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An Approach Merging the IDM-Related Knowledge

  • Xin NiEmail author
  • Ahmed SametEmail author
  • Denis CavallucciEmail author
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 572)

Abstract

Patents are one of the main innovation knowledge sources for engineers and companies. Inventive Design Method (IDM) – results from a research that extends from TRIZ and contains formal knowledge description components using ontologies, such as problems, partial solutions, and parameters. In this paper, we introduce IDM-Similar model that extends existing research work in IDM-related knowledge. A neural network named Word2vec and cosine similarity approach are used to build this model to compute the similarity among problems in wide range domains’ patents covering from the chemistry to mechanics and the computer to physics. Our model assumes that a partial solution of a patent could be used to solve the problem of another patent from a different domain if these two problems are similar enough. Experiments show that our model is a promising alternative to classical TRIZ for engineers to associate their problems in a field to solutions from patents of another field. Consequently, the step dedicated to solution concepts ideation is improved using our work.

Keywords

TRIZ Inventive Design Method Word2vec Similarity computation 

Notes

Acknowledgments

This work is supported by China Scholarship Council (CSC).

References

  1. 1.
    Yeap, T., Loo, G.H., Pang, S.: Computational patent mapping: Intelligent agents for nanotechnology. In: International Conference on Mems, Nano and Smart Systems. IEEE (2003)Google Scholar
  2. 2.
    Souili, A., Cavallucci, D., Rousselot, F.: A lexico-syntactic pattern matching method to extract IDM-TRIZ knowledge from on-line patent databases. Procedia Eng. 131(Complete), 418–425 (2015)CrossRefGoogle Scholar
  3. 3.
    Altshuller, G.S.: 40 Principles: TRIZ keys to technical innovation (Lev Shulyak et Steven Rodman, Trans.), 1st edn, 141p. Technical Innovation Center, Inc., Worcester (1998). ISBN-10: 0964074036Google Scholar
  4. 4.
    Zanni-Merk, C., Cavallucci, D., Rousselot, F.: Using patents to populate an inventive design ontology, Proceedings of the TRIZ Future Conference 2010, Bergamo, 3–5 November 2010, pp. 52-62. Elsevier Ltd. (2010)Google Scholar
  5. 5.
    Cavallucci, D., Khomenko, N.: From TRIZ to OTSM-TRIZ, Addressing complexity challenges in Inventive design. Int. J. Prod. Dev. 4, 4–21 (2007)CrossRefGoogle Scholar
  6. 6.
    Mikolov, T., et al.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
  7. 7.
    Dagan, I., Marcus, S., Markovitch, S.: Contextual word similarity and estimation from sparse data. In: Proceedings of the 31st annual meeting on Association for Computational Linguistics. Association for Computational Linguistics (1993)Google Scholar
  8. 8.
    Pilehvar, M.T., Jurgens, D., Navigli, R.: Align, disambiguate and walk: a unified approach for measuring semantic similarity. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol. 1 (2013)Google Scholar
  9. 9.
    Haveliwala, T.H.: Topic-sensitive PageRank. In: Proceedings of the 11th international conference on World Wide Web. ACM (2002)Google Scholar
  10. 10.
    Terra, E., Clarke, C.L.A.: Frequency estimates for statistical word similarity measures. In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology-Volume 1. Association for Computational Linguistics (2003)Google Scholar
  11. 11.
    Kessler, M.M.: An experimental study of bibliographic coupling between technical papers. No. 62 673TN1. Massachusetts Inst of Tech Lexington Lincoln Lab (1962)Google Scholar
  12. 12.
    Small, H.: Co-citation in the scientific literature: a new measure of the relationship between two documents. J. Am. Soc. Inf. Sci. 24(4), 265–269 (1973)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Lai, K.-K., Shiao-Jun, W.: Using the patent co-citation approach to establish a new patent classification system. Inf. Process. Manag. 41(2), 313–330 (2005)CrossRefGoogle Scholar
  14. 14.
    McGill, J.P.: Technological knowledge and governance in alliances among competitors. Int. J. Technol. Manag. 38(1), 69 (2007)CrossRefGoogle Scholar
  15. 15.
    Mowery, D.C., Oxley, J.E., Silverman, B.S.: Technological overlap and interfirm cooperation: implications for the resource-based view of the firm. Res. Policy 27(5), 507–523 (1998)CrossRefGoogle Scholar
  16. 16.
    Moehrle, M.G., et al.: Patent-based inventor profiles as a basis for human resource decisions in research and development. R&D Manag. 35(5), 513–524 (2005)CrossRefGoogle Scholar
  17. 17.
    Bergmann, I., et al.: Evaluating the risk of patent infringement by means of semantic patent analysis: the case of DNA chips. R&D Manag. 38(5), 550–562 (2008)CrossRefGoogle Scholar
  18. 18.
    Yoon, B., Park, Y.: A text-mining-based patent network: analytical tool for high-technology trend. J. High Technol. Manag. Res. 15(1), 37–50 (2004)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Shih, M.-J., Liu, D.-R., Hsu, M.-L.: Discovering competitive intelligence by mining changes in patent trends. Expert Syst. Appl. 37(4), 2882–2890 (2010)CrossRefGoogle Scholar
  20. 20.
    Yoon, B., Park, Y.: A systematic approach for identifying technology opportunities: keyword-based morphology analysis. Technol. Forecast. Soc. Change 72(2), 145–160 (2005)CrossRefGoogle Scholar
  21. 21.
    Gerken, J.M., Moehrle, M.G.: A new instrument for technology monitoring: novelty in patents measured by semantic patent analysis. Scientometrics 91(3), 645–670 (2012)CrossRefGoogle Scholar
  22. 22.
    Lee, S., et al.: Business planning based on technological capabilities: patent analysis for technology-driven roadmapping. Technol. Forecast. Soc. Chang. 76(6), 769–786 (2009)CrossRefGoogle Scholar
  23. 23.
    Magerman, T., Van Looy, B., Song, X.: Exploring the feasibility and accuracy of Latent Semantic Analysis based text mining techniques to detect similarity between patent documents and scientific publications. Scientometrics 82(2), 289–306 (2009)CrossRefGoogle Scholar
  24. 24.
    Souili, A., Cavallucci, D.: Automated extraction of knowledge useful to populate inventive design ontology from patents. TRIZ – The Theory of Inventive Problem Solving, pp. 43–62. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-56593-4_2CrossRefGoogle Scholar
  25. 25.
    Bultey, A., De Bertrand De Beuvron, F., Rousselot, F.: A substance-field ontology to support the TRIZ thinking approach. Int. J. Comput. Appl. Technol. 30(1), 113–124 (2007)CrossRefGoogle Scholar
  26. 26.
    Cavallucci, D., Rousselot, F., Zanni, C.: Initial situation analysis through problem graph. CIRP J. Manuf. Sci. Technol. 2(4), 310–317 (2010)CrossRefGoogle Scholar
  27. 27.
    Wikipedia Dataset. http://mattmahoney.net/dc/textdata.html. Accessed 13 Apr 2019
  28. 28.

Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  1. 1.ICUBE/CSIP, INSA of StrasbourgStrasbourgFrance
  2. 2.ICUBE/SDC, INSA of StrasbourgIllkirchFrance

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