improving ideas novelty based on otsm-triz model of … · 2017-03-08 · techniques and design by...

POLITECNICODIMILANO

DIPARTAMENTODIMECCANICA

Ph.D.CourseonMechanicalEngineering–XXVIIICycle

10.2012‐10.2016

Improving ideas novelty based on OTSM-TRIZ model of

contradiction

Candidate:

MehdiParvin

Supervisor:

Prof.GaetanoCascini

Tutor:

Prof.TullioTolio

PhD. Coordinator:

Prof.BiancaMariaColosimo

Abstract Smallandmedium‐sizedenterprises(SMEs)aremajorcontributorsto

industrialeconomies.Therefore,therehasbeenalong‐standinginterestintheempiricalliteraturetosupportR&Dengineers.TheobjectiveofthisresearchisbasedwithintosupporttheR&Dengineerstoimprovethepatentability(non‐obviousnovelty)ofideasduringideagenerationsession.BasedontheresultsofpreviousresearchinthefieldofPatentMappingandpatentanalysis,IdeationtechniquesandDesignbyAnalogy,andTRIZ,TechnicalContradictionMapwasdeveloped as the enrichment of Problem‐Solution Patent Map by thecontradiction concept. In addition, the procedure of building the map wasproposedbasedontheOTSM‐TRIZcontradictionmodel,theDesignbyAnalogymodelandPatentMapping.

Two experiments were planned and performed to study the map’susabilityandeffectiveness,andrepeatabilityof the map‐building process. Toperform the studies, the suggested map was built for Walker, as a sampletechnicalsystemthroughfollowingthedevelopedprocedure.

Inthefirstexperiment,fourdifferentmethods(Brainstorming,Problem‐SolutionMatrixMap,TechnicalContradictionMapandPatentTextFar‐Field)wereappliedandcompareeachoftheireffectiveness.Bycollectingdatafromexperiments, the efficiency of each method was estimated and evaluated inimprovingtheideationnoveltyofR&Dengineers.Inparticular,ithasreliedonthree variables; Novelty, Quantity, and Variety to assess the effect of eachmethod and these variables. First, it was estimated the model based on thecollected data in the first experiment: Usability of the map. The estimatesshowed that among four methods, introducing Technical Contradiction Mapprovidesthehighesteffectiveness in ideation ofNovelty,Quantity,aswellasVariety.

Also to be able to introduce the Technical Contradiction Map as abenchmark to the literature of ideation novelty, it was analyzed therepeatability of building the map. R&D engineers were involved in buildingthreeotherversionofthemapsbyfollowingtheproposedprocedure,andtheresultsofusageofthesemapswerecomparedtogetherwiththefirstmapbuiltby the researcher. As one would expect estimated results based on therepeatability experiment was very close across the groups applied the fourdifferentversionsofthemap.Infact,thisexperimentverifiestherepeatabilityof building the map, and if one uses the same method, conditions, andequipmentexplainedinthisresearch,willobtainthesameresults.

Acknowledgements

TheresearchworkdescribedinthisthesiswouldnothavebeenpossiblewithoutthehelpandguidanceofsomepeopletowhomIwouldliketoexpressmygratitude.

IwouldliketoexpressmythankstomysupervisorProf.GaetanoCasciniforhisimmenseknowledgeandintroducemeaninterestingresearchtopic.Alsoforallowing me to do this research in the KAEMART research group, whichconsistedofdifferentPh.D.studentsfrommethodsandtoolsforproductdesigngroup and the other groups in the mechanical engineering department ofPOLIMI.Itgavemetheopportunitytoprolongmyexchangestudieswhichhavebeenagreatexperience.

IamdeeplyindebtedtosomeofmypreviouscolleaguesandfriendsinIranianInstituteofInnovationandTechnologicalStudies(IIITS)forparticipatinginmyexperimentsandgivingmetheopportunitytogothroughtheircompaniestoperformmytests.

I would also like to express my sincere gratitude to my lovely family. Myparticularheartfeltthankstomylatefather,Prof.AmirParvin;mydearmotherNahidMobasserifortheirencouragementofmystudyprogressduringmylife;mybelovedwifeSaraSaliminaminforherunmeasurablehelpandsupport,andmychildrenAtenaandAmirhosseinformakingmeaquicklearnerofcertaingeneralmanagementskillsandnaturallyahappyfather.

Tableofcontent

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Table of Content: [1] Introduction ........................................................................................................................ 9

1.1 Problem background................................................................................................................... 10

1.1.1 The role of Patent analysis in idea patentability .............................................................. 10

1.1.2 The importance of idea patentability for Iranian SMEs ................................................... 11

1.2 Research framework and contribution ....................................................................................... 15

1.3 Research objective and questions .............................................................................................. 17

[2] State of the Art .................................................................................................................. 18

2.1 Patent Analysis ............................................................................................................................ 19

2.1.1 Patentability ..................................................................................................................... 20

2.1.2 Patent Analysis techniques .............................................................................................. 21

2.1.3 Patent Maps ..................................................................................................................... 27

2.2 Idea Generation .......................................................................................................................... 30

2.2.1 Idea characteristics and ideation Metrics ........................................................................ 32

2.2.2 Ideation Methods ............................................................................................................. 37

2.3 TRIZ and OTSM-TRIZ model of contradiction .............................................................................. 43

[3] Research Methodology ..................................................................................................... 48

3.1 Methodological proposal to improve the Novelty of design proposals ..................................... 49

3.1.1 Research contribution ...................................................................................................... 49

3.1.2 Developed model for the target contribution of the research ........................................ 51

3.1.3 Developed procedure for building Technical Contradiction map .................................... 57

3.2 Designing empirical study ........................................................................................................... 63

3.2.1 Research contribution sample ......................................................................................... 63

3.2.2 Plan of empirical studies .................................................................................................. 78

[4] Empirical Study ................................................................................................................. 86

4.1 Experiment I: Usability and effectiveness of proposed map ...................................................... 87

4.1.1 Ideation metrics measurement ........................................................................................ 88

4.1.2 Estimated results .............................................................................................................. 93

4.1.3 Data analysis .................................................................................................................... 97

4.2 Experiment II: Repeatability of the building the map ............................................................... 108

4.2.1 Ideation metrics measurement ...................................................................................... 110

4.2.2 Estimated results ............................................................................................................ 110

4.2.3 Data analysis .................................................................................................................. 113

4.3 Conclusion of Experiment I and Experiment II .......................................................................... 121

[5] Discussions and Conclusions ........................................................................................... 123

5.1 Summary ................................................................................................................................... 124

5.2 Research results and Discussion ............................................................................................... 126

5.3 Limitations and future developments ...................................................................................... 130

[6] References ...................................................................................................................... 132

6.1 References ................................................................................................................................ 133

[7] Appendix ......................................................................................................................... 145 7.1 Appendix A - Patent analysis survey in Iranian SMEs ............................................................... 146 7.2 Appendix B - Characteristics of Patent Map methods .............................................................. 161 7.3 Appendix C - The instruction of providing the Technical Contradiction Map ........................... 163 7.4 Appendix D - Walker Patents Profile......................................................................................... 164 7.5 Appendix E - Technical Contradiction Map ............................................................................... 169 7.6 Appendix F - Generated ideas (Experiment I&II) ...................................................................... 174

Listoffigures

6

List of Figures: Figure1‐Stepsinhumanreasoningbyanalogy. ................................................................................... 40

Figure2‐TheformulateContradictionaccordingtoOTSM‐TRIZ. ....................................................... 46

Figure3‐RelationsofResearchmodel,Designprocesssteps,andoriginalcontribution. ................. 50

Figure4‐Proposedmodelofresearch. .................................................................................................. 52

Figure5‐AconceptualdiagramofaTechnicalContradictionMap. .................................................... 56

Figure6‐SimplifiedproceduresforbuildingaTechnicalContradictionMap. ................................... 57

Figure7‐Asimplewalkingframe. .......................................................................................................... 64

Figure8‐Walkerforimprovedstairwaymobility. ................................................................................ 70

Figure9‐The30patentmatrixinformation. ......................................................................................... 74

Figure10‐Problem‐Solutionmatrixmap. ............................................................................................. 75

Figure11 ‐ Proposedcontradictionmap. ............................................................................................... 76

Figure12‐TheproposedthreeDimensionalTechnicalContradictionMap. ...................................... 77

Figure13‐Theresultsofgenealogytreeanalysisforateam‐ExperimentI. ..................................... 92

Figure14‐Graphicalrepresentationofassessingcriteriaoftwosessions‐ExperimentI. ............... 95

Figure15‐Theideatimelineofallgroup‐ExperimentI. ..................................................................... 96

Figure16‐NASAtaskloadIndexresultsofparticipants‐ExperimentI. ............................................ 96

Figure17‐NormalityoftheData‐ExperimentI. .................................................................................. 97

Figure18‐EstimatedresidualsfortheNoveltyregression:Homoscedasticitytest‐ExperimentI. .. 98

Figure19‐EstimatedresidualsfortheNoveltyregression:Normalitytest‐ExperimentI. .............. 99

Figure20‐EstimatedresidualsfortheNoveltyregression:Homoscedasticitytest‐ExperimentI. ............................................................................................................................................................ 102

Figure21‐EstimatedresidualsfortheQuantityregression:Normalitytest‐ExperimentI. .......... 103

Figure22‐EstimatedresidualsfortheVarietyregression:Homoscedasticitytest‐ExperimentI. 105

Figure23‐EstimatedresidualsfortheVarietyregression:Normalitytest‐ExperimentI. ............ 106

Figure24‐Graphicalrepresentationofassessingcriteriaoftwosessions‐ExperimentII. ............. 112

Figure25‐NormalityoftheData‐ExperimentII. ............................................................................... 113

Figure26 ‐ EstimatedresidualsfortheNoveltyregression:Homoscedasticitytest‐ExperimentII. ............................................................................................................................................................ 114

Figure27‐EstimatedresidualsfortheNoveltyregression:Normalitytest‐ExperimentII. .......... 115

Figure28‐EstimatedresidualsfortheQuantityregression:Homoscedasticitytest‐ExperimentII. ............................................................................................................................................................ 117

Figure29‐EstimatedresidualsfortheQuantityregression:Normalitytest‐ExperimentII. ......... 117

Figure30‐EstimatedresidualsfortheVarietyregression:Homoscedasticitytest‐ExperimentII. ............................................................................................................................................................ 119

Figure31‐EstimatedresidualsfortheVarietyregression:Normalitytest‐ExperimentII. ........... 120

Listoftables

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List of Tables: Table1‐ThreelevelsofthebenefitsofpatentanalysisforIranianindustries. .................................. 12

Table2‐TheInterviewwithresponsibleorganizationsforIranianinnovationsandinventions. .... 13

Table3‐SummaryofQuestionsofthesurveywithinIranianSMEsaboutpatentanalysis. .............. 14

Table4‐SummaryofR&Dengineers’knowledgeonpatentanalysis. ................................................. 15

Table5‐TypesofdesignresearchandDRMframework. ..................................................................... 16

Table6‐ThelogicbehindeachofTextMiningtechniques. .................................................................. 23

Table7‐RepresentativeExamplesofPatentMap. ................................................................................ 28

Table8‐Assessingmethodsofideas. ..................................................................................................... 34

Table9‐TheformulaforassessingNovelty,Variety,QualityandQuantity. ....................................... 36

Table10‐Examplesofnature‐basedandnon‐nature‐basedmethods. ............................................... 41

Table11‐ComponentsofClassicalTRIZandOTSM‐TRIZtheories. .................................................... 44

Table12‐ConceptsforappropriateAnalogs. ........................................................................................ 52

Table13‐DetailedprocedureforbuildingaTechnicalContradictionMap. ....................................... 58

Table14 ‐ Technicalsystemsclassifiedbydegreeofcomplexity. ........................................................ 64

Table15‐Theresultsofsearchingandrefiningthepatents. ............................................................... 65

Table16‐Resultsofextractedinformationfromapatent. ................................................................... 66

Table17‐Eightcategoriesofproblems. ................................................................................................. 70

Table18‐Sixcategoriesofsolutions. ..................................................................................................... 72

Table19‐DedicatetimeforbuildingtheTechnicalContradictionMapofaWalker. ......................... 78

Table20‐Similarpartsofexperiments. ................................................................................................. 80

Table21‐Theappliedformulaincurrentresearch. ............................................................................. 82

Table22‐Usabilityofproposedmapplan‐ExperimentI. ................................................................... 87

Table23‐ParticipantsProfile‐ExperimentI. ....................................................................................... 88

Table24‐TheNoveltyattributewithweightsandrelatedFBSLevels‐ExperimentI. ...................... 89

Table25‐The��scoresof4Groups‐ExperimentI. ............................................................................ 90

Table26–ThecalculationofthedegreeofNoveltyofoneoftheteams‐ExperimentI. ................... 90

Table27‐ThetemplatetableforassessingthedegreeofVarietyofteams‐ExperimentI. .............. 91

Table28‐ThefilledtemplatetableofassessingthedegreeofVarietyforateam‐ExperimentI..... 92

Table29–ThescoresofQuantity,Novelty,andVarietyforallteams‐ExperimentI. ....................... 93

Table30‐ThescoresofQuantity,Novelty,andVarietyrespecttothegroupwithdifferentstimuli‐ExperimentI. ....................................................................................................................................... 94

Table31‐EstimatedresultsofeffectsofdifferentmethodsonNovelty‐ExperimentI................... 100

Table32‐Estimatedresultsofimprovingideasfordifferentmethods:Novelty(clustering)‐ExperimentI. ..................................................................................................................................... 101

Table33‐Estimatedresultsofimprovingideasbasedondifferentmethods:Quantity‐ExperimentI. .......................................................................................................................................................... 103

Table34‐Estimatedresultsofimprovingideasfordifferentntmethods:Quantity(clustering)‐ExperimentI. ..................................................................................................................................... 104

Table35‐Estimatedresultsofimprovingideasfordifferentmethods:Variety‐ExperimentI...... 106

Table36‐Estimatedresultsofimprovingideasfordifferentmethods:Variety(clustering)‐ExperimentI. ..................................................................................................................................... 107

Table37‐RepeatabilityofbuildingthemapPlan‐ExperimentII(PartI). ....................................... 108

Table38‐Repeatabilityofbuildingthemapplan‐ExperimentII(PartII). ...................................... 109

Table39‐ParticipantsProfile‐ExperimentII. .................................................................................... 109

Listoftables

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Table40‐The��scoresof4Groupsofexperiment‐ExperimentII. ................................................ 110

Table41‐ThescoresofQuantity,Novelty,andVarietyforallteams‐ExperimentII. ...................... 111

Table42–ThescoresofQuantity,NoveltyandVarietyrespecttothegroupwithdifferentstimuli‐ExperimentII. .................................................................................................................................... 112

Table43‐Estimatedresultsofimprovingideasforthesamemethods:Novelty‐ExperimentII. .. 116

Table44‐Estimatedresultsofimprovingideasforthesamemethods:Quantity‐ExperimentII. 118

Table45‐Estimatedresultsofimprovingideasforthesamemethods:Variety‐ExperimentII. ... 121

Table46–Summaryofresultsandreflectingonexistingtheories. ................................................... 127

Chapter1:Introduction

9

Chapter1

[1] Introduction


10

1.1 Problem background

Theproblembackgrounddiscusses themotivations fortheresearch.The

motivationbehindcurrentresearchcanbediscussedbothasalivescientificissueand also a critical issue for companies. Supportive methods and tools for ideapatentability and similar research issues can be considered as the scientificdimension,andtheimportanceofideapatentabilityforindustriescanbeconsideredasapplicationdimension.Inthissection,thesetwoscopesarementioned.

1.1.1 The role of Patent analysis in idea patentability

PatentabilityofaninventionorasolutiongeneratedbyR&Ddepartmentsisacrucialissueforindustriesasitletsthemprotecttheirinvestigationsandcompetemoreactivelyinthemarket.Patentabilityisoneofcurrentresearchissuesinpatentanalysis domain that patent analysis, itself, is known as a tool for supportinginnovationandinventionandconsequentlyengineeringdesign(Reitzig,2005).Aninventioncanbeacceptedandregisteredasapatentwhenitsnoveltyisnon‐obviousfortheexpertsinthefield,anditsindustrialapplicationisvisible(Franzosi,2000).Everyinventionincludesatleastanovelsolutionforaproblemwhichitisnotmostlynon‐obviousfortheexpertsinthefield.Thiskindofinventionsmostlyusesmoreresourcesprovidedthetargetanddesiredexpectationsandperformances.Ontheotherhand,non‐obviousnovelideasaremostlythesolutionswhichprovidemorereturnswithsameorevenlessusageofthesamekindofresourcesorapplyingnewsort of resources. This kind of inventions exploits new physical principles andbehaviors.

Thepatentabilityofaninventionisstudiedasataskbasedontheexpertiseof theexperts in the fieldandworkthat can becomputerizedandperformedbysupportive software. The literature shows Non‐obviousness of novelty is a mostcriticalpartofapatentabilityofaninventionwhichmustbestudiedbytheexpertsinthefield,andthesoftwarecannotsubstitutetheirexpertise.Inotherwords,theliteratureshowswhileconsideringthenoveltyisknownasanexpertise‐basedtask;itcanbesupportedpartiallybysoftware(Pimenteletal.,2014),Butthestudiesforclarifying the non‐obviousness novelty is followed mostly by an expertise‐basedtaskwhichtheycannotbecomputerized.Thesupportivesoftwareforhighlightingthe newness and novelty of an invention use the solved problem, systems or itselements proposed by the solution or some characteristics of a solution such asfunctionandbehavioraskeywordsforpatentminingandtherearetoofewstudiestodiscussthekeywordsforhighlightingtheNon‐obviousnessofasolution.PatentMapsandnoveltydetectionaresomemethodsforhighlightingandsupportingthenewness and novelty of the patents. Some TRIZ‐based patent mining researches,approachthenon‐obviousnessofanoveltybyfocusingonsearchingandclarifyingthecontradiction(s)resolvedbyapatent(CasciniandRusso,2006).TRIZ(TheoryofInventiveProblem‐Solving)isknownanideationtechniqueofDesignbyAnalogy,uses the characteristics of solutions. Specifically, principles applied for resolvingcontradictions. This methodology discusses the inventions in five different levels


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whichthreeofthemcanbeconsideredasthenon‐obviousinventionsfordesignersinthefield.Also,therearefewtypesofresearchtodiscusstheeffectsofexpertiseinsearching and clarifying resolved contradictions of patents and consequently therelationsof levelsofresolvedcontradictionsontheNon‐obviousnessofapatent.Therefore, it isworthtostudythe roleofexpertise insearchingandhighlightinglevelsofresolvedcontradictionsandrelationofthemonthenon‐obviousnessofthenoveltyofpatents.

1.1.2 The importance of idea patentability for Iranian SMEs

Studying the patentability of an invention become a bigger issue whenthinkingaboutthescopeofSMEs(SmallandMediumEnterprises)whichareoneofthe critical sectors of industries. Nowadays the role of the SMEs in industrialdevelopment and the immense potential for growth are well known, so thegovernmentsaretryingtosupporttheSMEsindevelopingtheleadingindustriesbyconsideringdifferentencouragement(BennettandRobson,2003).SMEsaremostlyclassifiedbasedontheirnumberofemployeeswhichshowtheirlevelofaffordancesoninvestigatingformanyprofessionalmulti‐tasksandexpertisewhiletheyhavetocompete in the market with other sorts of companies. SMEs in the Europeancountriesaredefinedlessthan250employeesclassifiedintothreecategories;0‐9employees,10‐49employees,and50‐249employees(UNIDO,2003).AccordingtotheIranianStatisticalYearbookfor1999,whichisthescopeofthisresearch,IranianSMEscategoriesintofourclasses;1to9employees,10to49employees,50to99employees, and more than 100‐249 employees. The main part of manufacturingcompaniesinIranareintheSMEsector,andabout75%aresmallbusinesses.Also,approximately 63% of the human resources in the industrial enterprises isemployedintheSMEs(UNIDO,2003).

TheIraniantechnicalsectionhasmetvariousdifficultiesinrecentyears,butthemost importantone is thelow levelsofactivities in innovationandthesmallinvestmentfordoingR&Dtasks.StatisticsshowthetotalnumberofIranianpatentsinlocalofficeuptotheyear2013were47262patents(www.ip.ssaa.ir),whileonly677 patents registered in the international patent office information(www.orbit.com)andonly264ofthem(www.orbit.com)weregrantedintheworld.These results show only 0.55% of Iranian patents are granted in the world.According to this problem, two goals are defined; “upgrading productivity andhumanresourceefficiency,”and“upgradingtechnicalandprofessionalknow‐howandtheskilllevelofthelaborforce”(UNIDO,2003).Thesegoalsarepursuedintwoprimary policies; “reorganizing the training of labor to increase technical andprofessionalcompetenciesandtherebyachieveincreasedlevelsofproductivityandefficiency,”and“providingfacilitiesfornewindustrialSMEs”(UNIDO,2003).

PatentanalysisisamongtheissueswhichareconsideredasnecessaryskillsforengineerswhoareresponsiblefordifferenttechnicaltasksincludingR&DtaskssuchasQualitycontrolandnewproductdevelopment.ThemainbenefitsofpatentanalysisinIranianindustrieshavebeenclassifiedintothreecategories(Bagherietal.,2009).Table1showstheseadvantages.


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Table1‐ThreelevelsofthebenefitsofpatentanalysisforIranianindustries.

Competitive-level

information

Technical-level

information

Strategic-level

information

Identificationof

competitors Acquiringtechnical

informationfrom

patents

TechnologytrajectoryIdentificationofkey

inventor(s)

Avoidanceofinfringing

others’patents

Usingunprotected

technologies

Technological

orientationofmajor

companies

Identificationofpotential

licensorsAvoidingduplication

Tracinginfringements

IncreasingBargaining

powerintransferof

technology Source of ideas

Selectionofsuitable

partnersforstrategic

R&DalliancesCurrentawareness

Despiteconsideringpatentanalysisasoneoftherequiredskillsforengineersof SMEs, there is no empirical research to show the level of knowledge andapplicationofthistoolsintheIranianSMEs.Therefore,asurveywasperformedastheinitialentrancetotheissueinthescopeofthecurrentresearch.ThisstudywasconductedtoclarifymoretheexpectationofpatentanalysismethodsandtoolsforIranian industries and correspondingly the level of an acquaintance of R&DengineersinIranianSMEsonpatentanalysis.Thisreviewincludesdifferentparts(Details are available in Appendix A); the first section with the responsibleorganizationsforinnovationandinvention,thesecondpartwiththeIranianSMEs,and the third part with individual R&D engineers. The results of each part arediscussedinmoredetailinfollowing.

1. Preferred sector for using patent analysis (results of the survey within responsible organizations for innovation and inventions in Iran): Thisparthasconsistedofaninterviewwith20relatedresponsibleinfollowingorganizations:

ElitesFoundation;

TechcommitteeinExpediencyDiscernmentCouncil;

SciTechpark;MinistryofScienceandresearchandtechnology;

ResearchcenterofTehranpolytechnicuniversity;

IncubatorCentreofIranUniversityofScienceandTechnology;


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TehranUniversitySciTechparkandNANOCommittee;

MinistryofScienceandresearchandtechnology.

Thisinterview(Table2)withthesubjectofinventionandpatentshastaken40hours intotal,andtheresultsshow,despitegovernment financialsupport forgrantingpatentsandtheneedtothepatentinformationinIranianSMEs,thereisalow level of the acquaintance and usage on patent analysis. Table 2, shows thequestionsofthispartandalsoclarifiesthemostmeaningfulandconsensusresponsetothem.

Table2‐TheInterviewwithresponsibleorganizationsforIranianinnovationsandinventions.

Questions Results

1.TheleveloffinancialsupportsofgovernmentforgrantingapatentwithinIranianIndustry?

Mediumlevel

2.Thelevelofusageofpatentinformation(National/International)inIranianIndustry?

Lowlevel

3.ThemostpriorandpreferredsectorforusingpatentinformationamongIranianIndustry?

SmallandMediumEnterprises

4.ThelevelofthenecessityofpatentanalysisinIranianIndustry? Highlevel

5.ThelevelofusageofpatentanalysisinIranianIndustry? Lowlevel

6.ThelevelofanacquaintanceonthepatentanalysisinIranianIndustry? Lowlevel

TheTableshowsthat theresponsibleareawareof thenecessityofpatentanalysis for Iranian industries, but the usage and awareness are low in them.Moreover, theymostlyagree that IranianSMEsarethemostpreferredsector forapplyingpatentinformationtosolvetheirproblems.

2. The position of Iranian SMEs in using patent analysis (Result of the survey within Iranian SMEs):AfterclarifyingSMEsectoragainasthepreferredareaforusingpatentanalysisinthefirstpartofthesurvey,thesecondpartwasdonetoclarifythepositionofIranianSMEsinusingpatentanalysis.Thispartconsistedofaquestionnairewithtenquestionsinfoursections;generalcompanyinformation,thelevelofawarenessandusageofpatentanalysis,patentanalysispurposeandusingdatabases.25R&Dengineerscompletedthequestionnaire in 25 separate SMEs (or the engineers that they alsoconsideredresponsibleforR&Dtasksbesidestheirotherduties).Table3showsthesummaryofthequestionnaireanditsresults.


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Table3‐SummaryofQuestionsofthesurveywithinIranianSMEsaboutpatentanalysis.

Questions Results

1.ThenumberofemployeeinSMEcompany?‐10to49employees‐50to99employees

2.ThenumberofpatentsinIranianSMEcompany? 1‐5patent

3.Theresponsibledepartmentfortheinventions,newproductdevelopment,andpatentsinSMEcompany?

‐R&Ddepartment‐EngineeringdepartmentwithresponsibilityforR&Dtasksbesidesotherresponsibilities

4.TheLevelofanacquaintanceonthepatentanalysisinSMEcompany?

Lowlevel

5.RequestedandinterestedlevelforexploitingpatentinformationforSMEcompany?

Technicallevel(amongstrategic,Juridicalandcompetitive)

6.AnystandardoraspecificprocessforpatentanalysisprojectsinSMEcompany?

Withoutanystandardprocess

7.ThemainbenefitandexpectationsofpatentanalysisprojectinSMEcompany?

Twomainadvantages:‐ProposingaNovelpatentablesolution;‐Studyingpastresearchandfindingsolutionstoproblems

8.Forwhichstepoftheinventionprocess,thepatentanalysisisexpectedtobeused?

Ideagenerationbybecomingawareofexistingpossiblesolutions

9.TheprimarypurposeforusingpatentanalysisinSMEcompany?

RealizingNoveltiesofpatents

10.Themostuseddatabaseinthecompany? ‐USPTOandEPO

TheresultofthesurveyshowstheparticipatedSMEsintheinquiryhasatleastonepatent.TheyusuallyusedUSPTOandEPOdatabasesatthetechnicallevelforanalyzingthepatents.Thelevelofanacquaintanceonpatentanalysisislow,andthecompanieshavenotanystandardprocessforpatentanalysisprojects. Inthiscompanies, the principal purpose of the patent analysis is the to identify thenovelties in patented inventions, to exploit them for generating new patentablesolutionsandideas.

3. The knowledge and skills of R&D engineers (Result of a survey with R&D engineers):

Consideringtheresultsof thesecondpartof thesurvey, thethirdpartwas planned to observe the overall knowledge and skills of R&Dengineersinpatentanalysis.Thethirdpartwasdoneasaworkshop;thisworkshopwasperformedwith15R&DEngineersfromdifferentIranianSMEs.Someinformation(Table4)aboutthepatentanalysisknowledgeofparticipantswasgathered,andthenthepatentanalysiswaspresentedanddiscussedingroups.


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Table4‐SummaryofR&Dengineers’knowledgeonpatentanalysis.

Questions Results

1.Thenumberofpatentshasyoueverstudieduntilnow? Between1‐10

2.Thenumberofpatentanalysisprojectshaveyoueverparticipated?

Between1‐2

3.Thenumberofpatentdatabaseshasyoueverused? Between1‐2

4.Thenumberofpatentanalysissoftwareortoolshaveyoueverused?

0

As the Table shows in overall R&D engineers are not skillful in patentanalysis,andtheyevenhavereadverylessnumberofpatents.RespecttothegeneralpoliciesforIranianindustries(UNIDO,2003)fromonehandandtheresultsofthesurvey, it is logical to propose a contribution supporting R&D engineers inintroducingpatentable ideasusingpatentanalysistools.Also,asdiscussedintheprevious section, the scientific studies show few studies in searching andhighlightingthenon‐Obviousnessnoveltiesofpatents.Therefore, theobjectiveofthisinvestigationis,tosupportR&Dengineerstoproducemorenon‐obviousnovelideasbyusingpatentanalysis.

1.2 Research framework and contribution

ImprovingthepatentabilityofaninventiongeneratedbyR&Dengineersis

considered as the objective of this investigation. Patent analysis and ideationtechniquesarethetworelatedresearchfields.Problem‐SolutionPatentMapisoneofthetoolsofpatentanalysisatthetechnicallevelforimprovingthenoveltyofideasgeneratedbyR&Dengineers.TRIZisoneoftheknownmethodsbasedonDesignbyAnalogy model for improving the performances of engineers in solving inventiveproblemswhichincludingthecontradictorysituation.Literatureshow:

1. Problem‐SolutionPatentMapcanbeusedforsupportingR&Dengineersingeneratingnovelideas(Suzuki,2011),whileitwasnotdevelopedtohelpR&Dengineerstoproducenon‐obviousnovelideastobepotentialpatents.

2. Providing the previous solution of a technical system as stimuli toengineersinadesignsessioncanincreasetheQuantityofgeneratedideas(Simonton, 2010) and reduce the creativity (Jansson and Smith, 1991;Smithetal.,1993;DoboliandUmbarkar,2014).

3. Morethansources,analogsareusefulinqualityandappropriatenessofgenerated ideas by analogy for a problem (Casakin and Goldschmidt,1999;Casakin,2004).


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4. TRIZasamethodandtechniqueswasdevelopedtosupportengineersforresolvingthecontradictorysituationsintheevolutionpathofatechnicalsystem (Altshuller, 1984) which can result to non‐obvious novelinventions,butitsprocessforproblemdefinitionandProblem‐Solvingistoo systematic and time‐consuming to follow in every situation anddesignsessionbyengineers(Fricke,1993;Fricke,1996).

Accordingtothesummarymentionedabove,inthescopeofthisresearch,itisconsideredtoenrichtheProblem‐SolutionPatentMapofaparticulartechnicalsystemfor increasingthegenerationofnon‐obviousnovel ideasbyanewanalogaccording to the observed abstract patterns for resolving the contradictorysituations.

To address the objective of the current research and study the proposedcontribution,atype3studiesinthescopeofDRMframeworkisdefined.TheDRM(Design Research Methodology) consists of four phases (Blessing & Chakrabarti,2009),andthetypesofresearchprojectsinthiscontextaredefinedbasedonthekind of research activities in the different phases. Research clarification, theDescriptivestudyI,PrescriptiveStudy,andDescriptiveStudyIIarethefourphasesofresearchprojectsinDRM.ThedesignactivitiescanbeReview‐basedstudy,Initialstudy, or Comprehensive study in each of these four stages. The ResearchClarification phase points to formulate a realistic and alive research goal,Descriptive Study I clarifies the description of the existing situation, PrescriptiveStudyproposessolutionsforimprovingthecurrentsituationtowardsthedesiredsituation,andDescriptiveStudyIIassessestheeffectsofdevelopedsolutionrespectto the desiredsituation.Table5 (Blessing&Chakrabarti,2009)shows thescopeofcurrentresearch(Type3)respecttotheothersixtypesofstudiesinthisframework.

Table5‐TypesofdesignresearchandDRMframework.

In the scope of the current research, research clarification was studiedliterature‐based to clarify the necessity to approach patentability of an idea as a

Research Phases

Types Research

clarification

Descriptive

study I

Prescriptive

study

Descriptive

study II

1 Review‐based Comprehensive ‐ ‐

2 Review‐based Comprehensive Initial ‐

3 Review-based Review-based Comprehensive Initial

4 Review‐based Review‐basedReview‐based

Initial/ComprehensiveComprehensive

5 Review‐based Comprehensive Comprehensive Initial

6 Review‐based Review‐based Comprehensive Comprehensive

7 Review‐based Comprehensive Comprehensive Comprehensive


17

vividresearchgoal.ThereviewedliteratureshowedthatthesupportingtoolsarenotcapableenoughtoleadR&Dengineerstogeneratenovelideaswhichhavethehighpotentialitytobeacceptedaspatents.ThedescriptivestudyI,asthesecondphase, was performed literature‐based too, to compare the results and effects ofdifferentappliedtoolsandmethodsforsupportingthepatentabilityof ideas.ThereviewedliteratureshowedFull‐TextPatentandProblem‐SolutionPatentMapareusedforimprovingthenoveltyofideaswhilethereisnotdirectresearchoneffectsof them on the patentability of novel ideas. The prescriptive study, as the thirdphase,wasdonecomprehensivelythroughproposinganoveltoolforimprovingthepatentabilityofideas.ATechnicalContradictionMapandprocedureofbuildingthemapweretheproposedsolutionsinthisphase.Finally,theimpactsofthedevelopedmaprespecttothefirstsituationwerestudiedinthefourthphase,DescriptiveStudyII.

Theresultsofthetwofirstphases,researchclarification,andthedescriptivestudy I, were presented as state of the art in Chapter 2. The third phase, theprescriptivestudywasperusedcomprehensivelyinChapter3wherethetheoreticalandempiricalstructureoftheproposedsolutionwerediscussed.InChapter4,theempiricalperformancevaliditywasdiscussedastheresultsofthefourthphase,thedescriptive study II. Finally, Chapter 5, discusses some limitations and futurecorrespondingstudies.

1.3 Research objective and questions

This study is interested in finding and using non‐obvious novel ideas of

patentsofaparticularsystemforimprovingthepatentabilityofaninventionforthesame system. Therefore, the primary objective of this research is consideredimprovingthepatentabilityofaninventiongeneratedbyR&DengineersinIranianSMEs.Toapproachthisgoal,‘TechnicalContradictionMap”isdeveloped,andtwomain research questions are defined through a quantitative study and statisticalanalysis:

1. Can R&D engineers in Iranian SMEs improve Novelty within their ideas, through the use of an enriched Problem-Solution Patent Map by the ‘contradiction concept’?

2. Can Iranian R&D engineers build the proposed enriched Patent Map by following the developed procedure?

Twoexperimentsareplannedandperformed–theusabilityofthemapandthe repeatability of building the map – to answer and analyze these questions.SeveralhypothesestestsareusedtodeterminewhetherthereisenoughevidenceinpresentedsampleofcollecteddatafromtwoexperimentstoinferthattheusabilityoftheTechnicalContradictionMapisrightfortheentirepopulation.

Chapter2:Stateoftheart

18

Chapter2

[2] State of the Art


19

This chapter presents the results of the two first phases of the

research, research clarification and the descriptive study I, in the form ofa

summaryofthepreviousresearchthatthecurrentresearchbenefitsofthem.

Thisresearchisdefinedasanadvancementinoneoftheapplicationsofpatent

analysis by exploiting an enriched Design by Analogy model in solving

problems.Therefore,thischapterispresentedinthreeparts.Part1illustrates

thebasicterminologyandconceptsofapatent,patentanalysis,itsapplications

whichfollowbystudyingthedevelopedtoolsforsupportingthepatentability

of an invention. Part 2 concerns idea generation methods and its

advancements to get an idea for improving the patent analysis tools

specialized for supporting the patentability of an invention. Part 3, reviews

OTSM‐TRIZmodelofcontradictioninresolvinginventiveproblemstoreach

non‐obviousnoveltieswhichcanbeappliedtoimprovingthetargettool.

2.1 Patent Analysis

A patentisanagreementbetweenthegovernmentoritsresponsible

agency and patent owner; patent owner discloses and exposes the new

knowledge, technology, and relevant engineering science behind its patent,

andthegovernmentprotectstheexploitingrightofthepatentfortheowner

foracertainperiod(HufkerandAlpert,1994;Ernst,2003).EuropeanPatent

Office (EPO), the United States Patent and Trademark Office (USPTO), the

JapanPatentOffice(JPO)aresomeofthepublicoffices,andthepatentsare

accessible through different integrated, up‐to‐date sources of this office

(Abbasetal.,2014).

Patentsprovidevariousinformationsuchasthecontentofanexclusive

rightoranintellectualattributeright,andtechnicalinformationofbroadsort

of state‐of‐the‐artistic creation technology, which is used by companies to

knowthecompetitor'stechnologicaldevelopmentschemeorglobalstrategies

toplanR&Dprojects.However,usingpatentinformationisnoteasy,because

patentinformationintentionallyincludesexplicitexpressions;relatedtothe

natureofpatentsanditsspecificterminology,andalsorelatedtotherights

(Suzuki,2011).Also,thereiscontradictorydiscussionaboutadvantagesofthe

vastamountofpatentinformationwhichcanbehelpfulforgettingnewsights

andknowledgebutmustbereviewedandconsideredtoavoidanyproblemfor

newclaims(Cotropia,2005).

Patent analysis is a term to refer a set of task including searching

relevantpatents,extractingandanalyzingpatents’informationandpreparing

themtorespecttothebroadrangeofdecisionsinthetechnicalorstrategic


20

levels.AwideVarietyofapplicationsisdiscussedintheliteratureforpatent

analysis. Checking the patentability and Novelty of a new invention of the

company (Bonino et al., 2010), analysis of the competitors (Abraham and

Moitra,2001),studyingthepossiblegrowthofacompanyinaspecificperiod

(Ernst, 2003), studying the relation of the technological developments and

economic growth (Coussement and Van den Poel, 2008), recognizing the

trendsoffuturetechnologyinaparticularareaoftechnology(YoonandKim,

2011),clarifyingtheinfringements(Leeetal.,2013),studyingthetheproduct

evolutionandmarketopportunitiesrespecttothetechnologicaldevelopments

(Phaaletal.,2003),highlightingthepromisingpatentsfortechnologytransfer

(Trappey et al., 2013; Du and Ai, 2008) are some of the applications and

purposesofpatentanalysismentionedintheliterature.Inaddition,analysis

ofthepatentscanclarifytechnologicalfeatureandconnection,revealmarket

trends,showdirectionsfornoveltechnicalsolutionsandinfringementrisks,

highlight competitive positions, and support investment policies (Liu and

Shyu,1997;AbrahamandMorita,2001;Campbell,1983;Jung,2003;Daimet

al.,2006).

Checking the patentability of an invention is one of the technical

applications of patent analysis that is the interest of R&D engineers and

departments.DespitethebenefitofR&Ddepartmentstothisapplication,the

researchesinthisfieldshowsomelimitationswhicharemostlyrelatedtothe

methodsofextractingtheusefulandrelevantpatentsandtheir information

and analyzing and presenting the extracted information in a usable way.

Therefore, in following, the previous researches related to each one is

reviewedinmoredetaillevel.

2.1.1 Patentability

Respect to the rapid technology advancements in Industrial and

InformationTechnologyage,mostinventionsaredevelopedandbuiltonprior

patents.Therefore,thestudyisneededtoensurethateachnewpatentisnota

smallderivativeimprovementtoanexistingtechnologyorapriorpatentand

consequentlyitdoesnotdeprivethecurrentpatentholderofhis/herprofits

(GreenandScotchmer,1995).Therefore,patentsarealegaltitleforaperiod

that must cover three primary requirements by the experts in the field;

Novelty, Non‐obviousness, and Usefulness according to the patent law

(Samuelson,2004).

Legalimportanceofapatent,makethedefinitionandmeasurement

ofthecharacteristicsofapatent,specificallyNoveltyandNon‐obviousness,

averycriticalissue.Despitethisimportance,thereisnoagreementonthe


21

definition of these characteristics and their corresponding assessing

methods.

Optimal configuration of Novelty and Non‐obviousness and the

benefits of this configuration on overall welfare for various types of

industries are studied by theoretical economists (Scotchmer and Green,

1990; Green and Scotchmer, 1995). To determine whether a proposal

satisfiestheNoveltyandNon‐obviousnesscriteria,fourclassesoftheprior

art,aredefined(Franzosi,2000);Commonknowledge(alreadyknowntothe

expertsinthefield),Enhancedknowledge(canbeaccessedbyagoodexpert

whenconfrontedwithanewproblem),Hiddenknowledge(notknowntothe

mostexpertsinthefield),andfinallyPriorapplications.Noveltyisachieved

whentheinventionisnotintheclassesofCommonandEnhancedknowledge

in thetarget field.Similarly, todeterminewhetheraproposalsatisfiesthe

Non‐obviousnesscriterionanditissufficientlydifferentfromthepriorart,

the expert must conclude that the idea is not a simple derivation or

combinationofthepriorartwhichcanbelogicaltoanaverageexpertinthe

field(Franzosi,2000).Non‐obviousnessisachievedwhentheNoveltyisnot

in the classes of Common and Enhanced knowledge. The corresponding

assessmentconsidersCommonandEnhancedknowledgethroughchecking

the prior art in the field. It is worth to mention Common or Enhanced

awareness of another area of the art, can be used in a Non‐obviousness

NoveltyanditisnottobecheckedfortheNon‐obviousnessofaninvention

intargetfieldofart.TheoreticallycheckingtheNoveltyandNon‐obviousness

ofaninventiontobebeyondCommonandEnhancedknowledgeinatarget

fieldoftheart,doesnotseemacomplextask.Thedifficultybecomesevident,

consideringtheincreasingvolumeofpatentsinacertainfieldoftechnology

and not supportive computerized tool for effective and efficient search

(Bonino et al., 2010; Hunt et al., 2012; Wanner et al., 2008) and analysis

whicharediscussedinthefollowingsection.

2.1.2 Patent Analysis techniques

As mentioned, the patent analysis starts from searching relevant

patentsandcontinuesbyprovidingtherequiredinformationfortargetusers

atdifferent levelssuchasR&Dengineersandmanagers invarious formsof

text and graphs to let them decide in the strategic or technical level. The

generalprocessofpatentanalysiscomprisesoffourfollowingsteps(Liuetal.,

2011;Tsengetal.,2007):

1. Identificationofcandidateexistingpatentstoanalyze;2. Extractionofinformationfromthesecandidates;


22

3. Analysisoftheextractedinformationandassessingrelevance;4. Determining and presenting conclusions on whether and how this

informationaffectstheresearchconclusions.

Eachoftheabovestagesofpatentanalysisisataskwhichispursuedin

somemoredetaillevelsteps,whereasliteraturehasdiscusseddifficultiesof

each stage respect to the expertise, skills, and capabilities needed even by

experts in the field (Kostoff, 1998). Patent analysts with the various skill

required patent analysis tools with a different ability (Bonino et al., 2010).

Usingpatentanalysisautomatedtoolsassistandrelievethepatentanalysis

experts of the labour‐intensive and time‐consuming tasks of manually

searchingandanalyzingthepatents,andalsoacceleratetheanalysisprocess

(YoonandPark,2004).

Thetwofirststagesarecriticaltasksfromtheviewpointofsearching

andextractingrelevantpatentsorinformation;thefirststepaimsatreaching

tothemostappropriatepatentsandthesecondphaseaimsatretrievingthe

mostpromisinginformationofeachpatentrespecttotherequestedtargets.

