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Singling out individual inventors from patent data

Author

Listed:
  • Ernest Miguélez

    () (AQR-IREA. Department of Econometrics, Statistics and Spanish Economy. University of Barcelona, Av. Diagonal 690, 08034 Barcelona, Spain)

  • Ismael Gómez-Miguélez

    () (Signal Theory and Communications Department. Technical University of Catalonia, c/ Jordi Girona 1-3, 08034 Barcelona, Spain.)

Abstract

An increasing number of studies have sprung up in recent years seeking to identify individual inventors from patent data. Different heuristics have been suggested to use their names and other information disclosed in patent documents in order to find out “who is who” in patents. This paper contributes to this literature by setting forth a methodology to identify them using patents applied to the European Patent Office (EPO hereafter). As in the large part of this literature, we basically follow a three-steps 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 possible similar names; (3) the filtering stage, where additional information and different scoring schemes are used to filter out these potential same inventors. The paper includes some figures resulting of applying the algorithms to the set of European inventors applying to the EPO for a large period of time.

Suggested Citation

  • Ernest Miguélez & Ismael Gómez-Miguélez, 2011. "Singling out individual inventors from patent data," Working Papers XREAP2011-03, Xarxa de Referència en Economia Aplicada (XREAP), revised May 2011.
  • Handle: RePEc:xrp:wpaper:xreap2011-03
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    References listed on IDEAS

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    1. Ajay Agrawal & Iain Cockburn & John McHale, 2003. "Gone But Not Forgotten: Labor Flows, Knowledge Spillovers, and Enduring Social Capital," NBER Working Papers 9950, National Bureau of Economic Research, Inc.
    2. Lorenzo Cassi & Nicolas Carayol, 2009. "Who's Who in Patents. A Bayesian approach," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-00631750, HAL.
    3. Zvi Griliches, 1998. "Patent Statistics as Economic Indicators: A Survey," NBER Chapters,in: R&D and Productivity: The Econometric Evidence, pages 287-343 National Bureau of Economic Research, Inc.
    4. Jinyoung Kim & Sangjoon John Lee & Gerald Marschke, 2009. "International Knowledge Flows: Evidence from an Inventor-Firm Matched Data Set," NBER Chapters,in: Science and Engineering Careers in the United States: An Analysis of Markets and Employment, pages 321-348 National Bureau of Economic Research, Inc.
    5. Manuel Trajtenberg & Gil Shiff & Ran Melamed, 2009. "The "Names Game": Harnessing Inventors, Patent Data for Economic Research," Annals of Economics and Statistics, GENES, issue 93-94, pages 67-77.
    6. Grid Thoma & Salvatore Torrisi, 2007. "Creating Powerful Indicators for Innovation Studies with Approximate Matching Algorithms. A test based on PATSTAT and Amadeus databases," KITeS Working Papers 211, KITeS, Centre for Knowledge, Internationalization and Technology Studies, Universita' Bocconi, Milano, Italy, revised Dec 2007.
    7. Stéphane Maraut & Hélène Dernis & Colin Webb & Vincenzo Spiezia & Dominique Guellec, 2008. "The OECD REGPAT Database: A Presentation," OECD Science, Technology and Industry Working Papers 2008/2, OECD Publishing.
    8. Bottazzi, Laura & Peri, Giovanni, 2003. "Innovation and spillovers in regions: Evidence from European patent data," European Economic Review, Elsevier, vol. 47(4), pages 687-710, August.
    9. Raffo, Julio & Lhuillery, Stéphane, 2009. "How to play the "Names Game": Patent retrieval comparing different heuristics," Research Policy, Elsevier, vol. 38(10), pages 1617-1627, December.
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    Citations

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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.

    More about this item

    Keywords

    “Names game”; patent data; unique inventors; name matching algorithms;

    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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