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Identifying rapidly evolving technological trends for R&D planning using SAO-based semantic patent networks

Author

Listed:
  • Janghyeok Yoon

    (Pohang University of Science and Technology)

  • Kwangsoo Kim

    (Pohang University of Science and Technology)

Abstract

Patents constitute an up-to-date source of competitive intelligence in technological development; thus, patent analysis has been a vital tool for identifying technological trends. Patent citation analysis is easy to use, but fundamentally has two main limitations: (1) new patents tend to be less cited than old ones and may miss citations to contemporary patents; (2) citation-based analysis cannot be used for patents in databases which do not require citations. Naturally, citation-based analysis tends to underestimate the importance of new patents and may not work in rapidly-evolving industries in which technology life-cycles are shortening and new inventions are increasingly patented world-wide. As a remedy, this paper proposes a patent network based on semantic patent analysis using subject-action-object (SAO) structures. SAO structures represent the explicit relationships among components used in a patent, and are considered to represent key concepts of the patent or the expertise of the inventor. Based on the internal similarities between patents, the patent network provides the up-to-date status of a given technology. Furthermore, this paper suggests new indices to identify the technological importance of patents, the characteristics of patent clusters, and the technological capabilities of competitors. The proposed method is illustrated using patents related to synthesis of carbon nanotubes. We expect that the proposed procedure and analysis will be incorporated into technology planning processes to assist experts such as researchers and R&D policy makers in rapidly-evolving industries.

Suggested Citation

  • Janghyeok Yoon & Kwangsoo Kim, 2011. "Identifying rapidly evolving technological trends for R&D planning using SAO-based semantic patent networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(1), pages 213-228, July.
    • Handle: RePEc:spr:scient:v:88:y:2011:i:1:d:10.1007_s11192-011-0383-0
    DOI: 10.1007/s11192-011-0383-0
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    References listed on IDEAS

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    1. Janghyeok Yoon & Sungchul Choi & Kwangsoo Kim, 2011. "Invention property-function network analysis of patents: a case of silicon-based thin film solar cells," Scientometrics, Springer;Akadémiai Kiadó, vol. 86(3), pages 687-703, March.
    2. Roberto Fontana & Alessandro Nuvolari & Bart Verspagen, 2009. "Mapping technological trajectories as patent citation networks. An application to data communication standards," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 18(4), pages 311-336.
    3. G. M.P. Swann, 2009. "The Economics of Innovation," Books, Edward Elgar Publishing, number 13211.
    4. Albert, M. B. & Avery, D. & Narin, F. & McAllister, P., 1991. "Direct validation of citation counts as indicators of industrially important patents," Research Policy, Elsevier, vol. 20(3), pages 251-259, June.
    5. Karki, M. M. S., 1997. "Patent citation analysis: A policy analysis tool," World Patent Information, Elsevier, vol. 19(4), pages 269-272, December.
    6. Shiu-Wan Hung & An-Pang Wang, 2010. "Examining the small world phenomenon in the patent citation network: a case study of the radio frequency identification (RFID) network," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(1), pages 121-134, January.
    7. Reitzig, Markus, 2004. "Improving patent valuations for management purposes--validating new indicators by analyzing application rationales," Research Policy, Elsevier, vol. 33(6-7), pages 939-957, September.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Patent mining; Semantic patent similarity; Subject-action-object (SAO) structures; Patent network; Research and development (R&D) trend; Natural language processing (NLP);
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access

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