Also,thetwolaststagesareconsideredascomplextasksfromtheperspective

of transferring the searched and extracted data and information to useful

knowledge for users. Previous research in the field of the patent analysis

showsthetextminingtechniquesaremostlyusedtoobtaintheinformation

forthetwofirststages,andthevisualizationtechniquesaredevelopedtohelp

thetwolaststagesindescribingthepatentinformationvisuallyfordecision

makersortechnologyexperts.Infollowing,thepatentanalysistechniquesare

reviewed respectively into text mining techniques and visualization based

techniques.

Text mining techniques

Asmentionedthepatentanalysisisperformedinfourmainstages,and

theautomatedtoolsaredevelopedtoreducethelimitationofperformingeach

stage. Identification of candidate patents to be analyzed and Extraction of

information from these candidates are the two first stages that their

boundariesarestudiedrespecttothestructureofthecontentsofpatents.The

contentsofapatentarecategorizedinstructuredandunstructureddata.The

structured data contains determined and accurate information such as the

inventor,assignee,andcitationinformation.Thepartswhichmustbenarrated

and described such as title, abstract, claims, and description is the

unstructureddata(Liuetal.,2011;Tsengetal.,2007).Most limitationsare

relatedtothesearchingandretrievingunstructureddatawhicharethemain

bodyofthefirststages;searchingtherelevantpatentsandsearchingdataofa


23

patent through unstructured data. In other words, although some required

analysis of patents (aims of stages of 3 and 4 of patent analysis) can be

followed by searching and extracting patents or their accurate information

through structured data, most of the advanced requested analysis must be

donethroughsearchingandretrievingunstructureddata.

Text mining tools are developed for mining both structured and

unstructureddatatosupportthetwofirststages.Extractthestructureddata

fromthepatentreportiseasierthanunstructureddata(Tsengetal.,2007).

Respectively,threeapproachesareobservedindevelopingthecorresponding

tools;mappingbetweenthestructureddataandrequestedanalysisasmuch

aspossible,substitutingtheunstructureddatabysomeofthestructuredones

(when their relations are studied respect to the target), and finally using

naturallanguageprocessingtobepossibletodofurtheranalysisbyautomated

tools.Searchingpatentsthroughasetofpre‐definedcodessuchasIPC‐codes

insteadofsearchingbykeywordsisanexampleofdevelopedmethodsinthe

secondapproach.Textminingtoolsaremostlydevelopedinthedirectionof

thethirdapproach.

Textminingisaknowledge‐basedmethodforobtainingtheusefuldata

from the natural language text by using logical tools while recognizing

meaningful patterns from unknown textual data (Tseng et al., 2007;

Ghazinoory et al., 2013). Text mining tools are developed to be capable of

miningtextfrombothstructuredandunstructureddata(Tsengetal.,2007).

Textminingtoolsaredevelopedthroughfivegroupsoftechniques.Thelogic

behindeachofthesecategoriesofmethodsissummarizedinTable6.

Table6‐ThelogicbehindeachofTextMiningtechniques.

No. Technique Characteristics

1

Natural language

processing (NLP)

based techniques

‐Semantictextminingapproachbyusingcomputational

mechanismsandstructures;

PAT‐Analyzer for Identifying the resolved

contradictionsthroughthepatents(Casciniand

Russo,2006);

‐ Transforming the technical data in an easy word

compositionbyselectingthegrammaticalselectionofthe

textual data and producing the structural relation

between the elements (Masiakowski and Wang, 2013)

basedontwofollowingapproaches(YoonandKim,2011;

Parketal.,2013):


24

keyword based approaches; involving

predefinedkeywordsandkeyphrasesthatneed

proficientunderstanding;

Subject‐Action‐Object (SAO) based approaches;

analyzing unstructured data through

relationshipsofkeytechnologicalelements;

‐AdvantagesofNLPtechniques:

Powerfulforprocessingbigtextincludinglarge

amountoftextualdata;

‐Keywordbasedapproachessufferof:

Lexicalandgrammaticalobscurities;

Needtorealizethemeaningfulrelationbetween

thegrammaticalconstruction;

‐Developedversionstofollowtherelationsofnovelties

ofpatents:

Presenting the structural relationships among

components of patents by using semantic SAO

structures(Parketal.,2011);

Identifying the inventions that are extremely

novelbyusingsemanticSAOstructuresthrough

determining the gap with the current patents

andanewpatent(GerkenandMoehrle,2012);

TechTreeforshowingsimilaritiesofpatentsina

treeofpatentsandmappingtheirsimilaritiesby

usingsemanticSAOstructures(Choietal.2012);

Constructing a patent similarity and

dissimilarity matrix by measuring statistical

semanticSAOstructures(Yoonetal.,2013);

TechPerceptor for mapping similarities of

functions of patents through extracting the

function of patents and mapping their

similarities by using semantic SAO structures

(Parketal.,2013);

Product‐Function‐Technology (PFT) map for

mappingtechnologyroadbyusingsemanticSAO

structures(Choietal.,2013);

Extractingtheevolutiontrendsthroughranking

and classifying patents similarities based on

TRIZtrendsofevolutionbyusingSAOstructures

(Parketal.,2013).

2 Property function

based techniques

‐Grammaticaltextminingapproach;

‐Usingapropertytoexpressaparticularcomponentofa

systemandusingafunctiontorepresentaproperaction

ofthesystem(Dewulf,2011);

‐Advantagesofpropertyfunctionbasedtechniques:

Eliminatingtheneedtopredefinethekeywords

throughnaturallanguageprocessing

‐Somedevelopedversions:


25

TrendPerceptor for presenting invention

conceptsandtechnologicaltrendsbycomparing

thesimilaritiesofproperties,functionsandtheir

co‐occurrencesamongpatentsinanetwork(not

usable for a new patent having different

technological foundations) (Yoon and Kim,

2012).

3 Rule-based

techniques

‐Metadatatextminingapproach;

‐Capturingthedifferenceintrendsofpatentwithoutthe

need of expert information by using rules such as

associationrulemininganddegreeofchange


PatentTrendChangeMining(PTCM)tocompute

the similarity and dissimilarity of trends of

patent for two statute in two various period,

which uses patent fetcher to get International

PatentClassificationCode(IPC)foreachselected

keyword, patent transformer to transform the

PatentTextofHTMLformintextformandfilter

out irrelevant information, patent indicator

calculator module to determine the patent

values, and finally change detection module

definethetrendsofpatentchangebyusingrule

mining and discarding frequently mined

patterns(Shihetal.,2010).

Fuzzy Inference System (FIS) as a strategy

planningmethodbyusingfuzzyIF‐THENrulesof

learningalgorithmofKohonen(Kohonen,2012)

and primary related heuristic (Lin and Lee,

1991)torefinethestrategicrulesbyconsidering

indicators containing Patent Quantity (PQ),

Revealed Patent Advantage (RPA), Patent

Activity(PA),BeCitedRate(BCA),andRelative

CitationIndex(RCI)(YuandLo,2009).

4 Semantic analysis

based techniques

‐Grammaticaltextminingapproach;

‐Creatingrelationshipsamongdomainspecificconcepts

(Boninoetal., 2010)byrecognizing therelation within

patentsanddefiningthecomingtechnicaltrendsthrough

logicalcorrelatedparsedgrammaticalcomposition;

‐Dealingwithsemanticsinsteadoftechnicalkeywords;


Identifying the infringement by capturing the

occurrenceof dependencyrelationshipsamong

theelementsofclaimsectionsthroughmapping

hierarchical keyword vectors (utilizing

correspondence sign to recognize the relation

between the structured claim component and

unstructured text data), and a tree matching


26

algorithm to compare the component of claim‐

by‐claimbasis(Leeetal.,2013);

Semantic Intellectual Property Management

System (SIPMS) by discovering and mapping

relations among main concepts of the patent

data by transferring the texts to a semi‐

structured format and classifying them by an

indexing agent in three main processes,

including pre‐processing, patent analysis, and

inventionsupport(WangandCheung,2011).

A knowledge‐based software structure to help

theextractionofrelevantinformationofpatent

from repetitious, various, and un‐coordinated

data sources through a patentsystem ontology

using external information sources such as the

ontologyfield(Tadurietal.,2011,2012).

5 Neural networks

based techniques

‐Rule‐basedtextminingapproach;

‐ Making a trained patent network for checking the

Quality of patents with rule‐based approaches in

conjunction

‐Usingforpatenttaxonomyandtechnologyforecasting

(Cheetal.,2010).


UsingBackpropagationneuralnetworkstrained

to recognize the patents that are particular to

technology,throughdevelopedmodelalongwith

the identification of indicators including

InternationalPatentClassification(IPC)andthe

patentcitationsnumber(Trappeyetal.,2013).

As Table 6 shows the five groups of techniques, differ regarding the

issuetobesearchedandanalyzedinminingstructuredandunstructureddata

together; technical keywords and patterns, grammatical parsing, indicator

calculators and rules, and semantic concepts. It is also worth to take into

consideration, the most of the techniques developed for distinguishing

Novelty of patents and supporting patentability are in thegroup of Natural

languageprocessing(NLP)basedmethodsbyusingSAOstructureforfurther

analysis.DespitethelimitationsofNLP‐basedtextminingmethodsandtools,

the researches show they are extremely effective in processing large

documents containing huge volumes of textual data (Abbas et al., 2014). In

general,theNLP‐basedmethodsstillcannotcopecompletelywithlexicaland

grammatical ambiguities, and also lack in representing the semantic

relationshipsamongthegrammaticalstructures.TextminingbasedonNLPis

widely classified into two main approaches; keyword based and Subject‐

Action‐Object(SAO)based(Parketal.,2013).TheSAOmethodcananalyzethe

unstructured data by describing the link between the main technological


27

elements(YoonandKim,2011).IntheSAOstructure,informationisextracted

directlyfromthePatentText(Parketal.,2011).ByusingtheSAOstructures,

information can visualize the concepts in the Problem‐Solution format and

usuallybasedonTRIZ(Choietal.,2013).

Property function based techniques is the second group of the text

mining techniques. These techniques are also used to find and clarify the

inventionconceptbyusingapropertytoexpressaparticularcomponentofa

systemandusingafunctiontorepresentaproperactionofthesystem.This

technique is more preferable by eliminating the need to predefine the

keywordsthroughnaturallanguageprocessing(Dewulf,2011).

ConsideringtheresearchesindevelopingNLP‐basedtechniques(both

keywordbasedandSAObased)andProperty‐Functionbasedtechniquesfor

extractingthetechnicalinformationofpatentsandhighlightingtheirnovelty

and resolved the contradiction, a developing method for supporting

patentabilitycanbeexploitedofthesetechniques.

Visualization techniques

As mentioned the patent analysis is performed in four main stages

whichautomatedtoolsaredevelopedforeachstagetoreducethelimitation

ofperformingeachstage.Analysisoftheextractedinformationandassessing

therelevance,andDeterminingandpresentingconclusionsonwhetherand

howthisinformationaffectstheresearchoutcomes,arethestages3and4that

visualizationtechniquesareappliedtofacilitatethesetwostages.PatentMaps

and patent networks are the most used and known techniques for the

visualization purpose. These techniques exploited clustering methods,

bibliometric methods, quantitative frequency techniques, and ranking and

weighting approaches by distance, size and density indexes for nodes and

vectors.SomeofthetechniquesmentionedinTable6arepatentanalysistools

whicharedevelopedforallfourstagessuchasthedevelopedtoolsinSemantic

analysisbasedtechniquesandNeuralnetworksbasedtechnique.

2.1.3 Patent Maps

AsmentionedPatentMapsareresultsofsomeofthetextminingtools

topresentthefindingsandresultsofanalysisinamannerappropriatefor

thefinaluse;atitssimplestversion,ahumanexpertorautomatedtoolmay

use raw text, whereas, more powerful patent‐mapping approaches use

visualizations such as tables, trees, or graphs to illustrate relationships,

trends,andpossibleduplication,andsoguidetheoperatortonextstepsin

R&Dpolicy,training,businessstrategy,competitorresearch,or IPdefence


28

(Suzuki, 2011). There is more attention to implementing text mining

methodsforsupportingthetaskofpatentanalysisandPatentMapping(Lent

etal.,1997;Fattorietal.,2003;YoonandPark,2004).

ThemainpurposeofPatentMappingistohelpengineersrecognition

ofatextimmediatelyandeasily.PatentMapsintheformofgraphs,tables,

charts,andnetworks,providethecombinedinformationtorecognizethem

quicklyandmoreefficiently(Yoon,2010).PatentMapsarefocusedonthe

technological analysis, and they are presented in a single, two and more

dimensional matrix. The duty of summary involves quickly detecting the

topicandclassification(Tsengetal.,2007).APatentMapisusedtoreflect

the correlation between the patents by building the maps within the

keywordsandthekeyphrases(Changetal.,2010).Followingfeaturesare

listedasleastcharacteristicsofPatentMaps:

1. Usingpatentinformation;

2. Havingaclearpurposeofuse;

3. Consistingappropriatepatentinformationrespecttothepurposeofuse;

4. Containingorganizedpatentinformation;

5. Presentinginformationvisually.

The patent‐maps are built through using analytic, qualitative,

quantitative,andindexanalysisandmethods.Varioustechniquesandtools

havebeenintroducedforPatentMapping;patentvacuummaps(Yoonetal.,

2002),patentvacancymaps(Leeetal.,2009),GTM‐basedPatentMaps(Son

et al., 2012), and semantic Patent Maps (Bergman et al., 2008). Table 7

summarizesthemostusedPatentMapsaccordingtotheirform,analytical

method,andpurposes(Suzuki,2011).

Table7‐RepresentativeExamplesofPatentMap.

Representative Examples of Patent Map

No. Name Commonly

used form of presentation

Major analytical

method Overview Brief benefit

1 Element-Based

Map Illustration

Qualitative analysis

Showingthepositionofpatentsrespecttodifferentelementsofaproductonitsillustrationbyconsideringtechnicalandfunctionalinformationofthepatents.

Positioningofapatentanditsdifferencesfromexistingpatentsbyshowingonlypatents’numbersor/andholdernamesonthecorrespondingelements


29

2 Diagram of

Technological Development

Tree-structured

form


Showingthepasttechnicaladvancementinaspecifictechnologicalfieldandthepresenceofmainpatentsincludingexpiredpatents.

Checkingtheexistenceofpioneerpatentsandachievingthespillovereffectsofdevelopmentresults

3 Interpatent

Relation Map (Citation Map)

Tree-structured

form


Showingtherelationsamongcitationinformationofpatents.

Consideringtheusage(mechanism,function,…)oftheinformationofpriorpatents.

4 Matrix Map Matrix/graph


Quantitative analysis

Showingthevastnessofpatentnetworksbyacombinationofmultipleaspects;manufacturingpurpose,industrialcomponent,thefunctionalcomponent,theproblems,thesolutions,etc.

Positioningtherelevantkeypatentsrespectstoeachother,withthecorrespondingpatentinformationsuchasapatentholder,number,forasetofproblemsinincorporationwithasetofsolutions.

5 Systematized Art Diagram

Illustration Quantitative

analysis

Showingthesystemofartsbasedonpatentinformationconsideringgrantedpatentsincludingtechnicalelements,andreportnumberforaprimarypatenttocompletethetechnologicalcontents.

Presentingtheentireamountofpatentsdescribetoaparticularsetofartwhereas,summarizingIPrelevantactionatnationalinstituteandacademy.

6 Time Series

Map Graph


Showingpatentdocumentsforaparticularright‐holderinorderbasedoftheyearsofthefilingofthepatentapplication.

Analyzingthenumberofpatentapplicationsfiledandtrendsofinventors.

7 Twin Peaks

Analysis Map Graph


Showingagroupofthepatentcommunityaccordingtosomeaspecttodisclosesomenewaspects.

Showingtheprecedingorlaggingnatureoftechnologicaldevelopment,andalsodelayingingainingacompetitiveedgeinaspecificartinthescopeofacompanyunderthecorporatestrategyorinthescopeofacountry.

8 Maturation

Map Graph


Showingtheinterestleveloftheappropriatetechnologyintheindustrybyconsideringinventorsandtheirapplicationsfilednumberandtheyearfilingofpatentapplications.

Detectingsignsofchangeinthenumberofapplicationsfiledorthenumberofapplicants.

9 Ranking Map List/graph Quantitative

analysis

ShowingthestrengthleveloftechnologicaloftheapplicantortheinfluenceofIPintherelevanttechnicalfieldbyrankingthenumberofpatentsfiledbyelementorbytheapplicant.

Presentingtrendsoftechnologicaladvancementinatechnicalfieldbyleadingcompanies.

10 Share Map List/Graph Quantitative

analysis

Showingthedistributionofapplicationsfiledbyatechnologicalcomponent.

Presentingtheoneswhofiledanapplicationforapatentassociatetoaparticulartechnology.


30

11 Skeleton Map Tree-

structured form



Showinganumberofapplicationsfiledintheyearofthetimeofdivergencewithoutdisclosingtheyear.

Gainingacomprehensiveunderstandingofthespreadoftechnologicaldevelopment.

12 Radar Map Graph Quantitative

analysis

ShowinginIPpolicyamongdifferentorganizationandthedifferencesinthethemeoftechnicaldevelopmentacrosstime.

Comparingtheglobalcompetitivenessofenterprisesbyshowingthetechnicalfieldorganization.

Table7summarizestheinformationof12mostusedPatentMaps.Literaturedid

notdiscusstheresultsofempiricalstudiesoneffectsofvariousPatentMapson

theirultimatepurpose.AmongthemostusedPatentMaps,TheMatrixMapis

used for showing the technical information of patents for a target system by

using unstructured data of patents and analytical methods. The Problem‐

Solution Matrix Map is one kind of Matrix Maps which presents classes of

problems and solutions of patents to facilitate studying the Novelty of an

invention respect to its similar patents positioning on the same point of the

matrix.Itreviewspreciseproblemsdiscussedbythepatentandsimilarsolutions

(Bonino et al., 2010). (see detailed information on different Patent Maps in

AppendixB).

2.2 Idea Generation

As mentioned in the previous section, checking and supporting the

patentabilityofaninventionisoneoftheapplicationsofpatentanalysisinthe

industries,whichisthefocusofcurrentresearch.Toimprovethepatentability

ofideasandinventionsofacompany,thefocuscanbedefinedonsupporting

R&Dengineerstogeneratenon‐obviousnovel ideas.Thenon‐obviousnovel

ideaistheaimofsomeotherfieldsofresearchsuchaspsychologyanddesign

and reviewing the relevant investigations in this area can reveal some

directions for improving patent analysis respects to the application of

patentability.Fromtheotherhand,thepatentanalysisisdoneinfourstages

ofidentificationofcandidatepatentstobeanalyzed,extractionofdatafrom

the candidates, analysis of the extracted data, and preparing the research

conclusions. Text mining and visualization are two group of techniques are

studied to support patent analysis; text mining techniques are mostly

supportivetechniques fortwopreviousstages,andvisualizationtechniques

are mostly supportive techniques for two last stages. Therefore, the new

directions can be used to improve any of the four mentioned steps or the

relatedtechniques.


31

Ideasorsolutionsareproposedtosolveaparticularproblemorto

improve the current situation.Problems, ingeneral,aredivided intowell‐

defined and ill‐defined problems. These two kinds of problems are

distinguished by their characteristics such as wholly or partially specified

problem space, and feasibility and infeasibility of known and existing

solutions for solving them (Shelly and Bryan, 1964; Schön, 1983;

Goldschmidt,1997;Coyne,2005;Darlington andCulley,2004).Respect to

thesetwocategoriesofproblems, twomainapproachesaredevelopedfor

solvingproblems;typicalproblemsolvingbysearchingasolutiontofitthe

problem among existing and available solutions, and creative problem

solving to generate solution correctly respect to the requirements of the

problem.Problemsofdesignarerecognizedas ill‐definedproblemswhich

are open‐ended and must be approached by Creative Problem‐Solving in

regardstotheknownconceptsandlanguageofcognitionscience.Problems

whichareapproachedandsolvedinR&Ddepartmentsaredesignissuesin

thedirectionofachievingNewProductResearch,NewProductDevelopment,

ExistingProductUpdates,QualityChecksandInnovationwhichareknown

asprimary functionsofR&Ddepartmenttasks. It isreportedthatwithout

creativity in design, there is no potential for innovation (Mumford and

Gustafson,1988;Amabile,1996).

Ideagenerationmethodsaredevelopedinthefieldofpsychologyand

design simultaneously while these two areas have been influenced each

other. Psychology ismore interested insupporting individuals inproblem

solving and idea generation whereas the design is more interested in

characteristicsofresultsofideagenerationmethodsandsessions.Inamore

detailed level, the methods are developed to support generating ideas

respect to the required characteristics of solutions. An organization who

wants to survive and thrive in a challenging environment must develop

innovative solutions for existing and new problems. The required

components of a solution are defined respect to the ultimate purpose of

Problem‐Solving.Apatent,aninvention,anadvancementanddevelopment

ofanexistingproduct,oranextensionofanexistinginnovationintoanew

application,aretheexpectedresultsofProblem‐Solvingandideageneration

inthecompaniestoreachinnovations(Zhuangetal.,1999).

In this section, the relevant studies are reviewed respect to the

expectedcharacteristicsofideasastheresultsofideagenerationmethods,

andalsotheideationmethods.


32

2.2.1 Idea characteristics and ideation Metrics

Inthefieldofengineeringdesign,ideasorsolutionsaredefinedasthe

results and outputs of solving design problems. In this domain, design is

studiedthroughconsideringitsbroadsectionsusingtheterms,thedesign

problem/ task, the design process, the design type/output/proposal/

solution/ idea, the design activity/ move/ action, and the design

organization/team/personnel (Pahl and Beitz, 1984; Ulrich and Eppinger,

1995;Ullman,2002).Therefore,ideasorproposalsaretheoutputofadesign

processrespecttothedesignproblemortaskbyadesignteam.Toassess

design process, first, the characteristics of required design proposals and

ideasaredefined.

Somecommoncriteriaforassessingthegeneratedideasanddesign

proposals are discussed in the literature. In most research, the group

performanceisdefinedbyevaluatingtheproposalsregardingthenumberof

ideas (Nijstad et al. 2002; Shah et al. 2003; Perttula and Sipila 2007) and

Qualityofideas(Wierenga,1998,Shahetal.2003).Consequently,theQuality

ofanideaisdeterminedbyappropriatenessandoriginalityonthetargettask

(Massetti, 1996; Runco and Jaeger, 2012) and some situations

unexpectedness (Gero, 1996) and Non‐obviousness (Howard et al. 2006;

Howardetal.,2008).Someexaminationsinengineeringcharacterizethese

criteriabythelevelofmeetinggoals(Shahetal.,2003)andinventiveness

and orderliness (Sternberg, 1985). The four criteria of Novelty, Variety,

QualityandQuantityofideasanddesignproposalsareoneofthewell‐known

criteria for characterizing a design project through exploration and

expansion of design space (Shah et al., 2003). In this scope, Novelty is an

approach for highlighting the unusualness or unexpectedness an idea

comparestoasetoftargetideas(Shahetal.,2003)andregardingadesign

space, Novelty shows the well‐travelled or little‐travelled identification of

ideas in the design area (Nelson et al., 2009). Variety is a criterion for

studyingdissimilarityanddistanceofanideafromotherideasinasetunder

analysis(Shahetal.,2003)anditshowsthedegreeofexplorationinsolution

spacebyanidea.Quantityreferstothenumberofdifferentideasgenerated

(Shahetal.,2003).Qualityisacriterionforstudyingthedegreeoffeasibility

of an idea and the level of satisfying the design requirements which are

discussedasrelevanceorappropriatenessinotherinvestigation(Shahetal.,

2003).

It is expected design proposals solve design problems while the

solutioncanbeembodiedasaninnovationincludingapatentoratleastan


33

invention.Innovationisdefinedastheintendedapplicationofcreativityto

achieveaspecificgoal(Omanetal.,2013).Relevance(solutionaddressing

the problem), Novelty (solution different from past solutions), elegance

(solution capable of the pleasing observer), and generalizability (solution

applicable to other domains) are four criteria for assessing an innovation

(Cropley and Cropley, 2005). In engineering design, most innovations are

including inventions which can be granted whether as patents or not.

Innovationbasedonagrantedpatentismorepromisingforcompaniesdue

tothepossibilitytobemoreprotectedinthemarketfromcompetitors.From

this viewpoint, it is expected the characteristics mentioned above can be

representativeforapotentialideatobegrantedasapatent.

Apatentisalegaltitleforaperiodforaninventionthatmustcover

three primary requirements by the experts in the field; Novelty, Non‐

obviousness,andUsefulness(Industrialusage)accordingtothepatentlaw

(Samuelson, 2004). A patent is assessed through the position of the idea

respect to the prior art. Optimal configuration of Novelty and Non‐

obviousnessandthepositiveeffectsofthisconfigurationonoverallwelfare

for various types of industries are studied by theoretical economists

(Scotchmer and Green, 1990; Green and Scotchmer, 1995). To determine

whetheraproposalsatisfiestheNoveltycriteriontobedifferentfromprior

art, four classes of prior art are defined (Franzosi, 2000); Common

knowledge(alreadyknowntotheexpertsinthefield),Enhancedknowledge

(canbeaccessedbyagoodexpertwhenconfrontedwithanewproblem),

Hiddenknowledge(notknowntomostexpertsinthefield),andfinallyPrior

applications.Noveltyisachievedwhentheinventionisnotintheclassesof

CommonandEnhancedknowledgeinthetargetfield.Similarly,todetermine

whetheraproposalsatisfiestheNon‐obviousnesscriteriontobesufficiently

differentfrompriorart,theexpertmustconcludethattheideaisnotasimple

derivationorcombinationofpriorart that wouldbe logical toanaverage

expertinthefield(Franzosi,2000).Non‐obviousnessisachievedwhenthe

Novelty is not in the classes of Common and Enhanced knowledge. The

corresponding assessment considers Common and Enhanced knowledge

throughcheckingthepriorartinthefield.ItisworthtomentionCommonor

Enhanced awareness of another field of art, can be used in a Non‐

ObviousnessNoveltyanditisnottobecheckedfortheNon‐obviousnessof

aninventionintargetfieldofart.

Respecttothecharacteristicsofapatentmentionedabove,Engineering

and design fields, have proposed and applied different definitions and

assessing methods for Novelty and Non‐obviousness. The developed


34

definitionsandmethodsinthisfieldaimtosupporttheR&Dactivitiestowards

theacceptablepatentsandpromisinginnovations.Non‐obviousNoveltyand

Usefulness,areconsideredasthemaincomponentswhichmustbecoveredby

thecriteriadiscussedintheliterature.Non‐obviousNoveltycanbepresented

respecttothedefinitionsoftheNoveltyandVarietycriteriainthefourcriteria

framework(Quantity,Quality,Novelty,Variety)ofbecausethesetwocriteria

show unexpectedness and more exploration in design space, while the

UsefulnesscanbediscussedrespecttotheQualityinthescopeoffourcriteria

mentionedabove.Therefore,mostly inengineering design, the four criteria

areusedforassessingadesignproposaloradesignprocess.Respecttothe

different characteristics of design proposals mentioned in the literature,

variousmethodsalsoproposedforevaluatingthem.Table8summarizessome

ofthemostusedmethods.

Table8‐Assessingmethodsofideas.

Source Purpose Evaluation method Criteria

(Redelinghuys,1997)Assessingboth

theideasandthedesigner

Evaluatingdesignerrespectstoexpertsandassessingdesignfor

valuescomparetoengineeringrequirements

‐ProductQuality,‐Designerexpertise,‐Designercreativeeffort

(Besemer,1998;BesemerandO’Quin,

1999;O’QuinandBesemer,2006)

Assessingcreativity

UsingLikert‐typescalesystem

‐Novelty(originalandsurprise)‐ElaborationandSynthesis(organic,elegant,andwell‐crafted)‐Resolution(valuable,logical,useful,andunderstandable)

(SarkarandChakrabarti,2003)

Assessingcreativity

UsingSAPPhIREmodelandFBSFramework

‐Novelty‐Usefulness

(Shahetal.,2003)Assessinggroups

ofideas

Thesatisfactionassessingoffunctionalrequirementsbythe

numberofideas

‐Novelty‐Variety‐Quality‐Quantity

(VanDerLugt,2000;Vidaletal.,2004)

Assessinggroupsofideas

Determiningrelationsoffunctionsolutionsinagraphicallinkchart

‐Thelinksnumberamongideas‐Thelinktype‐Thedesigner'snumberassociatedperidea

(Vidaletal.,2004) Assessinggroups

ofideasDeterminingrelationsoffunction

solutionsbasedonlinkdensity

‐Ideasnumber‐Validideasnumber‐Denyideasnumber‐Notrelatedideas‐GlobalIdeasnumber

(Kaufmanetal.,2008)Assessinggroups

ofideasConceptsassessingfacingeachother

(byLikert‐typescalesystem)

‐Novelty‐Appropriateness‐Technicality‐Harmony‐ArtisticQuality

(Nelson,2009)Assessinggroups

ofideas

AssessingnewcombinationofVarietyandNovelty(Newapproach

ofShah’smetrics)

‐Novelty‐Variety‐Quality‐Quantity

(Srivathsavaietal.,2010)

Assessingproduct

Analyzingtheinterraterreliabilityandrepeatability

‐Novelty,‐Technicalfeasibility‐Originality

(Chulvietal.,2011)Assessingcreativity

Using0–3scaleratingsbyjudges‐Elementimportance‐Satisfactiondegreeofeachelement‐ThesuggesteddesignNovelty


35

AstheTable8shows,themostmethodsareusedforassessingthe

ideaswhilesomeofthemalsousedforevaluatingthedesigners.Someofthe

methodsareusedfordeterminingindividual ideaandsomeforagroupof

ideas which can be the outcome a design session. Besides, some of the

methodsusing simple scaling (veryhigh,high,medium, low,very low) by

experts,somemethodsrankideasbasedonsomemodelofdesign,andsome

methodsrankideasbasedonbothscalinganddesignmodels.Designmodels

suchasSAPPhIRE(statechange,action,parts,phenomenon, input,organs,

andeffect)andFBS(function–behaviour–structure)areusedforrankingthe

degree of criteria based on the covering concepts. The function (F) of a

technicalsystemis themotivation/purposeof itsexistencewhichsatisfies

the requirements, (i.e. what it is for) (Gero and Rosenman, 1990). The

behavior(B)isasequentialchangeofstates(Umedaetal.,1995),whatthe

systemdoestofulfilltheintentexpressedbythefunction(F).Thebehavioral

levelisbasedonthenetworkofalternativebehaviors(B)alldrivingfromthe

samefunctionalconcept.Thestructure(S)describesthecomponentsofthe

objectandtheirrelationships(GeroandKannengiesser,2004).

ThethreecriteriaofNovelty,Usefulness,andFeasibilityaremostly

commononthemetricspresented,butthemeaningofNoveltyaredifferent

in these methods. Most of the methods concentrate principally on the

identificationofNoveltyoftheideasandverylittlepartsproposeamethod

forassessingthedegreeofNovelty(ChakrabartiandKhadilkar,2003).

The method proposed by Shah is one of the methods which rather

covers the expected characteristics for a patent. This method proposed a

framework for defining the Novelty, Variety, and Quality based on the

functionofthesystemunderinvestigation,andsetofformulaforassessing

thedegreeofNovelty,Variety,andQualityofgroupsofideasbothpriorior

posteriori.Inotherwords,thismethodlettorankthedegreeofeachcriterion

basedonthedesignmodels.TheNoveltyofanideagenerationsessioninthis

methodisassessedconsideringthreemainissues;settingthereferenceideas

or solutions for comparison, defining the degree of Novelty of each idea

comparetotheotherideas,andmeasuringthetotaldegreeofNoveltyofall

proposedideasofanideagenerationsession.Noveltyisassessedrespectto

asetofideaswhichcanbepersonal(newnesscomparetotheotherideasof

thatperson),societal(newnessrespecttoideasorknowledgeofexpertsina

specific society), and historical (the first of its kind in the history of all

relevantsocieties)(Shahetal.,2003).Also,theNoveltyassessmentcanbe

approachedprioriorposteriori(Shahetal.,2003).Inprioriview,theentire

set of ideas is collected for evaluation by determining the unusualness or


36

expectedness, before examining any information for avoiding any bias. In

posterioriperspective, the keyattributesand theoccurrencesof themare

definedrespecttotheallgeneratedideasincorrespondingdesignsessions.

Finally,themeasurementoftotalNoveltyofanideagenerationsessionisa

combination of degree of Novelty of ideas and the numbers of their

occurrences.

RespectivelytoassessingthedegreeofNoveltyofanideageneration

session, the Variety is assessed considering three main issues; setting the

categoriesofVarietyofsolutions,definingthevalueoffordifferentlevelof

categories of Variety in a hierarchical structure, and measuring the total

degree of Variety of all proposed ideas of an idea generation session. The

AssessingQualityissimilartotheNoveltyandVariety.

BasedonShah’sassessingmetricand formula, the definitionof the

degreeofNovelty,Variety,andQualitystartsbydefinitionoffunctionsofthe

system under investigation. These criteria are then assessed based on the

weightsconsideredforthepre‐definedlevels.ThefinaldegreeofNoveltyand

VarietyofideagenerationsessionisachievedthroughShah’sformula(Shah

etal.,2003)whicharepresentedinTable9.

Table9‐TheformulaforassessingNovelty,Variety,QualityandQuantity.

No. The criteria Formula Description

1

Novelty (inposteriori

approach)

�� = ��

�

��

��

�

��

M1:TotalNoveltyscorem:Numberoffunctionsorattributesn:Numberofstagesfj:Weightsassignedtothevalueoffunctionorcharacteristictocalculateatotalscorepk:Weightsallocatedtotheimportanceofstages.

�� =��

��× 10

Cjk:TheideasnumberforfunctionjinstagekTjk:Theideasnumberforfunctionjforallstagesk

2 Variety �� = 10 ��

�

��

��/��

�

��

M3:Varietyscoreb�:BranchesnumberatlevelkS�:Levelscorek(10,6,3,1)m :TotalnumberoffunctionsM ��:MaximumVarietyscore

3 Quality �� = ��

�

��

��/(� × ��

�

��

)

MQ:QualitygradeSQ:NumbersofQualityscale(1‐10)fjandpk:Weightsoffunctionandstepm:Totalnumberoffunctionsn:Variableofthetotalnumberofideasproducedformeasurement

4 Quantity ‐ anumberofconceptsgenerated.


37

The Table 9 shows that higher Novelty can be achieved when the

occurrenceofinstancesofnewnessintheideasarelow,andthedegreeofthe

Noveltyishighaccordingtotheweights.HigherVarietycanalsobereached

whentheideascovermorecategoriesoffunctions,atahigherlevelofdegree

of Variety. Both final scores of Novelty and Variety show the portion of

NoveltyandVarietyonthesetof reference ideas; thesearethegenerated

ideasbyallteamsincurrentresearch.

NotconsideringallaspectsofdefinitionofnoveltyinNoveltycriterion

and the corresponding assessing method, no relation among Novelty and

timeline of an invention, not considering the different levels of value for

Novelty respect to the design abstract models (instead of bi‐situational of

yes‐no),aresomeoftheflawsofassessingmetricsforNovelty(Sarkarand

Chakrabarti, 2011). Among different assessing metrics presented and the

flaws,Shah’smetricwasselectedforthisresearch.Itisselectedbecauseof

theadequateandrelatedmeaningofbothNoveltyandVariety,thepossibility

ofimprovingwithFBSframework,andevaluationofgroupsofideas.

2.2.2 Ideation Methods

Inengineeringdesign,achievinganinnovationisfollowedbycreative

problem solving process including five main steps; identification of the

problem to be addressed, information gathering, idea generation, idea

evaluation and screening by alignment with strategy and feasibility, and

finally communication of the selected solution, implementation, and

commercialization(Amabile,1996;Finkeetal.,1992;MumfordandConnelly,

1991; Stain, 1967). In other words, one of the main stages of creative

problem solving is the ideation which is important to a Problem‐Solving

process(Majaro,1988;McAdam,2004).

When encountering a problem, people typically fall back on their

individual knowledge and experience, which is limited and subject to

cognitive biases (Parnes, 1988). It is believed that for individuals the

potentialityofcreativity,ideagenerationandProblem‐Solvingdependsonto

Domain‐Relevant knowledge and skills, personality variables, cognitive

factors,creativityskills,andtaskmotivation(Amabile,1983;Woodmanetal.,

1993; Ford, 1996). Therefore, different directions are studied in the

literature to improve the results of idea generation. These studies can be

classifiedintwomainapproaches;transmissiontogeneratingideas inthe

teams and developing supportive techniques and methods for idea

generation.


38

Thelogicbehindthefirstapproach,generatingideaingroups,isthat

by involving more people in the teams, the domain knowledge and skills,

creativityskillsandmotivationsareincreasedintermsofbothvolumeand

diversity, so the results will be improved (Nemeth, 1986; Amabile, 1988;

Kanter, 2000; Payne, 1990; Ancona and Caldwell, 1992; Woodman et al.,

1993; Wilson, 2000; Heinstrom, 2003). Mutual interactions for idea, goal,

strategy, and knowledge sharing in teams is a driver for idea generation

(Quinn,1985;Amabile,1998).Themorevariousgroup(genderandexpertise

Variety)resultstomorechaosintheimageofproblemandsituation,which

canbereachedtoideasifthechaosconvergestoconsistencyandconsensus

(Gilson,2001;Mumfordetal.,2001;Reiter‐PalmonandIllies,2004;Runco,

1986).

The logic behind the second approach, developing supportive

techniquesandmethods for idea generation, is thatexploiting thedomain

knowledge and skills, creativity skills, and other useful factors in idea

generationcanbeimprovedbyfollowingsomemethodsandtechniques.Itis

worthtakingintoaccountthatthesemethodsarealsodevelopedforusage

byindividualsandteams.Also,thescopeofsupportiveskillsisdifferentin

thesetechniquesrespecttothefivementionedstepsforCreativeProblem‐

Solvingatthebeginningofthissection.Sometechniquesaredevelopedto

supportthelevelofideagenerationwhilesomearedesignedtocover3or4

firststepsbysupportingdefiningorredefiningtheproblem,gatheringthe

necessaryinformationandthengeneratingideas(Colinetal.,2015).

Ideationmethodsaimtoexplicitlybroadenthesearchareaandguide

theteamtowardsmorecreativesolutions(Runco&Okuda,1988;Shalley&

Gilson, 2004) while providing an indication of progress towards an

acceptable solution (Yamamoto & Nakakoji, 2005). They achieve this by

providingaformalscaffoldandrulesaroundtheideagenerationprocess,and

theyrequire ideas tobe frequentlycaptured—orexternalized—ateach

stepthroughsketchesorsimilarmechanisms(Shahetal.,2001).Over300

ideationmethodsarelisted(Takahashi,1993),thoughonlyasmallsubsetof

thesearewidelyappliedinpractice.Thesemethodscanbeclassifiedinthe

followingapproaches(Shahetal.,2001):

Intuitive,whichaimatstimulatingsubconsciousthought:

o Germinal:aimtoeasebootstrappingasolution.Examplesare

Morphological Analysis (Zwicky, 1969), Brainstorming

(Osborn,1953),andtheK‐JMethod(Hogarth,1980).


39

o Transformational:adaptexistingsolutionstogeneratefresh

ideas. Examples are Checklists (Osborn, 1979), Random

Stimuli(DeBono,1970),andPMIMethod(DeBono,1970).

o Progressive: promote idea generation by repeated

application of a step of steps. Examples are Method 6‐3‐5

(Rohrbach, 1969), C‐Sketch (Shah, 1993), and the Gallery

Method(VanGundy,1988).

o Organizational: provide a meaningful structure around

generated.ExamplesareTheAffinityMethod(Mizuno,1988),

Storyboarding (Van Gundy, 1988), and Fishbone Diagrams

(FoglerandLeBlanc,1995).

Logical,whichfocusonsystematicallydecomposingtheprobleminto

components that can bebetter understoodand for whichsolutions

canbemoreeasilyderived.Theseinclude:

o History‐based methods that point designers to study past

solutions; examples are Design Catalogues (Pahl and Beitz,

1996)andTRIZ(Altshuller,1984).

o Analytical methods that guide them to explore variations of

theirstartingsolution;examplesareSIT,ForwardSteps,and

Inversion(Shahetal.,2001).

Success in Problem‐Solving is closely tied to the ability of the

participantstoengageindivergentthinking("thinkoutsidethebox");avoid

overly focusing on history, assumptions, and constraints (Cropley and

Cropley,2005),definedivergentthinkingas"branchingoutfromthegiven

toenvisagepreviously unknown possibilities,andarriveat unexpectedor

evensurprisinganswers,andthusgeneratingnovelty".

The logic behind most of the ideation methods and techniques is

discussedas“DesignbyAnalogy”inengineeringdesign.Extensiveresearch

inpsychologyhasattemptedtoclarifyhowpeoplegeneratesolutionsfornew

problemsandthemechanismisreportedasreasoningbyanalogy(Casakin

andGoldschmidt,1999;Eckertetal.,2005;ChristensenandSchunn,2007;

KelleyandLittman,2001).Theanalogyisknownasacomparisonbetween

two items in when they can be distinguished as similar, dissimilar, and

opposite in some traits or properties in a relational or causal structure

(Falkenhainer et al., 1989; Gentner and Markman, 1997; Hummel and

Holyoak, 1997; Blanchette and Dunbar, 2001; Gentner et al., 2001). The

people generate solutions for new problems through comparing the new

issuebytheirpreviousknowledgeandsolutions.DesignbyAnalogyusesthe

analogyfordesigning.Theproblemfieldistheprimarytargetoftheanalogy,


40

and the prior knowledge and experiences are the sources for searching a

potentialsolutiontotheproblem.TheprocessofDesignbyAnalogycanbe

pursuedwhensomeAnalogsaredecodedas similarities,dissimilaritiesor

oppositesindesigner’smemory.RetrievingtheappropriateAnalogsfromthe

previousknowledgeandexperiencesisthemostdifficultstepinDesignby

Analogy(Falkenhainer et al.,1989;HolyoakandThagard,1989;Markman

andGentner,1993;GentnerandMarkman,1997).Next,thedesignermust

map between the design problem and the source analog. Inferences and

designsolutionsarethengenerated.ThesestepsareillustratedinFigure1.

Figure1‐Stepsinhumanreasoningbyanalogy.

Accordingtothestepsasmentionedearlier,itcanbeconcludedthat

Design by Analogy is the mental attempt of a designer to infer the design

solutionaftermappingbetweenthedesigntarget(designproblem/task)and

thesourcesofanalogy,throughoneorsomeAnalogs.

FindingtheappropriateAnalogsandsourcesonthedesigntargetis

oneof the criticalaspectsofDesign by Analogy; the designer’s memory is

limited,butalso,mappingamongavailablesourcesinthedesigner’smemory

andthetargetproblemisataskwhichneedspreviousexperience.Sources

aremostlyextractedfrompriorknowledgeandexperiencesofthedesigner

storedinthememory,butexternalresourcessuchasdatabasescanbeused

too.Evenprofessionaldesignershavelimitedexperience,anditislogicalto

support them by resources available in various databases (Linsey et al.,

2012). The analogous domain may be from nature (Moreno et al., 2014).

