Identifying patent infringement using SAO based semantic technological similarities

Scientometrics

Volumn 90, Issue 2, 2012, Pages 515-529

  Park, Hyunseok ^a   Yoon, Janghyeok ^a   Kim, Kwangsoo ^a  

^a Pohang University of Science and Technology (POSTECH)   (South Korea)

Author keywords

Multidimensional scaling; Natural language processing; NLP; Patent analysis; Patent litigation; Patent mining; Patent risk; SAO; Subject action object

Indexed keywords

EID: 84855563371     PISSN: 01389130     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11192-011-0522-7     Document Type: Article

References (44)

1. Arundel, A. (2001). The relative effectiveness of patents and secrecy for appropriation. Research Policy, 30(4), 611-624.
2. 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.
3. 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.
4. Boslaugh, S., & Watters, P. A. (2008). Statistics in a nutshell. Sebastopol, CA: O'Reilly Media, Inc.
5. 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.
6. 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.
7. Carree, M. A., Klomp, L., & Thurik, A. R. (2000). Productivity convergence in OECD manufacturing industries. Economics Letters, 66(3), 337-345.
8. Carroll, J. D., & Green, P. E. (1997). Psychometric methods in marketing research: Part II, multidimensional scaling. Journal of Marketing Research, 34(2), 193-204.
9. Cascini, G., & Zini, M. (2008). Measuring patent similarity by comparing inventions functional trees. Computer-Aided Innovation (CAI), 277, 31-42.
10. Cascini, G., Fantechi, A., & Spinicci, E. (2004). Natural language processing of patents and technical documentation. Document Analysis Systems, VI, 89-92.
11. Chen, R. (2009). Design patent map visualization display. Expert Systems with Applications, 36(10), 12362-12374.
12. Crampes, C., & Langinier, C. (2002). Litigation and settlement in patent infringement cases. The RAND Journal of Economics, 33(2), 258-274.
13. Dao, T. N., & Simpson, T. (2002). Measuring similarity between sentences. http://www. codeproject. com/KB/string/semanticsimilaritywordnet. aspx.
14. Davidson, I., & Ravi, S. (2005). Agglomerative hierarchical clustering with constraints: Theoretical and empirical results. Knowledge Discovery in Databases: PKDD, 2005, 59-70.
15. Durham, A. L. (2004). Patent law essentials: A concise guide. Westport, CT: Praeger Publishers.
16. Ernst, H. (1998). Patent portfolios for strategic R & D planning. Journal of Engineering and Technology Management, 15(4), 279-308.
17. 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.
18. 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.
19. 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.
20. 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.
21. Johnson, S. C. (1967). Hierarchical clustering schemes. Psychometrika, 32(3), 241-254.
22. Kim, Y., Suh, J., & Park, S. (2008). Visualization of patent analysis for emerging technology. Expert Systems with Applications, 34(3), 1804-1812.
23. Kruskal, J. B. (1964). Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika, 29(1), 1-27.
24. Lai, Y. H., & Che, H. C. (2009). Modeling patent legal value by Extension Neural Network. Expert Systems with Applications, 36(7), 10520-10528.
25. Lanjouw, J. O., & Schankerman, M. (2001). Characteristics of patent litigation: A window on competition. The RAND Journal of Economics, 32(1), 129-151.
26. Lin, D. (2003). Dependency-based evaluation of MINIPAR. In Treebanks: Building and using parsed corpora (Vol. 20, pp. 317-332). Springer Netherlands.
27. 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.
28. Manning, C. D., Schutze, H., & MITCogNet. (1999). Foundations of statistical natural language processing (Vol. 59). Cambridge, MA: MIT Press.
29. Mead, A. (1992). Review of the development of multidimensional scaling methods. The Statistician, 41(1), 27-39.
30. Miller, G. A. (1995). WordNet: A lexical database for English. Communications of the ACM, 38(11), 39-41.
31. Moehrle, M. G. (2010). Measures for textual patent similarities: a guided way to select appropriate approaches. Scientometrics, 85(1), 95-109.
32. 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.
33. 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.
34. 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.
35. Stanford (2011). The Stanford Parser: A statistical parser http://nlp. stanford. edu/software/lex-parser. shtml. Accessed Feb 2011.
36. 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.
37. 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.
38. Wickelmaier, F. (2003). An introduction to MDS. Aalborg: Aalborg Universitetsforlag.
39. 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.
40. Yoon, B. (2008). On the development of a technology intelligence tool for identifying technology opportunity. Expert Systems with Applications, 35(1-2), 124-135.
41. Yoon, J., & Kim, K. (2011a). Generation of patent maps using SAO-based semantic patent similarity. Entrue Journal of Information Technology, 10(1), 19-27.
42. 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.
43. 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.
44. 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.