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The 1st Global Tech Mining Conference, Atlanta, USA
Analyzing Technology Evolution of Graphene Sensor Based on Patent Documents
Fang Shu1, Hu Zhengyin1, Pang Hongshen1, Zhang Xian1
1Chengdu Branch of the National Science Library, Chinese Academy of Sciences, Chengdu, 610041, China
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OUTLINE
Backgrounds
Methods
Empirical analysis (graphene sensor)
Conclusion and Further Works
Acknowledgement
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Backgrounds
Our Aims:
Classify the patents by technology evolution trees
Try to find emerging technology
Help to find the important patents
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Backgrounds
Young’s Work:
Young, Jong & Sang (2008) proposed a method of patent analysis for forecasting emerging technology, including:
building a set of patent documents;
extracting technology keywords;
clustering the patent documents;
forming a semantic network of technology keywords;
drawing technology evolution map.
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Backgrounds
Advantage of Young’s Method
simple operation ;
clear interpretation of the content ;
focusing on technical points ;
reflect the evolution of related technology clearly.
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Backgrounds
Disadvantage of Young’s Method
suspicion of circular reasoning ;
Ignoring distribution feature and semantic relations
of items;
k-Means clustering is not good for small sample.
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Methods
Our improved method:
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Methods
Our improved method:
Firstly, build a set of patent documents;
Secondly, extract keywords of technology;
Thirdly, cluster the patent documents;
This is the core improvement.
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Methods
Our clustering method:
Considering the distribution feature of patent classifications:
fij: the frequency of feature item i appears in the
document j; N:number of all documents in the collection; ni: the number of documents including feature item i.
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Methods
Our clustering method:
Considering the semantic relations between patent classifications:
L: the total number of feature items in the document j;
θim : semantic similarity value between feature item i and other feature item m.
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Methods
Our improved method:
Fourthly, form semantic network of keywords;
Lastly, draw technology evolution map.
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Empirical analysis
Firstly, build a set of patent documents. Retrieval policy :
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Empirical analysis
Secondly, extract keywords of technology.(see table 2).
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Empirical analysis
Thirdly, cluster the patent documents. Fourthly, form semantic network of keywords.
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Empirical analysis
Finally, draw technology evolution map.
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Empirical analysis
Find important patent documents:
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Empirical analysis
Compared with Young’s method A semantic network of keywords of graphene sensor
(Young’s method)
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Empirical analysis
Compared with Young’s method A technology evolution map(Young’s method)
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Conclusion
Our new method has the following advantages:
Avoiding the defect of circular reasoning;
Considering the distribution features and the
semantic features at the same time when clustering;
Using hierarchical clustering which is more suitable
for small samples.
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Further Works
Hope to formulate common standard that
helps experts to pick out keywords more
accurately ;
Try another methods to build semantic
relations or concept hierarchies of terms;
Try to apply the semantic relations of terms for further technology mining.
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Acknowledgement
Thanks for funding of “Intellectual property rights Information portal of CAS”
Thanks for the experts, including: Prof. Jinhui liu, Prof. Guoshen Chen, Prof. Ge lv,etc.
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Thank You for the attention!