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Identifying rapidly evolving technological trends for R&D planning using SAO-based semantic patent networks

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Abstract

Patents constitute an up-to-date source of competitive intelligence in technological development; thus, patent analysis has been a vital tool for identifying technological trends. Patent citation analysis is easy to use, but fundamentally has two main limitations: (1) new patents tend to be less cited than old ones and may miss citations to contemporary patents; (2) citation-based analysis cannot be used for patents in databases which do not require citations. Naturally, citation-based analysis tends to underestimate the importance of new patents and may not work in rapidly-evolving industries in which technology life-cycles are shortening and new inventions are increasingly patented world-wide. As a remedy, this paper proposes a patent network based on semantic patent analysis using subject-action-object (SAO) structures. SAO structures represent the explicit relationships among components used in a patent, and are considered to represent key concepts of the patent or the expertise of the inventor. Based on the internal similarities between patents, the patent network provides the up-to-date status of a given technology. Furthermore, this paper suggests new indices to identify the technological importance of patents, the characteristics of patent clusters, and the technological capabilities of competitors. The proposed method is illustrated using patents related to synthesis of carbon nanotubes. We expect that the proposed procedure and analysis will be incorporated into technology planning processes to assist experts such as researchers and R&D policy makers in rapidly-evolving industries.

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References

  • Abraham, B., & Moitra, S. (2001). Innovation assessment through patent analysis. Technovation, 21(4), 245–252.

    Article  Google Scholar 

  • Albert, M., Avery, D., Narin, F., & McAllister, P. (1991). Direct validation of citation counts as indicators of industrially important patents. Research Policy, 20(3), 251–259.

    Article  Google Scholar 

  • Bergmann, I., Butzke, D., Walter, L., Fuerste, J., Moehrle, M., & Erdmann, V. (2008). Evaluating the risk of patent infringement by means of semantic patent analysis: The case of DNA chips. R&D Management, 38(5), 550–562.

    Article  Google Scholar 

  • Bollobas, B. (1983). The evolution of sparse graphs (Graph theory and combinatorics). London: Academic Press.

    Google Scholar 

  • Breitzman, A., & Thomas, P. (2002). Using patent citation analysis to target/value ma candidates. Research-Technology Management, 45(5), 28–36.

    Google Scholar 

  • Cascini, G., Fantechi, A., & Spinicci, E. (2004). Natural language processing of patents and technical documentation. Lecture Notes in Computer Science, 3163, 508–520.

  • Cascini, G., & Zini, M. (2008). Measuring patent similarity by comparing inventions functional trees. Computer-Aided Innovation (CAI), 277, 31–42.

    Article  Google Scholar 

  • Chang, S., Lai, K., & Chang, S. (2009). Exploring technology diffusion and classification of business methods: Using the patent citation network. Technological Forecasting and Social Change, 76(1), 107–117.

    Article  Google Scholar 

  • Choi, S., Lim, J., Yoon, J., & Kim, K. (2010). Patent function network analysis: A function based approach for analyzing patent information. Cairo, Egypt: IAMOT 2010.

  • Daim, T., Rueda, G., Martin, H., & Gerdsri, P. (2006). Forecasting emerging technologies: Use of bibliometrics and patent analysis. Technological Forecasting and Social Change, 73(8), 981–1012.

    Article  Google Scholar 

  • Dewulf, S. (2006). Directed variation: Variation of properties for new or improved function product DNA, a base for ‘connect and develop’. Kortrijk, Belgium: ETRIA TRIZ Futures.

  • Diestel, R. (2005). Graph theory (graduate texts in mathematics). Heidelberg: Springer.

    Google Scholar 

  • Ernst, H. (1997). The use of patent data for technological forecasting: The diffusion of cnc-technology in the machine tool industry. Small Business Economics, 9(4), 361–381.

    Article  MathSciNet  Google Scholar 

  • Fontana, R., Nuvolari, A., & Verspagen, B. (2009). Mapping technological trajectories as patent citation networks. An application to data communication standards. Economics of Innovation and New Technology, 18(4), 311–336.

    Article  Google Scholar 

  • Freeman, L. (1979). Centrality in social networks conceptual clarification. Social networks, 1(3), 215–239.

    Article  Google Scholar 

  • Fujii, A., Iwayama, M., & Kando, N. (2007). Introduction to the special issue on patent processing. Information Processing and Management, 43(5), 1149–1153.

    Article  Google Scholar 

  • Gerken, J., Moehrle, M., & Walter, L. (2010). Patents as an information source for product forecasting: Insights from a longitudinal study in the automotive industry. In The R&D Management Conference 2010, Manchester, England: Managing science and technology.

  • Hopcroft, J., & Tarjan, R. (1973). Algorithm 447: Efficient algorithms for graph manipulation. Communications of the ACM, 16(6), 372–378.

    Article  Google Scholar 

  • Huang, Z., Chen, H., Yip, A., Ng, G., Guo, F., Chen, Z., et al. (2003a). Longitudinal patent analysis for nanoscale science and engineering: Country, institution and technology field. Journal of Nanoparticle Research, 5(3), 333–363.

    Article  Google Scholar 

  • Huang, M., Chiang, L., & Chen, D. (2003b). Constructing a patent citation map using bibliographic coupling: A study of taiwan’s high-tech companies. Scientometrics, 58(3), 489–506.

