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A hybrid similarity measure method for patent portfolio analysis

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  • Zhang, Yi
  • Shang, Lining
  • Huang, Lu
  • Porter, Alan L.
  • Zhang, Guangquan
  • Lu, Jie
  • Zhu, Donghua

Abstract

Similarity measures are fundamental tools for identifying relationships within or across patent portfolios. Many bibliometric indicators are used to determine similarity measures; for example, bibliographic coupling, citation and co-citation, and co-word distribution. This paper aims to construct a hybrid similarity measure method based on multiple indicators to analyze patent portfolios. Two models are proposed: categorical similarity and semantic similarity. The categorical similarity model emphasizes international patent classifications (IPCs), while the semantic similarity model emphasizes textual elements. We introduce fuzzy set routines to translate the rough technical (sub-) categories of IPCs into defined numeric values, and we calculate the categorical similarities between patent portfolios using membership grade vectors. In parallel, we identify and highlight core terms in a 3-level tree structure and compute the semantic similarities by comparing the tree-based structures. A weighting model is designed to consider: 1) the bias that exists between the categorical and semantic similarities, and 2) the weighting or integrating strategy for a hybrid method. A case study to measure the technological similarities between selected firms in China’s medical device industry is used to demonstrate the reliability our method, and the results indicate the practical meaning of our method in a broad range of informetric applications.

