Skip to main content
SpringerLink
Log in

Identifying patent infringement using SAO based semantic technological similarities

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

Companies should investigate possible patent infringement and cope with potential risks because patent litigation may have a tremendous financial impact. An important factor to identify the possibility of patent infringement is the technological similarity among patents, so this paper considered technological similarity as a criterion for judging the possibility of infringement. Technological similarities can be measured by transforming patent documents into abstracted forms which contain specific technological key-findings and structural relationships among technological components in the invention. Although keyword-based technological similarity has been widely adopted for patent analysis related research, it is inadequate for identifying patent infringement because a keyword vector cannot reflect specific technological key-findings and structural relationships among technological components. As a remedy, this paper exploited a subject–action–object (SAO) based semantic technological similarity. An SAO structure explicitly describes the structural relationships among technological components in the patent, and the set of SAO structures is considered to be a detailed picture of the inventor’s expertise, which is the specific key-findings in the patent. Therefore, an SAO based semantic technological similarity can identify patent infringement. Semantic similarity between SAO structures is automatically measured using SAO based semantic similarity measurement method using WordNet, and the technological relationships among patents were mapped onto a 2-dimensional space using multidimensional scaling (MDS). Furthermore, a clustering algorithm is used to automatically suggest possible patent infringement cases, allowing large sets of patents to be handled with minimal effort by human experts. The proposed method will be verified by detecting real patent infringement in prostate cancer treatment technology, and we expect this method to relieve human experts’ work in identifying patent infringement.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Arundel, A. (2001). The relative effectiveness of patents and secrecy for appropriation. Research Policy, 30(4), 611–624.

    Article  Google Scholar 

  • Bergmann, I., Moehrle, M. G., Walter, L., Butzke, D., Erdmann, V. A., & Furste, J. P. (2007). The use of semantic maps for recognition of patent infringements: A case study in biotechnology. Zeitschrift fur Betriebswirtschaft—Special issue, (4), 69–86.

  • Bergmann, I., Butzke, D., Walter, L., Fuerste, J. P., Moehrle, M. G., & Erdmann, V. A. (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 

  • Boslaugh, S., & Watters, P. A. (2008). Statistics in a nutshell. Sebastopol, CA: O’Reilly Media, Inc.

    Google Scholar 

  • Budanitsky, A., & Hirst, G. (2001). Semantic distance in WordNet: An experimental, application-oriented evaluation of five measures. In Proceedings of the Workshop on WordNet and Other Lexical Resources NAACL.

  • Buja, A., Swayne, D. F., Littman, M. L., Dean, N., Hofmann, H., & Chen, L. (2008). Data visualization with multidimensional scaling. Journal of Computational and Graphical Statistics, 17(2), 444–472.

    Article  MathSciNet  Google Scholar 

  • Carree, M. A., Klomp, L., & Thurik, A. R. (2000). Productivity convergence in OECD manufacturing industries. Economics Letters, 66(3), 337–345.

    Article  MATH  Google Scholar 

  • Carroll, J. D., & Green, P. E. (1997). Psychometric methods in marketing research: Part II, multidimensional scaling. Journal of Marketing Research, 34(2), 193–204.

    Article  Google Scholar 

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

    Google Scholar 

  • Cascini, G., Fantechi, A., & Spinicci, E. (2004). Natural language processing of patents and technical documentation. Document Analysis Systems, VI, 89–92.

    Google Scholar 

  • Chen, R. (2009). Design patent map visualization display. Expert Systems with Applications, 36(10), 12362–12374.

    Article  Google Scholar 

  • Crampes, C., & Langinier, C. (2002). Litigation and settlement in patent infringement cases. The RAND Journal of Economics, 33(2), 258–274.

    Google Scholar 

  • Dao, T. N., & Simpson, T. (2002). Measuring similarity between sentences. http://www.codeproject.com/KB/string/semanticsimilaritywordnet.aspx.

  • Davidson, I., & Ravi, S. (2005). Agglomerative hierarchical clustering with constraints: Theoretical and empirical results. Knowledge Discovery in Databases: PKDD, 2005, 59–70.

    Article  Google Scholar 

  • Durham, A. L. (2004). Patent law essentials: A concise guide. Westport, CT: Praeger Publishers.

    Google Scholar 

  • Ernst, H. (1998). Patent portfolios for strategic R&D planning. Journal of Engineering and Technology Management, 15(4), 279–308.

    Article  MathSciNet  Google Scholar 

  • Franzosi, R. (1994). From words to numbers: A set theory framework for the collection, organization and analysis of narrative data. Sociological methodology, 24, 105–136.

    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.

