View More View Less
  • 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 howgood@postech.ac.kr
  • | 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 janghyoon@postech.ac.kr
Restricted access

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.

  • Arundel, A 2001 The relative effectiveness of patents and secrecy for appropriation. Research Policy 30 4 611624 .

  • 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 BetriebswirtschaftSpecial issue, (4), 69-86.

    • Search Google Scholar
    • Export Citation
  • Bergmann, I, Butzke, D, Walter, L, Fuerste, JP, Moehrle, MG, Erdmann, VA 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
  • Boslaugh, S, Watters, PA 2008 Statistics in a nutshell O’Reilly Media, Inc. Sebastopol, CA.

  • 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.

    • Search Google Scholar
    • Export Citation
  • Buja, A, Swayne, DF, Littman, ML, Dean, N, Hofmann, H, Chen, L 2008 Data visualization with multidimensional scaling. Journal of Computational and Graphical Statistics 17 2 444472 .

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Carree, MA, Klomp, L, Thurik, AR 2000 Productivity convergence in OECD manufacturing industries. Economics Letters 66 3 337345 .

  • Carroll, JD, Green, PE 1997 Psychometric methods in marketing research: Part II, multidimensional scaling. Journal of Marketing Research 34 2 193204 .

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

    • Search Google Scholar
    • Export Citation
  • Cascini, G, Fantechi, A, Spinicci, E 2004 Natural language processing of patents and technical documentation. Document Analysis Systems VI:8992.

    • Search Google Scholar
    • Export Citation
  • Chen, R 2009 Design patent map visualization display. Expert Systems with Applications 36 10 1236212374 .

  • Crampes, C., & Langinier, C. (2002). Litigation and settlement in patent infringement cases. The RAND Journal of Economics, 33 (2), 258274.

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

    • Search Google Scholar
    • Export Citation
  • Davidson, I, Ravi, S 2005 Agglomerative hierarchical clustering with constraints: Theoretical and empirical results. Knowledge Discovery in Databases: PKDD 2005:5970 .

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Durham, AL 2004 Patent law essentials: A concise guide Praeger Publishers Westport, CT.

  • Ernst, H 1998 Patent portfolios for strategic R&D planning. Journal of Engineering and Technology Management 15 4 279308 .

  • Franzosi, R 1994 From words to numbers: A set theory framework for the collection, organization and analysis of narrative data. Sociological methodology 24:105136 .

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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
  • Hall, BH, Ziedonis, RH 2001 The patent paradox revisited: An empirical study of patenting in the US semiconductor industry, 1979–1995. The RAND Journal of Economics 32 1 101128 .

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, JJ, Ong, CS, Tzeng, GH 2006 Interval multidimensional scaling for group decision using rough set concept. Expert Systems with Applications 31 3 525530 .

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Johnson, SC 1967 Hierarchical clustering schemes. Psychometrika 32 3 241254 .

  • Kim, Y, Suh, J, Park, S 2008 Visualization of patent analysis for emerging technology. Expert Systems with Applications 34 3 18041812 .

  • Kruskal, JB 1964 Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika 29 1 127 .

  • Lai, YH, Che, HC 2009 Modeling patent legal value by Extension Neural Network. Expert Systems with Applications 36 7 1052010528 .

  • Lanjouw, JO, Schankerman, M 2001 Characteristics of patent litigation: A window on competition. The RAND Journal of Economics 32 1 129151 .

  • Lin, D. (2003). Dependency-based evaluation of MINIPAR. In Treebanks: Building and using parsed corpora (Vol. 20, pp. 317332). Springer Netherlands.

    • Search Google Scholar
    • Export Citation
  • 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. 201228). Emerald Group Publishing Limited.

    • Search Google Scholar
    • Export Citation
  • Manning, CD, Schutze, H MITCogNet 1999 Foundations of statistical natural language processing 59 MIT Press Cambridge, MA.

  • Mead, A 1992 Review of the development of multidimensional scaling methods. The Statistician 41 1 2739 .

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

  • Moehrle, M. G. (2010). Measures for textual patent similarities: a guided way to select appropriate approaches. Scientometrics, 85 (1), 95109.

    • Search Google Scholar
    • Export Citation
  • Moehrle, MG, 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
  • 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.

    • Search Google Scholar
    • Export Citation
  • Soo, VW, Lin, SY, Yang, SY, Lin, SN, Cheng, SL 2006 A cooperative multi-agent platform for invention based on patent document analysis and ontology. Expert Systems with Applications 31 4 766775 .

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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.

    • Search Google Scholar
    • Export Citation
  • Wallerstein, MB, Mogee, ME, Schoen, RA 1993 Global dimensions of intellectual property rights in science and technology National Academies Press Washington, DC.

    • Search Google Scholar
    • Export Citation
  • Wickelmaier, F 2003 An introduction to MDS Aalborg Universitetsforlag Aalborg.

  • Wu, Z., & Palmer, M. (1994). Verbs semantics and lexical selection. In Proceedings of the 32nd annual meeting on association for computational linguistics, Morristown (pp. 133138). Association for Computational Linguistics.

    • Search Google Scholar
    • Export Citation
  • 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, 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. (2011b). Identifying rapidly evolving technological trends for R&D planning using SAO-based semantic patent networks. Scientometrics. doi: .

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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 3750 .

    • 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

Manuscript submission: http://www.editorialmanager.com/scim/

  • Impact Factor (2019): 2.867
  • Scimago Journal Rank (2019): 1.210
  • SJR Hirsch-Index (2019): 106
  • SJR Quartile Score (2019): Q1 Computer Science Apllications
  • SJR Quartile Score (2019): Q1 Library and Information Sciences
  • SJR Quartile Score (2019): Q1 Social Sciences (miscellaneous)
  • Impact Factor (2018): 2.770
  • Scimago Journal Rank (2018): 1.113
  • SJR Hirsch-Index (2018): 95
  • SJR Quartile Score (2018): Q1 Library and Information Sciences
  • SJR Quartile Score (2018): Q1 Social Sciences (miscellaneous)

For subscription options, please visit the website of Springer

Scientometrics
Language English
Size B5
Year of
Foundation
1978
Volumes
per Year
4
Issues
per Year
12
Founder Akadémiai Kiadó
Founder's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Publisher Akadémiai Kiadó
Springer Nature Switzerland AG
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
CH-6330 Cham, Switzerland Gewerbestrasse 11.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 0138-9130 (Print)
ISSN 1588-2861 (Online)