a study of patent numbers forecasting by linear regression ... · patent analysis itself has become...

International Journal of Arts and Commerce Vol. 3 No. 8 October, 2014

207

A study of patent numbers forecasting by linear regression on cloud

storage technology

Liu, Kuotsan

Associate Professor

Graduate Institute of Patent

National Taiwan University of Science and Technology

Chen, Yingching

Graduate student of master’s degree

Graduate Institute of Patent

National Taiwan University of Science and Technology

Abstract

A patent numbers forecasting by linear regression is presented in this paper. A popular and short lifecycle

software technology, sharing link on cloud storage, was selected to demonstrate the research and results on

the main patentee diagram and technology-function matrix. The result shows that the linear model based on

numbers of inventors has high coefficient of determination. For a research and development proposal of a

company, how many patents should file could be easily determined by the forecasting patentees diagram and

the forecasting technology-function matrix.

Keyword：patent map, patent analysis, patent forecasting, cloud storage.

1. Introduction

Patentsare powerful to stop competitorsenter claimed scopesbased on their exclusive rights.A company owns

a big amount of patents is normal in modern industry. Famous companies,for example,International Business

Machines Corporation (IBM) and MicrosoftCorporation(Microsoft), each owns more than 100 thousands

patents. To accumulate sufficient number of patents and occupy a higher rank ofmain patentees in special

technical field is important to get a large marketshare.

It is necessary to make patent analysis before a research and development (R&D) project to guarantee no

International Journal of Arts and Commerce ISSN 1929-7106 www.ijac.org.uk

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block by competitors’ patents. However, this work is difficult and complex because of millions of patents in

database. There are 2.35 millions of patent publications in 2012, 9.2% growth on 2011, all over the world by

statistics of World Intellectual Property Office. Patent analysis itself has become a professional and research

field because its complexity and difficulty.

In patent analysis, patent maps are useful tools to visualize the distribution of patents, monitor the trend of

technological changes, infer the strategy of patent portfolios, and compare competitors by statistical charts or

diagrams. Patent maps shows macroscopic view of patents, and offer a company to determine the direction

of R&D. For example, a main patentees diagram shows the main competitors and their patent numbers, a

technology-function matrixshows the patent density on technical problems and solutions. All patent maps

give the views of patent on the drawing date, but the R&D objective setup is on a couple of years later. A

forecasting patent map is more helpful to determine the budget and the intended number of patent applications

for the R&D proposal.

This paper focus on patent numbers forecasting, a linear regression model was employed to do this work.

A popular software technology, sharing link on cloud storage, was selected to demonstrate the research and

results on the main patentee diagram and technology-function matrix.

Cloud storage has become the spotlight on IT industry. Huge amount of information is keeping

challenging the load of computer system, companies put their data in cloud storages instead of keep in hand,

files sharing link is necessary for them. IT industry is special in its short life time, a new company may

accumulate enough patents and become a main patentee in short term. Patent numbers forecasting would be

more important and usefulin this technology.

2. Methodology and data

2.1 Patent pool by search queries

The technical topicemployed for this study is sharing link in cloud storage. Search queries on US patent

publication database were in the following(search date: Dec.26,2013):

S1= (shar* adj3 (link* or URL or URI or hyperlink*)).DSC. 12,154 hits

S2=S1 not(vehicle* or GPS or sensor*).DSC. 8,975 hits

S3=S2 and (707* or 709*).UCM. 1,996 hits

S4=S3 not (adverti*).DSC. 1,423 hits

Where USPC(United States Patent Classification)707 is data processing: database, data mining, and file

management or data structures, UPC709 is multicomputer data transferring. Both are the most important

classesin cloud storage technology.


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2.2 An overview of main patentees and time evolution of patent publications

Table 1 is the top 20 patentees in the technical scope under the search query S4. Microsoft Corporation and

IBMoccupy top two in software technology, and own more than 50% of total patents. Google, Yahoo,

Facebook are famous companies in the world, but large gap behind top two. The rightest column shows

percentages of patent numbers after 2006 comparing to total. It shows that 11 patentees entered this technical

field after 2006, so we further limited the pool after 2006, which is the starting year to form

technology-function matrices in this paper.

