Abstract
In today’s competitive business environment, the timely identification of potential technology opportunities is becoming increasingly important for the strategic management of technology and innovation. Existing studies in the field of technology opportunity discovery (TOD) focus exclusively on patent textual information. In this article, we introduce a new method that tackles TOD via technology convergence, using both patent textual data and patent citation networks. We identify technology groups with high convergence potential by measuring connectivity between clusters of patents. From such technology groups we select pairs of core patents based on their technological relatedness, on their past involvement in convergence, and on the impact of their new potential convergence. We finally carry out TOD by extracting representative keywords from the text of the selected patent pairs and organizing them into the basic description of a new invention, which the potential convergence of the patent pair might produce. We illustrate our proposed method using a data set of U.S. patents in the field of digital information and security.
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References
Asthana, M. K., & Panda, D. M. R. (2002). Technology convergence: The human perspective. Delhi Business Review, 3(1), 1–18.
Beaudry D. N. (2007). The effects of technology convergence on markets. Dynamics of Globalization—AIB Northeast 2007 Conference (pp.1–16).
Bishop, C. M. (2006). Pattern recognition and machine learning. Berlin: Springer.
Bosworth, D. L. (1978). The rate of obsolescence of technical knowledge: A note. Journal of Industrial Economics, 26, 273–279.
Brookes, B. C. (1970). The growth, utility, and obsolescence of scientific periodical literature. The Journal of Documentation, 26, 283–294.
Cho, T. S., & Shih, H. Y. (2011). Patent citation network analysis of core and emerging technologies in Taiwan: 1997–2008. Scientometrics, 89, 795–811.
Choi, S., Yoon, J., Kim, K., Lee, J. Y. L., & Kim, C.-H. (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, 863–883.
Fouss, F., Francoisse, K., Yen, L., Pirotte, A., & Saerens, M. (2012). An experimental investigation of kernels on graphs for collaborative recommendation and semisupervised classification. Neural Networks, 31, 53–72.
Joo, S. H., & Kim, Y. (2010). Measuring relatedness between technological fields. Scientometrics, 83, 435–454.
Karvonen M., & Kässi T. (2011) Patent citation analysis as a tool for analyzing industry convergence. In Proceedings of Technology Management in the Energy Smart World (PICMET) (pp.1–13).
Kessler, M. M. (1963). Bibliographic coupling between scientific papers. American Documentation, 14, 10–25.
Kim, D., Lee, B., Lee, H. J., Lee, S. P., Moon, Y., & Jeong, M. K. (2012). Automated detection of influential patents using singular values. IEEE Transactions on Automation Science and Engineering, 9, 723–733.
Kim, D., Lee, B., Lee, H. J., Lee, S. P., Moon, Y., & Jeong, M. K. (2013). A graph kernel approach for the simultaneous detection of the core patent and patent groups. IEEE Transactions Intelligence Systems, (in press).
Kim, Y. G., Suh, J. H., & Park, S. C. (2008). Visualization of patent analysis for emerging technology. Expert Systems with Applications, 34, 1804–1812.
Knoke, D., & Burt, R. S. (1983). Prominence. In R. S. Burt & M. J. Minor (Eds.), Applied network analysis (pp. 195–222). Beverly Hills, CA: Sage Publications.
Kwon, O., Lee, B., Seo, J., Noh, K., Lee, J., & Kim, J. S. (2009). A method to make the genealogical graph of core documents from the directed citation network. Information—An International Interdisciplinary Journal, 12(12), 875–888.
Lee, C., Cho, Y., Seol, H., & Park, Y. (2012). A stochastic patent citation analysis approach to assessing future technological impacts. Technological Forecasting and Social Change, 79, 16–29.
Lee, M., Lee, S., Kim, J., Seo, D., Kim, P., Jung, H., et al. (2011). Decision-making support service based on technology opportunity discovery model. U- and E-Service, Science and Technology, 264, 263–268.
Lee, S., Yoon, B., & Park, Y. (2009). An approach to discovering new technology opportunities: Keyword-based patent map approach. Technovation, 29, 481–497.
Leicht, E. A., & Newman, M. E. J. (2008). Community structure in directed networks. Physical Review Letters, 100(11), 118703.
Pakes, A., & Schankerman, M. (1984). The rate of obsolescence of patents, research gestation lags, and the private rate of return to research resources. In Z. Griliches (Ed.), R&D, patents, and productivity (pp. 73–88). Chicago: University of Chicago Press.
Park, H. S., Ree, J. J., & Kim, K. (2012). Identification of promising patents for technology transfers using TRIZ evolution trends. Expert Systems with Applications, 40, 736–743.
Porter, A. L., & Detampel, M. J. (1995). Technology opportunities analysis. Technology Forecasting and Social Change, 49, 237–255.
Small, H. (1973). Co-citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for Information Science, 24, 265–269.
Theodoridis, S., & Koutroumbas, K. (2009). Pattern recognition (4th ed.). San Diego: Academic Press.
Tseng, Y. H., Lin, C. J., & Lin, Y. I. (2007). Text mining techniques for patent analysis. Information Processing and Management, 43, 1216–1247.
Yoon, B. (2008). On the development of a technology intelligence tool for identifying technology opportunity. Expert Systems with Applications, 35, 124–135.
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, 687–703.
Yoon, J., & Kim, K. (2011). Identifying rapidly evolving technological trends for R&D planning using SAO-based semantic patent networks. Scientometrics, 88, 213–228.
Yoon, J., & Kim, K. (2012). Detecting signals of new technological opportunities using semantic patent analysis and outlier detection. Scientometrics, 90, 445–461.
Yoon, B., & Park, Y. (2005). A systematic approach for identifying technology opportunities: Keyword-based morphology analysis. Technological Forecasting and Social Change, 72, 145–160.
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Kim, B., Gazzola, G., Lee, JM. et al. Inter-cluster connectivity analysis for technology opportunity discovery. Scientometrics 98, 1811–1825 (2014). https://doi.org/10.1007/s11192-013-1097-2
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DOI: https://doi.org/10.1007/s11192-013-1097-2