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The Software for Identifying Technological Complementarity Between Enterprises Based on Patent Databases

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Creativity in Intelligent Technologies and Data Science (CIT&DS 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1448))

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Abstract

In this paper, it is proposed to identify the technological complementarity of enterprises. The process of identifying potential partners is based on the comparison of cluster information, clustered with hLDA algorithm. To analyze an array of patents, it must first be loaded, parsed, and filtered by a specific class of patents. Then technical terms are extracted from data and clustered using the hLDA algorithm, and after clustering, an enterprises complementarity matrix is constructed. We developed software for clustering USPTO patent documents based on the hLDA method and identifying the technological complementarity of enterprises based on the comparison of cluster information.

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Acknowledgments

The reported study was funded by RFBR and the Administration of the Volgograd region according to the research projects 19-47-340007, 19-41-340016, and was funded by RSF according to the research projects 22-21-00855, 22-1-00808.

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Authors and Affiliations

  1. Volgograd State Technical University, 28 Lenina Ave, Volgograd, 400005, Russia

    Alexey Bezruchenko, Dmitriy Korobkin, Sergey Fomenkov, Sergey Kolesnikov & Sergey Vasiliev

Authors
  1. Alexey Bezruchenko
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  2. Dmitriy Korobkin
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  3. Sergey Fomenkov
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  4. Sergey Kolesnikov
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  5. Sergey Vasiliev
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Editor information

Editors and Affiliations

  1. Volgograd State Technical University, Volgograd, Russia

    Alla G. Kravets

  2. Volgograd State Technical University, Volgograd, Russia

    Maxim Shcherbakov

  3. Volgograd State Technical University, Volgograd, Russia

    Danila Parygin

  4. University of Patras, Patras, Greece

    Peter P. Groumpos

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Bezruchenko, A., Korobkin, D., Fomenkov, S., Kolesnikov, S., Vasiliev, S. (2021). The Software for Identifying Technological Complementarity Between Enterprises Based on Patent Databases. In: Kravets, A.G., Shcherbakov, M., Parygin, D., Groumpos, P.P. (eds) Creativity in Intelligent Technologies and Data Science. CIT&DS 2021. Communications in Computer and Information Science, vol 1448. Springer, Cham. https://doi.org/10.1007/978-3-030-87034-8_4

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  • DOI: https://doi.org/10.1007/978-3-030-87034-8_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87033-1

  • Online ISBN: 978-3-030-87034-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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