Abstract
Technology analysis is a process which uses textual analysis to detect trends in technological innovation. Co-word analysis (CWA), a popular method for technology analysis, encompasses (1) defining a set of keyword or key phrase patterns which are represented in technology-dependent terms, (2) generating a network that codifies the relations between occurrences of keywords or key phrases, and (3) identifying specific trends from the network. However, defining the set of keyword or key phrase patterns heavily relies on effort of experts, who may be expensive or unavailable. Furthermore defining keyword or key phrase patterns of new or emerging technology areas may be a difficult task even for experts. To solve the limitation in CWA, this research adopts a property-function based approach. The property is a specific characteristic of a product, and is usually described using adjectives; the function is a useful action of a product, and is usually described using verbs. Properties and functions represent the innovation concepts of a system, so they show innovation directions in a given technology. The proposed methodology automatically extracts properties and functions from patents using natural language processing. Using properties and functions as nodes, and co-occurrences as links, an invention property-function network (IPFN) can be generated. Using social network analysis, the methodology analyzes technological implications of indicators in the IPFN. Therefore, without predefining keyword or key phrase patterns, the methodology assists experts to more concentrate on their knowledge services that identify trends in technological innovation from patents. The methodology is illustrated using a case study of patents related to silicon-based thin film solar cells.
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References
Alcacer, J., & Gittelman, M. (2006). Patent citations as a measure of knowledge flows: The influence of examiner citations. Review of Economics and Statistics, 88(4), 774–779.
Bollobas, B. (1983). The evolution of sparse graphs. Graph theory and combinatorics (pp. 35–57). London: Academic Press.
Callon, M., Courtial, J., & Laville, F. (1991). Co-word analysis as a tool for describing the network of interactions between basic and technological research: The case of polymer chemistry. Scientometrics, 22(1), 155–205.
Callon, M., Courtial, J., Turner, W., & Bauin, S. (1983). From translations to problematic networks: An introduction to co-word analysis. Social Science Information, 22(2), 191.
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.
Chen, L., Tokuda, N., & Adachi, H. (2003). A patent document retrieval system addressing both semantic and syntactic properties. In: Proceedings of the ACL-2003 workshop on patent corpus processing (pp. 1–6). Japan: Sapporo.
Choi, S., Lim, J., Yoon, J., & Kim, K. (2010). Patent function network analysis: In: A function based approach for analyzing patent information. Egypt, Cairo: IAMOT.
de Marneffe, M., & Manning, C. (2008a). Stanford typed dependencies manual. Technical report.
de Marneffe, M., & Manning, C. (2008b). The stanford typed dependencies representation. In: Association for computational linguistics (pp. 1–8). Stanford: Stanford University.
Dewulf, S. (2006). Directed variation: Variation of properties for new or improved function product DNA, a base for ‘connect and develop’. Kortrijk, Belgium: ETRIA TRIZ Futures.
Diestel, R. (2005). Graph theory. Graduate texts in mathematics (vol. 173). Berlin: Springer-Verlag.
Freeman, L. (1979). Centrality in social networks conceptual clarification. Social Networks, 1(3), 215–239.
Hanneman, R., & Riddle, M. (2005). Introduction to social network methods. Riverside, CA: University of California, Riverside.
Hendrix, D. (2009). Institutional self-citation rates: A three year study of universities in the united states. Scientometrics, 81(2), 321–331.
Hirtz, J., Stone, R., McAdams, D., Szykman, S., & Wood, K. (2001). A functional basis for engineering design: Reconciling and evolving previous efforts. Research in Engineering Design, 13(2), 65–82.
Hopcroft, J., & Tarjan, R. (1973). Algorithm 447: Efficient algorithms for graph manipulation. Communications of the ACM, 16(6), 372–378.
Klein, D., & Manning, C. (2003). Fast exact inference with a factored model for natural language parsing. Advances in Neural Information Processing Systems, 15, 3–10.
Kostoff, R. (1998). The use and misuse of citation analysis in research evaluation. Scientometrics, 43(1), 27–43.
Lee, B., & Jeong, Y. (2008). Mapping Korea’s national r&d domain of robot technology by using the co-word analysis. Scientometrics, 77(1), 3–19.
Mann, D. (2002). Hands-on systematic innovation. Belgium: Creax press.
Neff, M., & Corley, E. (2009). 35 years and 160, 000 articles: A bibliometric exploration of the evolution of ecology. Scientometrics, 80(3), 657–682.
Pritchard, A. (1969). Statistical bibliography or bibliometrics. Journal of Documentation, 25(4), 348–349.
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.
Thompson, P., & Fox-Kean, M. (2005). Patent citations and the geography of knowledge spillovers: A reassessment. American Economic Review, 95(1), 450–460.
Tong, L., Cong, H., & Lixiang, S. (2006). Automatic classification of patent documents for triz users. World Patent Information, 28(1), 6–13.
Verhaegen, P. A., D’Hondt, J., Vertommen, J., Dewulf, S., & Duflou, J. R. (2009). Relating properties and functions from patents to triz trends. CIRP Journal of Manufacturing Science and Technology, 1(3), 126–130.
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.
Zhang, L., Janssens, F., Liang, L., & Glanzel, W. (2010). Journal cross-citation analysis for validation and improvement of journal-based subject classification in bibliometric research. Scientometrics, 82(3), 687–706.
Zhang, W., Qian, W., & Ho, Y. (2009). A bibliometric analysis of research related to ocean circulation. Scientometrics, 80(2), 305–316.
Acknowledgment
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. 2009-0088379).
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Yoon, J., Choi, S. & Kim, K. Invention property-function network analysis of patents: a case of silicon-based thin film solar cells. Scientometrics 86, 687–703 (2011). https://doi.org/10.1007/s11192-010-0303-8
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DOI: https://doi.org/10.1007/s11192-010-0303-8
Keywords
- Property
- Function
- Patent mining
- Patent analysis
- Technology analysis
- Natural language processing
- Social network analysis
- Technological trend