Suggested Citation

  • Zhang, Yi & Shang, Lining & Huang, Lu & Porter, Alan L. & Zhang, Guangquan & Lu, Jie & Zhu, Donghua, 2016. "A hybrid similarity measure method for patent portfolio analysis," Journal of Informetrics, Elsevier, vol. 10(4), pages 1108-1130.
    • Handle: RePEc:eee:infome:v:10:y:2016:i:4:p:1108-1130
    DOI: 10.1016/j.joi.2016.09.006
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    1. Fabry, Bernd & Ernst, Holger & Langholz, Jens & Köster, Martin, 2006. "Patent portfolio analysis as a useful tool for identifying R&D and business opportunities--an empirical application in the nutrition and health industry," World Patent Information, Elsevier, vol. 28(3), pages 215-225, September.
    2. Loet Leydesdorff & Duncan Kushnir & Ismael Rafols, 2014. "Interactive overlay maps for US patent (USPTO) data based on International Patent Classification (IPC)," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(3), pages 1583-1599, March.
    3. Janghyeok Yoon & Hyunseok Park & Kwangsoo Kim, 2013. "Identifying technological competition trends for R&D planning using dynamic patent maps: SAO-based content analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(1), pages 313-331, January.
    4. Intarakumnerd, Patarapong & Charoenporn, Peera, 2015. "Impact of stronger patent regimes on technology transfer: The case study of Thai automotive industry," Research Policy, Elsevier, vol. 44(7), pages 1314-1326.
    5. Xiao Zhou & Yi Zhang & Alan L. Porter & Ying Guo & Donghua Zhu, 2014. "A patent analysis method to trace technology evolutionary pathways," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(3), pages 705-721, September.
    6. Helen J. Peat & Peter Willett, 1991. "The limitations of term co‐occurrence data for query expansion in document retrieval systems," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 42(5), pages 378-383, June.
    7. Boyack, Kevin W. & Klavans, Richard, 2008. "Measuring science–technology interaction using rare inventor–author names," Journal of Informetrics, Elsevier, vol. 2(3), pages 173-182.
    8. Alexander I. Pudovkin & Eugene Garfield, 2002. "Algorithmic procedure for finding semantically related journals," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 53(13), pages 1113-1119, November.
    9. Luciano Kay & Nils Newman & Jan Youtie & Alan L. Porter & Ismael Rafols, 2014. "Patent overlay mapping: Visualizing technological distance," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(12), pages 2432-2443, December.
    10. Richard Klavans & Kevin W. Boyack, 2009. "Toward a consensus map of science," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(3), pages 455-476, March.
    11. Ying Huang & Jannik Schuehle & Alan L. Porter & Jan Youtie, 2015. "A systematic method to create search strategies for emerging technologies based on the Web of Science: illustrated for ‘Big Data’," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 2005-2022, December.
    12. Saaty, Thomas L., 1990. "How to make a decision: The analytic hierarchy process," European Journal of Operational Research, Elsevier, vol. 48(1), pages 9-26, September.
    13. Nees Jan Eck & Ludo Waltman & Ed C. M. Noyons & Reindert K. Buter, 2010. "Automatic term identification for bibliometric mapping," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(3), pages 581-596, March.
    14. M. M. Kessler, 1963. "Bibliographic coupling between scientific papers," American Documentation, Wiley Blackwell, vol. 14(1), pages 10-25, January.
    15. Marianna Makri & Michael A. Hitt & Peter J. Lane, 2010. "Complementary technologies, knowledge relatedness, and invention outcomes in high technology mergers and acquisitions," Strategic Management Journal, Wiley Blackwell, vol. 31(6), pages 602-628, June.
    16. Loet Leydesdorff & Lutz Bornmann, 2012. "Mapping (USPTO) patent data using overlays to Google Maps," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(7), pages 1442-1458, July.
    17. Kevin W. Boyack & Richard Klavans, 2010. "Co‐citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately?," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(12), pages 2389-2404, December.
    18. Chaomei Chen & Fidelia Ibekwe-SanJuan & Jianhua Hou, 2010. "The structure and dynamics of cocitation clusters: A multiple-perspective cocitation analysis," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(7), pages 1386-1409, July.
    19. Richard Klavans & Kevin W. Boyack, 2006. "Identifying a better measure of relatedness for mapping science," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(2), pages 251-263, January.
    20. Jaffe, Adam B, 1986. "Technological Opportunity and Spillovers of R&D: Evidence from Firms' Patents, Profits, and Market Value," American Economic Review, American Economic Association, vol. 76(5), pages 984-1001, December.
    21. Kevin W. Boyack & Richard Klavans & Katy Börner, 2005. "Mapping the backbone of science," Scientometrics, Springer;Akadémiai Kiadó, vol. 64(3), pages 351-374, August.
    22. Chyi-Kwei Yau & Alan Porter & Nils Newman & Arho Suominen, 2014. "Clustering scientific documents with topic modeling," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(3), pages 767-786, September.
    23. Henry Small, 1973. "Co‐citation in the scientific literature: A new measure of the relationship between two documents," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 24(4), pages 265-269, July.
    24. Kevin W. Boyack & Richard Klavans, 2010. "Co-citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately?," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(12), pages 2389-2404, December.
    25. Nakamura, Hiroko & Suzuki, Shinji & Sakata, Ichiro & Kajikawa, Yuya, 2015. "Knowledge combination modeling: The measurement of knowledge similarity between different technological domains," Technological Forecasting and Social Change, Elsevier, vol. 94(C), pages 187-201.
    26. Zhang, Yi & Porter, Alan L. & Hu, Zhengyin & Guo, Ying & Newman, Nils C., 2014. "“Term clumping” for technical intelligence: A case study on dye-sensitized solar cells," Technological Forecasting and Social Change, Elsevier, vol. 85(C), pages 26-39.
    27. Yi Zhang & Xiao Zhou & Alan L. Porter & Jose M. Vicente Gomila, 2014. "How to combine term clumping and technology roadmapping for newly emerging science & technology competitive intelligence: “problem & solution” pattern based semantic TRIZ tool and case study," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1375-1389, November.
    28. Chen, Dar-Zen & Huang, Mu-Hsuan & Hsieh, Hui-Chen & Lin, Chang-Pin, 2011. "Identifying missing relevant patent citation links by using bibliographic coupling in LED illuminating technology," Journal of Informetrics, Elsevier, vol. 5(3), pages 400-412.
    29. Martin G. Moehrle, 2010. "Measures for textual patent similarities: a guided way to select appropriate approaches," Scientometrics, Springer;Akadémiai Kiadó, vol. 85(1), pages 95-109, October.
    30. Fang Pei Su & Kuei Kuei Lai & R.R.K. Sharma & Tsung Hsien Kuo, 2009. "Patent priority network: Linking patent portfolio to strategic goals," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(11), pages 2353-2361, November.
    31. Hyunseok Park & Janghyeok Yoon & Kwangsoo Kim, 2013. "Identification and evaluation of corporations for merger and acquisition strategies using patent information and text mining," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(3), pages 883-909, December.
    32. van Eck, N.J.P. & Waltman, L., 2009. "VOSviewer: A Computer Program for Bibliometric Mapping," ERIM Report Series Research in Management ERS-2009-005-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    33. Ahlgren, Per & Colliander, Cristian, 2009. "Document–document similarity approaches and science mapping: Experimental comparison of five approaches," Journal of Informetrics, Elsevier, vol. 3(1), pages 49-63.
    34. Zhang, Yi & Zhang, Guangquan & Chen, Hongshu & Porter, Alan L. & Zhu, Donghua & Lu, Jie, 2016. "Topic analysis and forecasting for science, technology and innovation: Methodology with a case study focusing on big data research," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 179-191.
    35. Waltman, Ludo & van Eck, Nees Jan & Noyons, Ed C.M., 2010. "A unified approach to mapping and clustering of bibliometric networks," Journal of Informetrics, Elsevier, vol. 4(4), pages 629-635.
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