Table10showssomeofthemethodsofDesignbyAnalogyintwocategories

ofnature‐basedandnon‐nature‐basedmethods.


41

Table10‐Examplesofnature‐basedandnon‐nature‐basedmethods.

BioX analogies Non-BioX analogies

BiomimicryandAskNature WordTreeMethod

IDEA‐INSPIRE SCAMPERMethod

BiomimeticDesignThroughNaturalLanguageAnalysis

Synectics

Engineering‐to‐BiologyThesaurusandFunction‐BasedBiologicallyInspiredDesign

SearchenginesandAlgorithm‐BasedMethods

DesignbyAnalogytoNatureEngine(DANE)(ComputationalTool)

Visual‐BasedMethods

Bio‐TRIZTRIZ‐BasedMethods

Among the techniques are mentioned in the Table 10, TRIZ‐Based

methodsareknownasthemethodswhichareenrichedmethodsintermsof

usingthepreviousknowledgeandexperiencesstored inpatents inavery

abstract levelwhichmakeitpossible tobeusedforanalogyforVarietyof

problems whereas some predefined Analogs are also developed in these

methods forsolving inventive problemsand proposing non‐obvious novel

solutions.Duetotherelevanceofthesemodelstothecurrentresearch,they

arediscussedmoreinthefollowingsection.

The Quality and appropriateness of design solutions in Design by

Analogydependonthenatureof theAnalogs,thesourceandthemapping

among found information and the design target. To facilitate Design by

Analogy,differentAnalogs,sourcesandmappingmethodsarestudiedinthe

literature.Thestudiesaremorefocusedonthedomainofsources,typeof

sources,methodsfordefiningAnalogs.Theproblem,source,Analogsarethe

central concepts of models of Design by Analogy which are completed by

methods of extracting Analogs form the sources and methods of mapping

betweenproblemandsourcesthroughAnalogs.

Someresearchersstudiedtheroleofguidanceofanalogretrievalon

the Design by Analogy. The researches show the leadership can enhance

DesignbyAnalogy(Clementetal.,1994;Clement,1994;Linsey,2007;Linsey

et al., 2007; Linsey and Markman, 2008). Visual analogies improve design

solutionsforbothnovicesandengineeringexpertswhileshowhigherimpact

for learners (Casakin and Goldschmidt, 1999). The novice designers using

sketches of example designs generate more Novel, and higher Quality

solution compares to the other designer with text‐based example designs

(McKoyetal.,2001).Case‐DrivenAnalogyisusedmorerespecttoSchema‐

DrivenAnalogybynovices(Balletal.,2004).ACase‐DrivenAnalogyisknown


42

an analogy where a specific example is used to develop a new solution. A

Schema‐DrivenAnalogyisconsideredananalogywherethecharacteristics

of solutions are derived from some cases in an abstract level. The FBS

framework is one of the frameworks which is proposed to be used as

Schema‐Driven Analogy whereas the designer thinks about Function,

Behaviour, and structure of the system during designing (Gero &

Kannengiesser,2004).

Someresearchhasdiscussedtheeffectsofdifferentresourcesonthe

Quality of design solutions (Casakin and Goldschmidt, 1999; Leclercq and

Heylighen,2002;Casakin,2004;Eckertetal.,2005;ChristensenandSchunn,

2007;Tsengetal.,2008a;Chanetal.,2011;Linseyetal.,2012).Engineers

mostlyapplyCross‐Domainanalogiesinideagenerationprocesses(Casakin

and Goldschmidt, 1999; Leclercq and Heylighen, 2002; Christensen and

Schunn,2007).Close‐Domainanalogiesandexploitingtheknowledgeofthe

past solutions are often used in cost estimation, process planning and

evaluation of new product concepts (Eckert et al., 2005). Cross‐Domain

SpecificallyFar‐FieldAnalogyincreasesthenoveltyofsolutions(Chanetal.,

2011).ACross‐DomainAnalogyisappliedmorewhenthedesignersarenot

capable of solving the design problem (Tseng et al., 2008b; Linsey et al.,

2012).

Also, empirical studies demonstrated that professional designers

oftenuseanalogies(BlanchetteandDunbar,2001;LeclercqandHeylighen,

2002;Balletal.,2004;Eckertetal.,2005;ChristensenandSchunn,2007).

Experts apply analogies more significantly respects to novices whereas

novices tend to apply case‐driven analogies compare to Schema‐Driven

Analogies (Ball et al., 2004). To support the patent analysis tools for

application of patentability, rather than selecting just one or some special

method,itisworthtoconsiderDesignbyAnalogyastheprimarymodel.In

otherwords,improvingPatentMapwhichisthefocusofthecurrentresearch

forgeneratingpatentable ideasthroughsupporting thegenerationofnon‐

obvious novel ideas can be pursued by the main concepts of Design by

Analogy. As discussed Design by Analogy is the logic behind most of the

ideationmethods.Also,theTRIZ‐Basedmodelisrelevanttothescopeofthis

researchwhichisdiscussedinthefollowingsection.


43

2.3 TRIZ and OTSM-TRIZ model of contradiction

TRIZisanacronymof theLatintitle"TeoriyaResheniyaIzobretatelskikh

Zadach" of the Russian name "теория решения изобретательских задач,"meaning"Theoryoftheresolutionofinvention‐relatedtasks,"commonlystatedas"Theory of inventive Problem‐Solving." TRIZ is a methodology first proposed byGenrich Altshuller to identify patterns that repeatedly feature in the Problem‐Solving as evidenced by patent literature (Altshuller, 1984). As discussed before,TRIZ‐BasedmethodsareknownasatypeofDesignbyAnalogymethodswhichareenrichedregardingusingthepreviousknowledgeandexperiencesstoredinpatentsin a very abstract level whereas some predefined Analogs are also developed inthese methods for solving inventive problems and proposing non‐obvious novelsolutions.TRIZasatheory,abstractedobservedpatternsinpatentsinthreemainpostulates: the existence of Objective Laws of Engineering system evolution, thedynamicsofContradictionandtheconceptofResourcescharacterizingtheSpecificSituation (Khomenko and Ashtiani, 2007). Each engineering system evolves tosatisfythedesireofindividualidealityrequirements(characteristics).SomelawsofNatureconstituteanobstacletosystemevolution:inTRIZterms,thisisrepresentedbytheactionofananti‐systemthatsharessomepartswiththesystem‐of‐interest.The opposition of the anti‐system gets manifested through contradiction(s). Forcontinuingtheevolutiontowardsideality,thecontradictionsbetweensystemandanti‐system(eithertakenasawholeorjustassharedparts)mustbeovercomebytheavailableresourcesintheparticularsituation(Cascinietal.,2015).

OTSM‐TRIZextendsTRIZformodelingmorecomplexandmultidisciplinaryproblems(CavallucciandKhomenko,2007).OTSMisanacronymoftheLatinform"GeneralTheoryofpotentcogitate"oftheRussianname"Общаятеориясильногомышления,"commonlystatedas"GeneralTheoryofPowerfulThinking."Insomerecent researches, TRIZ and OTSM‐TRIZ are known as applied scientific theorieswhichareevolvedfromoriginalpatternsinthefieldofProblem‐Solving,andtheysupport users tosolvetechnicaland interdisciplinaryproblemsrespectively (SeeTable11,Cascinietal.,2015).Somenecessarycomponentofthebodyofknowledgeof these two applied scientific theories, such as assumptions, models, andinstruments/toolsaresummarizedinTable11(KhomenkoandAshtiani,2007).


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Table11‐ComponentsofClassicalTRIZandOTSM‐TRIZtheories.

Topic Classical TRIZ OTSM-TRIZ

Assumptions

ObjectiveLawsofEngineeringsystemevolutiondoexistandcouldbeusedforproblem‐solving;Contradictionshowstherootoftheproblem.Itshouldbedisclosedandresolved;SpecificSituationprovidesresourcesthatshouldbeusedtosolveaproblem.

AxiomsofModelsareusedduringthinkingprocess.Theproblemariseswhentypical,traditionalmodelscouldnotbeusedandshouldbechanged:

Group1:AxiomsonThinkingProcess((1)AxiomofImpossibility;(2)Axiomofrootofproblems;(3)Axiomofreflection;(4)Axiomofprocess.);Group2:AxiomsonworldVision((1)AxiomofUnity;(2)AxiomofDisunity;(3)AxiomofConnectedness.).

Models

SystemOperatormodelofsystemthinking;ClassicalTRIZModelsofProblemSolvingProcessdedicatedtodevelopingandorganizeotherproblem‐solvinginstrumentsintowholesystemefficientforsolvingtheproblemanddevelopthinkingskillsfurther:

‐TongsModel;‐HillModel;‐FunnelModel;‐ParallelModel.

ENVFractalModelisageneralandformalizedlanguagetodescribeproblemsandsolutions,realandimaginaryfactsandobjectsOTSMFractalModelofProblemSolvingProcessdedicatedtomanagingaproblem‐solvingprocessandharmonizetheapplicationofvariousinstrumentsevenoutofClassicalTRIZ.

Instruments

ForTypicalProblems:

‐Standards;‐PointersofEffects;‐MechanismofConvergence;‐…

ForNon‐TypicalProblems:‐ARIZ.

ForSmallProblemsituation(adozenofsub‐problems):

‐NewProblemTechnology;‐TypicalSolutionTechnology;‐ContradictionTechnology;‐ProblemFlowTechnology.

ForComplexProblems(hundredsofsub‐problems):

‐ProblemFlowNetworkApproach.

A necessary first step to Problem‐Solving is to analyze the problem, thedesiredgoal,andtheknowledgeaboutfillingthegapbetweentheproblemandthegoal.Specifically,aproblemmaybewell‐defined,byaclearperceptionofthecurrentsituation,thegoal,andhowtogetthere.Problemsthatarenotwell‐definedinallthreeaspectsareconsideredill‐structured(Jonassen,1997)andrequireexplorationandideation.Morethanclassifyingproblemstoowell/ill‐structuredproblems,TRIZandOTSM‐TRIZtheoriesrankproblemstotypical/non‐typicalandinventive/non‐inventiveproblems(Simon,1973;Khomenkoetal.,2007).Typical/non‐typicalandinventive/ non‐inventive are the classifications focused on the availability ofsolutions.Non‐typicalproblemsarethetypeofill‐structuredproblemsthatthereisnotypicalsolutionmodelforthataccordingtotheusualtypesofProblem‐Solvinginthe corresponding field of knowledge and technology. Inventive problems areknown as kind of non‐typical problems which the solution must cope with acontradictory situation for solving them. As patents are the non‐obvious novelsolutionsforproblems,theywereconsideredaspotentialsourcesforexploringthepatternsofsolutionsforinventiveproblemsoratleastnon‐typicalproblems.


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Modelingthecontradictorysituationofaninventiveproblemispursuedinthree levels; administrative, technical, and physical (Jonassen, 1997).Administrative,wherecontradictorydesiredoutcomesare identified but withnoknownwaytoachieve it; technical,wherebothsimultaneous useful andharmfuleffectsofanactionareidentifiedbutwithnoknownwaytoovercomethesituation;and Physical, where a property of an element make both desired and undesiredoutcomes but with no known way to cope with the situation (Altshuller, 1984;Terninkoetal.,1998;Salamatov,1999).

TRIZ and OTSM‐TRIZ models and instruments let the problem‐solversidentify and explicitly state non‐typical and inventive problems to propose non‐obvious novel solutions for them. The instruments propose models for firstmodellingandthenovercomingorbypassingthecontradictorysituations;OTSM‐TRIZ Model of contradiction is a model for formulating a contradictory situationsimultaneously in both technical and physical levels, and Separation principles(Separationinspace,intime,betweenthewholeandtheparts,uponConditions,…)areatoolforbypassingcontradictorysituations.AsmentionedOTSM‐TRIZmodelofcontradictionisamodelformodelingthecontradictorysituationofaninventiveproblemwhichisdevelopedbasedonENVmodel.ENVmodelisasimplemodelfordescribing a situation, an event, an element or a goal/ requirement precisely; EstandsforElement,NstandsforNameofthefeature,andVstandsforthevalueofthe feature. A four‐step process is proposed for formulating the contradictorysituation of an inventive problem simultaneously in both levels of technical andphysicalasmuchaspossibleclearbasedonENVmodel(CavallucciandKhomenko,2007).OTSM‐TRIZmodelofcontradictionformulatetheproblemconsideringinitialsituationA,achoicesituationB,andcontradiction;theinitialsituationAshowstheproblemandthechoicesituationBisconsideredasthesolutionthatisusuallyusedtoresolvethesituationAbutresultsfromanewproblemandcontradiction.Thesethreeconceptsshowtheparametershavetobeusedtomodelthesolution.Initialsituation A aims to clearly define the main requirement which is expected to besatisfied. This requirement is called Evaluation Parameter (EP1) (Cavallucci andKhomenko,2007),anditrepresentstheimprovingparameter.NewsituationBaimstoidentifyanewsituationBwhereEP1issatisfiedbyapplyingoneofalreadyknownsolutions/systems.ProblemsderivingfromsituationBaimstofindnewproblems(EP2)representsworseningparameter.Finally,ContradictionFormulation,aimstoselectonlysomeof(EP2)amongallrequirements/problems(EP2)extractedfromsituation B, the ones which are in conflict with the requirement (EP1) of thesituationA.BasedonOTSM‐TRIZmodelofcontradiction,acontradictorysituationcanbeformulatedasbelow(Figure2,BecattiniandCascini,2013):

<Control Parameter> of Element X should assume Value 1 in order to improve Evaluation

Parameter 1 of Element Y, but then Evaluation Parameter 2 of Element Z worsens. <Control Parameter> of Element X should assume Value 2 in order to improve Evaluation

Parameter 2 of Element Z, but then Evaluation Parameter 1 of Element Y worsens.


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Figure2‐TheformulateContradictionaccordingtoOTSM‐TRIZ.

Thecontradictioninthetechnicallevelisformulatedontherightsideofthemodelwhereasitsformulationatthephysicallevelisillustratedontheleftside.Inthismodel,animprovingparameterisapropertyofoneofthesystemcomponentsthat isexpected tobe improved by thesolution, andaworsening parameter is aproperty of a system component that prevents the improving parameter fromachievingthedesiredvalue.Theworseningandimprovingparametervalueshaveaninverserelationship.Acontrolparameterisacomponentsystempropertythatallowstrade‐offsbetweenimprovingandworseningparameters,anditispossibletocontrolvaluesofimprovingandworseningparametersthroughit.Resolvingthecontradictioncanbepursued inrightsideor leftsidebyapplyingtheseparationprinciples.

Literaturediscussesmore theclassicalTRIZ andOTSM‐TRIZmethodologyandtheircorrespondingmodelsofthinkingandtheirtools.ItisclaimedthatTRIZisgrowth and developed based on analyzing and classifying the level of resolvedcontradictionsinpatents.Thismainclaimwasnotissuedscientificallyyet.However,therearethereportsofsuccessfulcasestudiesbyusingTRIZforCreativeProblem‐Solving.ItisreportedthatTRIZradicallyenhancestheQualityandQuantityofidea‐generation(Souchkov,2007).TRIZprovidesusefulandnon‐obviousnovelsolutionsinshortertimewhereasincreaseseffectiveteamisworkingwhenwasappliedinagroup (Ilevbare, 2013). TRIZ was identified applicable technique when the ideageneration process is characterized by knowledge background of participants,opportunityoftryanderror,thoroughnessofideas,orelaborationofideaswhereasitwasobservedasuitabletechniqueforcontextscharacterizedbyhighknowledgebackgroundofparticipants,needforthoroughnessofideas,orelaborationofideas(Linetal.,2006).Inotherwords,oneofthedifficultiesofusingTRIZisassociatedwithitslearningandapplication(Ilevbare,2013).

Infurtherstudiesrespecttothemodelofcontradiction,theliteratureshowsthat there are studies for improving the various models for formulatingcontradictionsand theprocess formodeling the inventiveproblems (CasciniandRusso,2006;MaandTAN,2007;Cavalluccietal.,2008;MontecchiandRusso,2015).The contradiction model and matrix were used for idea generation (Kobayashi,2003;Dalyetal.,2012),buttheliterature isnotrichinreportingtheeffectof its


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usageontheNovelty,Quality,andappropriatenessofdesignandProblem‐Solving(Dalyetal.,2012).ArecentpaperclaimedthatusingOTSM‐TRIZgamesspecificallythe one which is based on OTSM‐TRIZ model of contradiction, increases thecreativityandVarietyofideas.Therefore,itcanbepromisingthatusingOTSM‐TRIZmodelofcontradictioncanincreasetheNoveltyandVarietyofideas(Belski,2011;Cascinietal.,2015;Dumasetal.,2016).

The OTSM‐TRIZ model of contradiction let the problem‐solvers model acontradictorysituationofaninventiveproblem.Fromtheotherhand,patentscanbethesolutionsforthecontradictorysituationofaninventiveproblem.ExploitingthismodelinexploringandhighlightingtheresolvedcontradictionsbyapatentcanenrichaPatentMapforreachingmorepatentableideasasaresultofatoolbasedontheDesignbyAnalogymodelofideageneration.

Chapter3:Researchmethodology

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Chapter3

[3] Research Methodology


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In this chapter, the prescriptive study as the third phase of research was

perused comprehensively, and the research contribution for increasing thepatentability of ideas is proposed. Then the set of required empirical studies forinvestigatingtheproposedcontributionareplannedanddiscussed.

3.1 Methodological proposal to improve the Novelty of design proposals

Increasingthepatentabilityofideagenerationanddesignsessionsareone

of theapplicationsmentioned for patent analysis,as identifiedas arequest fromIranian SMEs through a survey. Therefore, improving the patentability of ideagenerationanddesignsessionsthroughpatentanalysisisconsideredastheultimategoalofthisresearch.Patentsarealegaltitleforacertainperiodthatmustbecoverthree main requirements: Novelty, Non‐obviousness, and Usefulness. As such,increasing the patentability of ideas means increasing both the Usefulness andNovelty of ideas. However, the proposed Novelty should not be obvious for theexpertsinthefield.Noveltyandindustrialusagearethetwomainissuesthatmustbecoveredsimultaneously,whileNon‐obviousnessisconsideredasacharacteristicofaNovelidea.ThisresearchisfocusedontheNoveltyofideas.

3.1.1 Research contribution

The contribution of the current research is an improved Patent Map forsupportingR&Dengineersforimprovingthepatentabilityoftheirgeneratedideas.Todescribethiscontribution,variousdesignconceptsandmodelssuchasdesignprocess,characteristicsofdesignproposals,DesignbyAnalogy,patentanalysis,andPatentMap,andideationtechniquesmustbeconsideredinabigpicturealtogether.Figure 3 Shows the position of the contribution of this research respect to thementionedconceptsandmodels.

As the picture shows, the TC Map is the particular contribution of theresearch which is developed as one of Patent Map tools to be used as a designstimulus in part of the design process to increase the patentability of designproposals.

AsreviewedinChapter2,inengineeringdesign,achievinganinnovationisfollowed by creative problem solving process including five main steps;identification of the problem to be addressed, information gathering, ideageneration,ideaevaluationandscreeningbyalignmentwithstrategyandfeasibility,and finally communication of the selected solution, implementation, andcommercialization(Buhl,1960;Svensson,1974;Wilson,1980;Crawford,1984;Ray,1985;Cooper,1986;AndreasenandHein,1987;Cougeretal.,1993;Amabile,1996;Finkeetal.,1992;MumfordandConnelly,1991;Stain,1967).

Designproposalsaretheresultsofthefourfirststepswhichareknownasthestepsfordesigningtheconceptofsolution.Manydesigntechniquesandmethodshave been developed and applied in these four steps to increase the requiredcharacteristics of design proposals. The improved ideation and Problem‐Solving


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techniquesandmethodsaredevelopedtobeusedasadesignstimulusintheformofadesignprecedentoradesignprocedure.

Figure3‐RelationsofResearchmodel,Designprocesssteps,andoriginalcontribution.

Chapter 2 reviewed the characteristics of design proposals in three maingroups;Usefulness,Feasibility,andNovelty.Usefulnessshowstheappropriatenessandrelevanceoftheconceptofsolutionrespecttothedesigntaskandsolvingthetargetproblem.Feasibilityhighlightsthepossibilityandplausibilityofgeneratingthe developed concept as a concrete solution. Novelty clarifies the newness,unusualness, and unexpectedness of the developed concept respect to the set ofreference for the target users of the solution. As picture shows, respect to theultimate purpose of discussing the characteristics of the design proposals, thedifferentviewpoint,terms,meaning,andmeasuringmethodsaredeveloped,appliedandreviewedintheliterature.Patentabilityofanideacanbediscussedasaspecificlevel for these three groups of characteristics. Shah metric which is discussed inChapter 2, can be considered as precise meaning for these three groups ofcharacteristics with another term of Quality, Novelty, and Variety with insistingdifferentlyontherequiredconcepts,andasmuchasapossibleobjectiveformulaforassessingthem.

Inthescopeofthisresearchamongmanymetricsfordefiningandevaluatingthesecharacteristics,ShahmetricisselectedasthemeaningofNovelty,andsimilar


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objectiveformulaforassessingitaremorecompatiblewiththemeaningsbehindpatentabilityrequirementsregardingnon‐obviousnovelty,asdiscussedinChapter2.Noveltyisthemainconceptforpatentabilityandthereforethemostmetrics,andalsoShahmetricisfocusingonthiscriterion.Anon‐obviousnovelinventionhasachancetoberegisteredasapatent.NoveltymustbeprovedintheHiddenandPriorapplicationlevelofknowledgeofallfieldoftechnologiesamongthefourthlevelofCommon, Enhanced, Hidden, and Prior application (Franzosi, 2000). Non‐obviousnessmustbeprovedinthesamelevelsbut just inthespecificandtargetdomainoftechnology(Franzosi,2000).ThesetwoexpectedlevelsforNoveltyandNon‐obviousness, is discussed in the engineering design as Novelty and well‐travellinginbothproblemandsolutionspacesasNoveltyandVariety(Shahetal.,2003) which are mostly results of resolving contradiction by new resources in acontradictorysituationforcoveringtherequiredanddesiredvalueofrequirements(Altshuller, 1984). In other words, non‐obvious novelty can be represented byresolvingcontradictionforacombinationofnewproblemandnewsolution.

Respect to the essential and desired value of characteristics of a designproposal to be accepted as the patent regarding non‐obvious novelty, it seemsamongmanydesigntoolsandprecedents,Problem‐SolutionPatentMap(PS.Map)and OTSM‐TRIZ model of contradiction can be focused more on developing asupportivetool.Problem‐SolutionPatentMapprovideallproblemsandsolutionsfortechnicalsystemsbasedonallcorrespondingpreviouspatentsandconsequentlyincrease the possibility of absorbing the attention of problem‐solver to a newcombination of Problem‐Solution for a target system. Also, OTSM‐TRIZ model ofcontradictioncanhighlighttheresolvedcontradictionofthepatentsforeachpairofProblem‐Solution,andconsequently,increasethepossibilityofproposingthenon‐obviousconceptofsolutionforthecorrespondingpair.

Technical Contradiction Map, as the original contribution of currentresearch,isthecombinationoftheProblem‐SolutionPatentMapandOTSM‐TRIZmodel of contradiction for increasing the patentability of ideas regarding non‐obviousnoveltygeneratedbyR&Dengineers.Ontheotherhand,theTC.Amapisanewtool in thedomainofDesign by Analogymethods,andtherefore, itmust bediscussed based on the components of Design by Analogy model. Next sectiondiscussestheproposedcontributionbasedontheDesignbyAnalogymodel.

3.1.2 Developed model for the target contribution of the research

Asmentionedinchapter2,Increasingthepatentabilityofthedesignsolutionbysupportingdesigners inproposingnon‐obviousnovelsolutions istheultimategoalofthisinvestigation.ItislogicaltodeveloptheDesignbyAnalogymodelaboutthisaim. It isexpected that thedevelopedmodelconsiders themostappropriateAnalogs,source,andmappingmethod.Table12showstheconceptsforappropriateAnalogs,withthesourceandmappingmethodtoenrichDesignbyAnalogyfornon‐obviousnoveldesignsolutions.


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Table12‐ConceptsforappropriateAnalogs.

Fundamentals of

Design by Analogy

The developed concept for enriched

Design by Analogy for Novel, non-

obvious design solutions

Supportive logic

Analogs

Problem

Solution

Resolvedcontradiction

TosupportbothNoveltyand

Non‐obviousness

Source Relatedpatentstotargetdesignproblem TosupportNoveltyandnewness

Mappingbetween

analoganddesign

target

TechnicalContradictionMap

Toimprovepossibilityofcovering

newproblemsandalsoNoveltyand

Non‐obviousnesssolutionsfor

existingproblems

ExtractingAnalogs

fromthesourceOTSM‐TRIZmodelofcontradiction

Tosupportextractingproblems,

solutionsandcontradictionsfrom

patents

Novelty can be reached by proposing new solutions for existing or newproblemswhicharecoveredinthepatentsofatargetsystem,whilenon‐obviousnoveltycanbeconsideredasresolvinganewcontradictionorexistingcontradictionbynewsolutions.AstheTable12showstosupportnon‐obviousnovel ideas,thecontradiction is considered as one of the Analogs and therefore retrieving theresolvedcontradictionsandmappingthemarecoveredinthefundamentalconceptsofthemodel.Figure4showstheabove‐mentionedconceptsinthedevelopedmodel.

Figure4‐Proposedmodelofresearch.


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DesignbyAnalogyisdonewhenadesignermaponeorsetofAnalogsamongdesignproblemandsourcesofanalogy,andthentheideaswillbegeneratedthroughinference.Figure4suggeststhepreviouspatentsinafieldasasourcewhereastheproblem, solution and resolved contradictions of them as Analogs for proposingnon‐obviousnovelsolutions forthe targetdesignproblem.TosupportextractingtheproposedAnalogs,theOTSM‐TRIZmodelofcontradictionissuggestedandalsoforsupportingthemapping,PatentMappingisused.Eachcomponentoftheabove‐developedmodelisdiscussedinmoredetail.

The source: PreviouspatentsofatargetsystemareconsideredasthesourceforDesign

by Analogy in enriching the Patent Map for achieving more non‐obvious novelsolutions by designers. As discussed in Chapter 2, the patents are sources ofinformationconsistofbothtechnicalinformationsuchastheproblem,themeanstosolvetheproblem,andthecitedpatentsandinformationaboutthemarketsuchasthenameofholders,andtimeofregistration(Parketal.,2005).Therefore,theyhavecommonlystudiedinR&Dplanning;inthemacro‐levelstrategicanalysis,andinthemicro‐levelmodelingofspecificemergingtechnologies(LiuandShyu,1997;Wangetal.,1998;AbrahamandMorita,2001;Watanabeetal.,2001).

Theresearchshowsdesignersusethepreviousknowledgeandexperiences(Casakin and Goldschmidt, 1999; Leclercq and Heylighen, 2002; Casakin, 2004;Eckertetal.,2005;ChristensenandSchunn,2007;Tsengetal.,2008a;Chanetal.,2011;Linseyetal.,2012);relyingtheirpreviousknowledgeandexperiencesstoredin their memory, and external knowledge which are provided for them(Falkenhainer et al., 1989; Holyoak and Thagard, 1989; Markman and Gentner,1993; Gentner and Markman, 1997; Linsey et al., 2012). Patents are the type ofexternalknowledgefordesignerswhichcanbeprovidedforthemintwodifferentformsanddistance.

Theinformationofpatentscanbepresentedasrawinformationtodesignersas some individual sources for Case‐Driven Analogy or can be presented ascharacteristics of solutions by abstracting the information of the patents as thesourceforSchema‐DrivenAnalogy(Balletal.,2004;GeroandKannengiesser,2004).LiteratureshowsnovicestendtoapplyCase‐DrivenAnalogy(Balletal.,2004)whileSchema‐Driven Analogy is more effective on increasing Quantity, Quality andNoveltyandreducingfixation(Marslen‐Wilson&Tyler,1980;Oxman1990;Lane&Jensen,1993;Liikkanen&Perttula,2006;Zahneretal.,2010;Goldschmidt,2011;Howardetal.,2013).

Inaddition,theprovidedpatentscanbeselectedforClose‐DomainAnalogyby focusing on previous patents of a target system, or can be chosen for Cross‐Domain Analogy by focusing on more effective patents on the purpose of designproblem(CasakinandGoldschmidt,1999;LeclercqandHeylighen,2002;Eckertetal., 2005; Christensen and Schunn, 2007; Tseng et al., 2008b; Chan et al., 2011;Linsey et al., 2012). Engineers mostly apply Cross‐Domain analogies in ideageneration processes (Casakin and Goldschmidt, 1999; Leclercq and Heylighen,


54

2002;ChristensenandSchunn,2007).Close‐Domainanalogiesandexploitingtheknowledgeofthepastsolutionsareoftenusedincostestimation,processplanningand evaluation of new product concepts (Eckert et al., 2005). Cross‐DomainSpecificallyFar‐FieldAnalogyincreasestheNoveltyofsolutions(Chanetal.,2011).A Cross‐Domain Analogy is applied more when the designers are not capable ofsolvingthedesignproblem(Tsengetal.,2008b;Linseyetal.,2012).

AlthoughSchema‐DrivenandCross‐Domainanalogies seemmoreefficienton increasing the Noveltyof ideas, in thescopeof thecurrent research,Schema‐Driven of Close‐Domain sources are considered as the primary source for thedevelopedcontribution.Inotherwords,previouspatentsofatargetsystem(Close‐DomainAnalogy)asatypeofPatentMap(Schema‐DrivenAnalogy)areconsideredasthesourceforthisresearch.

The Analog: Three Analogs are selected for the study; the problem, solution, and

contradiction.Problemspacewhichcoversthecharacteristicsofbothproblemandsolutionmustbeexploredforcreativeproblem‐solving.Problemspaceistheareawheretheproblemsolvingbeaccomplishedanditincludesboththecurrentpartialsolutionsandpotentialsolutions.Theproblem(s)whichisaddressedbyeachpatentand the proposed solution(s) by the same patent can help designers to exploreproblemspaceofthetargetsystem.Consequently,thewholepreviouspatentsofatargetsystemcanrevealtheproblemareainabroaderscope.TheproblemofeachpatentismostlymentionedintheBackgroundsectionofthepatent.ThesolutionofeachpatentisdefinedintheClaimsectionofeachpatent,andtheconceptofthatismentionedintheSummarysection.

The contradiction is the third considered Analog for supporting thegenerationofnon‐obviousnovelsolutions.Theresolvedcontradiction,ifsearchedandfindinthepatents,cansupportdesignerstoexploretheproblemspacemoretechnicallybyconsideringtherelationsamongthecomponentsofthetargetsystem.Thecontradictioncannotbeseenexplicitinthepatentsandtheymustbeinferredbytheexpertiseofapatentanalystwhichismentionedinthefollowingsection.

The method of extracting Analogs from the sources: Althoughitisexpectedthattheinformationofproblemsandsolutionofthe

inventionaredescribedinapatent,noneofthemareincludedinthebibliographicalinformation. This data is only contained in the narrated parts which are un‐structureddataofthepatents.Anengineerorpatentanalystwhointendtoextractthis information must study all related patent documents for organizing the“problems correctly to be resolved by the invention” and “means for solving theproblem.”Usually,theproblemsarementionedintheBackgroundofeachpatentandcanbesearchedbykeywordssuchasProvide,Need,Improve.Furthermore,thegeneralsolutionconceptofapatentisusuallymentionedintheSummaryofpatentsandcanbesearchedbyInclude,Supportkeywordswithinthepatents.


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The resolved contradiction is not mentioned in the patents too, andextracting it fromthe patent information is notaneasy task. In the scopeof thisresearch, extracting the related information is going to be searched through theconceptualstepsbyR&DengineersbasedontheOTSM‐TRIZmodelofcontradiction.TheOTSM‐TRIZcontradictionisusedforformulatingaproblemtobesolved.OTSM‐TRIZmodelofcontradictionformulatetheproblemconsideringinitialsituationA,achoicesituationB,andcontradiction;theinitialsituationAshowstheproblemandthechoicesituationBisconsideredasthesolutionthatisusedusuallytoresolvethesituationAbutresultsinanewproblemandcontradiction.Thesethreeconceptsshowtheparametershavetobeusedtomodelthesolution.InitialsituationAaimsto clearly define the main requirement which is expected to be satisfied. Thisrequirement is called Evaluation Parameter (EP1) (Cavallucci and Khomenko,2007),anditrepresentstheimprovingparameter.NewsituationBaimstoidentifya new situation B where EP1 is satisfied by applying one of already knownsolutions/systems.ProblemsderivingfromsituationBaimstofindnewproblems(EP2)representsworseningparameter.Finally,ContradictionFormulation,aimstoselectonlysomeof(EP2)amongallrequirements/problems(EP2)extractedfromsituation B, the ones which are in conflict with the requirement (EP1) of thesituationA.BasedonOTSM‐TRIZmodelofcontradiction,acontradictorysituationcanbeformulated(BecattiniandCascini,2013)asmentionedinchapter2.

Patentsaremostlytheresultsofresolvedcontradictionsbutformulatingtheresolvedcontradictionfromtheinformationofapatentdocumentmustbeslightlydifferent from the above‐mentioned model. In the next part, the developedprocedure for extracting Analogs from patents are proposed with somemodificationsofthesesteps.

ReviewedliteratureinChapter2showed(Table6),themostofthedevelopedtext mining techniques for distinguishing Novelty of patents and specifically theresolved contradiction are in the group of SAO based NLP techniques whereaskeywordbasedNLPtechniquesandProperty‐FunctionbasedtechniquesarealsousedforminingtheinventionandNovelty.Inthescopeofthecurrentresearch,theconceptsbehindthesetechniquesareusedforextractingthecontradictionbyR&Dengineersmanually.

Method of analogy between the source and the Analog As mentioned in Chapter 2, analysis of the technological information of

patentdocumentsandpresentingthemvisuallyinakindofPatentMapsupportsunderstandingthepatentinformation(WIPO,2003).APatentMapcollectspatentdata through considering several aspects, and it is regarded as a type sources ofSchema‐DrivenAnalogy.AMatrixMapaskindofPatentMap,clusterspatentsbyconsideringaspectssuchasthefieldofmanufacturingpurpose,use,technologicalcomponent,a functionalcomponent,theproblemsandsolutions.BothstructureddataliketheapplicantnameandfilingdateandunstructureddatasuchasproblemandsolutionareusedastheaspectsforaMatrixMap.Therefore,aPatentMapis


56

builtbybothtextminingandvisualizationtechniques.Usually,theMatrixMapsbuiltinthetwo‐dimensionalgraphonpaperorscreen.

In this research, a three‐dimensional Matrix Maps which deals with threeaspectofproblem,solution,andcontradictionisproposed.Problemsandsolutionsare considered as the two first dimensions for clustering the patents. In general,“problemstobesolvedbytheinvention”canbeclassifiedbasedontherequestedmainandsecondaryfunctionsofthesystems.The“solutionsofproblems”canbegrouped into different categories, covering the improvement of a new system;developmentofprocessapplicationofnewelementorchangingofnewmaterials;theincreaseofsupportingmembers;improvementofnewformation,etc.(Suzuki,2011). These two dimensions are displayed on the main paper. The resolvedcontradictionforthesetofpatentsineachcrossamongtheclustersofproblemsandsolutions is considered as the third dimension which is displayed on othersupportivepapers.Figure5showstheoverallschemaofdevelopedmapconsistsofabubblegraphicalmatrixmapinmainpaperandContradictionMapsforeachcrossofthematrixmap.

Figure5‐AconceptualdiagramofaTechnicalContradictionMap.

AstheleftpartoftheFigure5shows,themainmapclassifiestherelevantpatents by combinations of classes of problems as the first aspect and classes ofsolutionsasthesecondaspect.Thenumberofpatentsineachcrossareintroducedby the size of a bubble. This type of quantitative Matrix‐Map supports theinterpretertorecognizeatalook,theproblems,andtechnicalcomponentandtheconcentration of means for solving problems. This kind of map also presentinformationfornewartwhenthelargestnumberofpatentapplicationswerefiledinsomecrosses,withalargevolumeofinformationdisclosed,whichsupportsR&Dstoreachrelevantinformation(Suzuki,2011).Itisalsopossibletogetinsightabout


57

thecompetitionsamongthecompanies in thecrosses witha big bubble.Thebigbubble also proposes the art is simple to improve technically in such crosses,althoughtheintellectualpropertymustbeconsideredmoreprecisely.Adversely,inthebubbleswithveryfewpatentapplications,itseemslesschancefornewartformoreimprovement.

TherightpartofFigure5showsthegraphsofresolvedcontradictionofthecrosses of the matrix map. Each contradiction graph consists of the worsening,improvingandcontrol parameters inthecomponents(orelements)of thetargetsystem.Acontrolparameterisacomponentsystempropertythatallowstrade‐offsbetween improvingandworseningparameters; it ispossible tocontrolvaluesofimprovingandworseningparametersthroughit.Improving,worseningandcontrolparameters together help engineers to understand the inventive problem. ThesesupportivepaperslettheR&Dengineersknowthetechnicalandinventiveproblemsofeachcrosstoresolvethemandgeneratenon‐obviousnovelsolutions.

To support understanding and to exploit theTechnical Contradiction Map(T.C Map) by R&D engineers for the first time, an instruction is developed. Theinstruction defines the main concepts and information provided in the Map andsomegeneraldirectionsforgeneratingnewideas(SeeAppendixC).

3.1.3 Developed procedure for building Technical Contradiction map

Based on the developed model of Design by Analogy specialized for non‐obvious novel ideas, a “Technical Contradiction Map” is proposed to facilitatemapping between Analogs and the design target by covering the information ofproblems,solutions,andresolvedcontradictionsofthepatentsrelatedtothetargetsystem.

Figure6‐SimplifiedproceduresforbuildingaTechnicalContradictionMap.


58

ATechnicalContradictionMapisathree‐dimensionalMatrixMapsconsistsproblem,solution,andresolvedthecontradictionofpatentsasitsthreedimensions.Similar toeachMatrixmap, threemainstages must be plannedand followed forbuildingtheTechnicalContradictionMap:(i)preparingtheresourcebyselectingthe appropriate patents, (ii) gathering target patent information by extractingAnalogsfromselectedpatents,andfinally(iii)buildingthemap.Tocoverallthesethree stages, a simplified procedure is developed that is shown in Figure 6. ThisFigure shows the three stages, their inputs, interventions, and outputs. Table 13showsindetailthestepsofeachofthestages.

Table13‐DetailedprocedureforbuildingaTechnicalContradictionMap.

Input Main

stages Main steps Detail steps Output

Target system

1-

Preparing

the

resource

Step 1:

Identifying

related

keywords to

the system

Considering“system’sname”;

Determiningthemainkeyword;

o (Nameofthesystem)+(Purposeofthesystem).

Target patents

Step 2:

Identifying

the CPC code

of the system

(Espacenet)

FindingouttheCPCcodeofthesysteminEspacenet;

o SearchingKeywordinthe“Classificationsearchpart”ofEspacenet.

o Checkingthedescriptionofcodetoensuretherelevanceofcodetothesystem.

Step 3:

Extracting

the related

patents

(Orbit)

Considering“Orbit”asthedatabaseforsearch;

Limitedpatentsearch;

o Bytitle,abstract,claims,descriptions,objectoftheinvention.

o Bykeyword.

o Alive&Granted.

o Between20years(validityofapatent).

Step 4: Refine

the patents

Eliminatingincompleteorwithoutdatapatents;

Eliminatingpatentsinotherlanguages,exceptEnglishpatents.;

Eliminatingnotrelatedpatentsbyreadingthetitle,abstractandseethefigures.

Step 5:

Selecting the

patents

Sortingthepatentsbythedateofpatents(newertoolder);

Selectingrandomly(onefromolder,onefromnewerandonefrommiddle);

Studyingthepatentsandcategorizetheproblemandsolution;

Intheproblemandsolutioncategories,wehavetherepetitionofcategories(redundant);

Studyingsomepatentsafterredundantformorecertainty.


59

Target patents

2-

Extracting

the

Analogs

Step 1:

Identify

Problem of

the patent

Definition:Theproblemdefinitionistheintendedimprovedbehaviorofthesystemunderinvestigationtobeachievedthroughtheproposedpatent.

Questionstolookfor:

o Whatistheultimatebenefitofapplyingforthepatentinthesystem?

o Whatneedorrequirementdoesthepatentsatisfy?

Comments:

o UsualLocationtolookfor:The“Background,”“Summary”and“Abstract”sectionsofthepatentapplication.

o Theusualformatinthepatent:Wordsthatcommonlyappearinproximitytotheproblemstatement:

Provide,Support,Need,Improve,Include,...

o Samplepattern:

Actionofchange(Preventing/Improving/…)

+Affectedfactors(InsufficientUsefulFunction/HarmfulFunction/Consumptionofexcessiveresources)

+Forreason(Ultimategoals). Contradiction formulation

Step 2:

Identify

Solution of

the patent

Definition:Thesolutionisthecombinationofthemeansproposedandappliedbypatentforsolvingtheproblem.

Questionstolookfor:

o Whatelementsorfunctionsareaddedorchangedinthesystemtosatisfytheproblem?

o Howhastheneedbeensatisfiedbythispatent?

o Howaretheinsufficient,harmful,orexcessivefunctionsofthesystemmodifiedbythispatent?

Comments:

o UsualLocationtolookfor:The“Summary,”“Background,”“Abstract”and"DetailedDescription"sectionsofthepatentapplication.

o Theusualformatinthepatent:Wordsthatcommonlyappearinproximitytothesolutionstatement:

Support,Include,Provide,Need,…

o Samplepattern:

Theactionofchange(Propose/Add/Develop/…)

+Somecomponent.


60

Step 3: Define

Undesired

outcome of

the patent

Definition:Aworseningparameterisapropertyofasystemcomponentthatpreventstheimprovingparameterfromachievingthedesiredvalue(asmuchasitisexpectedtopreventundesiredoutcome);theworseningandimprovingparametervalueshaveaninverserelationship.

Questionstolookfor:

o Whichsystemcomponentpropertyislimitingtheimprovingparameter?

o Whentheimprovingparameterprogresses,whichsystemcomponentpropertyregresses?(Itcouldbetheimprovingcomponentitself).

Comments:

o UsualLocationtolookfor:The“Background,""Detaileddescription"and"Claim"sectionofthepatentapplication.

o Theusualformatinthepatent:Wordsthatappearinproximitytotheworseningparameter:

Limited,Provide,Help,Reduce,…

o Samplepattern:

Nameoffeatureorpropertyofacomponentofsystem

+ofthe +nameofthecomponent.

Step 4:

Identify

Improving

Parameters &

elements of

the patent

Definition:Animprovingparameterisapropertyofoneofthesystemcomponentsthatisexpectedtopreventtheundesiredoutcomeinthesystem,butisnotsuccessful.

