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The Elaboration of the Patent Processing Instrument Based on Machine Learning Technology

Author

Listed:
  • Sheresheva, M.Y.

    (Lomonosov Moscow State University, Leninskie Gory 1-46, 119991, Moscow, Russia Author-2-Name: Gorlacheva, E.N. Author-2-Workplace-Name: Bauman Moscow State Technical University, 2nd Baumanskaya st. 5, 105005, Moscow, Russia Author-3-Name: Author-3-Workplace-Name: Author-4-Name: Author-4-Workplace-Name: Author-5-Name: Author-5-Workplace-Name: Author-6-Name: Author-6-Workplace-Name: Author-7-Name: Author-7-Workplace-Name: Author-8-Name: Author-8-Workplace-Name:)

Abstract

" Objective - While managing the innovation activity, it is necessary to base it on reliable sources of scientific and technical information, including patent research. However, the existing variety and scale of patent databases necessitate the development of an instrument that enables processing large volumes of patent information within limited timeframes. In these conditions, it is necessary to use machine learning (ML) technology to create a solid information base for management decisions. Methodology - The objective of the study presented in the paper was to propose an algorithm for processing patent data to improve the quality of patent research. The essence of the algorithm is that all necessary patents are ranked according to a relevance criterion, after which the researcher analyzes the already essential patents. Findings - The paper envisages the algorithm's practical realization using a gravity-driven power generator case. Findings indicate that the proposed new instrument enables a significant reduction in processing time for patent data. Novelty - The paper contributes to innovation management by integrating patent analytics and machine learning. Type of Paper - Empirical"

Suggested Citation

  • Sheresheva, M.Y., 2025. "The Elaboration of the Patent Processing Instrument Based on Machine Learning Technology," GATR Journals jber267, Global Academy of Training and Research (GATR) Enterprise.
    • Handle: RePEc:gtr:gatrjs:jber267
    DOI: https://doi.org/10.35609/jber.2025.10.3(5)
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    References listed on IDEAS

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    1. Trippe, Anthony J., 2003. "Patinformatics: Tasks to tools," World Patent Information, Elsevier, vol. 25(3), pages 211-221, September.
    2. Ricardo Hausmann & Muhammed A. Yildirim & Christian Chacua & Matte Hartog & Shreyas Gadgin Matha, 2024. "Global Trends in Innovation Patterns: A Complexity Approach," WIPO Economic Research Working Papers 80, World Intellectual Property Organization - Economics and Statistics Division.
    3. Ioanna Kastelli & Petros Dimas & Dimitrios Stamopoulos & Aggelos Tsakanikas, 2024. "Linking Digital Capacity to Innovation Performance: the Mediating Role of Absorptive Capacity," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(1), pages 238-272, March.
    4. Yuna Di & Yi Zhou & Lu Zhang & Galuh Syahbana Indraprahasta & Jinjin Cao & Haitao Ma, 2022. "Spatial Pattern and Evolution of Global Innovation Network from 2000 to 2019: Global Patent Dataset Perspective," Complexity, Hindawi, vol. 2022, pages 1-11, June.
    5. Yuna Di & Yi Zhou & Lu Zhang & Galuh Syahbana Indraprahasta & Jinjin Cao, 2022. "Spatial Pattern and Evolution of Global Innovation Network from 2000 to 2019: Global Patent Dataset Perspective," Complexity, John Wiley & Sons, vol. 2022(1).
    6. Shuai, Jing & Peng, Xinjie & Zhao, Yujia & Wang, Yilan & Xu, Wei & Cheng, Jinhua & Lu, Yang & Wang, Jingjin, 2022. "A dynamic evaluation on the international competitiveness of China's rare earth products: An industrial chain and tech-innovation perspective," Resources Policy, Elsevier, vol. 75(C).
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    JEL classification:

    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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