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      Disambiguation of patent inventors and assignees using high-resolution geolocation data

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      1 , 2 , 2 , 3 , a , 2 , 4

      Scientific Data

      Nature Publishing Group

      Technology, Economics

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          Abstract

          Patent data represent a significant source of information on innovation, knowledge production, and the evolution of technology through networks of citations, co-invention and co-assignment. A major obstacle to extracting useful information from this data is the problem of name disambiguation: linking alternate spellings of individuals or institutions to a single identifier to uniquely determine the parties involved in knowledge production and diffusion. In this paper, we describe a new algorithm that uses high-resolution geolocation to disambiguate both inventors and assignees on about 8.5 million patents found in the European Patent Office (EPO), under the Patent Cooperation Treaty (PCT), and in the US Patent and Trademark Office (USPTO). We show this disambiguation is consistent with a number of ground-truth benchmarks of both assignees and inventors, significantly outperforming the use of undisambiguated names to identify unique entities. A significant benefit of this work is the high quality assignee disambiguation with coverage across the world coupled with an inventor disambiguation (that is competitive with other state of the art approaches) in multiple patent offices.

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          Most cited references 46

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          The NBER Patent Citation Data File: Lessons, Insights and Methodological Tools

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            Patent Statistics as Economic Indicators: A Survey

             Zvi Griliches (1990)
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              International Knowledge Flows: Evidence From Patent Citations

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                Author and article information

                Journal
                Sci Data
                Sci Data
                Scientific Data
                Nature Publishing Group
                2052-4463
                16 May 2017
                2017
                : 4
                Affiliations
                [1 ]Department of Physics, The University of Houston , Houston, Texas, USA
                [2 ]IMT Institute for Advanced Studies , Lucca, Italy
                [3 ]Department of Managerial Economics, Strategy and Innovation, K.U. Leuven , Leuven, Belgium
                [4 ]Politecnico di Milano , Milan, Italy
                Author notes
                [a ] G.M. (email: gcmorrison@ 123456uh.edu ).
                []

                All authors contributed to the design of the method and reviewed the paper. G.M. performed the calculations, generated the figures, organized the data, and wrote the paper.

                Article
                sdata201764
                10.1038/sdata.2017.64
                5433392
                28509897
                Copyright © 2017, The Author(s)

                This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files made available in this article.

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                technology, economics

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