Questionstolookfor:

o Withoutconsideringthesolutionofthepatent,whichcomponentofthesystem,andwhichpropertyofthatcomponent,wereexpectedtopreventtheundesiredoutcome?

Comments:

o UsualLocationtolookfor:The"Summary,""DetailedDescription,""Background,""Abstract"and“Claims”sectionsofthepatentapplication.

o Theusualformatinthepatent:Wordsthatappearinproximitytotheimprovingparameter:

Support,Provide,Need,Stability,Include,...

o Samplepattern:



Step 5:

Identify

Worsening

Definition:Aworseningparameterisapropertyofasystemcomponentthatpreventstheimprovingparameterfromachievingthedesiredvalue(as


61

Parameter &

elements of

the patent

muchasitisexpectedtopreventundesiredoutcome);theworseningandimprovingparametervalueshaveaninverserelationship.

Questionstolookfor:



Comments:




o Samplepattern:



Step 6:

Identify

Control

Parameter of

the patent

Definition:Acontrolparameterisacomponentsystempropertythatallowstrade‐offsbetweenimprovingandworseningparameters,anditispossibletocontrolvaluesofimprovingandworseningparametersthroughit.

Questionstolookfor:

o Whichsystemcomponentpropertyaffectsbothimprovingandworseningparameterswithaninverserelationshipbetweenthetwoparameters?

Comments:

o UsualLocationtolookfor:The"DetailedDescription,“Summary”and“Background”sectionsofthepatentapplication.

o Theusualformatinthepatent:Wordsthatappearinproximitytothecontrolparameter:

Adjust,Provide,Support,…

o Samplepattern:



Step 7:

Formulate

Contradiction

of the patent

Comments:

o Filltheshapeaccordingtothecorrectnessoffollowingsentences:

<ControlParameter>ofComponentXshouldassumeValueinordertoimproveEvaluationparameterof


62

componentZ(improvingparameter),butthenEvaluationparameterofcomponentY(worseningparameter)worsens

<ControlParameter>ofComponentXshouldassumeAnti‐valueinordertoimproveEvaluationparameterofElementY,butthenEvaluationParameterofElementZworsens.

o Giverealvaluestovalueandanti‐value.

o Filltheblankboxesofthetemplatefigureofthecontradiction.

o Addoneofthemainpicturesofthepatentsbesidesthedrawngraphofthecontradiction.

Contradiction formulation

3-

Building

the map

Step 1:

Categorize

the extracted

problems of

patents

Quantitativeanalysisofextractedproblem

Classifythesimilarproblemsinoneclass

Classifythesimilarclasses

Technical Contradiction

Map

Step 2:

Categorize

the extracted

solutions of

patents

Quantitativeanalysisofextractedproblem

Classifythesimilarproblemsinoneclass

Classifythesimilarclasses

Step 3: Build

the matrix

map of

problems

and solutions

DedicateX‐axistoclassesofproblems

DedicateY‐axistoclassesofsolutions

WritetheQuantityofpatentsofeachcrossamongtheclassesofproblemandsolutionsinthecross

Step 4: Build

the

contradiction

graphs of

each cross

Positiontheresolvedcontradictionofpatentsofeachcrossonaseparatesheetofpaperasasupportivegraph

Mergeandtrimthecontradictionsofeachgraphbysimilarelements

As Table 13 shows, the three‐stage procedure for building the map isproposed in 16 steps together: preparing the resource in 5 steps, extracting theAnalogsin7steps,andbuildingthemapin4steps.Itisworthconsideringthatallthreestagesneedengineerexpertise,whilethemostprofessionalstageisthesecondstage.

The time needed for following the procedure and building the map is notconstantforanytechnicalsystem,anditdependsontheleastandenoughnumberofpatents forbuildingtherepresentativemap.Inotherwords,althoughthemapcanbebuiltbasedontheallretrievedrelevantpatents,itislogicaltostartwitharepresentativeone.Therepresentativemapcanbeconsideredasthemapwithleastandenoughnumberofpatentswhichcoverstheclassesofproblemsandsolutions.Thisaimiscoveredinstep5ofthefirststageoftheprocedurewhichreferredasselectingthepatentsafterredundancyintheclassesofproblemsandsolutions.


63

Therefore,thetimeneededforfulfillingtheproceduredependsontheleastand enough number of patents respect to the all relevant patents for the targettechnicalsystem.

Thestudytheefficiencyofdedicatedtime,thetimeneededforpreparingthemapcanbecomparedtothetimewhichisdedicatedtocheckingthepatentabilityofanewideaafterdesignsessioninR&Ddepartments.Astheexpertiseissocrucialforthesecondstageoftheprocedure, it is logicalthat themainemphasisfortheempiricalstudieswillbeconcentrateduponthisstage.

3.2 Designing empirical study

Theultimateobjectiveofthisresearchistoimprovethepatentabilityofidea

generationsessionsbyincreasingthegenerationofnon‐obviousnovelsolutionsforinventive problems of a target system. A Technical Contradiction Map is thesuggestedcontributionofthisresearchforthetargetobjective.Asetofempiricalstudies is needed to study the map’s usability and effectiveness and easiness ofrepeatingthemap‐buildingprocess.Toperformthestudies,thesuggestedmapwasbuiltforasampletechnicalsystemthroughfollowingthedevelopedprocedure.Thepreparedmapwasthenusedforstudyingtheusabilityandeffectivenessofthemap.Therefore, the prepared map for a sample technical system and the performedstructurearepresented,inadditiontothestructureofasetofstudiesforexaminingthemap’susabilityandeffectiveness,andtheeaseofrepeating themap‐buildingprocess.

3.2.1 Research contribution sample

Sample technical system The first step in preparing a Technical Contradiction Map is selecting a

technical system as a sample. Technical systems are classified into four levelsaccordingtotheircomplexity.Inthescopeofthisresearch,asimplesysteminlevelthreewaschosenasasampletechnicalsystem,inordertoshowtheusabilityoftheproposedcontributionforacompletetechnicalsystem.Table14showsthelevelofcomplexityoftechnicalsystems(HubkaandEder,2002);complexityisknownasthe required information to define a system (Kolmogorov, 1983), understand,predict,manage,design,and/orchangeit.


64

Table14 ‐ Technicalsystemsclassifiedbydegreeofcomplexity.

Complexity

Level System Feature Sample

1 Part,

Component

Theprimarysystemmanufacturedwithout

assemblyprocessBolt,BearingSleeve

2

Group

mechanism,

Sub‐assembly

Thesimplesystemthataccomplishessome

extrapurposes

Gearbox,Hydraulic

drive,Spindlehead

3 Apparatus,

Device

Thesystemwithsomepartsforclosed

function

Lathe,MotorVehicle,

Electricmotor

4

Plant,

Equipment,

Complex

machineunit

Thecomplexsystemthatperformssome

functions(containmachineandcomponent

thatorganizeafunctionalandspatialunity)

Hardeningplant,

Machiningtransferline,

Factoryequipment

A Walker (or walking frame) is a simple system in Level three of thecomplexityoftechnicalsystems;thisisconsideredasthesampletechnicalsystemofthisresearch.AWalkerisadeviceforhandicappedoroldpeoplewhoneedmoresupport to keep balance or stability during walking. On the requirementsof thisresearch’sempiricalstudies(describedinthefollowingsection),thesamplesystemmust be known and familiar with a wide range of engineers, so the Walker isconsideredasanappropriateoption.Figure7showsthestandardframe,alsoknownasthesimpleWalker.

Figure7‐Asimplewalkingframe.

Performed procedure for building Technical Contradiction Map for the sample technical system

As mentioned in the previous section, the procedure is proposed in threemainstages.Table15showstheresultsofthefirststageinsearchingandgatheringtherelativeandappropriatedpatents.


65

Table15‐Theresultsofsearchingandrefiningthepatents.

Preparing the

resource

Step 1: Identifying

related keywords to the system

Considering“system’sname”;

Determiningthemainkeyword;o (Nameofthesystem)+

(Purposeofthesystem).

Considering“Walker”asatechnicalsystem;

Determiningthemainkeyword;

Walker(Nameofthesystem)+Disability(Purposeofthesystem).

Step 2: Identifying the CPC code of the

system (Espacenet)

FindingouttheCPCcodeofthesysteminEspacenet;o SearchingKeywordin

the“Classificationsearchpart”ofEspacenet.

o Checkingthedescriptionofcodetoensuretherelevanceofcodetothesystem.

FindingouttheCPCcodeofthesysteminEspacenet;o SearchingKeyword(Walkerdisability)

inthe“Classificationsearchpart”ofEspacenet(A61H3/00).

o Checkingthedescriptionofcodetoensuretherelevanceofcodetothesystem(Appliancesforaidingpatientsordisabledpersonstowalkabout(apparatusforhelpingbabiestowalkA47D13/04;{orthopaedicdevicesforcorrectingdeformitiesof,orsupporting,limbsA61F5/0102};exercisingapparatusforthefeetortoesA63B23/10;{stairwaysorrampsE04F11/00}).

Step 3: Extracting the

related patents (Orbit)

Considering“Orbit”asthedatabaseforsearch;

Limitedpatentsearch;o Bytitle,abstract,

claims,descriptions,objectoftheinvention.

o Bykeyword.o Alive&Granted.o Between20years

(validityofapatent).

Considering“Orbit”asthedatabaseforsearch;o Limitedpatentsearch;o Bytitle,abstract,claims,descriptions,

objectoftheinvention.o Bykeyword“Walker2Ddisable+”.o Alive&Granted.o Between1994‐2014(20years’validity

ofapatent).

Step 4: Refine the patents

Eliminatingincompleteorwithoutdatapatents;

Eliminatingpatentsinotherlanguages,exceptEnglishpatents.;

Eliminatingnotrelatedpatentsbyreadingthetitle,abstractandseethefigures.

Eliminatingincompleteorwithoutdatapatents(4patents~%4);

Eliminatingpatentsinotherlanguages,exceptEnglishpatents(33patents~%33);

Eliminatingnotrelatedpatentsbyreadingthetitle,abstractandseethefigures(10patents~%10).

Selecting 54 out of 101 patents

Step 5: Selecting the

target patents

Sortingthepatentsbythedateofpatents(newertoolder);



Intheproblemandsolutioncategories,wehavetherepetitionofcategories(redundant);

Studyingsomepatentsafterredundantformorecertainty.

Sortingthe54patentsbythedateofpatents(newertoolder);



Intheproblemcategory(after19thpatent)andthesolutioncategory(after10thpatent),wehavetherepetitionofcategories(redundant);

Studying11patentsafter19thuntil30thpatent.

Selecting 30 out of 54 patents

AstheTable,15shows,among101foundpatentsbysearchingintheOrbit

database,around50%ofthem(54patents)couldbestudiedforbuildingthemap.Reading and extracting the required information from all 54 patents is a time‐consumingactivity,thereforeinthescopeofthissample,itisintendedtodecreaseanumberofpatentsforfurtherstudies.Theclassesofproblemsandsolutionsare


66

considered as concepts for reducing the Quantity of patents for the study. Thepatentswerefirstsorted,andthenthestudyselectedonepatentfromtheearliest,onefromthelatestandonefromthemiddle.Afterreducingtheclassesofproblemsand solutions, the analysis of more patents was completed. This supportive sub‐procedurereducedtheQuantityofpatentstobestudiedfrom54to30.

The second main stage of the procedure for extracting Analogs was thencompletedforthe30selectedpatents.Table16showstheperformedprocedureforone of the 30 selected patents; see detailed information of the 30 patents inAppendixD.

Table16‐Resultsofextractedinformationfromapatent.

Extracting the Analogs

Step 1: Identify Problem of the

patent

Definition:Theproblemdefinitionistheintendedimprovedbehaviorofthesystemunderinvestigationtobeachievedthroughtheproposedpatent.

Questionstolookfor:

o Whatistheultimatebenefitofapplyingforthepatentinthesystem?

o Whatneedorrequirementdoesthepatentsatisfy?

Comments:

o UsualLocationtolookfor:The“Background,”“Summary”and“Abstract”sectionsofthepatentapplication.

o Theusualformatinthepatent:Wordsthatcommonlyappearinproximitytotheproblemstatement:

Provide,Support,Need,Improve,Include,...

o Samplepattern:

Actionofchange(Preventing/Improving/…)

+Affectedfactors(InsufficientUsefulFunction/HarmfulFunction/Consumptionofexcessiveresources)

+Forreason(Ultimategoals).

Improvinguserstability,control,andeaseofusefor

navigatinginclinedsurfacesandstairways

(wasfoundinBackground,ClaimandSummary

sections)

Step 2: Identify Solution of the

patent

Definition:Thesolutionisthecombinationofthemeansproposedandappliedbythepatentforsolvingtheproblem.

Questionstolookfor:

o Whatelementsorfunctionsareaddedorchangedinthesystemtosatisfytheproblem?

o Howhastheneedbeensatisfiedbythispatent?

Addmechanically‐drivenfrontlegs

(wasfoundinSummarysection)


67

o Howaretheinsufficient,harmful,orexcessivefunctionsofthesystemmodifiedbythispatent?

Comments:

o UsualLocationtolookfor:The“Summary,”“Background,”“Abstract”and"DetailedDescription"sectionsofthepatentapplication.

o Theusualformatinthepatent:

Wordsthatcommonlyappearinproximitytothesolutionstatement:

Support,Include,Provide,Need,…

o Samplepattern:

Theactionofchange(Propose/Add/Develop/…)

+Somecomponent.

Step 3: Define Undesired outcome

of the patent


Questionstolookfor:



Comments:

o UsualLocationtolookfor:The“Background","Detaileddescription"and"Claim"sectionofthepatentapplication.



o Samplepattern:



InabilityofWalkertoproperlybalanceoninclined

surfaces

(wasfoundinBackgroundsection)

Step 4: Identify Improving

Parameters &

Definition:Animprovingparameterisapropertyofoneofthesystemcomponentsthatisexpectedtopreventtheundesired

Stabilityoninclined/stairwayssurfaces

ofWalkerframe


68

elements of the patent

outcomeinthesystem,butisnotsuccessful.

Questionstolookfor:

o Withoutconsideringthesolutionofthepatent,whichcomponentofthesystem,andwhichpropertyofthatcomponent,wereexpectedtopreventtheundesiredoutcome?

Comments:

o UsualLocationtolookfor:The"Summary,""DetailedDescription,""Background,""Abstract"and“Claims”sectionsofthepatentapplication.

o Theusualformatinthepatent:Wordsthatappearinproximitytotheimprovingparameter:

Support,Provide,Need,Stability,Include,...

o Samplepattern:


+ofthe+nameofthecomponent.

(wasfoundinBackgroundandSummarysections)

Step 5: Identify Worsening

Parameter & elements of the

patent


Questionstolookfor:



Comments:




o Samplepattern:

StabilityonnormalsurfacesofWalkerframe

(definedconceptuallybyresearcher)


69



Step 6: Identify Control Parameter

of the patent

Definition:Acontrolparameterisacomponentsystempropertythatallowstrade‐offsbetweenimprovingandworseningparameters,anditispossibletocontrolvaluesofimprovingandworseningparametersthroughit.

Questionstolookfor:o Whichsystemcomponentproperty

affectsbothimprovingandworseningparameterswithaninverserelationshipbetweenthetwoparameters?

Comments:o UsualLocationtolookfor:The

"DetailedDescription,“Summary”and“Background”sectionsofthepatentapplication.

o Theusualformatinthepatent:Wordsthatappearinproximitytothecontrolparameter: Adjust,Provide,Support,…

o Samplepattern: Nameoffeatureorpropertyof

acomponentofsystem +ofthe +nameofthecomponent.

HeightofWalkerlegs(frontlegs)

(wasfoundinDetailed

descriptionsection)

Step 7: Formulate Contradiction of

the patent

Comments:o Filltheshapeaccordingtothe

correctnessoffollowingsentences: <ControlParameter>of

ComponentXshouldassumeValueinordertoimproveEvaluationparameterofcomponentZ(improvingparameter),butthenEvaluationparameterofcomponentY(worseningparameter)worsens

<ControlParameter>ofComponentXshouldassumeAnti‐valueinordertoimproveEvaluationparameterofElementY,butthenEvaluationParameterofElementZworsens.

o Giverealvaluestovalueandanti‐value.

o Filltheblankboxesofthetemplatefigureofthecontradiction.

o Addoneofthemainpicturesofthepatentsbesidesthedrawngraphofthecontradiction.


70

ThelastcolumnofTable16highlightstheresultsofapatent:“Walker forimprovedstairwaymobility.”ThisWalkerwasadaptedtonavigatestairwaysandinclinedsurfaces;Figure8showsaperspectiveviewofanexemplaryembodimentoftheinvention.

Figure8‐Walkerforimprovedstairwaymobility.

Thethirdmainstageoftheprocedureformap‐buildingwasundertakenafteranalyzing all 30 patents on the extracted Analogs of problems, solutions andresolved contradictions of each patent. As mentioned before, the map is a three‐dimensional map. The first two dimensions are presented in the main paper bypositioningthepatentsrespecttotheclassesofproblemsandsolutions.Thenforeach cross occupied by at least one patent, the third dimension is provided bypresentingtheresolvedcontradictionsofpatentsonthecross.

Toproceed.First,theproblemsandsolutionsextractedforeachpatentareclassified,andthentheProblem‐SolutionMapofthemainpaperisbuilt.Tables17and 18 show the classes of extracted problems and solutions of the patents,respectively.

Table17‐Eightcategoriesofproblems.

No. Problem group name Problems

1

Reducing volume of Walker for non-using

period (4 patents)

ImprovingafoldableWalker(storageconfigurationandfoldableinallthreedimensionsandfold/unfoldbyuserindependently)

ImprovingaWalkerapparatuswithafoldingmechanism(thatallowstheWalkertobefoldedlaterallyinacompactmanner,andthatminimizesthenumberofrequiredpartswhileoptimizingrobustnessandlateralsupport)forusers

Improvingfold‐ability(configurebetweenfoldedstorageandunfoldedpositions)Walkerbyhandicappedpersonsoflimiteddexterity

ImprovingaWalkerforpatientswithdexterityproblemstoliftuptoastandingpositionandtoopenandcloseindividually


71

2 Supporting user for

normal walking (9 patents)

Preventingfallinginanydirectionswhilewalkingforaphysicallychallengedperson

Preventingcollapsingforuserwheresupportingweightisnecessaryduringnormalwalking

Preventinghealthproblems(stressontheback,shoulders,arms,wristsandhands)andimprovingsafetyissues(balanceandstabilitythroughthearmsandhandstrengthwhileleaningovertheWalker)ofthepatienthavingdifficultywithself‐sustainedwalking

Preventingcollapsingpatientandimprovingmobilityassistancetoprovidebalancedsupportforelderlyindividualsorotherdisabledpopulationsneedingwalkingassistance

Improvingcontrolofbalanceandbodyorientationduringhands‐freestandingandwalkingtoindividualswithcompromisedphysicalabilitiescausedbyinjuryordiseaseofthecentralnervoussystemorotherreasons

ImprovingaWalker/deviceforgaittrainingandwalkingimpairedpatient;canbeadjustedwideenoughtofitovertreadmillsorwheelchairs

Preventingthecollapseofhandicappedandphysicallyimpaired,withinsufficientlegstrengthtostandorwalkindependentlyduringambulation

ImprovingWalkermaneuverability(difficultorimpossiblegetcloseenoughtoobjectstotouchthem)fordisabledchildpatientsintheindoorenvironment(homeandschool)

ImprovingaWalkerforsupportthewalkingofamputees

3

Preventing collisions to obstacles during

walking (4 patents)

Preventingfallinguserwithpoorvision,orinlowlevelofilluminationintheenvironmentforusers

Improvingmobilityinpoorlylitareasbythepersonwithlimitedorpooreyesight

Improvinganambulatorydeviceforassistingphysicallychallengedusers(youngchildrenlearningtowalk,thosewhosufferlastingeffectsofinjuryandphysicalchallengesandtheelderly)inwalking,exerciseorotherwisegoingonfoot

Preventingfallingpatientwithdiminishedhearing/eyesightcapacityduringwalking

4

Supporting user for necessary sitting

motions (2 patents)

ImprovingaWalkertohaveaccesstoallpartsofthepatient'sbody(disabledand/orelderlytohaveashowerforadequatecleansing)toassurecompletebathing

Preventingfallingandriskingfurtherinjurybythepersonwhoneedsawalkingaidandneedstoswitchbetweenwalkingandsitting

5

Supporting user for walking on various

surfaces (4 patents)

Improvinguserstability,control,andeaseofusefornavigatinginclinedsurfacesandstairways

Improvingwalkingonthesoftsurfacessuchassandanddirt

Preventingpersonwhohasphysicaldifficultyinambulation(illness,injury,etc.)fallingandreducesnoise


72

Improvingtraversingstairsandsubstantiallylevelsurfacesforhandicappedpersons

6 Setting size of Walker

for user (2 patents)

Improvingfold‐ability(facilitatestorageandtransport)andstructuralstabilityofWalkerforbothchildandadultusers

ImprovingaWalkerthatsupportsthesizeofobesepersons(accommodateseveraldifferentsizedpersons),butcanbewellstoredandtransported

7

Walker’s Aesthetics and Ability to be

repaired (2 patents)

ImprovinggriphandlesofWalkerwhichiscostlyandtime‐consumingtoreplaceforsubsequentusers

ImprovingappearanceofWalker;morefriendlyforusers

8

Supporting user to stand for using

Walker and to sit after walking

(3 patents)

Improving(facilitating)disabledpersonrisingfromaseatedpositionorreturningtoaseatedposition

Improvingwalkingaidsandassistingapersonneedinghelptorisetoastandingposition

Preventingtheriskoffrequentfallsandinjuriesofliftedperson (elderly,personsrecoveringfromsicknessorsurgicalprocedures,personswithbalanceproblems)

Table 17 shows the problems of all 30 patents being classified into eightgroups (see the column labeled ‘problem group name’). Among the 8 groups, 3groupsofproblemsarerelatedtothemainfunctionoftheWalker(i.e.supportingusersinwalkingandusingtheWalker),2arerelatedtosupportiveotherfunctionswhileusingtheWalker,1isrelatedtothecustomizingtheWalkerfortheuser,andfinally,the2othergroupsarerelatedtoissuessurroundingabilitytoberepairedandtheWalker’saesthetics.

Table18‐Sixcategoriesofsolutions.

No. Solution group name Solutions

1 Customizing lower end of

Walkers’ legs (3 patents)

Addmechanically‐drivenfrontlegs

Addtheadapterwithameshthatincludesarunningsurface

AddaglideballintheformofaresilientballprovidedwithapluralityofholesforinstallingoverthelowerendofaWalker'sleg

2

Applying motion sensors (add illumination, alarm

system, signal device) (4 patents)

Addilluminationsourcetofocusonatargetarea,alocationemitter,aglobalpositioningsystem,atactilesignalemitterorasensordevice

Addtheintegratedilluminationmeans;alarmwiresanalarmandlights

Addamotionsensorapparatusandasignaldeviceoperatively

AsafetyWalkerwithanautomaticalertdevicecomprisingabatterypoweredlampassemblyandanaudiblealarmsystem

3 Applying new materials

(2 patents)

ProposeareplaceableandsanitarygriphandleforWalker,mobility,supportandseatingdevices

ProposeimproveddesignforanorthopedicWalkerthatpermitstheWalkertobeformedsubstantiallyfromapolymer

4

Applying body support Addaremovablemeshseatandalatchabledoorandmanynovelfeatures


73

devices (seat, belt, frames, handle,

...) (13 patents)

Addahandrailextension(Includesaclamp,aprimarysupport,andanangularsupport,andfoot)

Addanup‐rightWalker(includessideframeshavingabacklegframeandafrontlegframe)forsupportingapatientwithup‐rightpostureandcouplingwheels

Designashowerchair/WalkercombinationandfoldingWalkerhavingapivotedseat(asplittoiletseatwithagapatitsfrontend)

Addarisingsupportintegratedintoawalkingaid;havingapartthatisverticallyadjustableconnectingaslingorharness

DevelopamobilityapparatusorWalkerthatfacilitatesbothnormalgaitvelocityandanuprightposturewithalumbarandcervicalbelt

Proposeaphysicalassistancedeviceisconfiguredasawalkingaidandtosupportauserinaseatedposition

Proposeamodularandadaptiveapparatusforstabilityandbodyadjustmentaidwithsensoryinability

Proposealightweight,foldabledevicethatcanpartiallysupporttheweightofapatientduringrehabilitation

Proposeanadaptiveassistivewalkingdevice(aweight‐relievingWalker)insupportingtheuser'sbodyinspecificvariableamountsduringambulationwithouttheneedforbeinghandheldforpropulsion

ProposeaWalkerandaliftingarmattachedtotheWalkerthatextendsinanapproximatelyverticaldirectionfromtheWalkerforassistingaseatedpersontostand

Proposeanambulationaidwhichhasasupportstructurethatbothsupportsthepatient'sweightandismovablelaterallyontheframetoaccommodatesidewayshipmovementofthepatient'sgait

Proposeastand‐upWalkerforassistingtheweightinanuprightpositionwiththehandlesandliftspringmeans

5 Applying telescopic

structure (3 patents)

ProposeadjustableheightWalkerincludestwoassistingpartsandatransverselypartconnectingtotheassistingpartsatthefrontsides

ProposeadjustablewidthWalkerandfoldingtoacompactstateforstoragepurposesortravel

Proposeawalkingaiddevicehavingspring‐loadedandseparatelyadjustablerearlegs

6

Improvement folding mechanism of Walker

(pivot and joint) (5 patents)

ProposeaspecificsystemormethodforanarticulatingWalkermotionofarmsandlegs

Add telescoping legs, foldable handles, and body with a storageconfigurationthathaslessheight,depth,andwidththanitsfullydeployedconfiguration

Propose a foldable Walker apparatus having a Variety of optimizedfeatures relating to its folding mechanism, braking pad mechanismandbrakehousing,brakerodassembly,frameshape,andcollapsiblebasket

Propose a locking assembly for use with a Walker having foldable sidemembers

Propose a foldable Walker with a release mechanism (a paddle‐shapedleverarrangedtointeractwithlockingpinsbylateralmovementineitherdirection)forsaferandeasieroperation


74

Thesixcategories of solutionsareshown inTable 18;seecolumn labeled‘solutiongroupname.'Theclassesofthesolutionsareverycommonsolutionsforsuchengineeringsystemhoweverthedetailsmustbestudied.

Preparing the matrix of problems and solutions, and determining theQuantityofpatentsineachcrossoftheclassofproblemandsolution,wasthenextstepforbuildingthemap.Figure9showsthepreparedmatrixforthe30analyzedpatents.

Figure9‐The30patentmatrixinformation.

AsFigure9shows,inthecrossofeachclassofproblemsandsolution,the

Quantityofrelevantpatentsandthecodeof themarementioned.Forinstance,8

patents are distinguished relevant to the cross of the first class of the problems

“supporting user for normal walking” and fifth class of solutions “applying body

supportdevices”.Thepatentswithcodes3,8,13,16,20,21,24and26arethecodes

ofthese8patents.Itisworthmentioning;eachpatentappearsonceonthematrix.

ThebubblegraphicalschemaoftheProblem‐SolutionMatrixMapwaspreparedby

usingtheFigure9byExcelsoftware.Figure10demonstratesthedrawnmatrix.


75

Figure10‐Problem‐Solutionmatrixmap.

Figure 10 is a graphical representation of Figure 9, and it shows the

distributionofpatentsrespecttotheclassesofproblemsandsolutionsgraphically.

The third dimension as the other supporting graphs for each cross of the

Technical Contradiction Map was prepared by bringing all the analyzed resolved

contradictionofpatentsofeachcrossinapapertogether.Thereare10bubbleson

theMainmatrixoftheTechnicalContradictionMap,andrespectively,10supportive

graphsareexpectedforthismap(SeeAppendixEfordetailsofallgraphs).Figure

11showsoneofthesupportivegraphsforthecrossof‘supportinguserfornormal

walkingasaproblem’and‘applyingbodysupportdevicesasasolution.'Thiscross

consistsof8patentsandconsequently8contradictions.


76

Figure11 ‐ Proposedcontradictionmap.

AsseeninFigure11,tosimplifythegraphs,similarpartsofcontradictions

aremergedtosupportmoredesignersinanalyzingtheinventiveproblemsoftarget

technical systems and propose non‐obvious novel ideas by resolving the

contradictions.Thepositionof patentscan be followedby lookingat thegeneral

pictureofthecorrespondingpatentonthemergedmodelsofcontradiction.

Figure12showsthefinalpreparedschemaofTechnicalContradictionMap

fortheWalkerasthesampleofthefollowingempiricalstudies.Themainbubble

graph and the 10 graphical representations of contradictions of the patents

correspondingtoeachbubblearetogetherthe11pagesofTechnicalContradiction

MapofthesystemofWalker.


77

TheproposedmappresentedinfollowingFigures:

Figure12‐TheproposedthreeDimensionalTechnicalContradictionMap.


78

Dedicated time for following the procedure and building Technical Contradiction Map for the sample technical system

As mentioned Technical Contradiction Map is built by a three stages

procedure;Preparingtheresources,extractingtheanalogs,andbuildingthemap.

Following the procedure took around 35‐40 hours for the researcher. Table 19

showsthededicatedtimeapproximatelyforeachstage.

Table19‐DedicatetimeforbuildingtheTechnicalContradictionMapofaWalker.

1-Preparing the resource 2-Extracting the analogs 3- Building the map

Step 1: Identifying related

keywordstothesystem

Step2:IdentifyingtheCPCcodeof

thesystem(Espacenet)

Step 3: Extracting the related

patents(Orbit)

Step4:Refinethepatents

Step5:Selectingthepatents

Step 1: Identify Problem of the

patent

Step 2: Identify Solution of the

patent

Step3:DefineUndesiredoutcome

ofthepatent

Step 4: Identify Improving

Parameters & elements of the

patent

Step 5: Identify Worsening

Parameter & elements of the

patent

Step6:IdentifyControlParameter

ofthepatent

Step7:FormulateContradictionof

thepatent

Step 1: Categorize the extracted

problemsofpatents

Step 2: Categorize the extracted

solutionsofpatents

Step 3: Build the matrix map of

problemsandsolutions

Step 4: Build the contradiction

graphsofeachcross

10 Hrs. 20 Hrs. 5 Hrs.

35 – 40 Hrs.

It is worth considering the researcher is familiar with the procedure and

thereforethededicatedtimecanbeconsideredasthetimewhichisneededforan

expert.Inaddition,astheneededmentaltaskloadishigh,this40‐hourscanbedone

atleastin2weeksinsteadof1week(40hoursis1workingweek).

3.2.2 Plan of empirical studies

Research questions Improvingthepatentabilityofideagenerationsessions,throughincreasing

the generation of non‐obvious novel solutions for inventive problems of a targetsystem, is consideredas the objectiveof this research.ATechnicalContradictionMapisthesuggestedcontributionofthisresearchforthetargetobjective.Accordingto the considered objective and original contribution, the following researchquestionscanbepursued:

1. Research Question 1: Can R&D engineers in Iranian SMEs improve Novelty within their ideas, through the use of an enriched Problem-Solution Patent Map by the ‘contradiction concept’?

ForRQ1,itisworthmentioningthelackofanassessingsystemormatrixinthe literature for assessing non‐obvious novel ideas, which can be used by an


79

individual engineer and analyzer. Non‐obvious novelty is mostly evaluated byexpertsinthefieldsubjectivelyconsideringsomethepositionoftheidearespecttothe fourth level of knowledge. Therefore, it is considered to apply a degree ofNoveltyVarietyofideas,insteadofassessingnon‐obviousnovelties,inthescopeofthe empirical study. These degrees together can be used for showing the non‐obviousnoveltiesiftheassessingcriteriaandthecorrespondinglevelsaredefinedforthisaim.Thedetailsoftheassessingcriteriaarementionedinfollowingsections.

2. Research Question 2: Can Iranian R&D engineers build the proposed enriched Patent Map by following the developed procedure?

ThemapcanbeusefulforcompaniesifR&Dengineerscanbuilditforanytargetsystembyfollowingtheprocedure.Therefore,itisworthstudyingiftheycanfollowtheproposedstepsofproceduresformap‐building.

Proposed structure for the studies

GivenRQ1andRQ2,twoempiricalstudiesareplanned.Thefirstempiricalstudy investigates the usability and effectiveness of the Technical ContradictionMap;thesecondempiricalstudyinvestigatesrepeatabilityoftheprocessofbuildingthemap.

TostudytheusabilityandeffectivenessoftheTechnicalContradictionMap,theresultsofusingthemaparecomparedtosomeothermethodsusedforthesamepurpose in design and idea generation sessions. Idea generation usingbrainstormingasatechniqueisconsideredasthecontrolgroup,asmostdesignandideagenerationsessionsusethismethod(Lewisetal.,1975).TheProblem‐SolutionMatrixMapandPatentText(Far‐Field)areconsideredastheotherinterventionsfor comparison. The Far‐Field Analogy is considered as another intervention forcomparison, as literature discusses the effectiveness of Far‐Field Analogy onincreasingtheNoveltyandQuantityofideas(Chanetal.,2011).

TheProblem‐SolutionMatrixMapisconsideredasoneoftheinterventionsfor comparison as it is basic for a developed Technical Contradiction Map; it is,therefore,usefultostudytheeffectivenessofthedevelopedmapinrespecttothat.Intotal,theresultsoftheideagenerationsessionwiththeTechnicalContradictionMapwillbecomparedtothethreeotherinterventions,inordertostudythemap’susability and effectiveness: (i) idea generation session with Problem‐SolutionMatrixMap(ii) ideagenerationsessionwithPatent Text(Far‐Field)of thetargetsystem,and(ii)ideagenerationsessionwithbrainstorming.

Tocomparetheresultsofthefourconsideredinterventions,fourgroupsof7teams are planned, each consisting of 2 R&D engineers. The teams are asked togeneratepatentableideasintwosessions,each30minuteslongwitha15‐minutebreak in between. In the first 30 minutes, all teams generate ideas by applyingbrainstorming,however inthesecondsession;eachgroupwillgenerate ideasbyone of the considered interventions. Comparing the results of two sessions, theeffectivenessoftheTechnicalContradictionMapwillbestudied.


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Tostudy the repeatability, themap‐building process, thesame30 patentsused for sample study, are given to different R&D engineers to analyze themaccording to the developed procedure. The second main stage of procedure forextractinganalogsfromthepatentstakesaround1hour;itisnotexpectedthereforethattheparticipantswilldedicatetheirtimetoanalyzemorethanonepatent.Itisdesignedtoinvite90R&Dengineerstopursuetheprocedureforonlyonepatent.TheresultsofthefollowedprocedurebyR&Dengineersaregatheredasthreenewmaps.RepeatabilityisthencheckedbyanalyzingthesimilarityinresultsofusingthemapsaboutthedegreeofNoveltyoftheideas.Therefore,thesecondexperimentconsistsof2mainparts;extractingtheanalogsbyR&Dengineersasthefirstpart,andapplyingthebuiltmapsbasedontheresultsofpartoneasthesecondpart.Eachnewmapisgivento7newteamsof2R&Dengineerstoallowforcomparisonwiththe results from the seven first teams, which applied the built sample TechnicalContradictionMapbytheresearcherinthefirstexperiment.

Inthisexperiment,theteamsarealsoaskedtogeneratetheirideasintwo30‐minutesessions,likethefirstexperiment,toallowforcomparison.It isworthmentioning,beforerunningthefirstpartofthesecondexperiment,theobviousnessofthesentenceswascheckedinasampletext,byparticipating6R&Dengineers,andthesentencessimplifiedbasedontheiropinionsandsuggestions.Table20showstheschemaofcommonandsimilarpartsofexperimentsoneandtwo.

Table20‐Similarpartsofexperiments.

Participants of the studies For the designed empirical study, it is expected in total 194 R&D

engineers to participate in theexperiments. 56R&Dengineers in the firstexperiment(4groupseachoneconsistsof7teamsof2engineers),6R&DengineersfamiliarwithTRIZand90R&Dengineersforthefirstsessionofthesecondexperiment,and42R&Dengineersinthesecondsessionofthesecondexperiment(3groupseachoneconsistsof7teamsof2engineers).Itis worth mentioning the teams are selected randomly after the engineersacceptedtoparticipateintheexperiments.


81

Inrespectstotheconsideredproblemandtheultimateobjectiveofthe whole research, the R&D engineers are going to be invited for theexperimentsfromIranianSMEs.ToinvitetherequiredQuantityofengineerstotheexperiments,10Iraniancompaniesareco‐operatingwiththestudy.Itis expected that the target R&D engineers have around nine years’ workexperiencesindifferentpartsofindustriesincludingR&Ddepartments.

Idea generation task Thetaskispresentedasanoralpresentationinlessthan5minutesas

following: “In general the patentable idea is considered as a novel(/completely new) idea that is not obvious to the experts in the field andindustrialusageispredictedforthat.ConsiderWalkerasthetargetsystemand please propose as much as you can non‐obvious novel ideas that youthinktheyhavethepotentialtobeacceptedaspatent”.Inaddition,Figure7isgiventoparticipantsasawell‐knownFigureofaWalker.

Different stimuli Atthebeginningof thesecondpartof thedesignsession, the three

followingstimulipresentedtoteamsrandomly:‐Fulltextof5patentsofdomain/for7teams;‐Problem‐SolutionMapof30Far‐FieldpatentsofWalker/for7teams;‐TechnicalContradictionMapof30patentsofWalker/for7teams;‐Brainstorming/for7teams.

Data collection Inbothexperiments,participantsareaskedtofillthetablesofideas

onthesheetofpaperspreparedfordatacollection.Adescriptionoftheideaand its simple picture are the data asked to be filled by participants.Therefore, it can be considered that the data collection is done byparticipantsoftheexperiments.Inthiskindofdatacollection,theresearchertrusts the final report of the participants, and it is obvious that manymediatoryandnotcompletedideasaremissed.

Data processing Dataprocessingofdesignedempiricalstudiescanbedividedintotwo

parts;assessingtheresultsofeachideagenerationsessionbyeachteam,andstatisticalstudiesincomparingtheresultsofgroupstogether.Infollowingeachoneisdescribedinmoredetails.

The current research aims at increasing the patentability of ideasgenerated by R&D engineers in idea generation sessions. The researchfollows this aim through increasing the generation of non‐obvious novelideas.Itismentionedinadvancethattherearenospecificassessingmetricsfornon‐obviousnovelideasintheliterature.Ontheotherhand,theliteratureshowsresearchonmetricsystemsandcriteriaforassessingtheQualityandQuantityofideagenerationsessions(Chapter2).


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Non‐obvious novelty, which is the focus of this research, can beapproachedbyconsideringthetwocriteriaofNoveltyandVarietybecauseaccording to their definitions, they show unexpectedness and moreexplorationindesignspace.Quantityisalsoconsideredasanothercriterionfor assessing the results of different proposed interventions on ideageneration sessions to allow comparison of the results with relatedliterature.

Theposterioriapproachisselectedforthereference ideastostudytheNoveltyofideas.TobestrongerintheassessmentofNon‐obviousness,thedegreeofNoveltyofthenewideasarerankedaccordingtotheirnewness,inrespecttotheFBSframeworkandthegradegivenforthedegreeofVarietyof ideas on the more detail version of FBS (physical principle, workingprinciple, embodiment, and detail). To follow Shah’s assessing metric andformula, the functions of the sample target system (Walker) are defined,which is similar to the problems extracted from the patents. Novelty andVarietyarethenassessedbasedontheweightsconsideredforthelevelsofNovelty and Variety of ideas in respect to the FBS framework (details arementioned in Chapter 4). The final degree of Novelty and Variety of ideageneration of each team is achieved through Shah’s formula (Shah et al.,2003).Table21showstheappliedformulaincurrentresearch.

Table21‐Theappliedformulaincurrentresearch.

No. The criteria Formula Description

1

Novelty (inposteriori

approach)

�� = ��

�

��

��

�

��

M1:TotalNoveltyscorem:Numberoffunctionsorattributesn:Numberofstagesfj:Weightsassignedtothevalueoffunctionorcharacteristictocalculateatotalscorepk:Weightsallocatedtotheimportanceofstages.

�� =��

��× 10

Cjk:TheideasnumberforfunctionjinstagekTjk:Theideasnumberforfunctionjforallstagesk

2 Variety �� = 10 ��

�

��

� ��/��

�

��


ItshowsthathigherNoveltyisachievedforateamwhentheoccurrenceofinstancesofnewnessintheirideasarelow,andthedegreeoftheirNoveltyishighaccordingtotheweightsintheFBSframework.HigherVarietyisalsoreachedforateamwhentheideascovermorecategoriesoffunctions,atahigherlevelofdegreeofVarietyinrespecttothedetailversionoftheFBSframework.BothfinalscoresofNoveltyandVarietyshowtheportionofNoveltyandVarietywithrespecttothesetofreferenceideas;thesearethegeneratedideasbyallteamsincurrentresearch.

After the measuring the degree of Novelty and Variety of each team,statistical studies are needed. Statistical studies are pursued through the linear


83

regressionmodel.TheLinearregressionmodelisusedtoestimatetheimpactofanindependent variable on the value of a dependent variable. One of the simplestmethodsofthelinearregressionmodelisOrdinaryLeastSquares(OLS)thatweuseinthisproject.OLSmethodisbasedonthefittingastraightlinetoasampleofdatabyminimizingthesumofthesquaresofthedeviationsofthedatafromtheline.Thisequationhastheform:

� = � + �� + �� + �� + �� + �

whereYontheleft‐hand‐sideisourdependentvariablethatwearegoingtopredict;X�,X�moreover,soonareindeedourindependentvariablesusedtopredictit and ��,�� and so on are the coefficients that describe the impact of theseindependentvariablesonourdependentvariable�,andfinally�istheresiduals.

