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“Singling out individual inventors from patent data”

  • Ernest Miguélez

    ()

    (Faculty of Economics, University of Barcelona)

  • Ismael Gómez-Miguélez

    ()

    (Technical University of Catalonia)

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.

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File URL: http://www.ub.edu/irea/working_papers/2011/201105.pdf
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Paper provided by University of Barcelona, Research Institute of Applied Economics in its series IREA Working Papers with number 201105.

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Length: 39 pages
Date of creation: May 2011
Date of revision: May 2011
Handle: RePEc:ira:wpaper:201105
Contact details of provider: Postal: Tinent Coronel Valenzuela, Num 1-11 08034 Barcelona
Web page: http://www.ub.edu/irea/

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  1. 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.
  2. Jinyoung Kim & Sangjoon John Lee & Gerald Marschke, 2007. "International Knowledge Flows: Evidence from an Inventor-Firm Matched Data Set," Discussion Paper Series 0706, Institute of Economic Research, Korea University.
  3. 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.
  4. 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.
  5. Melamed, Ran & Shiff, Gil & Trajtenberg, Manuel, 2006. "The 'Names Game': Harnessing Inventors Patent Data for Economic Research," CEPR Discussion Papers 5833, C.E.P.R. Discussion Papers.
  6. 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.
  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. 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.
  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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