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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Altshuller, G.: The Innovation Algorithm: TRIZ, Systematic Innovation, and Technical Creativity. Technical Innovation Center, Worcester (1999)
Altshuller, G.: 40 Principles: Extended Edition. Technical Innovation Center, Worcester (2005)
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)
Narin, F., et al.: Patents as indicators of corporate technological strength. Res. Policy 16(2–4), 143–155 (1987)
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)
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)
Nagaoka, S., et al.: The process of innovation in Japan seen by inventors, RIETI Discussion Paper Series (2007)
Kiriyama, T.: IP information analysis (<special feature> patent information: analysis and effective utilization). J. Inf. Sci. Technol. Assoc. 60(8), 306–312 (2010)
Carpenter, M.P.: Citation rated to technologically important patents. World Patent Inf. 4, 160–163 (1981)
Muguruma, M.: The usefulness of patent forward citation analysis and its practical examples. J. Inf. Sci. Technol. Assoc. 56(3), 114–119 (2006)
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)
Ogawa, T., et al.: Finding basic patents using patent citations. IPSJ SIG Notes, Inf. Process. Soc. Jpn. 35, 41–48 (2005)
Albert, M.B.: Direct validation of citation counts as indicators of industrially important patents. Res. Policy 20, 251–259 (1991)
Tanaka, K.: Multi-viewpoint clustering of patent documents. IPSJ SIG Notes 4, 9–14 (2008)
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)
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)
Yamamoto, M., et al.: A journal paper filtering using the multiple information. IEEJ Trans. Electron. Inf. Syst. 131(6), 1250–1259 (2013)
Kleinberg, J.: Authoritative sources in a hyperlinked environment. J. ACM 46(5), 604–632 (1999)
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))
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)
Karamon, J., et al.: Link mining from networks of academic papers, Technical report of IEICE (2007). KBSE, 106 (473), 73–78
Kashima, H.: Mining graphs and networks. J. Inst. Electron. Inf. Commun. Eng. 93(9), 797–802 (2010)
Kajikawa, Y.: Utilization of citation information by link mining. J. Inf. Sci. Technol. Assoc. 60(6), 224–229 (2010)
Gettor, L.: Link mining: a new data mining challenge. ACM SIGKDD Explor. Newsl. 5(1), 84–89 (2003)
Outline of FI/F-term. https://www.jpo.go.jp/torikumi_e/searchportal_e/pdf/classification/fi_f-term.pdf. Accessed 23 Aug 2017
YUPASS. http://www.yupass.jp. Accessed 17 May 2017
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)
Ishioka, T.: Extended K-means with an efficient estimation of the number of clusters. Jpn. J. Appl. Stat. 29(3), 141–149 (2000)
K Means. http://stanford.edu/~cpiech/cs221/handouts/kmeans.html. Accessed 4 Mar 2018
scikit-learn. http://scikit-learn.org/stable/. Accessed 4 Mar 2018
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
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)