Abstract
Patent maps showing competition trends in technological development can provide valuable input for decision support on research and development (R&D) strategies. By introducing semantic patent analysis with advantages in representing technological objectives and structures, this paper constructs dynamic patent maps to show technological competition trends and describes the strategic functions of the dynamic maps. The proposed maps are based on subject-action-object (SAO) structures that are syntactically ordered sentences extracted using the natural language processing of the patent text; the structures of a patent encode the key findings of the invention and expertise of its inventors. Therefore, this paper introduces a method of constructing dynamic patent maps using SAO-based content analysis of patents and presents several types of dynamic patent maps by combining patent bibliographic information and patent mapping and clustering techniques. Building on the maps, this paper provides further analyses to identify technological areas in which patents have not been granted (“patent vacuums”), areas in which many patents have actively appeared (“technological hot spots”), R&D overlap of technological competitors, and characteristics of patent clusters. The proposed analyses of dynamic patent maps are illustrated using patents related to the synthesis of carbon nanotubes. We expect that the proposed method will aid experts in understanding technological competition trends in the process of formulating R&D strategies.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
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.
Bollobas B (1983) The evolution of sparse graphs (Graph theory and combinatorics). London: Academic Press.
Cascini, G., Fantechi, A., & Spinicci, E. (2004). Natural language processing of patents and technical documentation. Lect Notes Comput Sci, 3163, 89–92.
Cascini, G., & Zini, M. (2008). Measuring patent similarity by comparing inventions functional trees. IFIP International Federation for Information Processing, 277, 31–42.
Chang, P., Wu, C., & Leu, H. (2010). Using patent analyses to monitor the technological trends in an emerging field of technology: a case of carbon nanotube field emission display. Scientometrics, 82(1), 5–19.
Choi, S., Lim, J., Yoon, J., Kim, K. (2010). Patent function network analysis: a function based approach for analyzing patent information. In: IAMOT 2010, Cairo, Egypt.
Choi, S., Yoon, J., Kim, K., Lee, J. Y., & Kim, C. (2011). SAO network analysis of patents for technology trends identification: a case study of polymer electrolyte membrane technology in proton exchange membrane fuel cells. Scientometrics, 88(3), 863–883.
Feldman, R., & Sanger, J. (2007). The text mining handbook: advanced approaches in analyzing unstructured data. New York: Cambridge University Press.
Fujii, A., Iwayama, M., & Kando, N. (2007). Introduction to the special issue on patent processing. Inf Process Manage, 43(5), 1149–1153.
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.
Hair, F., Black, C., Babin, J., & Anderson, E. (2010). Multivariate data analysis (7th ed.). New Jersey: Pearson Education Inc.
Jeong, B., Lee, D., Cho, H., & Lee, J. (2005). A novel method for measuring semantic similarity for XML schema matching. Expert Syst Appl, 34(3), 1651–1658.
KIPO. (2010). K2E automatic translation. http://eng.kipris.or.kr/eng/other_service/k2e_automatic_translation.jsp.
Kostoff, R. (1998). The use and misuse of citation analysis in research evaluation. Scientometrics, 43(1), 27–43.
Kruskal, J. (1964). Nonmetric multidimensional scaling: a numerical method. Psychometrika, 29(2), 115–129.
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.
Lin, D. (2010). MINIPAR. http://webdocs.cs.ualberta.ca/~lindek/minipar.htm.
MacQueen, J. (1967) Some methods for classification and analysis of multivariate observations (vol. 1, pp. 14). California: University of California Press.
Mann, D. (2002). Hands-on systematic innovation. Belgium: Creax press.
Miller, G. (1995). WordNet: a lexical database for English. Commun ACM, 38(11), 41.
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.
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.
Park, H., & Jun, C. (2009). A simple and fast algorithm for K-medios clustering. Expert Syst Appl, 36(2), 3336–3341.
Park, H., Yoon, J., & Kim, K. (2012). Identifying patent infringement using SAO-based semantic technological similarities. Scientometrics, 90(2), 515–529.
Peters, H., & Van Raan, A. (1993a). Co-word-based science maps of chemical engineering. Part I: representations by direct multidimensional scaling. Res Policy, 22(1), 23–45.
Peters, H., & Van Raan, A. (1993b). Co-word-based science maps of chemical engineering. Part II: representations by combined clustering and multidimensional scaling. Res Policy, 22(1), 47–71.
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.
Salton, G., Wong, A., & Yang, C. (1975). A vector space model for automatic indexing. Commun ACM, 18(11), 613–620.
Savransky, S. (2000). Engineering of creativity: introduction to TRIZ methodology of inventive problem solving. Boca Raton: CRC Press.
Schmoch, U. (2009). Evaluation of technological strategies of companies by means of MDS maps. International Journal of Technology Management, 10, 4(5), 426–440.
Simpson, T., & Dao, T. (2005). WordNet-based semantic similarity measurement. http://www.codeproject.com/KB/string/semanticsimilaritywordnet.aspx.
Stanford (2010). The Stanford Parser: A statistical parser. http://nlp.stanford.edu/software/lex-parser.shtml.
STOPWORDS. (2010). English stopwords. http://www.ranks.nl/resources/stopwords.html.
Wanner, L., Baeza-Yates, R., Brugmann, S., Codina, J., Diallo, B., Escorsa, E., et al. (2008). Towards content-oriented patent document processing. World Patent Inf, 30(1), 21–33.
WIPO (2010). PCT Yearly Review 2009. http://www.wipo.int/export/sites/www/ipstats/en/statistics/pct/pdf/901e_2009.pdf2011.
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.
Yoon, J., & Kim, K. (2011). Identifying rapidly evolving technological trends for R&D planning using SAO-based semantic patent networks. Scientometrics, 88(1), 213–228.
Yoon, J., & Kim, K. (2012). Detecting signals of new technological opportunities using SAO-based semantic patent analysis and outlier detection. Scientometrics, 90(2), 445–461.
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.
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.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Yoon, J., Park, H. & Kim, K. Identifying technological competition trends for R&D planning using dynamic patent maps: SAO-based content analysis. Scientometrics 94, 313–331 (2013). https://doi.org/10.1007/s11192-012-0830-6
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11192-012-0830-6
Keywords
- Dynamic patent map
- Subject-action-object (SAO) structure
- Semantic patent similarity
- Natural language processing (NLP)
- Research and development (R&D) strategy