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Published byMichael Manning Modified over 9 years ago
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The 1st Global Tech Mining Conference, Atlanta, USA Analyzing Technology Evolution of Graphene Sensor Based on Patent Documents Fang Shu 1, Hu Zhengyin 1, Pang Hongshen 1, Zhang Xian 1 1 Chengdu 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 : f ij : the frequency of feature item i appears in the document j; N:number of all documents in the collection; n i : 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; θ im : 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!
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