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The proximity of ideas: An analysis of patent text using machine learning

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  • Sijie Feng

Abstract

This paper introduces a measure of the proximity in ideas using unsupervised machine learning. Knowledge transfers are considered a key driving force of innovation and regional economic growth. I explore knowledge relationships by deriving vector space representations of a patent’s abstract text using Document Vectors (Doc2Vec), and using cosine similarity to measure their proximity in ideas space. I illustrate the potential uses of this method with an application to geographic localization in knowledge spillovers. For patents in the same technology field, their normalized text similarity is 0.02-0.05 S.D.s higher if they are located within the same city, compared to patents from other cities. This effect is much smaller than when knowledge transfers are measured using normalized patent citations: local patents receive about 0.23-0.30 S.D.s more local citations than compared to non-local control patents. These findings suggest that the effect of geography on knowledge transfers may be much smaller than the previous literature using citations suggests.

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  • Sijie Feng, 2020. "The proximity of ideas: An analysis of patent text using machine learning," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-19, July.
    • Handle: RePEc:plo:pone00:0234880
    DOI: 10.1371/journal.pone.0234880
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    Cited by:

    1. A. Fronzetti Colladon & B. Guardabascio & F. Venturini, 2023. "A new mapping of technological interdependence," Papers 2308.00014, arXiv.org, revised Mar 2024.
    2. Hongshu Chen & Xinna Song & Qianqian Jin & Ximeng Wang, 2022. "Network dynamics in university-industry collaboration: a collaboration-knowledge dual-layer network perspective," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6637-6660, November.
    3. Jessica Birkholz & Jarina Kühn & Mariia Shkolnykova, 2022. "Exploration or Exploitation: Innovation Behavior of SMEs and Large Firms during the COVID-19 Crisis," Bremen Papers on Economics & Innovation 2203, University of Bremen, Faculty of Business Studies and Economics.
    4. de Rassenfosse, Gaétan & Palangkaraya, Alfons, 2023. "Do patent pledges accelerate innovation?," Research Policy, Elsevier, vol. 52(5).
    5. Ascione, Grazia Sveva, 2023. "Technological diversity to address complex challenges: the contribution of American universities to sdgs," MPRA Paper 119452, University Library of Munich, Germany.
    6. Nils Grashof & Alexander Kopka, 2023. "Artificial intelligence and radical innovation: an opportunity for all companies?," Small Business Economics, Springer, vol. 61(2), pages 771-797, August.
    7. Guangtong Li & L. Siddharth & Jianxi Luo, 2023. "Embedding knowledge graph of patent metadata to measure knowledge proximity," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(4), pages 476-490, April.

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