Table 1 patent numbers of top 20 patentees

rank Total After

2006

New

rank

Percentage

1 Microsoft Corporation 121 82 1 67.8%

2 International Business Machines Corporation 119 51 2 42.9%

3 Cisco Technology, Inc. 25 15 5 60.0%

4 Google Inc. 17 16 4 94.1%

5 PatentVC Ltd. 16 16 4 100.0%

6 Yahoo! Inc. 14 12 8 85.7%

7 Salesforce.com, Inc. 13 13 6 100.0%

9 Nortel Networks Limited 12 12 8 100.0%

9 Fujitsu Limited 12 7 16 58.3%

11 ACCENTURE GLOBAL SERVICES LIMITED 10 7 16 70.0%

11 AOL Inc. 10 10 9 100.0%

13 Sprint Communications Company L.P. 9 9 10 100.0%

13 Juniper Networks, Inc. 9 8 12 88.9%

14 NetApp, Inc. 8 8 12 100.0%

17 NEC CORPORATION 7 5 19 71.4%

17 Nokia Corporation 7 7 16 100.0%

17 SWsoft Holdings, Ltd. 7 7 16 100.0%

19 FACEBOOK, INC. 5 5 19 100.0%

19 Actifio 5 5 19 100.0%

20 DROPBOX, INC. 3 3 20 100.0%

Fig 1 is time evolution of patent publications, from 2006 to 2013. Light bubbles are patent numbers of data

processing,dark bubbles are multicomputer data transferring. Both are near linear increasing, multicomputer

data transferring has a higher positive slope.

International Journal of Arts and Comme

Fig.1 Time evolution of patent publica

2.3 Linear regression model

The linear model based on numbers of i

yi=α⋅xi + β (1)

whereyiis numbers of patent publication

coefficients of linear model.

3. Technology-function matrices for tw

Fig.2 is a technology function matrix

technologies, or technical solutions on

database maintenance, collaborative doc

Fig.3 is a technology function matrix

including:distributed data processing, c

memory, remote data accessing, n

computer-to-computer(c-to-c) session/co

routing.

45 41

57

69

57

76

0

20

40

60

80

100

120

140

2005 2006 2007 2008 2009

The numbers of patent p

merce ISSN 1929-7106

cations

f inventors was taken to make patent numbers fore

ion on year i, xi is numbers of inventor on year

two segmentson 2013

ix at 2013for data processing. We employed US

n x-axis, including: database and file access, dat

ocument database and workflow, data integrity, a

x at 2013 for multicomputer data transferring, i

computer conferencing, multicomputer data tra

network computer configuring, computer

connection establishing, c-to-c protocol implem

44

5948 51

63

72 6975

88

119

2009 2010 2011 2012 2013 2014

t publications from 2006 to 2012

www.ijac.org.uk

recasting in this paper.

ar i, α and β are two

SPC subclasses to be

atabase design, file or

, and file management.

, its USPC subclasses

transferring via shared

network managing,

lementing, c-to-c data

User

Typewritten Text

210


211

Fig.2 A technology-function matrix for data processing on 2013

We took 10% samples randomly, to get the problems of technological development after manual reading.

The problems on y-axis for both matrices are improved efficiency, providing a flexible system, simplify

operations, enhance security, tracking or monitor, enhanced system consistency and reliability. The search

query of each problem could be organized at the same time. For example, the query of improved efficiency is

(potim* or efficien* or effective* or (improve* near3(performance* or congest*)) or accelera*).DSC.

We got 653 patent publications under this query, and got the numbers of publications for USPC subclasses or

nodes on matrix. One publication may drop in more than one subclass.


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Fig.3 A technology-function matrix for multicomputer data transferring on 2013

In order to check the reliability of the matrix, we took 10% samples randomly again for each query, and read

samples manually to check whether their technical problems are consistent with the query goal. The correct

percentages for each problem are 75.4%, 78.4%, 70.6%,57.8%, 70.7%,66.0%. The total quality fall in

62.18% to 77.46% for 95% confidence interval.On the average, 70% quality may be not excellent, but 90%

labor cost saving is an important merit.

Fig.2 shows that in data processing, ‘database and file access’ has become the popular tehchnology for each

technical problems, ‘collaborative document database and workflow’ has not yet developed. Fig.3 shows that

in multicomputer data transferring, ‘computer network managing’ was the popular technology.If patent

numbers in ‘database and file access’or ‘computer network managing’are not detail enough, we could employ

UPC lower level subclasses to spread x-axis and get publication numbers on each nodes quickly under this

method.

The technology-function matrix shows patent numbers on all problem-solution nodes clearly. If one

company determines the topics of R&D under the matrix, the next question would be how many patent

applications should file to become a main patentee? It needs make patent numbers forecasting to answer this

question.


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4. Patent numbers forecasting by linear regressions

4.1 Linear regressions of three patentees

Patent numbers forecasting for every patentee can be got by using the linear regression model of formula (1).

Three main patentees, Microsoft, IBM, and Yahoo were selected to demonstrate the results and check their

reliability.

Fig.4 is the linear regression diagram onMicrosoft. We put the numbers of inventors and patent

publications from 1999 to 2013 in formula (1) to get α=0.2898, β= -0.3939. In this linear model, the

coefficient of determination R2=0.9342, R square indicates how well data points fit a statistical model, 0≤R

2≤1,

the higher value of R square, the stronger explanation of the linear model. The R square value shows a high

reliability.