    Article  Google Scholar 

  • Hung, S., & Wang, A. (2008). A small world in the patent citation network. In International Conference on IEEM 2008 (pp. 1–5). Singapore: IEEM.

  • Hung, S., & Wang, A. (2010a). Examining the small world phenomenon in the patent citation network: A case study of the radio frequency identification (rfid) network. Scientometrics, 82(1), 121–134.

    Article  Google Scholar 

  • Hung, S., & Wang, A. (2010b) Small world phenomenon in the radio frequency identification (rfid) patent citation network (pp. 23–26). Kyoto, Japan: International Conference on Mechanical and Electronics Engineering (ICMEE).

  • Karki, M. (1997). Patent citation analysis: A policy analysis tool. World Patent Information, 19(4), 269–272.

    Article  MathSciNet  Google Scholar 

  • Kayal, A., & Waters, R. (1999). An empirical evaluation of the technology cycle time indicator as a measure of the pace of technological progress in superconductor technology. IEEE Transactions on Engineering Management, 46(2), 127.

    Article  Google Scholar 

  • KIPO. (2010). K2e automatic translation. Retrieved from http://eng.kipris.or.kr/eng/other_service/k2e_automatic_translation.jsp. Accessed 1 Oct 2010.

  • Lee, S., Yoon, B., & Park, Y. (2009). An approach to discovering new technology opportunities: Keyword-based patent map approach. Technovation, 29(6–7), 481–497.

    Article  Google Scholar 

  • Li, X., Chen, H., Huang, Z., & Roco, M. (2007). Patent citation network in nanotechnology (1976–2004). Journal of Nanoparticle Research, 9(3), 337–352.

    Article  Google Scholar 

  • Liu, S., & Shyu, J. (1997). Strategic planning for technology development with patent analysis. International Journal of Technology Management, 13(5), 661–680.

    Article  Google Scholar 

  • Mann, D. (2002). Hands-on systematic innovation. Belgium: Creax press.

    Google Scholar 

  • Miller, G. (1995). Wordnet: A lexical database for english. Communications of the ACM, 38(11), 41.

    Article  Google Scholar 

  • Moehrle, M., & Geritz, A. (2004). Developing acquisition strategies based on patent maps. In Proceedings of the 13th International Conference on Management of Technology, (pp. 1–9). Washington, USA: R&D Management.

  • Moehrle, M., Walter, L., Geritz, A., & Muller, S. (2005). Patent-based inventor profiles as a basis for human resource decisions in research and development. R&D Management, 35(5), 513–524.

    Article  Google Scholar 

  • Narin, F. (1993). Technology indicators and corporate strategy. Review of Business, 14(3), 19–23.

    Google Scholar 

  • Narin, F. (1994). Patent bibliometrics. Scientometrics, 30(1), 147–155.

    Article  Google Scholar 

  • Porter, A., & Detampel, M. (1995). Technology opportunities analysis. Technological Forecasting and Social Change, 49(3), 237–255.

    Article  Google Scholar 

  • Reitzig, M. (2004). Improving patent valuations for management purposes––validating new indicators by analyzing application rationales. Research Policy, 33(6–7), 939–957.

    Article  Google Scholar 

  • Resnik, P. (1999). Semantic similarity in a taxonomy: An information-based measure and its application to problems of ambiguity in natural language. Journal of artificial intelligence research, 11, 95–130.

    MATH  Google Scholar 

  • RPCCT. (2010). Carbon nanotue (cnt): Recent trends. Daejun, Korea: Research and Policy Center for Chemical Technology.

    Google Scholar 

  • Savransky, S. (2000). Engineering of creativity: Introduction to triz methodology of inventive problem solving. Boca Raton, USA: CRC.

  • Shibata, N., Kajikawa, Y., Takeda, Y., & Matsushima, K. (2008). Detecting emerging research fronts based on topological measures in citation networks of scientific publications. Technovation, 28(11), 758–775.

    Article  Google Scholar 

  • Simpson, T., & Dao, T. (2005). Wordnet-based semantic similarity measurement. Retrieved from http://www.codeproject.com/KB/string/semanticsimilaritywordnet.aspx. Accessed 1 Oct 2010.

  • Trappey, C., Wu, H., Taghaboni-Dutta, F., & Trappey, A. (2011). Using patent data for technology forecasting: China rfid patent analysis. Advanced Engineering Informatics, 25(1), 53–64.

    Article  Google Scholar 

  • Yoon, J., Choi, S., & Kim, K. (2011). Invention property-function network analysis of patents: a case of silicon-based thin film solar cells. Scientometrics, 86(3), 687–703.

    Article  Google Scholar 

  • Yoon, B., & Park, Y. (2005). A systematic approach for identifying technology opportunities: Keyword-based morphology analysis. Technological Forecasting and Social Change, 72(2), 145–160.

    Article  Google Scholar 

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Acknowledgments

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. 2009-0088379).

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Authors and Affiliations

  1. Department of Industrial and Management Engineering, Pohang University of Science and Technology, San 31, Hyoja-dong, Nam-gu, Pohang, Kyungbuk, 790-784, Republic of Korea

    Janghyeok Yoon & Kwangsoo Kim

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  1. Janghyeok Yoon
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  2. Kwangsoo Kim
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Correspondence to Kwangsoo Kim.

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Yoon, J., Kim, K. Identifying rapidly evolving technological trends for R&D planning using SAO-based semantic patent networks. Scientometrics 88, 213–228 (2011). https://doi.org/10.1007/s11192-011-0383-0

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