Novelty, Quantity, and Variety are the three dependent variables areconsideredforthisstudy,sothethreeseparatelinearregressionmodelsareappliedto estimate the impact of each intervention (/method) on these dependentvariables.Infact,inthefirstregression,�isNovelty;inthesecondone,itisQuantity;andfinallyinthelastregressionmodel�isVariety.Inallthreeregressionmodels,��,��, �� and�� are dummy variables corresponding to the presence of eachmethod.Fourdifferentgroupsareinvolvedinthefirstexperiment;eachoneappliedone different method for idea generation, therefore three dummy variables areimposedtoestimatetheimpactofeachmethodtoimproveideationeffectivenessofR&D engineers. In particular, three dummy variables are put, instead of four,because one group or indeed one method is considered as a control group. Theestimates for each coefficient, ��, shows the contribution of the correspondingmethodwithrespecttothemethodthatisusedasthecontrolgroup.Eachdummyusesthevalue0or1,respectively,theabsenceorpresenceofsomecategoricaleffectorevent.Basedonabovementionedgeneraldefinition,theequationforNoveltyisasfollow:

Novelty = � + �� + �� + �� + �

Astheequationshowstheregressionfor'Novelty'consistsofthreedummyvariables,'ga,''gb'and'gc.'Infact,'Novelty'isthedataobservedfromthe1stideasevaluationacross fourrandomgroups(A,B,CandD),witheachgroupincludingsevenrandomteams(datahasbeenshowninChapter4)whereDstands forthegroup applying the ‘Brainstorming’ method, A stands for the group applying the‘Problem‐Solution Matrix Map’, B stands for the group applying the ‘TechnicalContradiction Map’ which is the target group, and finally C stands for the groupapplying‘PatentTextFar‐Field’.GroupD(brainstormingmethod)isconsideredasacontrol(reference)group.Therefore,threedummyvariables,'ga','gb'and'gc',aredefinedinwhichgatakes1forgroupAand0otherwise,andsimilarlyfor'gb'and'gc'.Infact,thesethreedummyvariablesmeasuretheexistenceofeachmethodwithrespecttogroupD.Forexample,'ga'takes1forgroupAandzeroforothergroups,


84

andthenmeasuresthedifferencebetweentheNoveltyofgroupAandD,andsoonfortheothergroups.��showsthecontributionof ‘Problem‐SolutionMatrixMap’techniquewithrespecttothecontrolgroup(Brainstorming)inimprovingideationeffectivenessofR&DengineerstogenerateNovelty.Analogously,��and��show,respectively, the contributions of the ‘Problem‐Solution Matrix Map’ and ‘PatentTextFar‐Field’ingeneratingNoveltywithrespecttothecontrolgroup.Asexpected,theestimatedcoefficientsfor��,��,and��shouldbepositiveandalsothemainhypothesisofthisresearchwhichis�� > ��andalso�� > ��.Moreformally,thetwofollowinggroupsofhypothesesaretestedforeachregressionmodel:

FirstGroupofhypotheses:H�: �� ≤ 0againstH�: �� > 0H�: �� ≤ 0againstH�: �� > 0H�: �� ≤ 0againstH�: �� > 0

SecondGroupofhypotheses:H�: �� ≤ ��againstH�: �� > ��H�: �� ≤ ��againstH�: �� > ��

whereH�istheNullHypothesisandH�istheAlternativeHypothesis,andtherejectionofNullHypothesisisexpected.

TheSTATAsoftwarewhichisadataanalysisandstatisticalsoftwareisusedtoestimatetheregressionmodelsandreportedoutputconsistsoffourmainpartsofinformation:(a)theR2value("R‐squared"row)whichrepresentstheproportionof variancein the dependent variable that can be explained by the independentvariable(technicallyitistheproportionofvariationaccountedforbytheregressionmodelaboveandbeyondthemeanmodel).However,R2isbasedonthesampleandisapositivelybiasedestimateoftheproportionofthevarianceof thedependentvariable, accounted for by the regression model (i.e., it is too large); (b) anadjustedR2value("AdjR‐squared"row),whichcorrectspositivebiastoprovideavaluethatwouldbeexpectedinthepopulation;(c)theFvalue,degreesoffreedom("F(3,24)")andstatisticalsignificanceoftheregressionmodel("Prob>F"row);and(d)thecoefficientsfortheconstantandindependentvariable("Coef."column),which is the necessary information to predict the dependent variable, using theindependentvariables,'ga','gb'and'gc'.

Theterms“significancelevel”or“levelofsignificance”refertothelikelihoodthatthechosenrandomsampleisnotrepresentativeofthepopulation.Thelowerthe significance level, the more confident one can be in replicating the results.Significancelevelsmostcommonlyusedineducationalresearcharethe.05and.01levels. If it helps, think of .05 as another way of saying 95/100 times that onesamples fromthepopulation, thisresultwillbeachieved. Similarly, .01suggeststhat 99/100times that onesamples from thepopulation, the sameresultwillbeachieved.ThesenumbersandsignscomefromSignificanceTesting,whichbeginswiththeNullHypothesis.


85

Forthefirstgroupofhypothesis,t‐testandforthesecondgroupofF‐testwillbe bone. Thet‐statisticis defined as theratio of the estimated coefficientanditsstandarderror.Thestandarderrorisanestimateofthestandarddeviationofthecoefficient.This test isameasureof the precisionof theregressioncoefficient. Itmeans that, ifacoefficient is largecompared to its standarderror,and then it isprobablydifferentfrom0.STATAreportsanaccompanyingp‐valueforthetestinsimpleregression.Theresultsarereportedtothreedecimalplacesofaccuracyforthep‐value.Therefore,avalueof0.000meansthep‐valueislessthan0.0005.Thisleadstothedecisionthatthenullhypothesisofazerocoefficientcanberejectedatanyreasonablesignificancelevel.Similarly,thenullhypothesiscanberejectedbyalargeF‐testresult.

Beforerunningtheestimatesandtestingthehypothesis,theassumptionsoftheclassicallinearregressionmodelmustbeclarifiedinordertogetreliableresultsandtest.Thefirstsetofassumptionsisconsideredastheso‐calledGauss–Markovassumptions.AssumptionsoftheClassicallinearregressionmodelareasfollows;theyarecheckedtobesuretheestimatedcoefficientsresultsareBLUE(BestLinearUnbiasedEstimators).

A.1: Linearity:� = � + �� + �� + �� + �� + �. There is a linear relationbetweenthedependentvariableandtheregressors.Thisassumptionisnotneededtobecheckedbecausebasedonthedefinitionofthemodel,itisalreadylinear.A.2:Fullrank:Thematrix�hasfullcolumnrank.Nooneofthekregressorcanbeexpressedasalinearfunctionfortheremainingk 1regressors.Basedonequation(2),thisassumptionisalsosatisfiedbecausedummyvariablesareused.A.3:Exogeneityoftheregressors: �[��|��. ��. ��. �� = 0].Theexpectedvalueof

therandomdisturbanceatobservationiisnotafunctionoftheregressorsobservedat anyobservation j (includingobservation i).Thismeans that in theerror term,somethinguncorrelatedwiththe��andcannotbepredictedbythe�� isshown.

Durbin–Wu–Hausman is the formal test tocheckExogeneity, but again, since theexplanatoryvariablesaredummy,thereisnoneedtocheckthisassumption.A.4:Homoscedasticity:Eachdisturbancehasafinitevariance��whichisconstantacrossobservations.Thedisturbance��isnotcorrelatedwiththedisturbance�� .In

fact, the error term has constant variance: �� (��)= �� for every i. To testHomoscedasticity for each regression, a graphical approach is used and also theBreusch‐Pagan/Cook‐Weisberg test. In case of heteroscedasticity, Huber/Whiteestimatorsorsandwichestimatorsofvariance(robuststandarderrors)todealwiththisissueareused.A.5: Normal distribution: The disturbances are normally distributed. Thisassumptionisnotstrictlynecessarysince,inlargesamples,theconditionsofCentralLimit Theorem will apply, and there is normality in the distribution of the mainstatistics.However,especiallyinasmallsample(inpresentedcase),itisnecessaryto check that the error terms are normally distributed: �|� ~�(0. ��) so thisassumptionischeckedforeachregression.

Chapter4:EmpiricalStudy

86

Chapter4

[4] Empirical Study


87

In this chapter, the descriptive study II was studied, and the empirical

performancevaliditywasdiscussedastheresultsofthefourthphaseofDRM.Theempiricalstudywasplannedandperformedthroughtwoexperimentsthateachoneispresentedindetailinthechapter.

4.1 Experiment I: Usability and effectiveness of proposed map

Experiment I is planned and performed to study the usability and

effectivenessofTechnicalContradictionMap(T.CMap)whichisthecontributionofcurrentresearchforimprovingthenon‐obviousnoveltyofgeneratingideasbyR&DengineersofSMEs.Table22presentstheperformedworkplan.

Table22‐Usabilityofproposedmapplan‐ExperimentI.

Groups First session (30 min) Break Second session (30 min)

A IdeaGeneration

(BrainstormingSession)7Teams,

2R&DEngineers

15min

IdeaGeneration(Problem‐SolutionMatrixMap)

7Teams,2R&DEngineers

B IdeaGeneration


2R&DEngineersIdeaGeneration

(TechnicalContradictionMap)7Teams,

2R&DEngineers

C IdeaGeneration



(PatentTextCross‐Domain)7Teams,

2R&DEngineers

D IdeaGeneration




2R&DEngineers

Total 56 R&D Engineers (28 teams, 2 R&D Engineers)

undergo the same treatment _

56 R&D Engineers (28 teams, 2 R&D Engineers) undergo the 4 different treatment

Asthetableshows,theexperimentwasintwomainparts.Inthefirstsection,allgroupstriedtogenerateasmanynewideasaspossibleaboutatargettechnicalsystem, Walker, by using the brainstorming method. In the second session, eachteam were asked to apply a stimulus except the teams were selected as controlgroup;teamsinGroupA,generatedideasbyusingregularProblem‐SolutionMatrixMap;teamsinGroupB,generatedideasbyusingtheTechnicalContradictionMap;teamsinGroupC,generatedideasbyusingdifferentPatentTextfromanotherfieldsuchasskateboard,unicycle,scooter,rollerskate,etc.;andfinallyteamsinGroupD,generated ideasabout theWalkerwithoutany involvement,as thecontrolgroup(seegeneratedideasofallgroupsinAppendixF).

56R&D Engineerswereasked toparticipate in the firstexperiment. Theywere randomly divided into four groups. Each group had 14 R&D Engineer in 7random teams; two individuals for each team. The allocating processes for eachgroupandtheteamshavebeendonecompletelyatrandom,andthereforethereisno systematic selection regarding their gender, age, education, degree and their


88

knowledgeinpatentanalysis.Table23showstheparticipantprofiles.From56totalmembers,45weremen,and11werewomen.Theywerebetween27to43yearsoldwhichcanbecategorizedasyoungandmiddle‐agedadults.Theycamefromseveralfields of study, but they were all engineers including Mechanical, Industrial,Electronics, Computer, Chemical, Aerospace, Marine, Metallurgical, and MaterialsEngineers.Someothercharacteristicsarementionedinthetable.

Table23‐ParticipantsProfile‐ExperimentI.

Participants Sex Average

age (year) Degree Field of study

Experiences in the field (year)

Knowledge in patent and patent

analysis

56

45M

35.4(27‐43)STD:4.4

41Master

13MechanicalEng.

9.2(7‐13)

STD:1.5

2High

12IndustrialEng.

12Medium8ElectronicEng.

5ComputerEng.

4ChemicalEng.

42Low11F

9Bachelor4AerospaceEng.

4MarineEng.

6PhD3MetallurgicalEng.

3MaterialsEng.

In followingafterreviewingtheappliedmethodandformulaforassessingtheexperimentresult, theobserveddataandstatisticalstudiesarepresentedformoreformalevidence.

4.1.1 Ideation metrics measurement

AsmentionedinChapter3,toassesstheresultsofeachteamandcomparetheresultsamongthegroups,thecombinationofShah’smetricandFBSframeworkisusedinthescopeofthisresearch.Followingthefinalappliedformulaisexplainedindetail.

Novelty: To calculate the degree of Novelty of each team, according to the Shah’s

formula,first,thecriticalortargetfunctions(orattributes)mustbelisted,andthenthe expected stages of a novelty for desired functions (or attributes) must bedefined.Theformulaneedstheweightsofthelistedfunctions(orattributes)basedontheir importance forthestudy,andalsothescores forthedefinedstagesofanoveltyforeachfunctionorattribute.Thescoresforeachstageofeachfunction(orattribute) is defined priori or posteriori. In priori view, the entire set of ideas iscollectedforevaluationbydeterminingtheexpectingunusualnessorexpectedness,beforeexamininganyinformationforavoidinganybias.Inposterioriperspective,the key attributes and the occurrences of them are defined respect to the allgeneratedideasincorrespondingdesignsessions.Basedonthepreparingnumbers,Noveltyofeachteamcanbecalculatedbasedonfollowingformula:


89

�� = ��

�

��

��

�

��

(1)

�� =��

��× 10 (2)

Where:M1:TotalNoveltyscorem:Numberoffunctionsorattributesn:Numberofstagesfj:Weightsassignedtothevalueoffunctionorcharacteristictocalculateatotalscorepk:WeightsallocatedtotheimportanceofstagesCjk:TheideasnumberforfunctionjinstagekTjk:Theideasnumberforfunctionjforallstagesk

Inthescopeofthisresearch,sixtargetattributesforexpectingnoveltiesofideasweredefinedandaccordingtotheirimportanceandexpectednovelties,theirweightsweredefined.Thenthestagesofexpectednoveltyforeachattributeweredefined in three levels of Function, Behavior, and Structure respect to the FBSframework.Table24,showsthedefinedattributesandstages.

Table24‐TheNoveltyattributewithweightsandrelatedFBSLevels‐ExperimentI.

No. Novelty

Attribute Wt. (fj)

Level 1 New Structure

Level 2 New Behavior

Level 3 New Function

Same Function-Change field Another Function

1 Support user

unbalance 0.25

Supportunbalanceduserbyusingmodular

partsmanually

Supportunbalanceduserbyactivatingmodularparts(electrically)

Supportuserbalancebyself‐

settingtothedisordersigns(smartsystem)

2 Support Walker

unbalance 0.25

Supportunbalanced

Walkerbyusingmodularlegsfitto

thesurfaces

SupportunbalancedWalker

byactivating(electrically)

modularlegsfitthesurfaces

SupportWalkerbalancebyself‐

settingtothesurfaceseffects(smartsystem)

3 Support Walker

storage & transport

0.15

SupportWalkerstorage&

transportbyusingfoldingparts

manually

SupportWalkerstorage&transport

byfoldingpartselectrically,etc.

SupportWalkerstorage&transportbyself‐settingtothe

locations(smartsystem)

4 Support user

body non-ergonomic

0.15

Supportnon‐ergonomicuserbodybysettingWalkerframe

manually

Supportnon‐ergonomicuserbodybysettingWalkerframe

electrically,etc.

Supportergonomicuserbodybyself‐

settingtothepositions

(smartsystem)

5 Supply Walker

propulsion 0.1

SupplyWalkerpropulsionby

liftinguser

SupplyWalkerpropulsionbypushinguser

SupplyWalkerpropulsionbyusing

engine

6

Support user routine

activities and user accessories

0.1

Supportroutineuseractivitiesby

holdinguseraccessories

manually

Supportroutineuseractivitiesby

holdinguseraccessorieselectrically

Supportroutineuseractivitiesbyself‐

settingtotheneeds(smartsystem)

Total 1

Asthetableshows,tobeabletodedicatetheideastoeachcellforassessingthedegreeofNoveltyofeachteam,accordingtotheconceptsofFunction,Behavior,


90

andStructure,themaincriterionforeachstageofeachattributeiswritteninthecorresponding cell in the table. In the level of Structure, the same Function isansweredbysameBehaviorbutnovelchangesinthestructureofthesystem.InthelevelofBehavior,thesameFunctionissatisfiedbynewprinciples.InthelevelsofStructureandBehavior,theusers’requirementsofsystemsarenotchangedwhilein the Function level, there is a new requirement or new value for samerequirements. In the scope of this research, the score of eachwas cell calculatedbasedonposterioriapproachbyapplyingFormula2.Table25showsthecalculatedscoresforeachcell.

Table25‐The��scoresof4Groups‐ExperimentI.

No. Novelty Attribute Wt. (fj)

Level 1 (New

Structure)

Level 2 (New

Behavior)

Level 3 (New

Function)

1 Support user unbalance 0.25 6.2 5.3 8.5

2 Support Walker unbalance 0.25 3.9 6.6 9.5

3 Support Walker storage & transport 0.15 1.5 8.8 9.7

4 Support user body non-ergonomic 0.15 4.0 6.4 9.5

5 Supply Walker propulsion 0.1 5.1 7.9 6.9

6 Support user routine activities and user accessories 0.1 5.5 5.0 9.5

Total 1

Table25wasappliedtocalculatethedegreeofNoveltyofeachteambasedontheFormula1.Table26showsthecalculationforateamasanexample.

Table26–ThecalculationofthedegreeofNoveltyofoneoftheteams‐ExperimentI.

no. Novelty Attribute Wt. (fj)

Level 1 Level 2 Level 3 Novelty Scores

1 Support user unbalance 0.25 0 0 0 0.25*((0*6.2)+(0*5.3)+(0*8.5))=0

2 Support Walker unbalance 0.25 1 1 0 0.25*((1*3.9)+(1*6.6)+(0*95))=2.6

3 Support Walker storage & transport 0.15 1 1 0 0.15*((1*1.5)+(1*8.8)+(0*9.7))=1.5

4 Support user body non-ergonomic 0.15 4 1 0 0.15*((4*4)+(1*6.4)+((0*9.5))=3.4

5 Supply Walker propulsion 0.1 1 0 1 0.1*((1*5.1)+(0*7.9)+((1*6.9))=1.2

6 Support user routine activities and

user accessories 0.1 2 3 0 0.1*((2*5.5)+(3*5)+((0*9.5))=2.6

Total 1 9 6 1 11.4

The number of each cell in Table 26 shows the corresponding number ofnovelattributes for that cell in thewholesetof solutions of the team. In the lastcolumn,thetotalscoreofNoveltyiscalculated.Similarly,byapplyingtheTable25,thedegreeofNoveltyofallteamswerecalculated.Theresultsareshowninnextsection.

Variety: To calculate the degree of Variety of each team, according to the Shah’s

formula, first,all ideasareanalyzed,and theircorresponding Functions,Physical


91

principle, Working principle, Embodiment, and details are drawn in a genealogytree.Inotherwords,eachideaisanalyzedin5levelswhichthenewsuggestionsinhigherlevels(meansFunctionandPhysicalprinciple),showthemorevarietyofidearespecttotheexistingandknownversionofthesystem.Therefore,theweightsforthelevelsmustshowthemorevarietyforhigherlevels.Mostly,literatureuses10,6,3and1forthelevelsofPhysicalprincipletodetail forassessingthedegreeofVarietyof ideas.Innextstep,theweightsmustbededicatedtoeachFunction.ByconsideringtheweightsfortheFunctionswerereferredinthetotalidea,thedegreeofVarietyofeachteamiscalculatedbasedonFormula3.

�� = 10 ∑ �� ∑ ��/��

�� (3)


InthescopeofExperimentI,afteranalyzingallideas,thelistoffunctionwasprepared, and then the template table for assessing the degree of Variety wasprovided.Table27showstheprovidedtemplateconsideringallrequiredweights.

Table27‐ThetemplatetableforassessingthedegreeofVarietyofteams‐ExperimentI.

Function

1-Support

user to Transport

2-Support walking in

any

surfaces

3-Support

user to sit

and stand

4-Support normal

walking

5-Prevent user

collision

& falling

6-Ergonomics

balance

7-Accessories

holder

8-Information

support

Weight

Sk

0.1 0.2 0.1 0.2 0.2 0.1 0.05 0.05

Physical Principles

10

Working Principles

6

Embodiment 3

Detail 1

Table27wasfilledforeachteamonce.Table28showsthefilledtableforoneoftheteamsasanexample.


92

Table28‐ThefilledtemplatetableofassessingthedegreeofVarietyforateam‐ExperimentI.

Function

1-

Support user to

Transport

2-Support walking in any surfaces

3-

Support user to sit

and stand

4-Suppo

rt norma

l walkin

g

5-Prevent user collision & falling

6-Ergonomics balance

7-Accessories holder

8-Informati

on

support

Weig

ht

Sk

0.1 0.2 0.1 0.2 0.2 0.1 0.05 0.05

Physical Principles

10 Mechanic

alMechanic

alMechanic

al_ _

Electrical

Chemical

_Mechanica

l_

Working

Principles 6 Portable Move Move _ _ Lighting Protect _ Protect _

Embodiment

3 Walkerframe

Walkerlegs

Walkerlegs

_ _Walkerframe

Walkerframe

_Walkerframe

_

Detail 1

Usingcomposit

ematerials

Walker

withfourwheels

Addtwo

wheelstofrontlegs

_ _

Contactled

lightsto

illuminatethe

pathatnight

Usingreflectorontheframe

_

Addtheumbrella

orroofduring

rain

_

Beforefollowingthecalculation,theresultsoftable28,mustbeshownintheformofthegenealogytreetomakepossibletocalculatethemaxquantityofvariety(M3)tobeusedinthecorrespondingformula.TheresultsofthegenealogytreeforateamisshowninFigure13.

Figure13‐Theresultsofgenealogytreeanalysisforateam‐ExperimentI.

BaseonFigure13andFormula3,thedegreeofVarietyoftheexamplewascalculatedas:

��= (10)*[(0.1)*((10*1)+(6*1)+(3*1)+(1*1))+(0.2)*((10*1)+(6*1)+(3*1)+(1*2))+(0.2)*((10*2)+(6*2)+(3*2)+(1*2))+(0.05)*((10*1)+(6*1)+(3*1)+(1*2))]/2=76.25

Similarly,thedegreeofVarietyofeachteaminbothsessionswascalculated.


93

4.1.2 Estimated results

The data were collected by team members during design session throughfillingthesolutionpapers,wheneverthemembersagreedonproposingsolutions.Adescription,aschema,andthetimeofappearanceofthesolutionswerethecollecteddatainsolutionpapers.Thedatacollectedforeachsessionseparately.Attheendofbothtwosessions,NASAsurveywascompletedbyparticipantsoftheteamstoo.ThedescriptionandschemaofeachsolutionwereanalyzedbyresearcherandthetotalscoreofNoveltyandVarietywerecalculatedbasedontheTables25and28.Table29 shows the results of calculation of Novelty and Variety for each 28 teamsorganizedbasedontheinterventioninthesecondsessiontoletfurtherstudies.

Table29–ThescoresofQuantity,Novelty,andVarietyforallteams‐ExperimentI.

Name Team

First session (Brainstorming)

Second sessions (various stimuli)

Two sessions

Quantity Novelty Variety Quantity Novelty Variety Quantity Novelty Variety

Group A (P.S)

1 16 11.4 67.8 7 7.2 70 23 18.6 137.8

2 8 13.4 29.2 4 5.3 80 12 18.7 109.2

3 12 8.7 28.2 5 7.6 50 17 16.3 78.2

4 17 12 59.5 5 4 42.5 22 16 102

5 7 4.9 60.5 4 3.6 40.5 11 8.5 101

6 22 5.6 77.3 11 6.9 67.3 33 12.5 144.6

7 6 15.5 51 3 3.4 60 9 18.9 111

Group B (T.C)

1 10 6.3 65.7 8 7.7 34 18 14 98.7

2 17 13.6 49.2 11 8.4 57.7 28 22 106.9

3 11 9 67 4 3.6 80 15 12.6 147.0

4 17 12 55 10 12.8 46.2 27 24.8 101.2

5 5 3.6 26 5 4.1 51 10 7.7 77.0

6 9 5.8 34.8 7 4.7 37.6 16 10.5 72.4

7 19 21 49.3 9 10.9 27.6 28 31.9 76.2

Group C (P.T)

1 10 6.7 49.3 3 4.3 30.5 13 11 79.8

2 10 6.9 49 8 7.2 70.5 18 14.1 119.5

3 10 9.6 39.3 2 1.9 40 12 11.5 79.3

4 18 11.9 35.3 6 5.3 34 24 17.2 69.3

5 5 4.8 31 4 3.3 31.5 9 8.1 62.5

6 15 14.6 56.5 6 6.1 21.3 21 20.7 77.8

7 11 7.5 42.3 5 5.9 50.5 16 13.4 92.8

Group D (B.S)

(Control Group)

1 5 3.8 21 4 3.7 40.5 9 7.5 61.5

2 17 12 49 3 2.7 30 20 14.7 79.0

3 8 6.7 47.3 3 2.1 30.5 11 8.8 77.8

4 12 7.6 64 3 3.3 60 15 10.9 124.0

5 10 9.1 61.3 4 3.8 40.5 14 12.9 101.8

6 16 15.1 59.8 3 2.5 30.5 19 17.6 90.3

7 12 8.4 62 2 1.6 10.5 14 10 72.5


94

ThetableshowsthescoreofQuantity,Novelty,andVarietyofeachteaminthreeconditions;firstsession,secondsession,andtwosessionstogether.Intotal,formostoftheteams,despitetheappliedinterventions,thescoresfortheallthreecriteriainthesecondsessionarelessthanthefirstsession.Furtherstudiescanbefollowedbytwoapproaches;investigationontheeffectofeachinterventionrespectto the control group in the second session, and investigating the effect of eachinterventionrespecttothecontrolgroupbyconsideringthetotalscoresforthetwosessionstogether.Two‐partdesignsessionwhereasapplyingbrainstormingforthefirstsession,reducestheeffectsofparticipants’expertiseintothetestastheytrytogenerate as much as possible ideas by first reflection on their mind in the firstsessionandtheeffectsofinterventionsandstimulicanbeobservedinthesecondsession. Also, the literature shows, without any intervention, the number ofgeneratedideasinabrainstormingsessiondecreaseafterhalfanhour,whilethebestideasaregeneratedinfirst15minutes(Howardetal.,2010).Moreover,stimuliwhich are prepared and applied during the early design stages, or when theparticipanthasbeenunabletosolvethedesignproblemforadifficultopen‐endeddesign problem (Tseng et al., 2008). Therefore, in the scope of Experiment I,Brainstormingisusedonlyatthebeginningoftheideationprocess,andformoreimprovement,thestimuliwerepresentedatthebeginningofthesecondsession.

To follow further studies, the scores of Quantity, Novelty and Variety are

calculatedfortheallteamsofagroupwiththesamestimulitogether.Table30showstheestimatedresultsforeachcriterionforthefourgroupsoftheexperimenttomakepossible comparison among the groups in the second approach for all sessionstogether.

Table30‐ThescoresofQuantity,Novelty,andVarietyrespecttothegroupwithdifferentstimuli‐ExperimentI.

Name First session Second sessions Two sessions

Quantity Variety Novelty Quantity Variety Novelty Quantity Variety Novelty

Group A (P.S)

Mean 12.6 53.3

Sco

re

71.4Mean 5.6 58.6

Sco

re

38Mean 18.1 112

Sco

re

109.4STD 6 18.7 STD 2.7 14.9 STD 8.5 22.7

Group B (T.C)

Mean 12.6 49.471.2

Mean 7.7 47.752.2

Mean 20.3 97.1123.4

STD 5.2 14.9 STD 2.6 17.6 STD 7.3 25.9

Group C (P.T)

Mean 11.3 43.262.2

Mean 4.9 39.834

Mean 16.1 8396.2

STD 4.2 8.9 STD 2 16.3 STD 5.3 18.7

Group D (B.S)

(Control Group)

Mean 11.4 52.1

62.8

Mean 3.1 34.6

19.5

Mean 14.6 86.7

82.3STD 4.2 15.2 STD 0.7 15 STD 4 20.8

Table 30 shows, almost the calculated average of Quantity, Novelty, andVarietyoftheteamsofallgroupinthefirstsessionaresimilar,andthereisnotabigdifferenceamongthem.However,thecorrespondingvalueforthesecondsessionwithvariousstimuliisdifferent.ThescoresofQuantityandNoveltyarehighestforthe group received Technical Contradiction Map (T.C Map) after the group with


95

Problem‐SolutionMap(P.SMap)whilethedegreeofVarietyofthesetwogroupsareso close but higher for the P.S Map. Considering the expecting meaning for non‐obvious novelty and the Shah metrics for Novelty and Variety, discussed inChapter3, it seems that T.C Map is more effective on the ultimate target of theresearchwhileitmustbestudiedthroughstatisticalstudies.Itisworthconsideringthatthelessvalueinthesecondsessionforallthreecriteriaareforthecontrolgroupwhichappliedbrainstormingforgeneratingideasinthesecondsessiontoo.

Respectivelytheanalysiscanbefollowedforthethirdcolumn,twosessionstogether.AgainthescoresofQuantityandNoveltyarehighestforthegroupreceivedT.C Map while the Variety is highest for the P.S Map group. Figure 14 showsgraphicallytheassessingcriteriaforthelastcolumn.

Figure14‐Graphicalrepresentationofassessingcriteriaoftwosessions‐ExperimentI.


96

Before performing statistical analysis, it is worth to look at the otherinformationgatheredduringandafterdesignsessions;thetimeofappearanceofsolutions,andtheNASAsurvey.

Figure15showstheperiodofemergingsolutionsinthesecondsession.Thetotaltimeisdividedintosixsessions.

Figure15‐Theideatimelineofallgroup‐ExperimentI.

ThefigureshowsT.CMapgrouprespecttotheothergroups,generatedthehigherpercentageoftheirideasamong6to20minute.Inotherwords,TechnicalContradictionMapincreasedthespeedof ideagenerationatthebeginningofthesessionwhilethememberscontinuegeneratingideauptotheend.

According to the NASA task load Index, the team members are asked torespondtosixquestions.Thequestionsaskabouttheattemptsandthefeelingofparticipants;demandingmentaltask,physicaldemandingtask,appropriatenessofthededicated time,success in fulfilling the taskrequirements, theharnessof thetask,andstressfulness.Figure16showstheresultsofthesurveyrespecttothefourvariousinterventions.

Figure16‐NASAtaskloadIndexresultsofparticipants‐ExperimentI.

0

5

10

15

20

0-5 6-10 11-15 16-20 21-25 26-30

Ide

as

Time

Idea Timeline

Problem-Solution Map Technical contradiction Map

Patent Text (Cross-field) Brainstorming (Control)


97

The best results for the T.C Map can be seen for the forth question which is thefeelingoftheparticipantsabouttheirsuccessesinfulfillingthetask.ThehardnessofT.CMapislessthanFullPatentText,anditishigherthanP.SMap.Inaddition,T.CMapisrankedaslessmentaldemandingrespecttoPatentText,whichcanbemeantthatT.CMapprovidedinformationmoreaccessible.

4.1.3 Data analysis

Statisticalanalysisusuallystartsbychecking thenormalityofdata.Thegathereddatainthefirstsessionwhereastheconditionofdesignsessionwasthesameforallthe 28 teams. The normality of data was studied through all scores of Quantity,Novelty,andVariety.Figure17showstheresultsofnormalitystudies.

Figure17‐NormalityoftheData‐ExperimentI.

As mentioned in Chapter 3, the statistical studies for usability andeffectivenessoftheproposedmap,TechnicalContradictionMap,isstudiedthroughan OLS model for the calculated scores of Novelty, Quantity, and Variety in theprevioussectionforeachteam.

0.0

00.2

50

.50

0.7

51

.00

Norm

al F

[(N

oveltyP

1-m

)/s]

0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)

Novelty - Part 10.0

00.2

50

.50

0.7

51

.00

Norm

al F

[(Q

uantity

P1

-m)/

s]

0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)

Quantity - Part 1

0.0

00.2

50

.50

0.7

51

.00

Norm

al F

[(V

ari

ety

P1-m

)/s]

0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)

Variety - Part 1

0.0

00

.25

0.5

00.7

51.0

0N

orm

al F

[(N

oveltyP

2-m

)/s]

0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)

Novelty - Part 2

0.0

00

.25

0.5

00

.75

1.0

0N

orm

al F

[(Q

ua

ntity

P2

-m)/

s]

0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)

Quantity - Part 20

.00

0.2

50

.50

0.7

51

.00

No

rma

l F

[(V

arie

tyP

2-m

)/s]

0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)

Variety - Part 2

0.0

00

.25

0.5

00

.75

1.0

0N

orm

al F

[(N

oveltyT

-m)/

s]

0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)

Novelty - Sum

0.0

00.2

50.5

00.7

51.0

0N

orm

al F

[(Q

uantity

T-m

)/s]

0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)

Quantity - Sum

0.0

00.2

50.5

00.7

51.0

0N

orm

al F

[(V

ari

ety

T-m

)/s]

0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)

Variety - Sum


98

Novelty: AsmentionedinChapter3,tostudytheeffectsofTechnicalContradictionMap

on the degree of Novelty of ideas of corresponding teams respect to the othergroups,anOLSmodelconsideringthreevariablesarerepresentingthethreeappliedmethodsrespecttothecontrolgroup,isused.ThemodelforestimatingNoveltyisasfollow:

Novelty = � + �� + �� + �� + �

where ��, �� and �� present, respectively, the contribution of ‘Problem‐Solution Matrix Map’, ‘Technical Contradiction Map’ and ‘Patent Text Far‐Field’methods with respect to the control group (Brainstorming). Since group B (whoreceivedtheTechnicalContradictionMapmethod)isthetargetgroup,theestimatedresultfor��willbeonthefocus.Thestudyispursuedthroughsetofhypothesesafterstudyingthevalidityofassumptionsofclassicallinearregressionmodelonthedataset.

1. Possibility of applying LOS model for statistical analysis: Novelty

2ofthe5assumptionsofaclassicallinearregressionmodelontheresidualsneeded to be tested in order to ensure a reliable interpretation of the testedhypotheses; Homoscedasticity and the normal distribution of the residuals weretested.

First,HomoscedasticityoftheresidualsoftheNoveltyregressionwastestedusingthegraphicalapproachandalsoBreusch‐Pagan/Cook‐Weisbergtest.Figure18 shows a graphical representation of the estimated residuals of the Noveltyregression.Itisclearthatthereisnosystematictrendfortheestimatedresiduals,indicatingthattheHomoscedasticityassumptionissatisfiedforthisvariable.

Figure18‐EstimatedresidualsfortheNoveltyregression:Homoscedasticitytest‐

ExperimentI.

The Breusch‐Pagan/Cook‐Weisberg test is also used to make sure theHomoscedasticity. This test is designed to detect any linear form of

-10

-50

510

15R

esid

uals

0 10 20 30group


99

heteroskedasticity. Breusch‐Pagan/Cook‐Weisberg tests the null hypothesis thatthe error variances of the regressions are all equal which indicatesheteroskedasticity,versusthealternativehypothesissayingthattheerrorvariancesareamultiplicativefunctionofoneormorevariables.Therefore,largevaluesofchi‐squareindicatepresentingoftheheteroskedasticity.Inourexample,thechi‐squarevalue is small enough to make sure that heteroskedasticity is not a problem, asshown in the graph. In this example, the chi‐square value is small, indicatingheteroskedasticityisprobablynotaproblem,asshowninthegraph.

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity Ho: Constant variance Variables: fitted values of Novelty chi2(1) = 2.20 Prob > chi2 = 0.1384

TheNormalityof theresiduals thenneededtobechecked.Bothgraphical(Figure19)andSkewness/KurtosistestsconfirmthenormalityoftheresidualfortheNoveltyregression.

Figure19‐EstimatedresidualsfortheNoveltyregression:Normalitytest‐ExperimentI.

Skewness/Kurtosis tests for Normality ------ joint ------ Variable | Obs Pr(Skewness) Pr(Kurtosis) adj chi2(2) Prob>chi2 -------------+--------------------------------------------------------------- resNormal1 | 28 0.2368 0.6552 1.74 0.4199

2. First Group of hypotheses: Novelty

ThefirstgroupofhypothesesstudiestheimpactofeachmethodontheOLSmodel respect to the control group by expecting positive coefficient for effectivemethodsontheOLSmodelofNovelty.Therefore,threehypothesesarestudied:

1. [H�: �� ≤ 0againstH�: �� > 0]2. [H�: �� ≤ 0againstH�: �� > 0]3. [H�: �� ≤ 0againstH�: �� > 0]

0.0

00

.25

0.5

00

.75

1.0

0N

orm

al F

[(re

sNorm

al1

-m)/

s]

0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)


100

Theabove‐mentionedhypothesesarestudiedthrought‐testonthebasisofOLS model by STATA software. Therefore, first, the both simple regression andregression with clustering analysis are done. Table 31 shows STATA output ofsimpleregressionanalysisfortheNoveltyacrossdifferentgroups.

Table31‐EstimatedresultsofeffectsofdifferentmethodsonNovelty‐ExperimentI.

Source | SS df MS Number of obs = 28 -------------+---------------------------------- F(3, 24) = 1.16

Model | 112.229643 3 37.409881 Prob > F = 0.3469 Residual | 776.502857 24 32.3542857 R-squared = 0.1263

-------------+---------------------------------- Adj R-squared = 0.0171 Total | 888.7325 27 32.9160185 Root MSE = 5.6881

------------------------------------------------------------------------------ Novelty | Coef. Std. Err. t P>|t| [95% Conf. Interval]

ga | 2.585714 3.040408 0.85 0.403 -3.68938 8.860808 gb | 5.571429* 3.040408 1.83 0.079 -.7036654 11.84652

gc | 1.914286 3.040408 0.63 0.535 -4.360808 8.18938 cons | 12.45714 2.149893 5.79 0.000 8.019981 16.8943

AsthetableshowsR2=0.126andAdjustedR2=0.017,whichmeansthatthethree dummy variables, explain 12.6% of the variability of the dependentvariable,'Novelty,' in the population. However, normally it isR2, not theadjustedR2,thatisreportedinresults.Inthisexample,F(3,24)=1.16andp=.35;thismeansthattheregressionmodelisnotstatisticallysignificantat90%levelofsignificance.Thisissuewillbedealtwithusingclusteringissue.Inaddition,onlythecoefficientofthedummyvariableforgroupB(T.Cmapastargetgroup)issignificantat90%levelofsignificance;butallofthemarepositiveandbiggerthanone.2.59forthedummy'ga'meansthatthemethodappliedforgroupAgenerates2.59moreNoveltiesthangroupD;however,itisnotsignificant.Also,5.57for'gb'meansthatthe method used for group B has the highest effect between other methods andproduces 5.57 more ideas than group D; hopefully this is the only significantcoefficient.Thisdifferenceis1.91forgroupCascomparedtogroupD,butagain,itisnotsignificant.Thepresenceofnosignificantcoefficientswillalsobeaddressedbyusingtheclusteringmethod.

Clusteringisamethodofgroupingasetofobjectssothatobjectsinthesamegroupas aclusteraremoresimilar toeachother, rather than to thoseobjects inother groups or clusters. Since the groups are truly random, regression can beestimatedbyclusteringwithrespecttoeachgroup.Infact,clusteringwithrespecttotheidvariabletakesadifferentnumberforeachgroup.Table32showstheSTATAoutputforthesameregressionasabove,butwithclustering.Itisclearthatallthreedummyvariablesaresignificantatthe99%confidencelevel.However,clusteringdoesnotchangetheestimatedcoefficients,andgroupBstillgenerated5.57moreNovelty than the control group (Brainstorming) and also more than the othermethodsappliedtogroupsAandC.


101

Table32‐Estimatedresultsofimprovingideasfordifferentmethods:Novelty(clustering)

‐ExperimentI.

Linear regression Number of obs = 28 F(0, 3) = . Prob > F = . R-squared = 0.1263 Root MSE = 5.6881

(Std. Err. adjusted for 4 clusters in id)

Robust NoveltyT | Coef. Std. Err. t P>|t| [95% Conf. Interval]

ga | 2.585714*** 3.47e-16 7.4e+15 0.000 2.585714 2.585714 gb | 5.571429*** 1.13e-15 4.9e+15 0.000 5.571429 5.571429 gc | 1.914286*** 1.28e-15 1.5e+15 0.000 1.914286 1.914286

cons | 12.45714*** 3.11e-16 4.0e+16 0.000 12.45714 12.45714 Tostudythefirstgroupofhypotheses,at‐testasasignificancetest,isdone.

Theteststatisticgiveninthetcolumnisatestthatthecoefficientissignificantlydifferentfromzero.STATAreportsanaccompanyingp‐valueforatwo‐tailtestinsimpleregression.Thatis,fortheslopecoefficient,thet‐statisticisatestof:[H�: β =0againstH�: β ≠ 0].Asthet‐valuesshowinTable31,only'gb'and'constant'term

coefficients are significant at, respectively, 90% and 99% confidence levels.Therefore, the null hypothesis is rejected (H�: �� ≤ 0 against H�: �� > 0) withextremelyhighconfidenceforthedifferencebetweengroupBandgroupD‐above90%infact.Butthenullhypothesisforgroups'ga'and'gc'cannotberejected.ThisimpliesthatthereisnosignificantdifferencebetweengroupsAandCwithgroupD.This indicates that only the Technical Contradiction Map is the effective method,comparedtotheothermethods.

3. Second Group of hypotheses: Novelty

Thesecondgroupofhypothesesstudiesthemagnitudeofeffectsof targetmethodrespecttotheothermethodsthroughfollowinghypotheses:

4. [H�: β� ≤ β�againstH�: β� > β�]5. [H�: β� ≤ β�againstH�: β� > β�]

Thesecondtypeofhypotheseswasapplied intwodifferentstyles;testinghypotheses4and5separately,andajointtestconsidering4and5together.Bothtypesoftestsarereportedasbelow:

Single test (4) ga - gb = 0 F( 1, 24) = 0.96 Prob > F = 0.3359

Single test (5) - gb + gc = 0 F( 1, 24) = 1.45 Prob > F = 0.2408

Joint test (4&5) ga - gb = 0 & - gb + gc = 0 F( 2, 24) = 0.82 Prob > F = 0.4524

AlargeF‐testwouldindicatethatthenullhypothesiscanberejectedwhichmeansthattheimpactoftheTechnicalContradictionMapissignificantlylargerthantheothermethods.Runninganotherregressionwithclusteringcanbeundertaken.


102

Quantity: Similar to the Novelty part, the different assumptions and hypothesis for

QuantityareestimatedandtestedthroughanOLSmodel:

Quantity = � + �� + �� + �� + �

where ��, �� and �� present, respectively, the contribution of ‘Problem‐Solution Matrix Map’, ‘Technical Contradiction Map’ and ‘Patent Text Far‐Field’methods with respect to the control group (Brainstorming). Since group B (whoreceivedtheTechnicalContradictionMapmethod)isthetargetgroup,theestimatedresultfor��willbeonthefocus.Thestudyispursuedthroughsetofhypothesesafterstudyingthevalidityofassumptionsofclassicallinearregressionmodelonthedataset.

1. Possibility of applying LOS model for statistical analysis: Quantity

Beforetestingthehypothesis,twoassumptionsregardingHomoscedasticityandthenormaldistributionoftheresidualsneedtobechecked.

Figure20showsthegraphicalviewoftheestimatedresidualsoftheQuantityregression; it is clear that it is distributed randomly over the groups andHeteroskedasticityisnotpresentinthisregression.

Figure20‐EstimatedresidualsfortheNoveltyregression:Homoscedasticitytest‐

ExperimentI.

TheBreusch‐Pagan/Cook‐Weisbergtestfollowssincethechi‐squarevalueissmall,indicatingthatheteroskedasticityisprobablynotaproblem,asshowninthegraphabove.

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity Ho: Constant variance Variables: fitted values of QuantityT chi2(1) = 2.34 Prob > chi2 = 0.1260

TheNormalityoftheresidualsoftheQuantityregressionandbothgraphical(Figure21)andSkewness/Kurtosistests,confirmnormalityoftheresidualfortheregression;normality,therefore,isnotofconcernforthisexperience.

-10

-50

510

15R

esid

uals

0 10 20 30group


103

Figure21‐EstimatedresidualsfortheQuantityregression:Normalitytest‐ExperimentI.

Skewness/Kurtosis tests for Normality ------ joint ------ Variable | Obs Pr(Skewness) Pr(Kurtosis) adj chi2(2) Prob>chi2 -------------+--------------------------------------------------------------- resQuantity1 | 28 0.3767 0.7803 0.91 0.6337

2. First Group of hypotheses: Quantity

ThethreehypothesesarestudiedforinvestigatingtheimpactofeachmethodontheOLSmodelrespecttothecontrolgroupbyexpectingpositivecoefficientforeffectivemethodsontheOLSmodelofQuantity:


Thet‐testisdonethrougharegressionmodel.Table33showstheestimatedresultsoftheanalysis.

