Skip to main content

A Study on Technology Structure Clustering Through the Analyses of Patent Classification Codes with Link Mining

  • Conference paper
  • First Online:
New Frontiers in Artificial Intelligence (JSAI-isAI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10838))

Included in the following conference series:

  • 1074 Accesses

Abstract

This paper provides the technology structure analysis and the technology field clustering through the analyses of patent classification codes with link mining method. Knowledge extraction from patent information has been made thus far, but conventional patent analysis methods depend on personal heuristic knowledge. It makes it hard to extract the technology structure. We are focusing on classification codes in the patent. They are assigned to capture the technology fields of patent. With the proposed method, we are succeeding in the clustering of various technology fields.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Altshuller, G.: The Innovation Algorithm: TRIZ, Systematic Innovation, and Technical Creativity. Technical Innovation Center, Worcester (1999)

    Google Scholar 

  2. Altshuller, G.: 40 Principles: Extended Edition. Technical Innovation Center, Worcester (2005)

    Google Scholar 

  3. Kawakami, H., et al.: Idea generation support system for implementing benefit of inconvenience by employing the theory of inventive problem solving. Trans. Soc. Instrum. Control Eng. 49(10), 911–917 (2013)

    Article  Google Scholar 

  4. Narin, F., et al.: Patents as indicators of corporate technological strength. Res. Policy 16(2–4), 143–155 (1987)

    Article  Google Scholar 

  5. Shide, K., et al.: The shift of positioning of Japanese general contractors’ R&D activities: a comparative value network analysis between condominium and semiconductor factory building markets. J. Archit. Plan. 76(668), 1929–1935 (2011)

    Article  Google Scholar 

  6. Kimura, H.: One approach of technology stocktaking and evaluation for corporate technology strategies: emphasizing future intentions and quantification through patent analysis. J. Sci. Policy Res. Manag. 26(1/2), 52–61 (2012)

    Google Scholar 

  7. Nagaoka, S., et al.: The process of innovation in Japan seen by inventors, RIETI Discussion Paper Series (2007)

    Google Scholar 

  8. Kiriyama, T.: IP information analysis (<special feature> patent information: analysis and effective utilization). J. Inf. Sci. Technol. Assoc. 60(8), 306–312 (2010)

    Google Scholar 

  9. Carpenter, M.P.: Citation rated to technologically important patents. World Patent Inf. 4, 160–163 (1981)

    Article  Google Scholar 

  10. Muguruma, M.: The usefulness of patent forward citation analysis and its practical examples. J. Inf. Sci. Technol. Assoc. 56(3), 114–119 (2006)

    Google Scholar 

  11. Sato, Y., et al.: A study of patent document score based on citation analysis. IPSJ SIG Notes, Inf. Process. Soc. Jpn. 59, 9–16 (2006)

    Google Scholar 

  12. Ogawa, T., et al.: Finding basic patents using patent citations. IPSJ SIG Notes, Inf. Process. Soc. Jpn. 35, 41–48 (2005)

    Google Scholar 

  13. Albert, M.B.: Direct validation of citation counts as indicators of industrially important patents. Res. Policy 20, 251–259 (1991)

    Article  MathSciNet  Google Scholar 

  14. Tanaka, K.: Multi-viewpoint clustering of patent documents. IPSJ SIG Notes 4, 9–14 (2008)

    Google Scholar 

  15. Yamashita, Y.: Text mining technology for patent analysis and patent search: patent search and patent analysis service patent integration. J. Inf. Process. Manag. 52(10), 581–591 (2010)

    Article  Google Scholar 

  16. Yamamoto, M., et al.: A journal paper filtering using the profile revised by patent document information. IEEJ Trans. Electron. Inf. Syst. 130(2), 358–366 (2010)

    Google Scholar 

  17. Yamamoto, M., et al.: A journal paper filtering using the multiple information. IEEJ Trans. Electron. Inf. Syst. 131(6), 1250–1259 (2013)

    Google Scholar 

  18. Kleinberg, J.: Authoritative sources in a hyperlinked environment. J. ACM 46(5), 604–632 (1999)

    Article  MathSciNet  Google Scholar 

  19. Eto, M.: A new co-citation measure based on structures of citing papers. Inf. Process. Soc. Jpn. Database 49, 1–15 (2008). (SIG 7 (TOD 37))

    Google Scholar 

  20. Ueda, I.: “Active mining utilizing the patent classification IPC, F1, F Term” on the basis of the cognitive processes of the examiner In: Proceedings of SIG-FAI, Japanese Society for Artificial Intelligence, vol. 46, pp. 13–21 (2001)

    Google Scholar 

  21. Karamon, J., et al.: Link mining from networks of academic papers, Technical report of IEICE (2007). KBSE, 106 (473), 73–78

    Google Scholar 

  22. Kashima, H.: Mining graphs and networks. J. Inst. Electron. Inf. Commun. Eng. 93(9), 797–802 (2010)

    Google Scholar 

  23. Kajikawa, Y.: Utilization of citation information by link mining. J. Inf. Sci. Technol. Assoc. 60(6), 224–229 (2010)

    Google Scholar 

  24. Gettor, L.: Link mining: a new data mining challenge. ACM SIGKDD Explor. Newsl. 5(1), 84–89 (2003)

    Article  Google Scholar 

  25. Outline of FI/F-term. https://www.jpo.go.jp/torikumi_e/searchportal_e/pdf/classification/fi_f-term.pdf. Accessed 23 Aug 2017

  26. YUPASS. http://www.yupass.jp. Accessed 17 May 2017

  27. Pelleg, D., Moore, A.W.: X-means: extending K-means with efficient estimation of the number of clusters. In: Proceedings of the Seventeenth International Conference on Machine Learning, pp. 727–734. Morgan Kaufmann Publishers Inc., San Francisco (2000)

    Google Scholar 

  28. Ishioka, T.: Extended K-means with an efficient estimation of the number of clusters. Jpn. J. Appl. Stat. 29(3), 141–149 (2000)

    Article  Google Scholar 

  29. K Means. http://stanford.edu/~cpiech/cs221/handouts/kmeans.html. Accessed 4 Mar 2018

  30. scikit-learn. http://scikit-learn.org/stable/. Accessed 4 Mar 2018

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Masashi Shibata .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Shibata, M., Takahashi, M. (2018). A Study on Technology Structure Clustering Through the Analyses of Patent Classification Codes with Link Mining. In: Arai, S., Kojima, K., Mineshima, K., Bekki, D., Satoh, K., Ohta, Y. (eds) New Frontiers in Artificial Intelligence. JSAI-isAI 2017. Lecture Notes in Computer Science(), vol 10838. Springer, Cham. https://doi.org/10.1007/978-3-319-93794-6_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-93794-6_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93793-9

  • Online ISBN: 978-3-319-93794-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics