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Mapping (USPTO) patent data using overlays to Google Maps

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  • Loet Leydesdorff
  • Lutz Bornmann

Abstract

A technique is developed using patent information available online (at the U.S. Patent and Trademark Office) for the generation of Google Maps. The overlays indicate both the quantity and the quality of patents at the city level. This information is relevant for research questions in technology analysis, innovation studies, and evolutionary economics, as well as economic geography. The resulting maps can also be relevant for technological innovation policies and research and development management, because the U.S. market can be considered the leading market for patenting and patent competition. In addition to the maps, the routines provide quantitative data about the patents for statistical analysis. The cities on the map are colored according to the results of significance tests. The overlays are explored for the Netherlands as a “national system of innovations” and further elaborated in two cases of emerging technologies: ribonucleic acid interference (RNAi) and nanotechnology.
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  • Loet Leydesdorff & Lutz Bornmann, 2012. "Mapping (USPTO) patent data using overlays to Google Maps," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(7), pages 1442-1458, July.
    • Handle: RePEc:bla:jinfst:v:63:y:2012:i:7:p:1442-1458
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    Cited by:

    1. Xu Bai & Jinxi Wu & Yun Liu & Yihan Xu, 2020. "Research on the impact of global innovation network on 3D printing industry performance," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 1015-1051, August.
    2. Loet Leydesdorff & Duncan Kushnir & Ismael Rafols, 2014. "Interactive overlay maps for US patent (USPTO) data based on International Patent Classification (IPC)," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(3), pages 1583-1599, March.
    3. Sangsung Park & Sunghae Jun, 2017. "Technology Analysis of Global Smart Light Emitting Diode (LED) Development Using Patent Data," Sustainability, MDPI, vol. 9(8), pages 1-15, August.
    4. Yanto Chandra, 2018. "Mapping the evolution of entrepreneurship as a field of research (1990–2013): A scientometric analysis," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-24, January.
    5. Loet Leydesdorff & Daniele Rotolo & Ismael Rafols, 2012. "Bibliometric perspectives on medical innovation using the medical subject Headings of PubMed," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(11), pages 2239-2253, November.
    6. Balázs Lengyel & Tamás Sebestyén & Loet Leydesdorff, 2015. "Challenges for regional innovation policies in Central and Eastern Europe: Spatial concentration and foreign control of US patenting," Science and Public Policy, Oxford University Press, vol. 42(1), pages 1-14.
    7. Zhang, Yi & Shang, Lining & Huang, Lu & Porter, Alan L. & Zhang, Guangquan & Lu, Jie & Zhu, Donghua, 2016. "A hybrid similarity measure method for patent portfolio analysis," Journal of Informetrics, Elsevier, vol. 10(4), pages 1108-1130.
    8. Øivind Strand & Inga Ivanova & Loet Leydesdorff, 2017. "Decomposing the Triple-Helix synergy into the regional innovation systems of Norway: firm data and patent networks," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(3), pages 963-988, May.
    9. Rotolo, Daniele & Hicks, Diana & Martin, Ben R., 2015. "What is an emerging technology?," Research Policy, Elsevier, vol. 44(10), pages 1827-1843.
    10. Loet Leydesdorff & Floortje Alkemade & Gaston Heimeriks & Rinke Hoekstra, 2015. "Patents as instruments for exploring innovation dynamics: geographic and technological perspectives on “photovoltaic cells”," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(1), pages 629-651, January.
    11. Jongho Lee & Keun Lee, 2021. "Is the fourth industrial revolution a continuation of the third industrial revolution or something new under the sun? Analyzing technological regimes using US patent data [Vertical integration and ," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 30(1), pages 137-159.
    12. Ignacio Rodríguez-Rodríguez & José-Víctor Rodríguez & Niloofar Shirvanizadeh & Andrés Ortiz & Domingo-Javier Pardo-Quiles, 2021. "Applications of Artificial Intelligence, Machine Learning, Big Data and the Internet of Things to the COVID-19 Pandemic: A Scientometric Review Using Text Mining," IJERPH, MDPI, vol. 18(16), pages 1-29, August.
    13. Daniele Rotolo & Ismael Rafols & Michael Hopkins & Loet Leydesdorff, 2014. "Scientometric Mapping as a Strategic Intelligence Tool for the Governance of Emerging Technologies," SPRU Working Paper Series 2014-10, SPRU - Science Policy Research Unit, University of Sussex Business School.
    14. Werner Ebeling & Andrea Scharnhorst, 2014. "Modellierungskonzepte der Synergetik und der Theorie der Selbstorganisation," Papers 1412.2399, arXiv.org.

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