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“Singling out individual inventors from patent data”

Author

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  • Ernest Miguélez

    (Faculty of Economics, University of Barcelona)

  • Ismael Gómez-Miguélez

    (Technical University of Catalonia)

Abstract

An increasing number of studies in recent years have sought to identify individual inventors from patent data. A variety of heuristics have been proposed for using the names and other information disclosed in patent documents to establish “who is who” in patents. This paper contributes to this literature by describing a methodology for identifying inventors using patents applied to the European Patent Office (EPO hereafter). As in much of this literature, we basically follow a three-step procedure: (1) the parsing stage, aimed at reducing the noise in the inventor’s name and other fields of the patent; (2) the matching stage, where name matching algorithms are used to group similar names; and (3) the filtering stage, where additional information and various scoring schemes are used to filter out these similarly-named inventors. The paper presents the results obtained by using the algorithms with the set of European inventors applying to the EPO over a long period of time.

Suggested Citation

  • Ernest Miguélez & Ismael Gómez-Miguélez, 2011. "“Singling out individual inventors from patent data”," IREA Working Papers 201105, University of Barcelona, Research Institute of Applied Economics, revised May 2011.
    • Handle: RePEc:ira:wpaper:201105
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    File URL: http://www.ub.edu/irea/working_papers/2011/201105.pdf
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    References listed on IDEAS

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    Cited by:

    1. Ventura, Samuel L. & Nugent, Rebecca & Fuchs, Erica R.H., 2015. "Seeing the non-stars: (Some) sources of bias in past disambiguation approaches and a new public tool leveraging labeled records," Research Policy, Elsevier, vol. 44(9), pages 1672-1701.

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    More about this item

    Keywords

    “Names game”; patent data; unique inventors; name matching algorithms. JEL classification:C8; J61; O31; O33; R0.;
    All these keywords.

    JEL classification:

    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • J61 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Geographic Labor Mobility; Immigrant Workers
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • R0 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General

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