Fig.4 Linear regression diagram on patent numbers of Microsoft

Fig.5 is the linear regression diagram of IBM. The coefficients in formula (1) are determined by the same

method ,α=0.3612, β=-0.3478 for Microsoft. In this linear model, the coefficient of determination

R2=0.9133, it also shows a high reliability.

Sample Predictive Value


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Fig.5 Linear regression diagram on patent numbers of IBM

Fig. 6 is the linear regression diagram of Yahoo. The coefficients in formula (1) are α=0.3512, β=0.1045.

In this linear model, the coefficient of determination R2=0.9451, which is high reliability again.

Fig. 6 Linear regression diagram on patent numbers of Yahoo

4.2 Patent numbers forecasting in the next three years

The patent numbers forecasting for thirteen patentees in the next three years can be easily calculated after the

coefficients had been determined. We regarded the average number of inventors in the latest five years as xi to



International Journal of Arts and Comme

get the number of patents yi. Fig.7 is pate

In Fig.7, there would be 8.82,9.05,8

publications by IBM. It shows that the

However, the others are not difficult

fourth patentee, less than 20. If one com

least 9 applications in the next three ye

applications. The R&D budget can be d

get a higher rank by the forecasting diag

Fig.7 patent publication forecasting in

4.3Patent numbers forecasting on inte

A forecast technology function matrix

forecasting matrix, three interested node

0 20

Microsoft Corporation

IBM

Google

Cisco

salesforce.com

Yahoo

AOL Inc.

Sprint Comm.

Juniper Networks

ACCENTURE GLOBAL

NEC CORPORATION

FACEBOOK

DROPBOX

51

16

15

13

12

10

9

8

7

5

5

3

Estimated Applicants dist

2006~2013 Publication

merce Vol. 3 No. 8

atent publication forecasting in 2014-2016, and to

,8.04 publications in the next 3 years by Micro

e top two patentees are difficult to surpass.

lt to catch up,the third patentee will accumulate a

ompany intends to become a top ten patentee in 20

years. If one wishes to become a top five paten

determined under the number of patent applicatio

agram.

in 2014-2016

terested technology and function

ix can be formed by using the linear model of form

des were selected to illustrate the results.

40 60 80 100

82

51

8.82

6.80

9.05

6.65

8.04

7.16

ants distribution of U.S. publications in 2016

blications estimated 2014 estimated 2015 estimated 2

October, 2014

total patents in 2016.

crosoft, 6.80,6.65,7.16

e approximately20, the

2016, he should file at

tentee, it will need 18

tions. One patentee can

ormula (1). Fig. 8 is a

100

8.04

imated 2016

User

Typewritten Text

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216

Fig.8 A forecast technology-function matrix

In Fig.8, the first node is ‘database and file access’ to solve ‘improved efficiency’ problems. On this node,

there will be 19, 20, 19 patents in the next three years, and reach 212 on 2016. The second node is ‘computer

conferencing’ on ‘tracking or monitor,’ this node has a higher increasing rate, will reach 126 on 2016. The

third node is ‘computer network managing’ on ‘enhance security,’ this node has a lower increasing rate, 66

patents on 2016.

The other nodes are not difficult to forecast by the same model. The forecasting matrices year by year

visualize not only patent densities but also patent increasing rates. A quick growing up bubble indicates hot

topic of technology and function.

5. Conclusions

Patent numbers forecasting is important for research and development. A simple but high reliable linear

regression method was illustrated in this paper. It is very helpful to determine how many patent applications

should file in a couple of years, and further determine the R&D budget.

A linear regression model based on the numbers of inventors on main patentees predicts the new ranking in

the next years.

The linear regression model applies to the nodes of technology-function matrix to get a forecast matrix in the

future. Some low density nodes in this year may grow up to high density in the next years. The forecast

matrix can low down the risk of R&D comparing to rely only on a present matrix.


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The IT industry is characterized in its short lifetime cycle. Both famous and unknown companies usually

appear in the main patentee diagram. It don’t need a long term to become a main patentee even for a nameless

company. Patent numbers forecasting are more powerful and necessary in this technical field.

Acknowledgement

This study is conducted under the “Cloud computing systems and software development project (3/3)” of

the Institute for Information Industry which is subsidized by the Ministry of Economy Affairs of the

Republic of China.

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Trappeya, Charles V., Hsin-Ying Wua, FatanehTaghaboni-Duttab, Amy J.C. Trappeyc(2011), Using patent

data for technology forecasting: China RFID patent analysis, Advanced Engineering Informatics,

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Xiea, Zhongquan, KumikoMiyazahia(2013), Evaluating the effectiveness of keyword search strategy for

patent identification, World Patent Information, 35(1), pp.20-30.

Liu, Kuotsan, Yen, Yunxi(2013), A quick approach to get a technology-function matrix for an interested

technical topic of patents. International Journal of Arts and Commerce, 2(6),pp.85-96.

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