Table33‐Estimatedresultsofimprovingideasbasedondifferentmethods:Quantity‐

ExperimentI.


Model | 128.857143 3 42.952381 Prob > F = 0.4030 Residual | 1014.85714 24 42.2857143 R-squared = 0.1127 -------------+---------------------------------- Adj R-squared = 0.0017

Total | 1143.71429 27 42.3597884 Root MSE = 6.5027

QuantityT | Coef. Std. Err. t P>|t| [95% Conf. Interval]

ga | 3.571429 3.475864 1.03 0.314 -3.602403 10.74526 gb | 5.714286 3.475864 1.64 0.113 -1.459546 12.88812 gc | 1.571429 3.475864 0.45 0.655 -5.602403 8.74526

_cons | 14.57143 2.457807 5.93 0.000 9.498764 19.64409

Table 33 shows the corresponding coefficient for Technical ContradictionMapis5.71morecomparedtocontrolgroup,D;Furthermore,itisalsolargerthantheothergroupsthatare3.57and1.57,respectively,forgroupsAandC.Inthiscase,noneoftheestimatedparametersaresignificantateventhe90%confidencelevel

0.00

0.25

0.50

0.75

1.00

Nor

mal

F[(

resQ

uant

ity1-

m)/

s]

0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)


104

andsoclusteringisneededforthisvariable.R2is0.11;thisspecifiedregressiononlyexplains11%ofthevariationinQuantity,though.Table34showstheresultsoftheclustering, and as previously discussed, clustering corrects non‐significantcoefficients,butestimatedcoefficientsarenotaffectedbyclustering;groupBstillhasthehighestbetacomparedtoothergroups.

Table34‐Estimatedresultsofimprovingideasfordifferentntmethods:Quantity

(clustering)‐ExperimentI.

Linear regression Number of obs = 28 F(0, 3) = . Prob > F = . R-squared = 0.1127 Root MSE = 6.5027

(Std. Err. Adjusted for 4 clusters in id)

Robust QuantityT | Coef. Std. Err. t P>|t| [95% Conf. Interval]

ga | 3.571429 1.97e-15 1.8e+15 0.000 3.571429 3.571429 gb | 5.714286 2.24e-15 2.5e+15 0.000 5.714286 5.714286 gc | 1.571429 1.97e-15 8.0e+14 0.000 1.571429 1.571429

_cons | 14.57143 1.86e-15 7.8e+15 0.000 14.57143 14.57143

As t‐values show in Table 33, 'ga', 'gb', 'gc', and 'constant' term are allsignificant; the null hypothesis of the first group of hypotheses is rejected. ThisindicatesthatallthreemethodsaremoreeffectivethanBrainstormingandapplyingthesemethodsimprovetheQuantitymorethanBrainstorming.

3. Second Group of hypotheses: Quantity

AnF‐testwasundertakentoseewhethertheTechnicalContradictionMapwasmoreeffectivethantheothermethodsthroughtwohypotheses:

4. [H�: �� ≤ ��againstH�: �� > ��]5. [H�: �� ≤ ��againstH�: �� > ��]

Twodifferenttestswererun,thattestedhypotheses4and5separately,andajointtestthatconsidered4and5together.Bothtypesoftestsarereportedas:

Single test (4) ga - gb = 0 F (1, 24) = 0.38 Prob > F = 0.5434 Single test (5) - gb + gc = 0 F (1, 24) = 1.42 Prob > F = 0.2450 Joint test (4&5) ga - gb = 0 & - gb + gc = 0 F (2,24) = 0.71 Prob > F = 0.5014

A largeF‐testwould indicatethat thenull hypothesiscanberejected; thismeansthattheimpactoftheTechnical Contradiction Mapissignificantlylargerthantheothermethods.


105

Variety: Finally, the same approach was repeated to test the effectiveness of each

methodthroughVariety.

1. Possibility of applying LOS model for statistical analysis: Variety

Beforetestingthehypothesis,twoassumptionsregardingHomoscedasticityandNormaldistributionoftheresidualsneededtobechecked.Figure22showsthegraphicalviewoftheestimatedresidualsoftheVarietyregression;it'sobviousitisdistributedrandomlyoverthegroupsandHeteroskedasticityisnotpresentinthisregression.

Figure22‐EstimatedresidualsfortheVarietyregression:Homoscedasticitytest‐

ExperimentI.

The Breusch‐Pagan/Cook‐Weisberg test followed; the chi‐square value issmall,indicatingthatheteroskedasticityisprobablynotaproblem,asshowninthegraphabove.

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity Ho: Constant variance Variables: fitted values of Variety chi2(1) = 0.26 Prob > chi2 = 0.6111

Normality of the residuals of the Variety regression, and both graphical(Figure23)andSkewness/Kurtosistests,confirmnormalityoftheresidualfortheregression;however,normalityisnottheresearchconcernforthisexperience.

-40

-20

020

4060

Res

idua

ls

0 10 20 30group


106

Figure23‐EstimatedresidualsfortheVarietyregression:Normalitytest‐ExperimentI.

Skewness/Kurtosis tests for Normality ------ joint ------ Variable | Obs Pr(Skewness) Pr(Kurtosis) adj chi2(2) Prob>chi2 -------------+--------------------------------------------------------------- resVariety1 | 28 0.0790 0.7472 3.52 0.1718

2. First Group of hypotheses: Variety

Theeffectsofeachappliedmethodrespecttothecontrolgroup,arestudiedthrough t‐test by estimating a simple dummy regression model consideringfollowinghypotheses:


Table35showstheresults;surprisingly,inthiscase,theestimatedbetaforgroupAisalmosttwiceasbigastheoneestimatedforgroupB.ThisimpliesthattheProblem‐Solution Matrix Map is more effective than the Technical ContradictionMapregardingVariety.Itisalsotheonlycoefficientthatissignificantatthe95%significant level. Other betas were not significant therefore clustering wasundertakenforthisvariablebeforetestingthehypothesis.However,R2is0.23;thismeansthatthespecifiedregressiononlyexplains23%ofthevariationinVariety.

Table35‐Estimatedresultsofimprovingideasfordifferentmethods:Variety‐

ExperimentI.


Model | 3532.74717 3 1177.58239 Prob > F = 0.0937 Residual | 11821.8125 24 492.575522 R-squared = 0.2301 -------------+---------------------------------- Adj R-squared = 0.1338 Total | 15354.5597 27 568.687397 Root MSE = 22.194

VarietyT | Coef. Std. Err. t P>|t| [95% Conf. Interval]

ga | 25.25714 11.86321 2.13 0.044 .7726711 49.74161 gb | 10.44524 11.86321 0.88 0.387 -14.03923 34.92971 gc | -3.7 11.86321 -0.31 0.758 -28.18447 20.78447

_cons | 86.7 8.38856 10.34 0.000 69.38686 104.0131

0.00

0.25

0.50

0.75

1.00

Nor

mal

F[(r

esV

arie

ty1-

m)/s

]

0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)


107

SincetheestimatedcoefficientsarenotsignificantforbothgroupsBandD,before testing hypothesis, clustering was undertaken. Based on the clusteringresults,thehypothesiswastestedinTable36.

Table36‐Estimatedresultsofimprovingideasfordifferentmethods:Variety(clustering)

‐ExperimentI.

Linear regression Number of obs = 28 F(0, 3) = . Prob > F = . R-squared = 0.2301 Root MSE = 22.194 (Std. Err. adjusted for 4 clusters in id)

Robust VarietyT | Coef. Std. Err. t P>|t| [95% Conf. Interval]

ga | 25.25714 2.14e-14 1.2e+15 0.000 25.25714 25.25714 gb | 10.44524 1.34e-14 7.8e+14 0.000 10.44524 10.44524 gc | -3.7 1.27e-14 -2.9e+14 0.000 -3.7 -3.7 cons | 86.7 1.24e-14 7.0e+15 0.000 86.7 86.7

AsTable36shows,GroupAstillhasthehighestbetacomparedtotheothergroups.Ast‐valuesshowinTable36,'ga','gb'and'constant'termareallsignificant;thenullhypothesisofthefirstgroupofthehypothesiscanberejected,exceptforgroup C. As shown, the estimated beta for group C is negative, thus the nullhypothesiscannotberejected(Hypothesis3).ItindicatesthatinregardstoVariety,Patent Text Far‐Field is less effective than even Brainstorming. A test wasundertakentoseewhethertheTechnicalContradictionMapwasmoreeffectivethantheothermethods.

3. Second Group of hypotheses: Variety

AnF‐testwasundertakentoseewhethertheTechnicalContradictionMapwasmoreeffectivethantheothermethodsthroughtwohypotheses:

4. [H�: �� ≤ ��againstH�: �� > ��]5. [H�: �� ≤ ��againstH�: �� > ��]

Twodifferenttestswererunagain,testinghypotheses4and5separately,and then a joint test that considered 4 and 5 together. Both types of tests arereportedas:

Single test (4) ga - gb = 0 F( 1, 24) = 1.56 Prob > F = 0.2239 Single test (5) - gb + gc = 0 F( 1, 24) = 1.42 Prob > F = 0.2448 Joint test (4&5) ga - gb = 0 & - gb + gc = 0 F( 2, 24) = 2.98 Prob > F = 0.0699


108

A large F‐test would indicate that the null hypothesis should be rejected,however,inthiscase,itisnotpossibletoshowthattheTechnicalContradictionMapismoreeffectivethanothermethods.Basedonthistest,itcanonlybeinterpretedthattwobetasforgroupsAandBarenotthesame,butitcannotbesaidthatthebetaforgroupBislargerthanbetaofgroupA.However,asshowninTable36,thebetaforgroupAistwiceasbigthangroupB.

4.2 Experiment II: Repeatability of the building the map

Experiment II is planned and performed to study the repeatability of

buildingtheTechnicalContradictionMap.Therepeatabilityofbuildingthemapisstudied through the existence of significant differences among the effects ofdifferentmapsdevelopedbydifferentengineers.AsmentionedinChapter3,exceptthemapwhichwasdevelopedbytheresearcher,3othermapsweregenerated,eachoneby30otherR&Dengineers.Topursuethestudy,theeffectsofthreenewmapsarestudiedrespecttothemapwhichwasdevelopedbytheresearcherforthefirstexperiment. It is worth considering; every 30 engineers developed a map byfollowingprocedure.Theprocedure,alsodevelopedinthescopeofthisresearchforextractingthemaincontradictionwasresolvedbythatpatent.Everyengineerbyfollowing procedure analyzed a patent and formulated the main contradiction.Therefore, the Experiment II consists of two part; first part for studying andsimplifying the following the procedure, and the second part for analyzing thedifferencesamongtheusageoffourdevelopedmaps.Table37showsthetotalplanofExperimentII.

Table37‐RepeatabilityofbuildingthemapPlan‐ExperimentII(PartI).

Steps First session

(45 min) Second sessions

(45 min)

Groups 6patentsrandomlyselectedandexaminedbyTRIZ

experts(6Groups,6R&DEngineers)

30patentsexaminedformappingtheinformation

(3Groups)

30R&DEngineers,30Patent



Total 6 R&D Engineers undergo the same treatment 90 R&D Engineers undergo the same treatment

Tomakepossibletocomparetheresultsofusageofthe3newmapswiththepreviousmap,thestructureofExperimentIIwasplannedas thestructureof theExperimentI.Thetestwasdoneintwo‐sessiondesigneachone30minutes.Theparticipantsgenerated their ideas throughBrainstorming in the first sessionandfollowedthedesigningbyapplyingoneofthefourdevelopedmapswhichdedicatedtothemrandomly.Inaddition,thequantityoftheteamsandthemembersofeach


109

teamwereplannedlikewisethefirstexperiment.Table38showstheplanofsecondpartofExperimentII.

Table38‐Repeatabilityofbuildingthemapplan‐ExperimentII(PartII).

First session (Brainstorming/ 30 min)

Break Second sessions

(various T.C Map/ 30 min)

Group A

IdeaGeneration(BrainstormingSession)


15min

IdeaGeneration(T.CMatrixMap)


Group B


7Teams,2R&DEngineersIdeaGeneration(T.CMatrixMap)


Group C


7Teams,2R&DEngineersIdeaGeneration(T.CMatrixMap)


Group D

IdeaGeneration (BrainstormingSession)

Controlgroup7Teams,2R&DEngineers

IdeaGeneration(T.CMatrixMap)

Controlgroup7Teams,2R&DEngineers

56 R&D Engineers (28 teams, 2 R&D Engineers) undergo the same treatment

_ 56 R&D Engineers (28 teams, 2 R&D

Engineers) undergo the same treatment

The allocating processes for each group and teams have been donecompletelyat random; therewasnosystematicselectionregarding theirgender,age,education,degreeandtheirknowledgeinpatentanalysis.Table39showstheparticipants’profilesforthispartoftheresearch;inordertocomparewithpreviousgroups,thesamedistributionofpeoplewasalmostused,asinthefirstexperiment.In total, there were 56 participants (40 men and 16 women). Participants werecategorizedbyageintoyoungandmiddle‐agedadults(25‐44)andalsoclassifiedinthreedegreesofeducation(master,bachelor,andPh.D.).Followingtheliterature,participantsfromseveralfieldsofstudywereused,butallwereengineersincludingMechanical, Industrial, Electronics, Computer, Chemical, Aerospace, Marine,MetallurgicalandMaterialsEngineers.

Table39‐ParticipantsProfile‐ExperimentII.

In followingafterreviewingtheappliedmethodandformulaforassessingtheexperimentresult, theobserveddataandstatisticalstudiesarepresentedformoreformalevidence.

Participants Sex Average

age (year) Degree Field of study

Experiences in the field (year)

Knowledge in patent and patent

analysis

56

40M

36.2(25‐44)STD:4.5

40Master

14MechanicalEng.

9(5‐13)

STD:1.9

4High

10ElectronicEng.

15Medium9IndustrialEng.

6CivilEng.

5AerospaceEng.

37Low16F

8Bachelor4MaterialsEng.

3ChemicalEng.

8PhD3ComputerEng.

2MetallurgicalEng.


110

4.2.1 Ideation metrics measurement

As reviewed in Chapter 3 and similarly to the measurement metrics wereappliedinExperimentI,ShahmetricsempoweredbyFBSframeworkisused.ThemainstructureandtheformulaforassessingtheNovelty,VarietyareapproximatelythesameforExperimentIandExperimentII.TheonlydifferenceistheprovidedscoresforthelevelsofNovelty.Table40showsthepreparedscoresintheposterioriapproachforassessingthedegreeoftheNoveltyofteams.

Table40‐The��scoresof4Groupsofexperiment‐ExperimentII.

No. Novelty Attribute Wt. Level 1

(New Structure) Level 2

(New Behavior) Level 3

(New Function)

1 Support user balance 0.25 3.1 7.1 9.8

2 Support Walker balance 0.25 3.8 6.9 9.3

3 Support Walker storage & transport 0.15 0.6 9.4 10

4 Support user body ergonomic 0.15 3.8 7 9.2

5 Supply Walker propulsion 0.1 5 8 7

6 Support user routine activities

and user accessories 0.1 4.7 5.4 9.9

Total 1

4.2.2 Estimated results

Likewise, the first experiment, the data were collected by team membersduringdesignsessionthroughfillingthesolutionpapers,wheneverthemembersagreedonproposingsolutions.Adescription,aschema,andthetimeofappearanceofthesolutionswerethecollecteddatainsolutionpapers.

ThedescriptionandschemaofeachsolutionwereanalyzedbyresearcherandthetotalscoreofNoveltyandVarietywerecalculatedbasedonthepreparedtableandformula.TheQuantity,Novelty,andVarietyscoreswerecalculatedforfirstandseconddesignsessionsseparatelyandthese twosessions together.Table41showsthecalculatedscores.Todofurtherstudies,thegroupwhichappliedthemapproducedbyaresearcherinExperimentIisconsideredasthecontrolgroup.ThisgroupisconsideredasGroupDinthetable.


111

Table41‐ThescoresofQuantity,Novelty,andVarietyforallteams‐ExperimentII.

Name Team

First session (Brainstorming)

Second sessions (various T.C Map)

Two sessions

Quantity Novelty Variety Quantity Novelty Variety Quantity Novelty Variety

Group A

1 18 10.4 38.0 7 5.5 52.0 25 16 90

2 11 12.5 65.0 9 5.8 38.0 20 18 103

3 9 6 50.7 5 4.1 50.0 14 10 101

4 17 9.9 44.0 11 12.4 48.0 28 22 92

5 10 10.1 53.7 6 7.1 34.0 16 17 88

6 13 7.6 44.3 10 10.9 47.0 23 19 91

7 14 9.6 48.5 8 8.1 55.5 22 18 104

Group B

1 13 8.3 43.3 9 7.9 46.7 22 16.2 89.9

2 8 5.8 65.5 7 6.3 38.3 15 12.1 103.8

3 12 9.1 64.0 9 10.5 40.3 21 19.6 104.3

4 13 8 71.0 7 6.1 61.0 20 14.1 132.0

5 9 8.6 51.3 4 4.4 80.0 13 13 131.3

6 14 13.1 43.2 5 3.4 50.0 19 16.5 93.2

7 17 12.4 45.0 10 11.1 53.7 27 23.5 98.7

Group C

1 10 8.1 43.8 6 5.2 52.5 16 13.3 96.3

2 14 12.6 60.3 8 8.8 50.3 22 21.4 110.6

3 11 8.3 47.7 7 5.4 71.0 18 13.7 118.7

4 8 6.7 44.7 4 4.2 35.0 12 10.9 79.7

5 18 12.5 62.3 8 7.4 31.0 26 19.9 93.3

6 14 10 56.5 10 13.2 53.7 24 23.2 110.2

7 9 4.4 47.7 5 8.6 50.5 14 13 98.2

Group D (Control Group)

1 10 6.3 65.7 8 7.7 34 18 14 98.7

2 17 13.6 49.2 11 8.4 57.7 28 22 106.9

3 11 9 67 4 3.6 80 15 12.6 147.0

4 17 12 55 10 12.8 46.2 27 24.8 101.2

5 5 3.6 26 5 4.1 51 10 7.7 77.0

6 9 5.8 34.8 7 4.7 37.6 16 10.5 72.4

7 19 21 49.3 9 10.9 27.6 28 31.9 76.9

Like the shows, the scores for the first session are close to the calculatedscoresof the firstdesignsessionof theExperiment I.Alsotheresultofusingthethreenewdevelopedmapsseemsimilartotheresultsofthecontrolgroup.Table42summarizesmeanandstandarddeviationoftheobserveddataasinitialdescriptivestatisticsregardingthesecondexperiment.


112

Table42–ThescoresofQuantity,NoveltyandVarietyrespecttothegroupwithdifferent

stimuli‐ExperimentII.

Name First session Second sessions Two sessions

Quantity Variety Novelty Quantity Variety Novelty Quantity Variety Novelty

Group A (T.C))

Mean 13.1 49.2

Sco

re

65.8Mean 8 46.4

Sco

re

53.9Mean 21.1 95.5

Sco

re

119.7STD 3.4 8.6 STD 2.2 7.7 STD 4.9 6.8

Group B (T.C)

Mean 12.3 54.862.2

Mean 7.3 52.949.6

Mean 19.6 107.6114.8

STD 3 11.8 STD 2.2 14.2 STD 4.6 17.2

Group C (T.C)

Mean 12 51.862.5

Mean 6.9 49.152.9

Mean 18.9 101115.4

STD 3.5 7.7 STD 2 13.2 STD 5.3 13.1

Group D (T.C)

(Control Group)

Mean 12.6 49.4

66.6

Mean 7.7 47.7

51.1

Mean 20.3 97.2

117.7STD 5.2 14.9 STD 2.6 17.6 STD 7.3 25.9

Asonecanseefromthecolumn,itseemsthereisnosignificantdifferencebetweenthemregardingNovelty,Quantity,andVarietyinthesecondsessionandalso the two sessions together; statistical studies needed for any further claim.Figure 24 also shows the graphical representation of the data for each variableacrossgroups.

Figure24‐Graphicalrepresentationofassessingcriteriaoftwosessions‐

ExperimentII.

The figure shows there is no significance difference between groupsobservedinthesecondexperiment.


113

4.2.3 Data analysis

In this section, a linear regression method is applied, including dummyvariables. The estimated results, testing the assumptions of the classical linearregression model and finally, hypothesis tests regarding each variable arepresented.

The normality of gathered data is studied through the data of the first sessionwhereas the condition of design session was the same for all the 28 teams. ThenormalityofdatawasstudiedthroughallscoresofQuantity,Novelty,andVariety.Figure25showstheresultsofnormalitystudies.

Figure25‐NormalityoftheData‐ExperimentII.

Figure25showsthedistributionalplotofthedataforastandardizednormaldistribution; it demonstrates that the data are normally distributed. Therefore,furtherstatisticalstudiesaredonebycheckingthesetsofhypothesesforallCriteriaNovelty,Quantity,andVariety.

0.0

00.2

50.5

00.7

51.0

0N

orm

al F

[(N

ove

ltyB

S-m

)/s]

0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)

0.0

00

.25

0.5

00

.75

1.0

0N

orm

al F

[(Q

ua

ntity

BS

-m)/

s]

0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)

0.0

00

.25

0.5

00

.75

1.0

0N

orm

al F

[(V

arie

tyB

S-m

)/s]

0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)

0.0

00.2

50

.50

0.7

51

.00

No

rmal F

[(N

oveltyT

CM

-m)/

s]

0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)

0.0

00

.25

0.5

00

.75

1.0

0N

orm

al F

[(Q

ua

ntity

TC

M-m

)/s]

0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)

0.0

00.2

50

.50

0.7

51

.00

Norm

al F

[(V

ariety

TC

M-m

)/s]

0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)

0.0

00.2

50.5

00.7

51.0

0N

orm

al F

[(N

ove

ltyT

-m)/

s]

0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)

0.0

00.2

50.5

00.7

51.0

0N

orm

al F

[(Q

uantity

T-m

)/s]

0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)

0.0

00.2

50.5

00.7

51.0

0N

orm

al F

[(V

ariety

T-m

)/s]

0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)

Novelty-

Novelty-

Novelty-

Quantity-

Quantity-

Quantity-

Variety-

Variety-

Variety-


114

Novelty: The novelty is estimated through regression model with the explanatory

variablesbeingthethreedummyvariablesregardinggroupsA,B,andCandgroupD(thecontrolgroup):

Novelty = � + �� + �� + �� + �

As each group used the same method (T.C Map), the smaller number forcoefficientsβ�,β�andβ�isexpected,becausethesecoefficientsshowthedifferencebetween groups A, B, and C with respect to group D. Before discussing thehypotheses,checkingtheassumptionsofregressionisneeded.

1. Possibility of applying LOS model for statistical analysis: Novelty

HomoscedasticityandNormaldistributionoftheresidualsaretestedastwoassumptionsoffivepossibleones.HomoscedasticityoftheresidualsoftheNoveltyregression was tested using a graphical approach and also Breusch‐Pagan/Cook‐Weisbergtest.Figure26belowshowsagraphicalrepresentationoftheestimatedresidualsoftheNoveltyregression.Itisclearthatthereisnosystematictrendforthe estimated residuals, indicating that the Homoscedasticity assumption issatisfiedforthisvariable.

Figure26 ‐ EstimatedresidualsfortheNoveltyregression:Homoscedasticitytest‐

ExperimentII.

Asbefore,theBreusch‐Pagan/Cook‐Weisbergtestwasusedtomakesureofthe Homoscedasticity. A large value for chi‐square indicates presenting ofheteroskedasticity.Since in thisexercise, the chi‐squarevalue issmall.Thereforeheteroskedasticitywouldnotprobablybeaproblem(asseeninthegraph).

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity Ho: Constant variance Variables: fitted values of NoveltyT chi2(1) = 0.00 Prob > chi2 = 0.9714

-10

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TheNormalityoftheresidualswillnowbeexamined.Bothgraphical(Figure27) and Skewness/Kurtosis tests confirmed the normality of the residual for theNoveltyregression.

Figure27‐EstimatedresidualsfortheNoveltyregression:Normalitytest‐Experiment

II.

Skewness/Kurtosis tests for Normality ------ joint ------ Variable | Obs Pr(Skewness) Pr(Kurtosis) adj chi2(2) Prob>chi2 -------------+--------------------------------------------------------------- resNovelty2 | 28 0.9530 0.0318 4.65 0.0976

2. Group of hypotheses: Novelty

Thefollowinggroupofhypothesesstudiesthesimilarityamongtheresultsofapplyingdifferentmapsfortherepeatabilityofbuildingthemap:

1. [H�: �� ≠ 0againstH�: �� = 0]2. [H�: �� ≠ 0againstH�: �� = 0]3. [H�: �� ≠ 0againstH�: �� = 0]

4. [H�:�� ≠ �� ≠ ��andagainstH�: �� = �� = �� = 0]

Hypotheses1to3aresingletests;eachparameterisdifferentfromzero,andsincetheestimatedparametershouldbeclosetozero,thenullhypothesiscanberejectedandthereforethealternativehypothesiscanbeaccepted.Inotherwords,thereneedstobenodifferencebetweencontrolgroupDandothergroups,A,B,andC.Inaddition,Hypothesis4testsusethejointtestforallthecoefficientstogether,and represents that they are jointly different from group D. However, it is notnecessarytostudythehypothesis4,butitisonlyforbeingmoreaccurate.

Table43showstheresultsofaregressionmodelofNoveltyacrossdifferentgroupsandcomparesthiswithgroupD.

0.00

0.25

0.50

0.75

1.00

Nor

mal

F[(r

esN

orm

al2-

m)/

s]

0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)


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Table43‐Estimatedresultsofimprovingideasforthesamemethods:Novelty‐

ExperimentII.



-------------+---------------------------------- Adj R-squared = -0.1024 Total | 578.618571 27 21.4303175 Root MSE = 4.8606

NoveltyT | Coef. Std. Err. t P>|t| [95% Conf. Interval]

ga | 1.328571 2.598115 0.51 0.614 -4.033675 6.690818 gb | .4714286 2.598115 0.18 0.858 -4.890818 5.833675 gc | -.4 2.598115 -0.15 0.879 -5.762247 4.962247

_cons | 17.55714 1.837145 9.56 0.000 13.76546 21.34882

Thereisnobigdifferencebetweentheestimatedcoefficients,thusitcanbeinterpreted that those coefficients are not significantly different from zero; thisindicates that there is no significant difference between groups A, B, and C withgroupD.Thesignificanceoftheresultsdoesnotneedtobecheckedbecauseitisonlythevalueofthecoefficientsbeingsmallwhichistheconcernoftheresearch.

Fromrunningfourtypesofhypothesis,theresultsarereportedasbelow:

Single test (1) ga = 0 F( 1, 24) = 0.26 Prob > F = 0.6138 Single test (2) gb = 0 F( 1, 24) = 0.03 Prob > F = 0.8575 Single test (3) gc = 0 F( 1, 24) = 0.02 Prob > F = 0.8789 Joint test (4) ga = gb = gc = 0 F( 2, 24) = 0.16 Prob > F = 0.9198

AlargeF‐testforeachhypothesisindicatesthatitispossibletorejectthenullhypothesiswhichmeansthatthereisnosignificantdifferencebetweengroupsA,B,andCwithD.Asexplainedbefore,asthereisnointerestonthesignificantresults,anotherregressionwithclusteringdoesnotneedtobeundertaken.TheestimatedresultsbasedonTable43areenoughforthesecondexperiment.

Quantity: AsintheNoveltypart,thesignificancedifferenceamongthefourdeveloped

mapsisstudiedthroughestimatingaregressionmodelforQuantityaftercheckingthevalidityofassumptionsforregressionanalysis.

1. Possibility of applying LOS model for statistical analysis: Quantity

Similar to the previous experiment, before testing the hypotheses, twoassumptionsregardingHomoscedasticityandNormaldistributionoftheresidualsneedtobechecked.Figure28showsthegraphicalviewoftheestimatedresiduals


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oftheQuantityregression;it'sobviousitisdistributedrandomlyoverthegroups,with no kind of Heteroskedasticity in this regression being observed. Breusch‐Pagan/Cook‐Weisberg test also shows that heteroskedasticity is probably not aproblemsincethechi‐squarevalueissmall.

Figure28‐EstimatedresidualsfortheQuantityregression:Homoscedasticitytest‐

ExperimentII.

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity Ho: Constant variance Variables: fitted values of QuantityT chi2(1) = 0.05 Prob > chi2 = 0.8204

TheNormalityoftheresidualsoftheQuantityregressionandbothgraphical(Figure29),andSkewness/Kurtosistestsconfirmnormalityoftheresidualfortheregression;normalityisnottheresearchconcernforthisexperience.

Figure29‐EstimatedresidualsfortheQuantityregression:Normalitytest‐ExperimentII.

Skewness/Kurtosis tests for Normality ------ joint ------ Variable | Obs Pr(Skewness) Pr(Kurtosis) adj chi2(2) Prob>chi2 -------------+--------------------------------------------------------------- resQuantity2 | 28 0.9475 0.0580 3.87 0.1441

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00.

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ntity

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2. Group of hypotheses: Quantity

ThesamehypothesesasintheNoveltypartasfollowsweretested:


4. [H�:�� ≠ �� ≠ �� andagainstH�: �� = �� = �� = 0]

Hypotheses1to3aresingletests;thetestlookedatwhethereachparameterisdifferentfromzero.Givenitwasneededtoshowthattheestimatedparametershouldbeclosetozero,theinterestwastorejectthenullhypothesisandtoacceptthealternativehypothesis.Thismeansthat,inthecaseofrepeatabilityofbuildingthemap,nodifferencewillbeobservedbetweencontrolgroupDandothergroups,A, B, and C. In addition, Hypothesis 4 tested the use of the joint test for all thecoefficientstogetherandrepresentedthattheyarejointlydifferentfromgroupD.

Table44showstheestimatedresults,andasonewouldexpect,thereisnosignificantdifferencebetweengroups.Moreover,inthiscase,noneoftheestimatedparameters are significant at even the 90% confidence level. Clustering can beappliedtothisvariable,althoughitisnotnecessary,becausetheresearchismostlyinterested in the size of the coefficients, and not theirsignificant level. However,R2is0.026whichisverylow;themainreasonisthatbecausethosemethodsarealmostthesame,andwhencomparingagainstthecontrolgroup,onlyaminorpartofthevariationinQuantityhasbeenexplained.

Table44‐Estimatedresultsofimprovingideasforthesamemethods:Quantity‐

ExperimentII.



-------------+---------------------------------- Adj R-squared = -0.0960 Total | 780.964286 27 28.9246032 Root MSE = 5.6305 ----------------------------------------------------------------------------- QuantityT | Coef. Std. Err. t P>|t| [95% Conf. Interval]

-------------+---------------------------------------------------------------- ga | 1.571429 3.009622 0.52 0.606 -4.640125 7.782983 gb | .7142857 3.009622 0.24 0.814 -5.497268 6.92584 gc | -.7142857 3.009622 -0.24 0.814 -6.92584 5.497268

_cons | 19.57143 2.128124 9.20 0.000 15.1792 23.96366

Fourtypesofthehypothesisaretested,andtheresultsarereportedbelow.AlargeF‐testforeachhypothesisindicatesthatthenullhypothesiscanberejected,meaningthereisnosignificantdifferencebetweengroupsA,B,andCwithD.


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Variety: AsintheNoveltyandQuantityparts,thesignificancedifferenceamongthe

fourdevelopedmapsisstudiedthroughestimatingaregressionmodelforVarietyaftercheckingthevalidityofassumptionsforregressionanalysis.

1. Possibility of applying LOS model for statistical analysis: Variety

Here also two assumptions regarding Homoscedasticity and Normaldistributionoftheresidualsneededtobecheckedasbefore.Figure30showsthegraphicalviewoftheestimatedresidualsoftheVarietyregression;itisclearthatitis distributed randomly over the groups and no kind of Heteroskedasticity isobserved in this regression. The same results obtained from the Breusch‐Pagan/Cook‐Weisbergtestshowsthatheteroskedasticityisprobablynotaproblemsincethechi‐squarevalueissmall.

Figure30‐EstimatedresidualsfortheVarietyregression:Homoscedasticitytest‐

ExperimentII.

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity Ho: Constant variance Variables: fitted values of VarietyT chi2(1) = 0.03 Prob > chi2 = 0.8870

-20

02

04

06

0R

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du

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TheNormalityoftheresidualsoftheVarietyregressionandbothgraphical(Figure31)andSkewness/Kurtosistestsconfirmnormalityoftheresidualfortheregression;normalityisnottheresearchconcernforthisexperienceaswell.

Figure31‐EstimatedresidualsfortheVarietyregression:Normalitytest‐ExperimentII.

Skewness/Kurtosis tests for Normality ------ joint ------ Variable | Obs Pr(Skewness) Pr(Kurtosis) adj chi2(2) Prob>chi2 -------------+--------------------------------------------------------------- resVariety2 | 28 0.0260 0.0609 7.42 0.0244

2. Group of hypotheses: Variety

Finally,fourhypothesesweretestedfortheestimatedcoefficientsasfollows:


4. [H�:�� ≠ �� ≠ �� andagainstH�: �� = �� = �� = 0]

Hypotheses 1 to 3 are single tests; the research tested whether eachparameter is different from zero. It was needed to show that the estimatedparametershouldbeclosetozero,withthenullhypothesisbeingrejectedandthealternativehypothesisbeingaccepted.Thismeansthat,givenabeliefthatthesameresults for each group will occur, for the repeatability of building the map, nodifferencebetweencontrolgroupDandothergroups,A,B,andCshouldbeseen.Inaddition,Hypothesis4testedtheuseofthejointtestforallthecoefficientstogether,and represents that they are jointly different from group D. Table 45 shows theSTATAreportforregressionanalysisforVariety.

0.0

00

.25

0.5

00.

75

1.0

0N

orm

al F

[(re

sVa

riety

2-m

)/s]

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Table45‐Estimatedresultsofimprovingideasforthesamemethods:Variety‐ExperimentII.



-------------+---------------------------------- Adj R-squared = -0.0367 Total | 7725.30631 27 286.122456 Root MSE = 17.223

------------------------------------------------------------------------------ VarietyT | Coef. Std. Err. t P>|t| [95% Conf. Interval]

ga | -12.0881 9.205936 -1.31 0.202 -31.08821 6.912023 gb | -10.45952 9.205936 -1.14 0.267 -29.45964 8.540595 gc | -6.647619 9.205936 -0.72 0.477 -25.64774 12.3525

_cons | 107.6119 6.50958 16.53 0.000 94.17679 121.047

ResultsforVarietyarequitedifferentfromNoveltyandQuantity.AsseeninTable 45, there are larger numbers of all the groups compared to the estimatedresultsofNoveltyandQuantity.Inparticular,itis‐12forgroupA,meaningthatintermsofVariety,12morevarietiesforgroupAarepresentincomparisontogroupD,andsimilarlyforgroupBandD;however,noneofthemaresignificant.

Fourtypesofhypothesisweretestedwiththeresultsreportedbelow.AlargeF‐test foreach hypothesis indicates that the null hypothesiscanbe rejected; thismeansthatthereisnosignificantdifferencebetweengroupsA,B,andCwithD.


4.3 Conclusion of Experiment I and Experiment II

The ultimateobjective of this research is considered improving the

patentability of an invention generated by R&D engineers in Iranian SMEs. Toapproach this aim, ‘Technical Contradiction Map’ is developed, and two mainresearch questions are defined. Respectively two sets of empirical studies areplannedandperformedtostudytheresearchquestions.

Experiment I, compared the effects of four interventions on the designsession;Brainstorming+Problem‐SolutionMatrixMap(GroupA),Brainstorming+TechnicalContradictionMap(GroupB/Targetgroup),Brainstorming+PatentTextFar‐Field(GroupC),andBrainstorming+Brainstorming(GroupD/Controlgroup).Among the four methods, Technical Contradiction Map provided the highest


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effectiveness in Novelty and Quantity. Also, Problem‐Solution Map provided thehighesteffectivenessinVariety.Specifically,theestimatedcoefficient forgroupB(Brainstorming + Technical Contradiction Map) was 5.57; this means that thiscomposition of methods provides more ideas than group D for Novelty. Thecorresponding estimated coefficients for group A (Brainstorming + Problem‐SolutionMatrixMap)and groupC(Brainstorming +Patent Text Far‐Field)were,respectively, 2.59 and 1.91. Besides the highest effectiveness of the TechnicalContradiction Map in comparison to other methods, the estimated coefficient forthisoneistheonlysignificantcoefficientinthisregression.

Additionally, twogroupsofhypothesesweretested todetermine whetherthereisenoughevidenceinthesampleofcollecteddatabasedontheexperimentsandestimations,toinferthattheusabilityoftheTechnicalContradictionMapistruefortheentirepopulation.ThetestsbasedontheF‐teststatisticalsoconfirmedthehighesteffectivenessoftheimprovedversionofTechnicalContradictionMap.TheF‐test showed the impact of the Technical Contradiction Map was significantlylarger than the other methods. This study’s estimates and hypotheses testssupportedtheeffectivenessoftheTechnicalContradictionMapcomparedtoothermethodsregardingNoveltyandQuantity.

Experiment II studied the repeatability of building the TechnicalContradiction Map through investigating some maps produced by many R&Dengineers discarding their level of expertise on building the map whereas theyfulfilled their task by following procedure. As it can be expected, the estimatedresults were very close across the different groups for Novelty and quantity andvariedonVariety.Thismakessensebecausethesamemethodswereappliedacrossdifferentgroups,andthereforetheestimatedresultsforeachgroupwerealmostthesame.Infact,thisexperimentverifiesthereplicationcapabilityofbuildingthemap;ifoneusesthesamemethod,conditionsandequipmentexplainedinthisresearch,thesameresultswillbeobtained.

Chapter5:Discussionsandconclusions

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Chapter5

[5] Discussions and Conclusions


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Small and medium‐sized enterprises (SMEs) are main contributors to

industrial economies. Therefore, therehas beena long‐standinginterest in theempirical literature to support R&D engineers. Improving the patentability of aninventiongeneratedbyR&Dengineersisoneofresearchissuesinthisfield,whichisconsideredastheobjectiveofthisresearchtoo.BasedontheresultsofpreviousresearchdiscussedinChapter2inthefieldofPatentMappingandpatentanalysis,IdeationtechniquesandDesignbyAnalogy,andTRIZ,itisconsideredtoenrichtheProblem‐Solution Patent Map of a specific technical system for increasing thegenerationof non‐obvious novel ideasbyanewanalogaccordingtotheabstractobservedpatternsforresolvingthecontradictorysituations.TheproposedmapandtheprocedureforprovidingitwerediscussedinChapter3.Chapter4discussedtheeffects of the developed map on the performances of R&D engineers in terms ofgenerating ideas with a higher degree of Novelty and variety, simultaneouslystudied the performance of R&D engineers in building the developed map bythemselves. This chapter discusses some limitations and future correspondingstudies.

5.1 Summary

The ultimateobjective of this research is considered improving the

patentability of an invention generated by R&D engineers in Iranian SMEs. Toapproachthisobjective,‘TechnicalContradictionMap’isdeveloped,andtwomainresearchquestionsaredefinedthroughaquantitativestudyandstatisticalanalysis:

1. Can R&D engineers in Iranian SMEs improve Novelty within their ideas, through the use of an enriched Problem-Solution Patent Map by the ‘contradiction concept’?

2. Can Iranian R&D engineers build the proposed enriched Patent Map by following the developed procedure?

Twoexperimentswereplannedandperformedtostudythemap’susabilityandeffectivenessandrepeatabilityofthemap‐buildingprocess.

Toperformthestudies,thesuggestedmapwasbuiltforWalker,asasampletechnicalsystemthroughfollowingthedevelopedprocedure.Astheresultsofthefirstmainstageoftheprocedureofbuildingthemap,among101foundpatentsbysearchingintheOrbitdatabase,around50%ofthem(54patents)wereconsideredasnot‐noisypatentsforbuildingthemap.Astheresearchisthetypeofexploratoryresearch,thisnumberisalsoreducedbasedonthesufficientpatentsineachclassofproblemsandsolutions.Thepatentswerefirstsorted,andthenthestudyselectedone patent from the earliest, one from the latest and one from the middle. Afterreducingtheclassesofproblemsandsolutions, theanalysisofmorepatentswascompleted. This supportive sub‐procedure reduced the Quantity of patents to bestudied from 54 to 30. The second and third main stages of the procedure forextractingAnalogsandbuildingthemapwerethencompletedforthe30selectedpatents.Itisworthmentioningintotalthesolvedproblemswereclassifiedinto8


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groups,thesolutionswereclassifiedin6categorieswhereasthe30patentsweredistributedin10crossesof48possiblecrosses.Therefore,thefinalmapconsistsofthemainpapertoshowthepositionsofpatentsonthecrossesand10supportivegraphstoillustratetheresolvedcontradictions.Thepreparedmapwasthenusedforstudyingtheusabilityandeffectivenessofthemap.

TostudytheusabilityandeffectivenessoftheTechnicalContradictionMap,theresultsofusingthemapwerecomparedtosomeothermethodsusedforthesame purpose in design and idea generation sessions. Idea generation usingBrainstormingasatechniqueisconsideredasthecontrolgroup,asmostdesignandidea generation sessions use this method (Howard et al., 2010). The Problem‐Solution Matrix Map and Patent Text (Far‐Field) were considered as the otherinterventions for comparison. The Far‐Field Analogy was considered as anotherintervention forcomparison,as literaturediscusses theeffectivenessofFar‐FieldAnalogy on increasing the Novelty and Quantity of ideas (Chan et al., 2011).Engineers mostly apply Cross‐Domain analogies in idea generation processes(Casakin and Goldschmidt, 1999; Leclercq and Heylighen, 2002; Christensen andSchunn,2007).Cross‐DomainSpecificallyFar‐FieldAnalogyincreasesthenoveltyofsolutions (Chan et al., 2011). A Cross‐Domain Analogy is applied more when thedesignersarenotcapableofsolvingthedesignproblem(Tsengetal.,2008b;Linseyet al., 2012). The Problem‐Solution Matrix Map was considered as one of theinterventionsforcomparisonasitisbasicforadevelopedTechnicalContradictionMap. In total, the results of the idea generation session with the TechnicalContradiction Map were compared to the three other interventions, in order tostudy the map’s usability and effectiveness: (i) idea generation session withBrainstorming,(ii)ideagenerationsessionwithProblem‐SolutionMatrixMap,and(ii)ideagenerationsessionwithPatentText(Far‐Field)ofthetargetsystem.