  • Hall, B. H., & Ziedonis, R. H. (2001). The patent paradox revisited: An empirical study of patenting in the US semiconductor industry, 1979–1995. The RAND Journal of Economics, 32(1), 101–128.

    Article  Google Scholar 

  • Huang, J. J., Ong, C. S., & Tzeng, G. H. (2006). Interval multidimensional scaling for group decision using rough set concept. Expert Systems with Applications, 31(3), 525–530.

    Article  Google Scholar 

  • Johnson, S. C. (1967). Hierarchical clustering schemes. Psychometrika, 32(3), 241–254.

    Article  Google Scholar 

  • Kim, Y., Suh, J., & Park, S. (2008). Visualization of patent analysis for emerging technology. Expert Systems with Applications, 34(3), 1804–1812.

    Article  Google Scholar 

  • Kruskal, J. B. (1964). Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika, 29(1), 1–27.

    Article  MATH  MathSciNet  Google Scholar 

  • Lai, Y. H., & Che, H. C. (2009). Modeling patent legal value by Extension Neural Network. Expert Systems with Applications, 36(7), 10520–10528.

    Article  Google Scholar 

  • Lanjouw, J. O., & Schankerman, M. (2001). Characteristics of patent litigation: A window on competition. The RAND Journal of Economics, 32(1), 129–151.

    Article  Google Scholar 

  • Lin, D. (2003). Dependency-based evaluation of MINIPAR. In Treebanks: Building and using parsed corpora (Vol. 20, pp. 317–332). Springer Netherlands.

  • Majewski, S. E., & Williamson, D. V. (2004). Incomplete contracting and the structure of R&D joint venture contracts. In Professor G. Libecap (ed.), Intellectual property and entrepreneurship (advances in the study of entrepreneurship, innovation & economic growth, Vol. 15, pp. 201–228). Emerald Group Publishing Limited.

  • Manning, C. D., Schutze, H., & MITCogNet. (1999). Foundations of statistical natural language processing (Vol. 59). Cambridge, MA: MIT Press.

    MATH  Google Scholar 

  • Mead, A. (1992). Review of the development of multidimensional scaling methods. The Statistician, 41(1), 27–39.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Moehrle, M. G. (2010). Measures for textual patent similarities: a guided way to select appropriate approaches. Scientometrics, 85(1), 95–109.

    Google Scholar 

  • Moehrle, M. G., 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 

  • Richardson, R., & Smeaton, A. F. (1995). Using WordNet in a knowledge-based approach to information retrieval. Dublin City University School of Computer Applications Working Paper CA-0395.

  • Soo, V. W., Lin, S. Y., Yang, S. Y., Lin, S. N., & Cheng, S. L. (2006). A cooperative multi-agent platform for invention based on patent document analysis and ontology. Expert Systems with Applications, 31(4), 766–775.

    Article  Google Scholar 

  • Stanford (2011). The Stanford Parser: A statistical parser http://nlp.stanford.edu/software/lex-parser.shtml. Accessed Feb 2011.

  • Tsourikov, V. M., Batchilo, L. S., & Sovpel, I. V. (2000). Document semantic analysis/selection with knowledge creativity capability utilizing subject-action-object (SAO) structures. United States Patent No. 6167370.

  • Wallerstein, M. B., Mogee, M. E., & Schoen, R. A. (1993). Global dimensions of intellectual property rights in science and technology. Washington, DC: National Academies Press.

    Google Scholar 

  • Wickelmaier, F. (2003). An introduction to MDS. Aalborg: Aalborg Universitetsforlag.

    Google Scholar 

  • Wu, Z., & Palmer, M. (1994). Verbs semantics and lexical selection. In Proceedings of the 32nd annual meeting on association for computational linguistics, Morristown (pp. 133–138). Association for Computational Linguistics.

  • 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., & 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. doi:10.1007/s11192-011-0383-0.

  • Yoon, B., & Park, Y. (2004). A text-mining-based patent network: Analytical tool for high-technology trend. The Journal of High Technology Management Research, 15(1), 37–50.

    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 

Download references

Acknowledgments

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

Author information

Authors and Affiliations

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

    Hyunseok Park

  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

    Janghyeok Yoon & Kwangsoo Kim

Authors
  1. Hyunseok Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Janghyeok Yoon
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kwangsoo Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kwangsoo Kim.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Park, H., Yoon, J. & Kim, K. Identifying patent infringement using SAO based semantic technological similarities. Scientometrics 90, 515–529 (2012). https://doi.org/10.1007/s11192-011-0522-7

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-011-0522-7

Keywords

JEL Classification

Search

Navigation