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  • 1 Korea Institute of Intellectual Property, KIPS Center, Yeoksam-dong, Gangnam-gu, Seoul 135-980, Republic of Korea janghyoon@gmail.com
  • 2 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
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Abstract

In the competitive business environment, early identification of technological opportunities is crucial for technology strategy formulation and research and development planning. There exist previous studies that identify technological directions or areas from a broad view for technological opportunities, while few studies have researched a way to detect distinctive patents that can act as new technological opportunities at the individual patent level. This paper proposes a method of detecting new technological opportunities by using subject–action–object (SAO)-based semantic patent analysis and outlier detection. SAO structures are syntactically ordered sentences that can be automatically extracted by natural language processing of patent text; they explicitly show the structural relationships among technological components in a patent, and thus encode key findings of inventions and the expertise of inventors. Therefore, the proposed method allows quantification of structural dissimilarities among patents. We use outlier detection to identify unusual or distinctive patents in a given technology area; some of these outlier patents may represent new technological opportunities. The proposed method is illustrated using patents related to organic photovoltaic cells. We expect that this method can be incorporated into the research and development process for early identification of technological opportunities.

  • Albert, M, Avery, D, Narin, F, McAllister, P 1991 Direct validation of citation counts as indicators of industrially important patents. Research Policy 20 3 251259 .

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Aleskerov, E., Freisleben, B., & Rao, B. (2002). Cardwatch: A neural network based database mining system for credit card fraud detection. In IEEE (pp. 220226).

    • Search Google Scholar
    • Export Citation
  • Altschuller, G 1984 Creativity as an exact science: The theory of the solution of inventive problems Gordon and Breach New York.

  • Barnett, V, Lewis, T, Abeles, F 1979 Outliers in statistical data. Physics Today 32:73 .

  • 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 550562 .

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cascini, G, Fantechi, A, Spinicci, E 2004 Natural language processing of patents and technical documentation. Document Analysis Systems VI:508520 .

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cascini, G, Russo, D, Zini, M 2007 Computer-aided patent analysis: Finding invention peculiarities N Leon-Rovira eds. Trends in computer aided innovation Springer Boston 167178 .

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cascini, G, Zini, M 2008 Measuring patent similarity by comparing inventions functional trees. IFIP International Federation for Information Processing 277:3142.

    • Search Google Scholar
    • Export Citation
  • Chandola, V, Banerjee, A, Kumar, V 2009 Anomaly detection: A survey. ACM Computing Surveys (CSUR) 41 3 158 .

  • Choi, S., Lim, J., Yoon, J., & Kim, K. (2010). Patent function network analysis: A function based approach for analyzing patent information. In IAMOT2010, Cairo, Egypt.

    • Search Google Scholar
    • Export Citation
  • Christensen, C, Leslie, D 1997 The innovator's dilemma Harvard Business School Press Boston.

  • Franses, P, Kloek, T, Lucas, A 1998 Outlier robust analysis of long-run marketing effects for weekly scanning data. Journal of Econometrics 89 1–2 293315 .

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fujii, A, Iwayama, M, Kando, N 2007 Introduction to the special issue on patent processing. Information Processing & Management 43 5 11491153 .

  • 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.

    • Search Google Scholar
    • Export Citation
  • Hodge, V, Austin, J 2004 A survey of outlier detection methodologies. Artificial Intelligence Review 22 2 85126 .

  • Karki, M 1997 Patent citation analysis: A policy analysis tool. World Patent Information 19 4 269272 .

  • Kruskal, J 1964 Nonmetric multidimensional scaling: A numerical method. Psychometrika 29 2 115129 .

  • Lee, S, Yoon, B, Park, Y 2009 An approach to discovering new technology opportunities: Keyword-based patent map approach. Technovation 29 6–7 481497 .

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Leung, K, Leckie, C 2005 Unsupervised anomaly detection in network intrusion detection using clusters Australian Computer Society, Inc. Sydney 333342.

    • Search Google Scholar
    • Export Citation
  • Lin, D. (2010). Minipar. http://webdocs.cs.ualberta.ca/∼lindek/minipar.htm. Accessed 1 Oct 2011.

  • Mann, D 2002 Hands-on systematic innovation CREAX Press Leper.

  • Mann, D 2003 Better technology forecasting using systematic innovation methods. Technological Forecasting and Social Change 70 8 779795 .

  • Miller, G 1995 Wordnet: A lexical database for English. Communications of the ACM 38 11 3941 .

  • Moehrle, M., & Geritz, A. (2004). Developing acquisition strategies based on patent maps. In Proceedings of the 13th international conference on management of technology (pp. 19), Washington, DC, USA.

    • Search Google Scholar
    • Export Citation
  • 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 513524 .

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mogee, M, Kolar, R 1994 International patent analysis as a tool for corporate technology analysis and planning. Technology Analysis & Strategic Management 6 4 485504 .

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Narin, F 1994 Patent bibliometrics. Scientometrics 30 1 147155 .

  • Park, B 2002 An outlier robust GARCH model and forecasting volatility of exchange rate returns. Journal of Forecasting 21 5 381393 .

  • Radauer, A, Walter, L 2010 Elements of good practice for providers of publicly funded patent information services for SMEs-selected and amended results of a benchmarking exercise. World Patent Information 32 3 237245 .

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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.

    • Search Google Scholar
    • Export Citation
  • Savransky, S 2000 Engineering of creativity: Introduction to TRIZ methodology of inventive problem solving CRC Boca Raton .

  • Schuh, G., & Grawatsch, M. (2004). TRIZ-based technology intelligence. In Proceedings of the 13th international conference on management of technology, Washington, DC, USA.

    • Search Google Scholar
    • Export Citation
  • Sekar, R., Gupta, A., Frullo, J., Shanbhag, T., Tiwari, A., Yang, H., et al. (2002). Specification-based anomaly detection: A new approach for detecting network intrusions. In Proceedings of the 9th ACM conference on computer and communications security, New York, USA.

    • Search Google Scholar
    • Export Citation
  • Simpson, T., & Dao, T. (2005). Wordnet-based semantic similarity measurement. http://www.codeproject.com/KB/string/semanticsimilaritywordnet.aspx. Accessed 1 Oct 2011.

    • Search Google Scholar
    • Export Citation
  • Siris, V, Papagalou, F 2006 Application of anomaly detection algorithms for detecting SYN flooding attacks. Computer Communications 29 9 14331442 .

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stanford. (2010). The Stanford parser: A statistical parser. http://nlp.stanford.edu/software/lex-parser.shtml. Accessed 1 Oct 2011.

  • Yoon, B 2008 On the development of a technology intelligence tool for identifying technology opportunity. Expert Systems with Applications 35 1–2 124135 .

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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 687703 .

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yoon, J, Kim, K 2011 Generation of patent maps using SAO-based semantic patent similarity. Entrue Journal of Information Technology 10 1 1927.

    • Search Google Scholar
    • Export Citation
  • Yoon, J, Kim, K 2011 Identifying rapidly evolving technological trends for R&D planning using SAO-based semantic patent networks. Scientometrics 88 1 213228 .

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yoon, B., & Park, Y. (2004). Morphology analysis approach for technology forecasting. In 2004 IEEE international engineering management conference (Vol. 2, pp. 566570), Singapore.

    • Search Google Scholar
    • Export Citation
  • Yoon, B, Park, Y 2005 A systematic approach for identifying technology opportunities: Keyword-based morphology analysis. Technological Forecasting and Social Change 72 2 145160 .

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yoon, B, Yoon, C, Park, Y 2002 On the development and application of a self-organizing feature map-based patent map. R&D Management 32 4 291300 .