Tocomparetheresultsofthefourconsideredinterventions,fourgroupsof7teamswereplanned,eachconsistingof2R&Dengineers.Theteamswereaskedtogeneratepatentableideasintwosessions,each30minuteslongwitha15‐minutebreak in between. In the first 30 minutes, all teams generated ideas by applyingBrainstorming,howeverinthesecondsession;eachgroupgeneratedideasbyoneof the considered interventions. Comparing the results of two sessions, theeffectiveness of the Technical Contradiction Map was studied. Specifically, thisresearch tests the hypothesis that using a Technical Contradiction Map afterBrainstormingismoreeffectiveingeneratingideasthanothertechniques,suchastheProblem‐SolutionMatrixMap,PatentTextFar‐FieldandalsoBrainstorming.Bycollecting data based on an experiment and using a statistical model, theeffectiveness of each technique in improving the ideation of R&D engineers inIranianSMEsintermsofNovelty,VarietyandQuantitywasexamined.

Tostudytherepeatabilityofthemap‐buildingprocess,thesame30patentsused for sample study, were given to different R&D engineers to analyze themaccordingtothedevelopedprocedure.Respecttothetimeneededforanalyzing1patentandalsothetimelimitationforinvolvingtheR&Dengineersinthestudy,onepatent was given to one R&D engineer to follow the second main stage of the


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procedureandextractingtheAnalogs.Therefore,90R&Dengineerswereinvolvedinthestudytopursuetheprocedureforonlyonepatent.TheresultsofthefollowedprocedurebytheR&Dengineersweregatheredasthreenewmaps.‘Repeatability’wasthencheckedbyanalyzingthesimilarityinresultsofusingthemapsintermsof Novelty, Quantity, and Variety of the ideas. Therefore, the second experimentconsistsof2mainparts;extractingtheAnalogsbyR&Dengineersasthefirstpart,andapplyingthebuiltmapsbasedontheresultsofpartoneasthesecondpart.Eachnewmapwasgivento7newteamsof2R&Dengineerstoallowforcomparisonwiththe results from the seven first teams, which applied the built sample TechnicalContradictionMapbytheresearcherinthefirstexperiment.Inthisexperiment,theteamswerealsoaskedtogeneratetheirideasintwo30‐minutesessions,likethefirstexperiment,toallowforcomparison.

Toelaborateonthisfurther,thenextsectionbrieflyreviewstheresults,andtheresultsrespecttotheliterature.Finally,thelimitationsofthecurrentresearchandfuturelinesofpossibleresearcharelaidout.

5.2 Research results and Discussion

As mentioned in the previous section, the ultimate aim of the current

researchwaspursuedthroughtwomainresearchquestions.Therespondsforeachmainresearchquestionisfollowedthroughtheassumptionsbehindtheproposedoriginalcontributionoftheresearch.Moreover,therearesomeexpectationsbeyondresearchquestions,whichletreflectionsonexistingtheoriesbasedontheobservedresultsofthedesignedandperformedexperiments.

Twoexpectationscanbediscussedbeyondtheresearchquestionswhereaseachonecanbefollowedindifferentlevels.Possibilitytoextractthepreviousandexistingcontradictionfromthepreviouspatentsofatechnicalsystem,possibilitytopresenttheextractedcontradictionofthepatentsofasysteminausableconfiguration,andlevel of effectiveness of success of awareness of R&D engineers of previous andexistingcontradictioninevolutionpathofatechnicalsysteminpatentabilityoftheirideasforimprovementofthesystemarethethreemainexpectations.

Eachexpectationcanbeapproachedindifferentlevels.Followingthepossiblelevelsforeachexpectationarementioned:

Expectation1:possibilitytoextractthepreviousandexistingcontradictionfromthepreviouspatentsofatechnicalsystem:

Level1:Possibilitytoextractsystematicallyandmanuallythemainresolvedcontradictionsofasimplemechanicalsystem;Level 2: Possibility to extract systematically all resolved contradictions ofanytargettechnicalsystem;

Expectation2:possibilitytopresenttheextractedcontradictionofthepatentsofasysteminausableconfigurationforR&DengineersnotfamiliarwiththeTRIZandTRIZcontradictionmodel:


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Level 1: Usability of the provided information for R&D engineers familiarwithTRIZ;Level2:UsabilityoftheprovidedinformationforR&DengineersnotfamiliarwithTRIZwithsomeguidelines;Level3:UsabilityoftheprovidedinformationforR&DengineersnotfamiliarwithTRIZwithoutanyguidelines;

Expectation3:ThelevelofeffectivenessofawarenessofR&Dengineersofpreviousandexistingcontradictioninevolutionpathofatechnicalsysteminthepatentabilityoftheirideasforimprovementofthesystem:

Level1:Levelofsuccessonthepatentabilityofgeneratedideasrespecttothecharacteristicsofgeneratedideas;Level2:LevelofsuccessonthepatentabilityofgeneratedideasrespecttotheconditionsofrealR&Dprojects;Level3:LevelofsuccessonthepatentabilityofgeneratedideasrespecttothesuccessindexesofR&Ddepartments;

Thecurrentresearchisgoingtoreflectinjustsomeoftheabove‐mentionedlevels based on the type and domain of possible empirical studies. Table 46,highlightsthelevelsofeachexpectationwhichareaddressedinthisresearch,therelationofthemwiththeperformedexperiments,themainobservedresults,andsomereflectionsonexistingtheories.

Table46–Summaryofresultsandreflectingonexistingtheories.

Expectations beyond the research question

The relevant part of experiments to the

expectation Observed results Reflecting on theories

Expectation 1: possibility to extract the previous and existing contradiction from the previous patents of a technical system

Level 1

Possibility to extractsystematically andmanually the mainresolvedcontradictions of asimple mechanicalsystem:

Systematic stepsto retrieve theleastandenoughrelevant patentsof the patents ofthe targetmechanicalsystem.

Systematicmanually stepsto extract themain resolvedcontradictions

Preparing Experiment:

Following systematicsteps for retrieving theleast and enoughrelevant patents forWalker by theresearcher.

Following systematicsteps for extracting themain resolvedcontradictions of 30patentsofWalkerbytheresearcher.

Possibilitytofollowthe systematicsteps for bothpurposes.

Followingsystematicstepsforreaching least andenough relevantpatent takesaround10hoursforWalker.

The problem,solution, andimprovingparameter arefounddirectly,theworseningparameters andcontrolparameter

‐ Expertise is needed forfollowing the bothsystematicsteps:

Classifying theproblem andsolutions anddedicatingthepatentstotheclasses.

Interpreting theworsening andcontrol parameterwhen they are notfound.

‐Thededicatedtimeforreaching the relevantpatentscanbecomparedwith the time forchecking thepatentability of ideas in


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(consists of adefinition,keywords,supportivequestions, mostprobable place,example,patterns,…).

are found moiety,andtheundesiredresult is rarelyfound.

currentactivitiesinR&Ddepartments.

Experiment II:

Following systematicsteps for extracting themain resolvedcontradictions of 6patents of Walker by 6R&D engineers familiarwithTRIZ.

Following systematicsteps for extracting themain resolvedcontradictions of 30patentsofWalkerby90R&D engineers notfamiliarwithTRIZ.

Possibility toextract the maincontradictionmanually.

115 R&Dengineers areinvolved in thetest, but 90engineersfulfilledcompletely theprocedure.

About 78% of R&Dengineers are able tofollow the procedureand it must beimproved.

Level 2

Possibility to extractsystematically allresolvedcontradictions of anytarget technicalsystem.

‐ ‐ ‐

Expectation 2: possibility to present the extracted contradiction of the patents of a system in a usable configuration for R&D engineers not familiar with the TRIZ and TRIZ contradiction model

Level 1

Usability of theprovided informationfor R&D engineersfamiliarwithTRIZ.

‐ ‐ ‐

Level 2

Usability of theprovided informationfor R&D engineersnotfamiliarwithTRIZwith some guidelinesintheformTechnicalContradiction MapT.CMap:

Structuralpresentation ofrelevantpatents

As a three‐dimensionalpatent mapbased onProblem‐Solution PatentMap

Applying thegraphical OTSM‐TRIZ model ofcontradiction

Experiment I:

ComparingT.CMaprespecttoBrainstorming,M.SMap,Far‐fieldPatentFullTextbychecking:‐UsabilityofT.CMap

Quantity

Novelty

Variety‐EffectivenessofT.CMap

Quantity

Novelty

Variety

‐ The effects ofdifferent interventionsonQuantityinorders:

1.T.CMap2.P.SMap3.Far‐field PatentFullText4.Brainstorming‐ The effects ofdifferent interventionsonNoveltyinorders:

1.T.CMap2.P.SMap3.Far‐field PatentFullText4.Brainstorming‐ The effects ofdifferent interventionsonVarietyinorders:

1.P.SMap2.T.CMap3.Brainstorming

T.CMapismoreeffectivethan others in QuantityandNoveltyandit:

Proves again thatstructuralrepresentation ofprecedents iseffective onincreasing Novelty

(Doboli andUmbarkar,2014).

Proves graphicalOTSM‐TRIZ model ofcontradiction canhelp to understandthe contradictions

clearer (Cavallucciand Khomenko,2007).

Proves that PatentMaps can be usablefortechnicalpurposes

(Tsengetal.,2007).


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4.Far‐field PatentFullText.

Rejects Far‐field ismore effective thanclose‐field becausethe structural andgraphicalpresentation is alsoeffectiveon theeffectofprecedents.

Experiment II:

StudyingT.CMap:‐UsabilityofT.CMap

Quantity

Novelty

Variety

‐ApplyingtheT.Cmapin a 1‐hour designsession (30 minbrainstorming, 30 minapplyingT.Cmap),theresultscanbereportedas:

Quantity:

Mean:20.3

STD:7.3‐Variety:

Mean:97.1

STD:25.9‐Novelty: Mean:123.4

T.C Map, discarding thelevel of expertise in itsproduction,isusableandeffective on increasingthe ideas characteristicsrespect to thebrainstorming.

Level 3

Usability of theprovided informationfor R&D engineersnotfamiliarwithTRIZwithout anyguidelines.

‐ ‐ ‐

Expectation 3: The level of effectiveness of awareness of R&D engineers of previous and existing contradiction in evolution path of a technical system in patentability of their ideas for improvement of the

system

Level 1

The level of successonthepatentabilityofgenerated ideasrespect to thecharacteristics ofgenerated ideas, byusingaT.CMap:

Applying OTSM‐TRIZ model ofcontradiction

Applying forclose‐fieldpatents

Experiment I:

ComparingT.CMaprespecttoBrainstorming,M.SMap,Far‐fieldPatentFullTextbychecking:‐UsabilityofT.CMap

Quantity

Novelty

Variety‐EffectivenessofT.CMap

Quantity

Novelty

Variety

Like the level 2 ofexpectation2.

T.CMapismoreeffectivethan others in QuantityandNoveltyandit:

Rejects Far‐field ismore effective thanclose‐field becausethe structural andgraphicalpresentation are alsoeffectiveon theeffectofprecedents

Far‐field is moreeffective thanBrainstorming (Chanet al., 2011) and itmore effective thanP.SMap.

Experiment I:

StudyingT.CMap:‐UsabilityofT.CMap

Quantity

Novelty

Variety

Like the level 2 ofexpectation2.

To study more stronglythe effectiveness of T.CMap.


130

Level 2

Level of success onthe patentability ofgenerated ideasrespect to theconditions of realR&Dprojects.

‐ ‐ ‐

Level 3

Level of success onthe patentability ofgenerated ideasrespecttothesuccessindexes of R&Ddepartments.

‐ ‐ ‐

Astableshows,theempiricalstudies inthescopeof thecurrentresearch,

approachtheonelevelofeachofthreeexpectationsbeyondtheresearchquestions.Throughobservedresults,thereflectiononexistingtheoriesismentionedinthelastcolumn. The levels of approaching each expectation were defined as generalandwideaspossible.

Although the levels are defined general, it was not to address themcompletelybecauseofsomelimitations.TheaccessibilitytotheR&Dengineerswasoneofthemainlimitationsbehindperformingtheexperiments.BysupportsofanIranianresearchcenter,theaccessibilitywasprovidedforsomeIranianSMEs.Itisworthconsidering,theaccessibilitytoIranianR&Dengineerswasalsocriticalfortheultimateaimoftheresearchasthemainproblemoftheresearch,wassearchedandstudied in thescopeof IranianSMEs;Theexistenceof the sameproblemonSMEssectordespitethegeographicregioncanbestudied.

5.3 Limitations and future developments

Therearesometechnicalandmethodological limitations thatwouldgive

rise toseveralpotentialtypesof futurestudies.Following limitationsareadirectcontinuationoftheworkperformedinthisthesisandcouldberesearchedproposalsfor future studies. It is important to note the methodological limitations of thestudies involved in this thesis. The boundaries of this study were thosecharacteristics of design or methodology that could impact or influence theinterpretationoftheresearchfindings.Inparticular,twomainissuesregardingdatacollection (environmental issue) and also analyzing the collected data(methodological issue) were identified. As previously discussed, a significantlimitationinthislineoftheresearchprogramistherelianceonthemeasuresandidentifications used to collect and formulate the data, as well as the sample ofengineers.Keyconcernsregardingthefirstissueofdatacollectionareasfollows:thesizeofthesample;theuseofself‐reporteddata;thecategoriesandknowledgeoftheparticipants;andculturalandothersimilartypesofbias.

Sample size:Inthisresearch,56peopleparticipatedinthefirstexperimentandwererandomlydividedintofourgroups.Thisisafairsamplesizeforthiskindofexperimentalresearch,assupportedbypreviousliterature(Massetti,


131

1996;Nijstadetal.,2002; Perttulaetal.,2007).Althoughhavingusedseveralhypothesestest,itwouldbedifficulttofindsignificantrelationshipsbetweenthedata.However,thesamplesizeislessrelevantinqualitativeresearch.

Self-reported data: The participants were separated randomly into fourgroups;eachgrouphad14peoplein7randomteams(2peopleperteam).However,as there isarelianceonconducting aqualitativeresearchstudyandgathering the data, self‐reporteddata is limitedby the fact that it canrarelybeindependentlyverified.Inotherwords,whatparticipantssaidintheexperimentswastakenintoaccount.

Categories and knowledge of the participants: A broad range ofparticipantswereused;nosystematicselectionregardingtheirgender,age,education,degreeandalsotheirknowledgeinthepatentanalysiswasmade.However, as some of the participants did not have enough knowledge ofpatent ideation, bias and diversity in them analyze and point of view wasexpected.

Cultural and another type of bias:Allocatingprocessesforeachgroupandteams have been done completely at random, participantsall have biases,whethertheyareconsciousofthemornot.Itismainlyduetolocalculturalbiasandevenplaceandtimeofrunningtheexperiments.

Another significant limitation of this study is the reliance on the existingmeasuresandidentificationsusedintheliteraturetocollectandformulatethedata.TheworkbyShahetal. (2003) in ideationmetricswasessential,but flawswerefound in the Variety metric in some researches like Nelson et al., 2009. It wasmentionedintheirstudythat"fornormalizingagroupscoretheVarietycanonlybecalculatedforasetofmultipledesignideasandtheaverageVarietyscoreisnotastheVarietyscoreonlyappliestothesetitself.”SoitisrecommendedtoredefinetheVarietymetrictoperformitmorerobust.

Also,theproposedPatentMapsrelyonmanualwork,thereforedecreaseitsoperational efficiency. To solve this problem and for future studies, it isrecommended to develop software to perform the proposed approach in thisresearchmoredirectly.Thisreducesthemanualworkandallowswhoarefamiliarwithpatentanalysisandtextmining,toprofitfromtheresearchresults.

132

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[7] Appendix

146

7.1 Appendix A - Patent analysis survey in Iranian SMEs

7.1.1 Interview and results

I. General information

Nameofinterviewer:

Nameofinterviewee:

Placeofinterview:

Dateofinterview:

II. Questions

1.TheleveloffinancialsupportsofgovernmentforgrantingapatentwithinIranian

Industry?

□Low□Medium□High

2.Thelevelofusageofpatentinformation(National/International)inIranianIndustry?

□Low□ThelevelofthenecessityofpatentanalysisinIranianIndustry□High

3.ThemostpriorandpreferredsectorforusingpatentinformationamongIranian

Industry?

□Microenterprises(0‐9employees)

□Smallenterprises(10‐49employees)

□Medium‐sizedEnterprises(50‐249employees)

4.ThelevelofthenecessityofpatentanalysisinIranianIndustry?


5.ThelevelofusageofpatentanalysisinIranianIndustry?


6.ThelevelofanacquaintanceonthepatentanalysisinIranianIndustry?


147

1. The level of financial supports of government for granting a patent within

Iranian Industry?

Answer Choices Responses Numbers

Low 20% 4

Medium 80% 16

High 0% 0

Total 100% 20

2. The level of usage of patent information (National/International) in Iranian

Industry?


Low 75% 15

Medium 25% 5

High 0% 0

Total 100% 20

0%

20%

40%

60%

80%

100%

Low Medium High

1.ThegovernmentfinancialsupportforgrantingapatentinIranianIndustry?

0%

10%

20%

30%

40%

50%

60%

70%

80%

Low Medium High

2.Theusageofapatentinformation(National/International)inIranianIndustry?

148

3. The most prior and preferred sector for using patent information among

Iranian Industry?


Microenterprises(0‐9employees) 0% 0

Smallenterprises(10‐49employees) 45% 9

Medium‐sizedEnterprises(50‐249employees) 55% 11

Total 100% 20

4. The level of the necessity of patent analysis in Iranian Industry?


Low 0% 0

Medium 30% 6

High 70% 14

Total 100% 20

0%

10%

20%

30%

40%

50%

60%

Micro enterprises(0-9 employees)

Small enterprises(10-49 employees)

Medium-sizedEnterprises

(50-249 employees)

3.Whichsectorismoreinterestedinpatentinformation?

0%

10%

20%

30%

40%

50%

60%

70%

80%

Low Medium High

4.TheneedofpatentanalysisinIranianIndustry?

149

5. The level of usage of patent analysis in Iranian Industry?


Low 80% 16

Medium 20% 4

High 0% 0

Total 100% 20

6. The level of an acquaintance on the patent analysis in Iranian Industry?


Low 75% 15

Medium 25% 5

High 0% 0

Total 100% 20

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

Low Medium High

5.TheusageofpatentanalysisinIranianIndustry?

0%

20%

40%

60%

80%

Low Medium High

6.ThelevelofanacquaintanceonthepatentanalysisinIranianIndustry?

150

7.1.2 Questionnaire and results

World’sPioneeringindustriesafterpassing“efficiency”,“Quality”,and“flexibility”paradigmsarenowadaysininnovationera.Ininnovationera,competitionandresistanceinmarketsdependoncompanies’managementsbalanceinthreefieldsof“inventionandtechnologydecoding”,“creativeandinnovativedesign”and“intellectualpropertymanagement”.Therefore,thisquestionnaireispreparedtostudyandevaluategeneralandspecialconditionsofpatentownercompaniesinIran,andwewillappreciateifyoucanhelpusinimprovinganddevelopingofscientificeffortsinIranbyallottingsometimetocompletethequestionnaire. Itshouldbementionedthatcompanies’informationwillbekeptsecretandwillonlybeusedforresearchreasons.Theresultsandconclusionsoftheresearchwillbesenttoyousubsequentlyincaseyourcompanycompletesthequestionnaire.

General information

Fieldofstudy:Name:

Degree:Age(year):

Experiencesinthefield(year):Sex:

I. Company specifications

1.ThenumberofemployeeinSMEcompany?

□10to49employees□50to99employees□100‐249employees

2.ThenumberofpatentsinIranianSMEcompany?

□1‐5□5‐10□10‐15

3.Theresponsibledepartmentfortheinventions,newproductdevelopmentand

patentsinSMEcompany?

□ IntellectualPropertyDepartment

□ InnovationManagementDepartment

□ ResearchandDevelopmentDepartment

□ EngineeringDepartment

II. Patent analysis level




151

□Strategiclevel

□Competitivelevel

□Technicallevel

□Juridicallevel


□Yes□No

III. Patent analysis purpose


□Preventingreworkanddecreasingresearchcosts

□Conductingstudiestoanupperlevelofknowledge

□Proposinganovelsolution

□Consideringnewaspectsofinvention

□Awarenessoftechnicaltrendoftechnologyinothercountries

□Securefieldsofinvestment

□Studyingpastresearchandfindingsolutionstoproblems

□Identifyingtestifiedinventionsasnewevents


□Identificationofproblem

□Identificationoflimitationsandcriterions

□Findingpossiblesolutions

□Creatingideas

□Studyingfacilities

□Selectinganapproach

□Creatinganinitialsample

□Refinement

9.ThemostimportantpurposeforusingpatentanalysisinSMEcompany?

□Identificationofinnovationinpatentedinventions

□Identificationoftechnologyvacuityandimportantpoints

□Analysingpatentregistrationtrends

152

□AnalysingQualityofregisteredinventionstospecificationofR&Dtasks

□Forecastingtechnicalimprovementsinaspecificfield

□Strategicplanningfortechnology

□Extractinginformationfromregisteredinventiontoidentifyinfringements

□Identificationofpromisingregisteredinventions

□TechnologyRoadmap

□Identificationoftechnologycompetitors

IV. Database

10. Themostuseddatabaseinthecompany?

□ USPTO□EPO□Other

1. The number of employee in SME company?


10to49employees 36% 0

50to99employees 64% 16

100‐249employees 0% 9

Total 100% 25

0%

10%

20%

30%

40%

50%

60%

70%

10 to 49 employees 50 to 99 employees 100-249 employees

1.ThenumberofemployeeinSMEcompany?

153

2. The number of patents in Iranian SME company?


1‐5 60% 15

5‐10 28% 7

10‐15 12% 3

Total 100% 25

3. The responsible department for the inventions, new product development and

patents in SME company?


IntellectualPropertyDepartment20% 5

InnovationManagementDepartment16% 4

ResearchandDevelopmentDepartment52% 13

EngineeringDepartment 12% 3

Total 100% 25

0%

10%

20%

30%

40%

50%

60%

70%

1-5 5-10 10-15

2.ThenumberofpatentsinIranianSMEcompany?

0%10%20%30%40%50%60%

IntellectualProperty

Department

InnovationManagementDepartment

Research andDevelopmentDepartment

EngineeringDepartment

3.Theresponsibledepartmentfortheinventions,newproductdevelopmentandpatentsinSME

company?

154

4. The Level of an acquaintance on the patent analysis in SME company?


Low 76% 19

Medium 24% 6

High 0% 0

Total 100% 25

5. Requested and interested level for exploiting patent information for SME

company?


Strategiclevel 20% 5

Competitivelevel 8% 2

Technicallevel 60% 15

Juridicallevel 12% 3

Total 100% 25

0%

10%

20%

30%

40%

50%

60%

70%

80%

Low Medium High


0%

20%

40%

60%

80%

Strategic level Competitive level Technical level Juridical level


155

6. Any standard or a specific process for patent analysis projects in SME company?


Yes 0% 0

No 100% 25

Total 100% 25

7. The main benefit and expectations of patent analysis project in SME company?


Preventingreworkanddecreasingresearchcosts 0% 0

Conductingstudiestoanupperlevelofknowledge 0% 0

ProposingaNovelsolution 56% 14

Consideringnewaspectsofinvention 12% 3

Awarenessoftechnicaltrendoftechnologyinothercountries 0% 0

Securefieldsofinvestment 0% 0

Studyingpastresearchandfindingsolutionstoproblems 32% 8

Identifyingtestifiedinventionsasnewevents 0% 0

Total 100% 25

0%

20%

40%

60%

80%

100%

120%

Yes No


0%

10%

20%

30%

40%

50%

60%

Preventingrework anddecreasingresearch

costs

Conductingstudies to anupper level

ofknowledge

Proposing anovel

solution

Consideringnew aspectsof invention

Awarenessof technical

trend oftechnology

in othercountries

Secure fieldsof

investment

Studyingpast

research andfinding

solutions toproblems

Identifyingtestified

inventionsas newevents


156

8. For which step of the invention process, the patent analysis is expected to be

used?


Identificationofproblem 12% 3

Identificationoflimitationsandcriterions 0% 0

Findingpossiblesolutions 28% 7

Creatingideas 60% 15

Studyingfacilities 0% 0

Selectinganapproach 0% 0

Creatinganinitialsample 0% 0

Refinement 0% 0

Total 100% 25

9. The most important purpose for using patent analysis in SME company?


Identificationofinnovationinpatentedinventions 60% 15

Identificationoftechnologyvacuityandimportantpoints 0% 0

Analysingpatentregistrationtrends 8% 2

AnalysingQualityofregisteredinventionstospecificationofR&Dtasks 0% 0

Forecastingtechnicalimprovementsinaspecificfield 12% 3

Strategicplanningfortechnology 0% 0

Extractinginformationfromregisteredinventiontoidentifyinfringements 12% 3

Identificationofpromisingregisteredinventions 0% 0

TechnologyRoadmap 8% 2

Identificationoftechnologycompetitors 0% 0

Total 100% 25

0%

20%

40%

60%

80%


157

10. The most used database in the company?


USPTO 48% 12

EPO 40% 10

Other 12% 3

Total 100% 25

0%10%20%30%40%50%60%70%

9.ThemostimportantpurposeforusingpatentanalysisinSMEcompany?

0%

10%

20%

30%

40%

50%

60%

USPTO EPO Other

10.Themostuseddatabaseinthecompany?

158

7.1.3 Workshop, questionnaire and results

General information

Fieldofstudy:Name:

Degree:Age(year):

Experiencesinthefield(year):Sex:

Patent analysis

1.Thenumberofpatentshasyoueverstudieduntilnow?

□1‐5□5‐10□10‐15


□1‐5□5‐10□10‐15

3.Thenumberofpatentdatabaseshaveyoueverused?

□1‐5□5‐10□10‐15


□0□1□2

7.1.3.1 Results

1. The number of patents has you ever studied until now?


1‐5 53% 8

5‐10 40% 6

10‐15 7% 1

Total 100% 15

0%

10%

20%

30%

40%

50%

60%

1-5 5-10 10-15

Thenumberofpatentshasyoueverstudieduntilnow?

159

2. The number of patent analysis projects have you ever participated?


1‐2 100% 15

3‐4 0% 0

5‐6 0% 0

Total 100% 15

3. The number of patent databases have you ever used?


1‐2 100% 15

3‐4 0% 0

5‐6 0% 0

Total 100% 15

0%

20%

40%

60%

80%

100%

120%

1-2 3-4 5-6


0%

20%

40%

60%

80%

100%

120%

1-2 3-4 5-6

3.Thenumberofpatentdatabaseshaveyoueverused?

160

4. The number of patent analysis software or tools have you ever used?


0 100% 15

1 0% 0

2 0% 0

Total 100% 15

0%

20%

40%

60%

80%

100%

120%

0 1 2


161

7.2 Appendix B - Characteristics of Patent Map methods

7.2.1 Ranking of Patent Map methods

Patent Map Requirements

Total

score of

Patent

Maps Patent Map

Major

analytical

method

Commonly

used form of

presentation

Scalable

up to

100

patents

Doable by

consultants

in 8 hours

(30

patents)

Readable

by R&D

engineers

in 1 hour

(30

patents)

Represent

the

Novelty

Represent

the

inventive

steps

Time

perspective

Element‐

basedMap

Qualitative

analysisIllustration 2 3 2 2 2 0 11

Mapof

Technological

Development

Qualitative

analysis

Tree‐

structured

form

1 2 2 2 2 3 12

Inter‐patent

Relations

Map

Qualitative

analysis

Tree‐

structured

form

1 2 2 1 1 0 7

Matrix Map

Qualitative

analysis

Quantitative

analysis

Matrix/

Graph 3 3 3 3 3 1 16

Systematized

ArtDiagram

Quantitative

analysisIllustration 2 2 2 2 2 0 10

Time‐Series

Map

Quantitative

analysisGraph 2 1 1 0 0 3 7

TwinPeaks

AnalysisMap

Quantitative


Maturation

Map

Quantitative


RankingMapQuantitative

analysisList/graph 1 1 1 0 0 3 6

ShareMapQuantitative

analysisList/Graph 1 1 1 0 0 3 6

SkeletonMap

Quantitative

analysis

Qualitative

analysis

Tree‐

structured

form

2 2 3 1 1 3 12

RadarMapQuantitative


0=Irrelevant 1= Low relation 2=Medium relation 3=High relation

162

7.2.2 Representative examples of Patent Map

Representative Examples of Patent Map

No. Name Presentation

form

Main analytical

method Main benefit

Useful for generating

Novel ideas

according to the

problem and

solution presented in

patents?

(Yes/No/Partly)

1Element‐

BasedMapIllustration

Qualitative

analysis

‐Thesummaryofrelatedtechnology

‐Thesituationofone’sownpatentandits

differencewithotherpatents

No

2

Diagramof

Technological

Development

Tree‐

structured

form

Qualitative

analysis

‐Thepioneerpatentsforcachingthepotential

‐ThespillovereffectsofdevelopmentresultsPartly

3

Interpatent

RelationMap

(Citation

Map)

Tree‐

structured

form

Qualitative

analysis

‐Theinformationofcitedperson

‐Thepriorartcitedsection

‐Theusageofpriorartinformation

NO

4 Matrix Map Matrix/graph

Qualitative

analysis

Quantitative

analysis

Therelatedkeypatents(combinationof

problemswithsolutions)Yes

5Systematized

ArtDiagramIllustration

Quantitative

analysis

Theentireamountofpatentsassociatedtoa

preciseareaofartandreviewtherelated

intellectualpropertyactivitiesatuniversities

andgovernmentalorganizations

NO

6TimeSeries

MapGraph

Quantitative

analysis

Theapplicantstrendanalysis,patentnumber

ofapplicationsfiledandpatentsissued.NO

7TwinPeaks

AnalysisMapGraph

Quantitative

analysis

‐Theprecedenttechnologicalprogressof

companiesunderbusinessstrategy

‐Thedelayofcountriesforreachingan

aninternationalcompetitiveadvantageina

preciseart

NO

8Maturation

MapGraph

Quantitative

analysis

Identifythenumberofapplicationsfiledand

thesignsofchangeNO

9 RankingMap List/graphQuantitative

analysis

Thetechnologicalprogresstrendsinleading

organizationNO

10 ShareMap List/GraphQuantitative

analysis

Theapplicationholderinformationand

relatedpatentforaspecifictechnologyNO

11 SkeletonMap

Tree‐

structured

form

Quantitative

analysis

Qualitative

analysis

Toobtainacompleteperceptionoftherange

oftechnologicalimprovement.NO

12 RadarMap GraphQuantitative

analysis

‐Thefocusedfundingontechnicalfieldand

laboronaspecialtechnologyindifferent

organization

‐Thecomparisonofinternational

competitivenessbetweenthecompanies

NO

163

7.3 Appendix C - The instruction of providing the Technical

Contradiction Map

The map consists of a main matrix and some supportive graphs. It is suggestive to

exploiting the map in following steps:

Look at the main Matrix:

o The classes of problems which are addressed in the patent;

o The classes of means and solutions which are used in the patents for solving

the problems;

1. Think about any new problems which must be addressed by the system;

2. Think about any general idea for structure of the system for same classes of

the problems or new problems;

o The size of the bubbles is representative for the quantity of patents in each

cross of problem-solution (The bubbles with very few patent applications,

have less chance for new art for more improvement, whereas, the largest

number of patent applications were filed in some crosses represents a large

volume of information disclosed);

o For more technical information for each cross, go to the corresponding

supportive graph;

Look at the supportive Graphs:

o Each patent resolved a main contradiction. A contradiction can be clarified

by three parameters; improving, worsening, and control.

o Improving parameter; is a property of a system component that is expected

to be improved by the solution, consists of 2 parts in the top of the graph:

A component of the system in a box;

A property of that component in a box;

o Worsening parameter; is a property of a system component that prevents

the improving parameter from achieving the desired value, consists of 2

parts in the bottom of the graph:

A component of the system in a box;

A property of that component in a box;

o The worsening and improving parameter values have an inverse

relationship.

o Control parameter; is a system component property that allows trade-offs

between improving and worsening parameters and it is possible to control

values of improving and worsening parameters through it; it is written on

the line that connects the improving parameter and worsening parameter

of contradiction of a patent.

o A picture of each patent is presented next to the line of control parameter.

3. Propose new ideas for addressing and resolving the contradictions in a

cross.

164

7.4 Appendix D - Walker Patents Profile

169

7.5 Appendix E - Technical Contradiction Map

174

7.6 Appendix F - Generated ideas (Experiment I&II)

7.4.1 Experiment I: Group A

Team Treatment Idea descriptions

Time of

generating

ideas

(Out of 30 min)

no. of

Ideas

1 B.S

FoldableandportableWalker 2

16

AdjustableWalkerlegsfordifferentpersons 4

AdjustableWalkerlegsforuseonthestairways 5

Usingdampersystemtoimproveusage 6

TelescopiccapabilityforchangingWalkertowalkingstick 7

Walkerwithhandleheatingorcooling 9

Joinwheeltotransportandinclinedsurfaces 10

Usematerialsandlightweightalloystoreduceweight 11

Addafoldingseatfortiredness 12

Addasensortofeelthetypeofpath 14

Addshoppingbasket 16

Addlightstomoveatnight 18

Announcinghelpatfallingtime 19

Useasanelectricmotor 22

Addtaximeterandaccessories 26

Addtheumbrellaorroofduringrain 27

1 P.S

Addchildren'slock 9

7

Automaticadjustableheightwiththeuser'sbody 11

Addmusclestimulationsystem 13

AddMP3playertoexcitemovement 14

Nanocoverageforeasiercleaning 18

Addfoldingtoilet 23

AddGPStoidentifythepath 28

2 B.S

Walkerswithwheelsandsteeringandbraking 2

8

RetractabletelescopicWalker 5

Equippedwithnavigationandmonitorsystemandlocationand

guidetrack8

Equippedwithmedicationreminders 10

Consideringthesystemsendalertsandmessagestoaphone

numberatthetimefalling12

Walkerwithumbrellaandawnings 18

Usingasascooter 20

Theabilityoftransformingtoseat 25

2 P.S

Useofnewlightermaterials(composites) 10

4Automaticadjustableheightwiththeuser'sbody 20

Sensingthedistancetoobstacles 25

Locktoconnecttotheescalatorfortheclimbingandcomedown 30

3 B.S

ModularfoldingWalker 2

12Usingnewmaterials(nanotechnologyandcomposites)toreduce

weightandreduceenvironmentalissues4

Walkerupperbodyconnectiontoreducepressureonthehands 6

175

Walkerconnectionlegs 9

WalkerWheelforeasiermobility 11

AddGPStoidentifythepath 15

Walkie‐talkiesaddshort‐rangecommunicationsystem 17

Addaudiosystemandhands‐freecommunicationonWalker 19

Integratedpedometerandatimertohelptreatmentprogram 23


Addtheumbrella 27

Addseatstorest 30

3 P.S

Addwarningsystemforfallprotection 10

5

UserMonitoringvitalsigns 15

Installlightingsystemsforuseindarkenvironments 22

Automaticsizeadjustmentbasedonuserrecognition 25

PortableWalker 30

4 B.S

AddMP3playerforfun 2

17

SmartWalkertodetectphysicalsituationofusertowarnincase

ofemergency3

Withadjustableheightandsizeforeachuser 5

Addsmallbasket 8

Walkerwithawnings 9

Addwirelesstelephoneforhomeuse 10

Addglovesinsteadofhandlefordisabledpersons 12

SmartWalkertodetectionobstacles 17

AbilitytoassemblyanddisassemblyofWalkerbyuser 19

UsingWalkerlegsasacane(Multi‐purpose) 20

Abilityofassemblyintoaseat 21

Theabilitytorecognizedisabilitiesandnotifythefamily 22

SmartWalkerforblindpeople 24

Havingaremotetrackingsystemtodetectthepositionofuserby

others26

Self‐cleaningability(usingNanomaterials) 27

Withalarmorwarningsystemtouseonshoppingcentreor… 29

Energyconversionkinematictopowertheheatingorcooling

(HVAC)30

4 P.S

Addcontrolmechanisminhandlepartforsitting(hydraulic) 5

5

FoldableWalkerusingthetelescopemechanism 10

Telescopiclegsdesign 15

Walkerconvertstothetoiletforemergencies 20

Levelsdetectionandtheabilitytoswitchtothedifferentlevels 25

5 B.S

Walkerwithaseat 3

7

Walkertoclimbthestairs 8

Walkerwithalarms 13

FoldingWalker 18

Addahandletobalancewhiledoinghouseholdchores 20

Walkerwiththeabilitytocontrolvitalsigns 25

SpringWalkerforUnevensurfaces 30

5 P.S

Walkerwithwheelsandspeedcontrol 8

4Addasafetybelt 10


Addtrackingsystem 20

6 B.S WheelWalker 1 22

176

Gridandlightweightbody 2

Compositebodystyle 3

Addholderofmobile 5

Withspecialseatrest 7

Walkerwithwheelsandseat 9

FoldingWalker 11

UserMonitoringvitalsigns 13

Reflectiveandphosphorusstyle 15

Addsmallhandleforputtingshoppingbags 17

Anti‐sweathandle 19

AutomaticWalker 20

AddGPS 21

Withalarmsystem 21

MotorizedWalker 22

Formovingstairs 23

Exoskeletons 24

Withmassagers 25

WithAir‐Bag 26

Walkersnowplow 27

Walkerwithawnings 29

Addcoolingsystem 30

6 P.S

FoldableWalker 3

11

Useoflightermaterialstoreduceweight 4

Walkerwithcontrolbrakesonhandle 6

AddGPS 9

Adjustableheight 12

Usetheseatforsitting 15

Usingthreewheelsforclimbingstairs 16

Possibilityofsensingheartrateandbloodpressure 19

Addmultimediafeatures 22

IncreasetheheightofWalkerhandleintounderarmsforeaseof

movement24

Addmobileholder 29

7 B.S

Addtheumbrellaorroofduringrain 2

6

Walkerwithfourwheels 3

Addtwowheelstofrontlegs 6

Usingcompositematerials 9

Usingreflectorontheframe 16

Contactledlightstoilluminatethepathatnight 23

7 P.S

Usinggreenandeco‐friendlymaterials(BioComposite) 10

3TwoWalkerlegsconnectedtobodyofuser 20

Changeablefrontlegsforclimbingstairs 30

177

7.4.2 Experiment I: Group B


Time of

generating ideas

(Out of 30 min)

no. of

Ideas

1 B.S

Madebycarbonfibreandfolding 4

10

Withheatedhandlesandwristhook 6

Capableofclimbingstairs 7

Withvitalsignsmonitoring 9

Addsensorsforblindperson 14

Addrechargeablelithiumbatteryorsolar 18

Seattorelaxduringwalking 20

RadioandMP3player 22

Watertankfordrinkingandtakingdrugs 26

ConvertibilitytotheLuge 30

1 T.C

Usingslidersystemforwalkingonallsurfaces 5

8

Usesmallelectromotorandsensorandtosetfourlegs

independently7

Helpingthepatienttositbyusingfoldingsystem 8

Helpingusertorestthroughtelescopicfoldingsystemofseat(3

to4pieces)10

Helptomoveatdifferentsurfacesbyusingnewmaterialfor

Walkerlegs14

UsingAir‐bagineachWalkerlegs 17

Adjusttheheightofuser'shandusingtheanglesensor 20

Walkersizesettingusingmotionsensors 25

2 B.S

AddMP3andLCD 2

17

Installtheelectricalgeneratorforpowersupply 3

Wheelsforeasymovement 4

FoldableWalkerforsittingandstand 5

Installthefanforcooling 7

SpringWalkertoreduceimpact 8

Lightsforuseatnight 10

Addaswivelchairfortherest 12

Addanalarmconnectedtoamobilephoneincaseoffalling 13


AddasmallboxinfrontofWalkertoputthenecessary

equipment19

Addanelectronicdevicetoinformeatthedrug 20

Newdesignoffrontlegstouseinthestairs 22

Walkerwithsensorstodetectobstaclesfortheelderlypeople 25

Addabaseandwheelforusing 26

Walkerlegsattachedtotheknees 27

Withmassagers 29

178

2 T.C

Possibilitytoadjustthelegswithsurfaces 8

11

Walkerhandlesstayparallelwithfloorandbodystaybalanced 9

Usingporousmaterialsforup‐rightWalker 11

Forincreasemanoeuvrabilityandbalanceofuser,changethe

steeringwheelfromfronttoback(Trolley)13

Whenthearmcollectedusingahandleveronthehandle 15

Nestinglegsforrestingtime 16

Foldingplatformforsitting 17

Improvednavigationinthedarkenvironmentbyusing

materialsofphotocell20

Addleverlocksinthefrontarmandcontrolledbyahandlefor

stairs23

Usingflexiblepadsonthebottomoflegsdependsonground 25


3 B.S

Addsupportsfordisabledpeople 1

11

Addpedometer 2

FoldableWalker 4

EquippedWalkerwithanairbagtopreventfalls 6

Theabilitytotransformfromfourtotwolegs 8

Useoflightermaterials 13

Useheatandcoldinsulation 17

Insteadoffourlegsusingaflatplateforunevensurfaces 21

Usingreflectivematerial 25

Addfoldableseat 27

Walkerwithwheelinfrontlegsandfrictionbreaksforrearlegs 29

3 T.C

AddGPSinordertoidentifyrightdirection 10

4

Usingphosphorusmaterial(luminous)inWalkerframeto

avoidcollisionswithvehiclesatnight16

Themodularcomponentsinsteadofintegratedcomponentsfor

easyrepairthedamagedsection22

Foldingseatforsittinginanemergencycase 26

4 B.S

Addahandlebikeforeasyuse 3

17

EmbedaseatforWalker 6

Embedwheelsformorespeedandbrakehandle 7

Addlights,hornandbasket 8

Dualwheelsforclimbingstairs 9

FoldableWalker 12

Addsafetybelt 13

Changeabletoascooter 14

Changeabletoacane 15

RadioandLCD 16

Addsidemirrorstoavoidanaccidentwithamotorbikefrom

behind17

Usingslideheightadjustmentfordifferentpeople 19

Addfoldablesmalltable 20

DesignlikeTwinstrollers 23

179

Addtheumbrella 24

AddGPSforAlzheimer's 25

ElectricWalkerwithfootrestforstairs 30

4 T.C

Usingthermochromics’materials 6

10

Usingphotochromicmaterials 7

TheuseofshapememoryalloySMAtoimprovetheassembly

anddisassemblyWalker8

SmartWalkertodetectobstacles 10

Addacamerawithazoomin‐zoomoutcapabilityforelderly

peoplewithlowvision12

AddaWalkerwheelwithadjustablespeed 15

Walkertoclimbstairs(chainchangebike) 17

Forcrossingtheslipperysurfaces,usingspecialWalkerlegs 21

Apowersupplyandcontrolequipmentfordisabledpeople 25

Addamemoryforstoringinformationofpeoplewith

Alzheimer'sdisease30

5 B.S

InstallGPSandconnecttopoliceandhospital 3

5

Asapantsformechanicalreinforcementofmuscleswhile

walking6

SuchasSolomoncarpetbutflexibleandelectronic 16

Usesuchasextraequipmentlikebeds,wheelchairs,crutches,

fishingholder20

Suchasabackpacksthatreducetheweight 27

5 T.C

UsingelasticmaterialsforWalkerframe 9

5

Usinghigh‐strengthandlightweightmaterialsfor

Compensationweight15

UsingspringforWalkerlegsforincreasingbettermoving

forward18

Smartmaterialsforcoolingandheatinguser 20

Telescopicfrontandrearlegstoenablemanoeuvreupand

downonslopes25

6 B. S

FoldableWalker 3

9

Useoflightandunbreakablematerials 5

UsingantiactivateX‐RAYdevicematerials 7

UsingNon‐metallicmaterialforlegstoreduceweight 10

Addelectroniccircuitandthermalsensors 14

Adddataprocessorofheartbeat 17

AddGPSandnavigationfacilities 20

Improvethesystemofwheelsandengineinstallation 24

Walkerwithflexibilityfordifferentsurfaces 28

6 T.C

TheuseoflightweightmaterialstoreduceweightWalker 5

7

Walkerwithtwoseparatelegstohelpknee 8

Walkerwithgelatinousmaterialthatdoesnotinjurewhenuser

fall10

SmartWalkerwiththedrugprogram 14

Walkerwithresiliencematerialsforbetterfeelingofuser 20

180

Walkerlegswithspecialmaterialsforhousecleaning 25

Walkerwithfoldableandintegratedframe 28

7 B.S

Walker'shandlewithsensorstosenseenergyofusertoprevent

theuserfalling1

19

Walkerheightadjustablelegsfordifferentuser 2

Walkerwithasystem(anelectriclift)tomakelighterweight

feelduringliftingWalkertouser3

SmartWalkerwithautomaticsurfacedetectionforadjusting

thelevelofconnectionoflegstoeachsurfaces5

Walkerrobotwithflexiblelegsforclimbingstairs 8

Walkerwiththevitalsigns(bloodpressure,pulse,...) 10

Walkerwithawarningsystemtothosearoundtheuseratthe

timeoffallingorimbalance12

Foldingandassemblies’Walkerforeasytransport 13

WalkerwiththewarningandalerttheuserifWalkerstandson

thesurfaceisunsuitable15

Walkerwithlightingsystemfornightuseanddarkplaces 17

Walkerwiththeabilitytoadjustallaspectsoflegsandrodsto

adapttodifferentpeople(suchaschildrenandadults)18

Walkerwithaprotectiveumbrella(sun,rainandsnow) 20

Walkerwithabilitytoestimatethedistancetothetarget,for

example,acameraisinstalledonWalker22

WalkerequippedwithaGPStolocate 23

Walkerwithincreasingabilityofhandsforpeoplewithweak

hands25

Walkerwithheatingandcoolingsystems 26

Walkerequippedwithafoldingseatforthenecessaryuser

tiredness27

Walkerwithasystemthatwillmaintainbalanceduringwalking 28

Walkerwithacollisionwarningsystemtobarrier 30

7 T.C

Useofnewmaterialsformorecomfortgriphandleandprevent

slippingonthehandle5

9

Withahandleintheseat(Walkerwithseat)thatpushupthe

seattomoveandtohelpuserstand6

Addaspecialhandletohelpgetupfromthegroundandusea

Walker8

Ribbedwheeldesignforwinterandsummertoprevent

slippingondifferentsurfacessuchasmountainbike10

Walkerframewithwirematerialstohelpbendingandfolding

duringwalkingandkeepstrength15

AddAirbagsforthecollision 20

AddaT‐shapedbasetothefrontoftheWalker(tripodismore

stableandeasytouse)22

Usingpolymericmaterialsforlightnessandpreventrusting 25

Walkerwithplasticarmsandlegsandfrontframeonaccordion

shape28

181

7.4.3 Experiment I: Group C


Time of generating

ideas

(Out of 30 min)

