Skip to main content
Log in

Detecting signals of new technological opportunities using semantic patent analysis and outlier detection

  • Published:
Scientometrics Aims and scope Submit manuscript

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.

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

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • 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 

  • Aleskerov, E., Freisleben, B., & Rao, B. (2002). Cardwatch: A neural network based database mining system for credit card fraud detection. In IEEE (pp. 220–226).

  • Altschuller, G. (1984). Creativity as an exact science: The theory of the solution of inventive problems. New York: Gordon and Breach.

    Google Scholar 

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

    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 

  • Cascini, G., Fantechi, A., & Spinicci, E. (2004). Natural language processing of patents and technical documentation. Document Analysis Systems, VI, 508–520.

    Article  Google Scholar 

  • Cascini, G., Russo, D., & Zini, M. (2007). Computer-aided patent analysis: Finding invention peculiarities. In N. Leon-Rovira (Ed.), Trends in computer aided innovation (pp. 167–178). Boston: Springer.

    Chapter  Google Scholar 

  • Cascini, G., & Zini, M. (2008). Measuring patent similarity by comparing inventions functional trees. IFIP International Federation for Information Processing, 277, 31–42.

    Google Scholar 

  • Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys (CSUR), 41(3), 1–58.

    Article  Google Scholar 

  • 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.

  • Christensen, C., & Leslie, D. (1997). The innovator’s dilemma. Boston: Harvard Business School Press.

    Google Scholar 

  • 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), 293–315.

    Article  Google Scholar 

  • Fujii, A., Iwayama, M., & Kando, N. (2007). Introduction to the special issue on patent processing. Information Processing & 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.

  • Hodge, V., & Austin, J. (2004). A survey of outlier detection methodologies. Artificial Intelligence Review, 22(2), 85–126.

    Article  MATH  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  • Kruskal, J. (1964). Nonmetric multidimensional scaling: A numerical method. Psychometrika, 29(2), 115–129.

    Article  MATH  MathSciNet  Google Scholar 

  • 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 

  • Leung, K., & Leckie, C. (2005). Unsupervised anomaly detection in network intrusion detection using clusters (pp. 333–342). Sydney: Australian Computer Society, Inc.

    Google Scholar 

  • Lin, D. (2010). Minipar. http://webdocs.cs.ualberta.ca/~lindek/minipar.htm. Accessed 1 Oct 2011.

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

    Google Scholar 

  • Mann, D. (2003). Better technology forecasting using systematic innovation methods. Technological Forecasting and Social Change, 70(8), 779–795.

    Article  Google Scholar 

  • Miller, G. (1995). Wordnet: A lexical database for English. Communications of the ACM, 38(11), 39–41.

    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, DC, USA.

  • 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 

  • Mogee, M., & Kolar, R. (1994). International patent analysis as a tool for corporate technology analysis and planning. Technology Analysis & Strategic Management, 6(4), 485–504.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Park, B. (2002). An outlier robust GARCH model and forecasting volatility of exchange rate returns. Journal of Forecasting, 21(5), 381–393.

    Article  Google Scholar 

  • 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), 237–245.

    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.

    Google Scholar 

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

    Book  Google Scholar 

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

  • 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.

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

  • Siris, V., & Papagalou, F. (2006). Application of anomaly detection algorithms for detecting SYN flooding attacks. Computer Communications, 29(9), 1433–1442.

    Article  Google Scholar 

  • 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), 124–135.

    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  MathSciNet  Google Scholar 

  • Yoon, J., & Kim, K. (2011a). Generation of patent maps using SAO-based semantic patent similarity. Entrue Journal of Information Technology, 10(1), 19–27.

    Google Scholar 

  • Yoon, J., & Kim, K. (2011b). Identifying rapidly evolving technological trends for R&D planning using SAO-based semantic patent networks. Scientometrics, 88(1), 213–228.

    Article  Google Scholar 

  • Yoon, B., & Park, Y. (2004). Morphology analysis approach for technology forecasting. In 2004 IEEE international engineering management conference (Vol. 2, pp. 566–570), Singapore.

  • 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 

  • 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), 291–300.

    Article  Google Scholar 

Download references

Acknowledgments

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

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kwangsoo Kim.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Yoon, J., Kim, K. Detecting signals of new technological opportunities using semantic patent analysis and outlier detection. Scientometrics 90, 445–461 (2012). https://doi.org/10.1007/s11192-011-0543-2

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-011-0543-2

Keywords

JEL Classification

Navigation