no. of

Ideas

1 B.S

Walkerwithaseatandaplacetoputfood(foodtable

Slider)2

10

WithsensorsonthehandleWalkertomeasureblood

pressure,heartrate,temperatureandbloodsugarclose

andsaveandsendviamobile

5

Foldingcapabilityandbecomeapipetocarryina

backpack6

Thepatient'sweightandapplyingpressureonthehandle

andbaseWalker,forenergystorageandconversionto

electricalenergythatisrequiredWalker,forexample,for

useinsensorsorelsewhere

8

Walkerconvertstocanewhenthepatientisfeelingbetter 10

Addatransparencyguardtopreventsplashingwater

duringraintime13

Changetheframetothetriangular‐shaped(3wheels) 20

Addacanopy 22

Thepossibilityofdoublewidthwithatelescopicchange 26

Adjustablelegsfordifferentsurfaces(handlestayparallel

tofloor)forkeepingbalance28

1 P.T

Walkerwithownervoicerecognitionsystemforcoming

totheowner15

3Controlsystem,automaticspeedregulationandthe

rechargeablebatteryformove20

Walkerwithanimbalancesensortoactandkeeppatient 25

2 B.S

Addacomfortableandsofttohandleforuser 1

10

Handleswithanti‐sweat 2

Addanumbrella 4

Changeablelegsfordifferentsurfaces 7

Thepossibilityofchangetoacane 11

convertibletoscooters 15

Audioandvideoentertainmentsystemsforuser 17

AddGPS 19

Addsuitablelegsforuseonstairs 25

Addcargo 27

2 P.T

Addlight 10

8

AddAirbag 12

Foldableseatforrest 15

Adjustableheightfordifferentuser 16

Walkerwithholedetection 19

Walkerspringlegs 22

FoldableWalker 26

Addaportabletoilet 28

3 B.S

WalkersmartfordifferentagesbyaddingLCD(forkids

cartoonplay,foradultsshowvitalsigns)2

10

SmartWalkerformovingatdifferentlevels(stairsand

slopes)5

Theabilitytocoverdistances(sidewalktobusstairsand

...)7

Addfan,umbrella,sunroof,... 8

IncreaseflexibilityWalkertocarryontheplaneandcar 10

Walkermodularcomponentsforeasytransport 13

182

Addequipmentforphysiotherapy 15

Addairbag 18

UsenewmaterialforstrengthWalkerframe 23

AddMobileholder 26

3 P.T

Walkerwithshockabsorberstoreducetheimpactsof

movementondifferentsurfaces15

2Addwheelswithpowersupplyfromhandle(manual

transmission)30

4 B.S

Equippedtomonitorforentertainment 2

18

AddaGPStoWalker 3

Addwheeltofrontlegs 4

Equippedsensorofvitalsignsforsendinginformationto

thehealthcentres6

Withtheopening,closingandfoldingcapability 7

Poweredbyloudspeakertoamplifythevoiceofthe

elderly8

Puttingautomaticawningstoprotectfromlightandrain 10

Policecallincaseofgettinglost 12

Equippedwithcoolingandheatingsystem 14

Usinglightcompositematerials 15

Setthealarmsforusingmedicines 17

Withthelightingsystematnight 18

Usereflectivebody 20

Foldableseatforrest 22

Addfirst‐aidkit 25

Walkerwithengineforeasymoving 27

Self‐cleaningsystem 28

Anti‐theftsystem 30

4 P.T

Walkerwithfoldingchairsforrelaxing 12

6

Electricwheelchairbyputtingalightengine 15

Addadigitaldevice(smartphoneorGPS) 18

Addwheeltorearlegsinordertobalancethepatient

duringmove20

Usingwheelsofatankforroughpaths 25

Addalarmsystemandconnecttotheambulanceandfire

fighting30

5 B.S

Usinglightweightcarbonmaterials 2

5

Addingwheelandbrakesystem 4

Newdesignhandleframetorelyontheforearminstead

ofthewristforwalking13

NewdesigntoavoidbendinguseronWalkers(longer

handles)19

Usingarobotconnectedtotheuserbrainandlegsfor

maintainbalance26

5 P.T

Walkerwithspecialwheelssnowyandicyconditions 8

4

Walkerwithonewheelinfrontandtwowheelsatthe

rearsothatahorizontalshaftattachedtothefrontwheel

anditsconductivityWalker

14

Walkerpoweredbybatterywiththree‐wheels 20

Retractableandportablemotorized 24

6 B.S

Portablefoldingbag 2

15

Walkerlegsspringforlowerpowerconsumptiontomove 3

Ergonomicdesignforthespine 5

Equippedwithairbagsafetysystemtopreventphysical

injuries7

Softcoverwithelastic 9

Equippedwithsafetybelt 11

Equippedwithwheelsandbrakesandhorn 15

Equippedwithvideoandsound 17

Withspecialpartforpersonalbelongings 19

183

SmartWalkerforblindpeople 22

WithGPS 24

Equippedwiththemedicationusagealarm 25

Smartalertsystemvitalsigns 27

Adjustablelegsfortheescalatorsandramps 29

Equippedwithportablechairforrefreshment 30

6 P.T

Walkertobeabletoski 10

6

Walkeralsobeusedforcoastalhotspots 17

WalkerwithJackdeviceforuseatvariouslevelsand

helpingtostandandsit19

Walkerstandsinterchangeablefordifferentlevels 20

AdjustableWalker(widthandlength)forthelargeand

smallperson25

Useautomaticsystemtostandandsit(likeCitroencar) 27

7 B.S

Walkerwithwheelandbrakes 1

11

Jointheseatforemergencies 4

LiftingcategoriesWalkertothepatient'sarmpittohelp

thedisabled7

Addamotorformovingautomatically 9

Walkerwithcontrolandmoveinadifferentdirectionfor

peoplewithdisabilities12

AdjustableHeight 15

Walkerwiththreewheelsforclimbingstairs 19

Addmp3andradiotransmitter 23

AddGPSformissingpatient 25

AddabasketinfrontofWalkertoputmobileand

personalbelongings27

Foldingcapabilitiesandportability 30

7 P.T

CombinedWalkerandwheelchair 12

5

Addasmallwheelattheendofeachlegsforeaseof

movement17

Walkerwithcontrolsystemandsensorstomonitoruser

performance20

Addaremotecontrolsystemforchildrenorpeoplewith

disabilities25

Addingafallprotectionsystemtoact(airbags) 30

184

7.4.4 Experiment I: Group D


Time of

generating ideas

(Out of 30 min)

no. of

Ideas

1 B.S 01

Adjustablebaseandlegs 5

5Withanalarmsystematthetimeofimbalance 10

AddingradiotoWalker 15

Allowsthemeasurementofvitalsigns 20

InstallGPStoavoidmissing 25

1 B.S 02

TheuseoftelescopicbaseforWalkers 2

4Usingnewmaterialforlighterweight 5

Helptomoveatdifferentlevelsbasedsensor 9

Increasesafetyandreflectorandlampbase 18

2 B.S 01

PortableFoldingWalker 1

17

LightweightWalker 3

Walkerforusingatthebeach 4

Walkerhasashower 6

Walkerwitfan 8

Walkerwithfoldablebed 10

ModularWalkertouseinnewdemand 11

Walkerturnedintocane 13

Walkerwithaheater 15

Walkerwithseat 17

Adjustablesystemsfordifferentheight 19

Walkerturnedtothetent 20

Addabasketforadditionalequipment 22

Walkerawnings 24

Walkerlegstoride(skating) 26

Walkerwithahornandsiren 28

Walkermotorized 30

2 B.S 02

Warningsystemsinhospitals 7

3MeasuringvitalsignsbyconnectingamonitortoWalker 11

SettingthesizeoftheWalkerwithfingerprint 15

3 B.S 01

FoldingWalkertouseinthesubway 3

8

Walkerwithtelescopicheightadjustment 7

Walkerwithadifferentlegforheightadjustmentonslopesand

stairs10

WheeledWalkerwithbrakes 13

TheradioonWalkerforfun 17

Interchangeablelegsforhomeuse 20

Lightweightconstructionwithnewmaterial 25

Joinumbrellaforspecificweather 30

3 B.S 02

Helpthepatientforthesittingpositioninthenecessary

conditionbyaddingaseat5

3Equippedwithwheellocks 10

Useofcompositebodyformorestrength 15

4 B.S 01

Usableasacarriage 3

12

VitalsignssensorsinstalledonWalkerframeforinformingthe

relativesandmedicalcentres5

AppropriateWalkerforclimbingstairs 7

Addablinkerforwarning 10

Walkerwithbaskettoputthepurchase 14

Walkerwithhappyandattractivecolourschemetomake

happiness17

185

Walkermadeoflightweightandsturdy 19

WithGPS 22

Withanti‐sweathandle 25

Apop‐upseatfortiredness 26

Anumbrellatoavoidtherain 28

Changeabletotheelectricwheelchair 30

4 B.S 02 Withtheconnectiontotheuser'swaist 15

3Walkerwearablewithairbag 20

Walkerforusingonsnow 30

5 B.S 01

ExciteusersbyplaymusicwithputtingatablettoWalker 2

10

Addaawningsforprotectionfromrainandsun 4

AddspringtoWalkerlegstoavoidimpacts 8

Walkerwithmedicineremindersystem 12

Addusersupportfordisabilities 16

Addlightstomoveatnight 18

Withfoldableseat 20

Withfingersensordimensions 22

Walkertopreventslipping 24

Walkerwithaspecialmaterialtoavoidfreezeframe 26

5 B.S 02

Three‐dimensionaltriangularlegstopassthestairs 5

4Addafoldablebed 8

Joinconsoletokeepmobile 12

Addanalternatortostoreelectricityanduseflashlightsfor

Mobile15

6 B.S 01

Addvitalsignssuchasheartratesensor 2

16

AddboardsonWalker 4

Walkerfoldingandassemblyforeasytransport 6

Addalightingsystemfornightuse 9

Abilitytoalerttheusertofindtheappropriatesurfaces 13

Withseatforrest 15

Addabaskettoputthepurchase 18

Walkerwarningatthetimeoffalling 20

Theabilitytotransformthecane 20

Foruseinescalator 21

Walkergridbodyandlightweight 22

Walkerwithachangeablelegforvariouslevels 25

Adjustablesizesforchildrenandadult 26

ModularBody 27

Joinwheelsformorespeed 28

Walkerwithfirstaidequipment 30

6 B.S 02 Addbrakestocontrolspeed 2

3Addhornandluminoussystem 7

SmartWalkerwithtabletandGPS 10

7 B.S 01

Bodylightweightforpeoplewithdisabilities 5

12

HeatingandcoolingWalkerframe 8

Suitableforslipperysurfaces 10

Suitableformovingstairs 13

ThefoldingandpotableWalker 15

Withsafetybelt 17

WithGPS 20

WithLCD&MP3 22

Addaprotectiveumbrella 25

Medicinetimealertstopatient 26

Addlightsfornight 28

Usereflectivematerialindarkplaces 30

7 B.S 02 Walkerwithdetachablebaseandconvertiblebedforinjection 20

2Walkerwithadjustableseatheighttositonthefloorandstand

up24

186

7.4.5 Experiment II: Group A


Time of

generating

ideas

(Out of 30 min)

no. of

Ideas

1 B.S

DiversityinWalkerbodycolour 2

18

Changetheangleofhandlestohelpbalancetheuser 4

Adjustableheight,foreaseofclimbingstairs 5

Foldinglegsandbody 7

Abilitytoinstalllightsfornightvision 10

Installingalarmorbeeptonotifythefamily 11

InstallpagertocontacttheEmergencyCentretohelpor

ambulance11

Theabilitytobecomechairtorelax 12

Fittedwithabaskettoholdmarketbasket 13

Installnavigationwithaddressingcapability 15

Lightbodyweightwithcarboncompounds 17

Walkerwithcoversforhands(gloves)onthehandle 18

Walkerwithmirrorsonthebody 19

ResistanttireswithwearresistantonendofWalkerlegs 20

Ergonomichandlesthatpreventsweatingandslipping

hands22

ThepossibilityofinstallingawningsonWalker 24

Walkerbodywithcellphoneplace 27

Walkerwithmusicplayerandplaysetonhandles 29

1 T.C 01

Adjustabledistancebetweenthelegsandbodywithspring

mechanism14

7

RemoveoneofthelegsandcreateWalkertripodtoreduce

weight17

Addasteeringwheelatthetopofthecentralaxisanda

tripodonthebottom19

Theuseofpolymeralloysinsteadofsteelalloysinthebody

inordertobelightweight22

AddGPS 23

Addadditionalhandlestopracticeexercisemovements 24

AddaWalkerbelttokeepbalancetheupperbodyof

patient28

2 B.S

TheuseoflightandstrongmaterialinWalkerdesignfor

obesepeople1

11

Walkerhandleswiththermalglovesforwinter 3

Walkerwithawning 4

Addmobileholder 5

Walkerhandletobeloweredandraisedwiththesizeofthe

patient8

187

Atthesametime,canbeusedwithandwithoutwheelsand

controlledbyaswitch10

Walkerelectricwithspeedadjustmentwheelbyaswitch 14

Addingasmallcomputerscreentomonitorroadconditions 17

Addsensorsmonitoringheartrate 21

Addafallingwarningandbalancesensors 24

Addsensorstothelegsforunderstandingthesituationof

theroad27

2 T.C 01

WalkeralwayscleanusingNanocoating 7

9

Walker'shandlecoolinginthesummer 9

WithoxygenholderontheWalkerlegs 13

Addingalarmsforusingdrugs 19

Convertibletoachair 21

Designthetipofthelegsforeasymoveontheasphalt 24

Flexibilityandformabilityforrehabilitation 26

Walkerergonomicdesigntofitdifferentbody 28

Specialinsoledesignforslipperysurfaces 30

3 B.S

Intelligentsystemtoinformhealthcarecentreorfamily 2

9

Walkerwiththeslidingjointsformorespace 5

DesignedfoldableWalker 6

Walkerwithtwofrontlegsonwheels 9

Walkerwithasmallseatforachild 12

UsingattractivecolouringWalkerframeforpatientmorale

boost15

DesignWalkerwithlightweightmaterial 18

Addingseatbelts 23

Addabagandshoppingcart 25

3 T.C 01

Walker'sopeningandclosinghorizontallytorelax 9

5

Patientprotectionsystemtoavoidfallingonthemove 14

Walkerwheels’legswithself‐cleaningsystem 17

Addingalightforusingindarkplaces 23

Walkerheightadjustableonspringyform 27

4 B.S

Usethesmallwheelstomoveeasier 1

17

RetractableWalkerfortransport 2

Walkerwithreflectivelegsfornightvision 5

Addamonitorvitalsigns 6

AddGPS 7

Addashoppingcart 9

Addaholdertoputthefood 11

AddaplacetositinthefrontofWalker 13

Walkerwithwheelsandbrakes 16

Foldablelikeapram 18

Lightandstrong(likethefuselage) 20

Walkerwithfoldingseats 22

Thesoftandreplaceablehandles 24

Addmovingtable 25

188

AddAirbags 27

Addradioandrecorder 28

Handleswithfallwarningsystem 30

4 T.C 01

Addawirelesssystemtotransmitpatientinformation 8

11

Likeakidscooterbutwithhigherseat 10

Walkerwiththeopenspaceinthefrontandcloseinthe

back15

Addedodometerandtestfacilitiestostrengthenthe

musclesoffeet18

Walkerwithspeedcontrolwheel 20

Userfriendlyandtheabilitytotuneupofbyuser 23


Walkerfortheblindandtransmitasenseoftheearth 26

Legspaceisautomaticallyretractingwhenyouwalk 27

SettheWalkerdimensionswithafingerprintsensor 29

Installalarmstopreventcollisionwithobstacles 30

5 B.S

Walkerwheelchairswithadjustablespeed 3

10

Walkerwithhandbrake 5

Addareflector 7

Walkerwithbottlesplaceorsmallbasket 9

Addanumbrellaforprotectrainandsun 11

AddLCDforfun 14

Walkerwithresilientlegs 18

Walkerwithlightfordarkenvironment 21

Self‐controlWalker 24

Walkerwithdifferentlegsfordifferentsurfaces 26

5 T.C 01

Walkerforrampsandroll 5

6

SuitableWalkersclimbingstairs 8

Walkerwiththeabilitytoadjusttotheambient

temperature(frostandheat)12

WalkerwithapathmemoryforAlzheimer's 15

Walkerwithadjustablephysicalcharacteristicsofthe

patient19

Walkerjointtothebodylikeanarmour 25

6 B.S

LockingWalkertothebodyofthepatient 2

13

DesignforfoldingandmultipleuseofWalker 4

Addabrakingsystems 6

Addingaccessorieslikebeeps,lights… 8

Walkerwithshoppingbags 10

Walkerwithfoldableumbrella 12

Walkerwiththegridframe 14

FoldingWalkerfortravel 16

Walkerwithwatertankanddrugbox 18

WalkerframewithheatandcoldInsulator 20

Walkerframewithaerodynamicdesign 22

Walkerframewithsmallchair 24

189

PortableWalkerforthejourney 26

6 T.C 01

GuidedWalkerwithtraineddogs 9

10

Walkerwithatelescopicsystemtoadjust 12

Walkerwiththeabilitytotransformintoawheelchairand

viceversa16

Walkerattachedtothelegswithnohandles 19

VersatilerehabilitationWalker 21

SkatingWalker 23

Walkerfornormalwalkingontheice 24

Walkerforbath 27

InterchangeableWalkerframe 29

ElectricWalker 30

7 B.S

Addlightingequipment 2

14

FoldableWalker 3

Walkertripodwithwheels(twofrontwheelsclosetoeach

other)5

Walkerwithawnings 7

WalkerwithGPS 8

Withleveldetectionsensorandobstacles 11

Usingthecompositematerialstolightenthebodyweight 14

Walkerwithtelescopichandle 16

UsingtheLEDsforbetterillumination 18

Addthemp3playerforentertainment 19

Addasmalltableforstudy 22

Walkersolarbattery 25

TheabilityofgettinghitbythebodyframeofWalker 28

Walkerwitharemovableplasticendlegs 30

7 T.C 01

Walkersemi‐automaticforliftingfromthebedandchairs 8

8

ErgonomicWalkersettingwithbody 12

Walkerwithleaningback 16

WalkerExo‐skeleton 19

Walkerself‐drivingwithengine 22

Walkerflexiblewithazigzagmotion 26

WithpedaltosettheWalkerlegsandhandles 29

190

7.4.6 Experiment II: Group B


Time of

generating

ideas

(Out of 30 min)

no. of

Ideas

1 B.S

Madebycarbonfiberandfolding 4

10

Withheatedhandlesandwristhook 6

Capableofclimbingstairs 7

Withvitalsignsmonitoring 9

Addsensorsforblindperson 14

Addrechargeablelithiumbatteryorsolar 18

Seattorelaxduringwalking 20

RadioandMP3player 22

Watertankfordrinkingandtakingdrugs 26

ConvertibilitytotheLuge 30

1 T.C 04

Usingslidersystemforwalkingonallsurfaces 5

8

Usesmallelectromotorandsensorandtosetfourlegs

independently7

Helpingthepatienttositbyusingfoldingsystem 8

Helpingusertorestthroughtelescopicfoldingsystemofseat

(3to4pieces)10

Helptomoveatdifferentsurfacesbyusingnewmaterialfor

Walkerlegs14

UsingAir‐bagineachWalkerlegs 17

Adjusttheheightofuser'shandusingtheanglesensor 20

Walkersizesettingusingmotionsensors 25

2 B.S

AddMP3andLCD 2

17

Installtheelectricalgeneratorforpowersupply 3

Wheelsforeasymovement 4

FoldableWalkerforsittingandstand 5

Installthefanforcooling 7

SpringWalkertoreduceimpact 8

Lightsforuseatnight 10

Addaswivelchairfortherest 12

Addanalarmconnectedtoamobilephoneincaseoffalling 13


AddasmallboxinfrontofWalkertoputthenecessary

equipment19

Addanelectronicdevicetoinformeatthedrug 20

Newdesignoffrontlegstouseinthestairs 22

Walkerwithsensorstodetectobstaclesfortheelderlypeople 25

Addabaseandwheelforusing 26

Walkerlegsattachedtotheknees 27

Withmassagers 29

191

2 T.C 04

Possibilitytoadjustthelegswithsurfaces 8

11

Walkerhandlesstayparallelwithfloorandbodystay

balanced9

Usingporousmaterialsforup‐rightWalker 11

Forincreasemaneuverabilityandbalanceofuser,changethe

steeringwheelfromfronttoback(Trolley)13

Whenthearmcollectedusingahandleveronthehandle 15

Nestinglegsforrestingtime 16

Foldingplatformforsitting 17

Improvednavigationinthedarkenvironmentbyusing

materialsofphotocell20

Addleverlocksinthefrontarmandcontrolledbyahandlefor

stairs23

Usingflexiblepadsonthebottomoflegsdependsonground 25


3 B.S

Addsupportsfordisabledpeople 1

11

Addpedometer 2

FoldableWalker 4

EquippedWalkerwithanairbagtopreventfalls 6

Theabilitytotransformfromfourtotwolegs 8

Useoflightermaterials 13

Useheatandcoldinsulation 17

Insteadoffourlegsusingaflatplateforunevensurfaces 21

Usingreflectivematerial 25

Addfoldableseat 27

Walkerwithwheelinfrontlegsandfrictionbreaksforrear

legs29

3 T.C 04

AddGPSinordertoidentifyrightdirection 10

4

Usingphosphorusmaterial(luminous)inWalkerframeto

avoidcollisionswithvehiclesatnight16

Themodularcomponentsinsteadofintegratedcomponents

foreasyrepairthedamagedsection22

Foldingseatforsittinginanemergencycase 26

4 B.S

Addahandlebikeforeasyuse 3

17

EmbedaseatforWalker 6

Embedwheelsformorespeedandbrakehandle 7

Addlights,hornandbasket 8


FoldableWalker 12

Addsafetybelt 13

Changeabletoascooter 14

Changeabletoacane 15

RadioandLCD 16

Addsidemirrorstoavoidanaccidentwithamotorbikefrom

behind17

Usingslideheightadjustmentfordifferentpeople 19

192

Addfoldablesmalltable 20

DesignlikeTwinstrollers 23

Addtheumbrella 24

AddGPSforAlzheimer's 25

ElectricWalkerwithfootrestforstairs 30

4 T.C 04

Usingthermochromics’materials 6

10

Usingphotochromicmaterials 7

TheuseofshapememoryalloySMAtoimprovetheassembly

anddisassemblyWalker8

SmartWalkertodetectobstacles 10

Addacamerawithazoomin‐zoomoutcapabilityforelderly

peoplewithlowvision12

AddaWalkerwheelwithadjustablespeed 15

Walkertoclimbstairs(chainchangebike) 17

Forcrossingtheslipperysurfaces,usingspecialWalkerlegs 21

Apowersupplyandcontrolequipmentfordisabledpeople 25

Addamemoryforstoringinformationofpeoplewith

Alzheimer'sdisease30

5 B.S

InstallGPSandconnecttopoliceandhospital 3

5

Asapantsformechanicalreinforcementofmuscleswhile

walking6

SuchasSolomoncarpetbutflexibleandelectronic 16

Usesuchasextraequipmentlikebeds,wheelchairs,crutches,

fishingholder20

Suchasabackpacksthatreducetheweight 27

5 T.C 04

UsingelasticmaterialsforWalkerframe 9

5

Usinghigh‐strengthandlightweightmaterialsfor

Compensationweight15

UsingspringforWalkerlegsforincreasingbettermoving

forward18

Smartmaterialsforcoolingandheatinguser 20

Telescopicfrontandrearlegstoenablemaneuverupand

downonslopes25

6 B.S

FoldableWalker 3

9

Useoflightandunbreakablematerials 5

UsingantiactivateX‐RAYdevicematerials 7

UsingNon‐metallicmaterialforlegstoreduceweight 10

Addelectroniccircuitandthermalsensors 14

Adddataprocessorofheartbeat 17

AddGPSandnavigationfacilities 20

Improvethesystemofwheelsandengineinstallation 24

Walkerwithflexibilityfordifferentsurfaces 28

6 T.C 04

TheuseoflightweightmaterialstoreduceweightWalker 5

7Walkerwithtwoseparatelegstohelpknee 8

Walkerwithgelatinousmaterialthatdoesnotinjurewhen

userfall10

193

SmartWalkerwiththedrugprogram 14

Walkerwithresiliencematerialsforbetterfeelingofuser 20

Walkerlegswithspecialmaterialsforhousecleaning 25

Walkerwithfoldableandintegratedframe 28

7 B.S

Walker'shandlewithsensorstosenseenergyofuserto

preventtheuserfalling1

19

Walkerheightadjustablelegsfordifferentuser 2

Walkerwithasystem(anelectriclift)tomakelighterweight

feelduringliftingWalkertouser3

SmartWalkerwithautomaticsurfacedetectionforadjusting

thelevelofconnectionoflegstoeachsurfaces5

Walkerrobotwithflexiblelegsforclimbingstairs 8

Walkerwiththevitalsigns(bloodpressure,pulse,...) 10

Walkerwithawarningsystemtothosearoundtheuseratthe

timeoffallingorimbalance12

Foldingandassemblies’Walkerforeasytransport 13

WalkerwiththewarningandalerttheuserifWalkerstands

onthesurfaceisunsuitable15

Walkerwithlightingsystemfornightuseanddarkplaces 17

Walkerwiththeabilitytoadjustallaspectsoflegsandrodsto

adapttodifferentpeople(suchaschildrenandadults)18

Walkerwithaprotectiveumbrella(sun,rainandsnow) 20

Walkerwithabilitytoestimatethedistancetothetarget,for

example,acameraisinstalledonWalker22

WalkerequippedwithaGPStolocate 23

Walkerwithincreasingabilityofhandsforpeoplewithweak

hands25

Walkerwithheatingandcoolingsystems 26

Walkerequippedwithafoldingseatforthenecessaryuser

tiredness27

Walkerwithasystemthatwillmaintainbalanceduring

walking28

Walkerwithacollisionwarningsystemtobarrier 30

7 T.C 04

Useofnewmaterialsformorecomfortgriphandleandpreventslippingonthehandle

5

9

Withahandleintheseat(Walkerwithseat)thatpushuptheseattomoveandtohelpuserstand

6

AddaspecialhandletohelpgetupfromthegroundanduseaWalker

8

Ribbedwheeldesignforwinterandsummertopreventslippingondifferentsurfacessuchasmountainbike

10

Walkerframewithwirematerialstohelpbendingandfoldingduringwalkingandkeepstrength

15

AddAirbagsforthecollision 20

AddaT‐shapedbasetothefrontoftheWalker(tripodismorestableandeasytouse)

22

Usingpolymericmaterialsforlightnessandpreventrusting 25

Walkerwithplasticarmsandlegsandfrontframeonaccordionshape

28

194

7.4.7 Experiment II: Group C

Team Treatment Idea descriptions Time of generating ideas

(Out of 30 min) no. of Ideas

1 B.S

UsingsomewheelsintheformofaU‐

shapedattheendoflegs1

10

Walkerwithdetachableseat 5

Walkerforcyclingandwalkingatthesame

time7

Sensortoadjusttheheightandsize 10

FoldingsofasWalker 15

Walkerwithslidingsystemtoopenand

close18

Walkerwithroughendlegsforslippery

surfaces21

WalkerthatcanbeusedtoSportsactivities 24

Walkerwithholderintheback 27

Walkerwithsturdymaterialand

lightweight30

1 T.C 02

MultilevelWalkerfordifferentmodes 9

6

Walkerwithreplaceablelegsindifferent

surfaces14

Walkerwithtripod 17

Electrichandlesforliftingandstanding

patient22

Walkerwithdetachablehandlesforusing

asacane26

Walkerwithpuzzlesystemforchangesin

differentshapeofseating30

2 B.S

Puttingthecart 1

14

WalkerwithGPStofindpath 3

Walkerwithchangeableanglesaccording

tothebodyofpatient5

Addlightingequipment 7

Installtheelectricalgeneratoronthelegs

tosupplyelectricityduringwalking9

Automaticwashingsystem 11

Walkerwithtents 12

Walkerwithcustomizablehandlesto

armpit16

Connectiontothebodyofthepatient 19

Walkerwithhangingseattohelpthe

patientwithweakfeet21

Walkerwithscreentoseetheroute 24

195

FoldableWalkerasabackpack 26

Walkerwithfrontlegsfixedinfrontand

rearlegswithwheelsinUshape28

Walkerwithbumpertoavoidcollision 30

2 T.C 02

Walkerwithfootmassager 6

8

Walkerwithglassandwipers 7

WalkerwithHeatingandcoolingsystems 10

Walkerwiththeabilitytobecome

Motorcycles15

ThecombinationoftreadmillandWalker 17

Walkerwiththelegsself‐settingforthebus

ride20

UseofspringinthebodyofWalker 24

Walkerlightweightpolymer 27

3 B.S

Walkerstoclimbtheramp 3

11

Walkerwithhandlestabilitycontrol 5

FoldableWalkerforgettingonthecar(like

theblindpeople)8

Connecttotheupperbodyinsteadof

connectingtohandsandarmstoreducethe

strainonhandsandfatigue

10

Walkermadebycarbonfiber 13

GridandstyleWalker 17

Walkerwithbrakeandwheels 19

Walkerforwalkingonthesnow 23

Walkerforskiing 24

Equippedwithsafetybelts 28

Walkerwithmusicplayer 30

3 T.C 02

Walkerwithreplaceableveneer 9

7

Walkerwithassemblyanddisassembly

capability11

Walkerforthebeach 16

Walkerhandleswithcapabilitytousefor

fishingrod19

Walkerwalkingstick 22

Walkerwithhangingseatandfolding 25

Healthyreplaceablehandles 29

4 B.S

WheeledWalkertomoveeasier 1

8

Walkerpartiallyretractable 3

Walkerforschoolwithbackrestandopen

front8

Walkerself‐sizeforkids 10

Addseatforemergencies 13

Walkerwithinterchangeablebody

especiallyatthebaseandhandles16

Withlightsandhorn 21

196

Walkerwithabalancesystemduring

walking25

4 T.C 02

Walkerfortherapy 7

4Walkerforwalkingonthegrass 12

Patient'sbodyholderWalker 18

WearableWalker 24

5 B.S

Walkerwithaseat 2

18

Guidingthepatientswiththehandlesfrom

sides3

Drugwarningsystem 6

Withreflectiveandphosphoricframe 10

Theuseofnewmaterialssuchas

nanotechnologyandcompositestoreduce

weightandreduceenvironmentalissues

12

WithAirbag 15

Interchangeablebase 17

Maintainbalancewhiledoinghousehold

choreswithextrahandle19

ProtectiveWalkerfromwind,rainandsun 20

Withtelescopicbase 22

MotorizedWalkertoeasymove 24

Slidingsystemsforclosing 25

OpenfrontbaseWalker 26

Walkerfordisabledsports 27

Walkerstoclimbthestairs 27

Walkercombinationswheelchairfor

disabledpeople28

Addlightsformovingatnight 29

Attachingthesmallbasket 30

5 T.C 02

DesignaWalkertomoveinbothsittingand

standingposition10

8

WalkerBike 12

Walkerwithtallhandlestothearmpitslike

crutches15

Walkerwithsemi‐uprightpositionfor

moving19

WalkerDouble(withnurse) 21

WalkerchairsandFoldingsofas 23

WalkerwithHybridEngine 26

Walkerwithsensingcamera 29

6 B.S

Designforuseonstairs 1

14

Addwalkie‐talkiesshort‐range

communicationssystem2

Addfirstaidboxes 5

Walkerwiththreewheels 7

Walkerwithalarms(Voice,vibration) 10

197

Lightweightcompositebody 13

Walkerwithaspringybaseforuneven

surfaces17

Hands‐freeaudioandvideocommunication

onWalker19

Walkerwithpedometerandtimertohelp

treatmentprogram21

Connectingtothefeetinsteadofhands 24

Withaparttoaddseattorelax 25

Withanti‐theftsystem 26

Addaholderforshopping 28

Useheatandcoldinsulationmaterialsin

bodyframe30

6 T.C 02

Walkercalculatethenumberofstepsand

calories6

10

Walkerforcarryingheavyloads,suchas

lifts9

Walkerforusedinwatertherapy 11

Walkeropenedthefrontframetoaccess

thetable17

ConvertibleWalkertothescooter 20

Walkerwithelasticbodytogettheblowsof

theroad22

Walkerstairliftsautomatic 24

MultifunctionalWalker,crutches,

wheelchair,etc.26

Walkerwithattachedwheelsthegroundon

theimbalancedposition28

Walkerforwalkingandsimultaneously

rehabilitationcapability30

7 B.S

WheeledWalkerwithseat 2

9

FoldingWalker 3

Withcoolingsystem 5

Walkerfoldedwithfragmentationstructure 7

Addlocatingandpositioningsystemswith

GPS10

Withspecialseatrest 14

Walkerwithcontrolofvitalsystem 17

Addthecartandmobilephone 20

Addumbrellasorcanopies 24

7 T.C 02

Exoskeletons 8

5

Walkerwithmagneticcapabilityandgroundclearance

14

AppropriateWalkertouseforalllevelsandsurfaces

17

Walkerwithmemoryandrouting 21

Walkerwithanti‐theftalarmbyannouncingthepolicestation

26

198

7.4.8 Experiment II: Group D

Team Treatment Idea descriptions Time of generating ideas

(Out of 30 min)

no. of

Ideas

1 B.S

FoldableWalker 2

13

Theself‐assemblyanddisassemblyWalker 6

Withtelescopiclegs 8

Withdisposablecoatedhandles 11

Theabilitytoinstallindifferentplacesfor

differentpurposes13

Walkerforchildren 16

Walkerlikego‐kart 18

Walkerwithhelpedbarsfrombehindfor

children19

Withthecontrollerabovethechild 22

WiththetransmitteronaWalkertofindthe

child24

Walkerwithcompositematerial 25

Walkerwithfallprotection 28

Addairbagtoprotectpatient 30

1 T.C 03

AdjustableWalkermotionwiththepatient's

head5

9

Walkerforusingintheallsurfaces 8

Semi‐automaticWalkerwithasteeringwheel

insteadofaU‐shapedhandle9

Walkerwithsemi‐seatedpositionwithoneleg

andmovingonwheels13

Walkerwithfoldingfrontlegsonthestairsfor

goingupthestairs16

Walkerasaclothingwithminimumvisibility 19

Walkerwhichholdthepatientbykeepinghis

shoulder22

HandlesandleverstosteertheWalker 26

Walkerframeandwheelsmadebyhollow

materialforlightness28

2 B.S

Walkerwithlightseatpendant 3

8

CircularWalkerinsteadofsquareone 6

TriangularWalkerinsteadofsquareone 8

Addastudytable 11

Addanalarmtonotifythepatient'sneeds 14

Addasmallbaseholderforbalance 18

Walkerwithalonghandletoarmpit 23

Walkerforstairclimbing 27

199

2 T.C 03

Usingalevertowalkbyhandsforhelping

peoplewithweakfeet10

7

AdequateWalkertotheangleofgait 14

Walkerwithaflexiblebodyinmotion 17

Bodywithspringymaterial 21

ThebodymadefromNanoforcleaningand

lightness23

Intelligentbodytotheambientforadjusting

thetemperature26

Automaticadjustmentdimensionswith

physicalconditions28

3 B.S

Walkerwithaspecialframeforgettinghit 1

12

Walkerwithahandleonthesidetosupport

user3

AddReflective 4

Addlightsforusingatnight 6

Addfallwarningsystem 9

Addthealarmfortakingdrugs 13

AddFoldingchairs 16

Addabrakeandabasket 17

AddGPS 19

Addsnowchainsforwheels 23

Addawning 27

AdddifferentholeintheWalkerframeto

adjustbyuser30

3 T.C 03

WithbootsinsteadofWalkerlegsandgloves

insteadofWalkerhandles6

9

Walkervariableendlegsondifferentsurfaces 9

Addlateralhandlesforbalance 12

Addadditionalfoldablelegsforuserbalance 15

Semi‐automaticWalkertoguidetheelderly

peopletodestination18

Walkercapableoffloatingonwater 21

Walkerwithlifting 24

WalkerRobot 27

Walkerwithwheelslikearmytanks 30

4 B.S

Withthefoldabilityforhandleandlegs 2

13

Convertibletoseat 4

Convertibletobed 5

Walkerwithawnings 7

Rockingchairwithwheels 10

Walkerwithashoppingcart 11

Abilitytoaddsensorstodetectobstacles 14

Addhornandlights 16

Adddeviceformeasuringheartrate 18

AddPedometer 20

200

Addoxygen‐masksystemforemergencies 22

Walkerwithheightsensor 26

Walkerwithmedicalalarmsystem 29

4 T.C 03

Withtheabilitytobalancethebodyonuneven

surfaces9

7

Withtheabilitytobalancethebodyinthepool

andwatertreatment13

ElectricWalkerwithsolarcells 17

Walkermultiuser 19

Walkerself‐controlwithouttheneedfor

guidance21

Addaudioandvideosystems 25

Addingheatandcoldinsulationtohandles 30

5 B.S

Addreflectivelightsandhornandcart 1

9

Walkerfordifferentsizes 4

Addbackrest 7

Uselikescooters 10

Walkerwithhandleslikegloves 13

Fittothepatient'shandsandfeet 18

Self‐navigationWalker 22

Connecttothehealthcentersandhome 25

Witharemotecontrol 27

5 T.C 03

Addanantifreezesolutiontouseinicy

surfaces8

4

Automaticsystemaccordingtothesurfaces

(withorwithoutwheel)15

Withanelasticbody 20

Newbodydesignwithdifferentlegstobalance

patient26

6 B.S

Walkerformovingstairs 1

14

Addasmallboxforputtingdownthestuffof

patient2

Addthespeedcontrol 4

Balancesensortopreventfalling 6

Walkerwithwheelsandbrakeknob 9

Automaticelectricmotor 10


Guidingwithasteeringleverlikeanaircraft 15

WithGPS 17

Obstacledetectionsensor 19

Theabilitytobecomecrutches 23

Walkerwithhandlesanti‐sweat 25

Walkerwithspecificlightweightmaterial 27

Withreplacementpartsforchangingbyuser 30

6 T.C 03 Withergonomichandlesforbody 5

5Designdifferentforusingindifferentsurfaces 9

201

Walkerfortheelderlyandbalancethebody

fromabove16

Electricmotorsforliftingpatientsfrombeds

andchairs19

Forsettingsize,usethefingerprint 25

7 B.S

RadioandGPSforAlzheimer's 2

17

Designhandleslikebikeswithbrake 4

Withalarmsystem 6

Designfishnetframeandstrong 8

AssembledanddisassembledlikeIkeaproduct 10

Withtelescopicframe 13

Withfoldablelegs 14

Controlbodyfromthebackofpatient 16

AddaseatforWalker 19

Withheatingandcoolingappliances 20

Changeabletothescootersshape 23

MotorizedWalker 25

Limitingthespeedofthewheelswiththe

program26

Exoskeletons 27

Ergonomicdesignfortheplacementofthe

spine28

Withlonghandleforleaningonthearm 29

Walkerwithwebcamtocallhome 30

7 T.C 03

Walkerwithspringybody 5

10

Walkerwithsuspensionsystem 7

Walkerwithpedalforadjustingdimensions 9

Walkerwithsoundamplificationdevices 12

Walkerwithinsulationcoating 16

Walkerwithanti‐sliplegs 18

ThescooterWalkerusestowalkbyleaning

forwardandviceversa21

Walkerwiththealarminunbalancedsituation 24

Walkerself‐guidedautomatic 27

Walkerwithlevertosteerleftandright 30

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