DECISION SUPPORT MODEL FOR INTELLECTUAL PROPERTY MANAGEMENT
Abstract
This article examines the pressing issue of improving the effectiveness of decision support in intellectual property management. A well-founded definition of the term decision support is provided in the context of intellectual property management using artificial intelligence technologies. An original decision support model for managing intellectual property assets in government agencies is presented, taking into account the basic stages of intellectual property management and the requirements specific to public administration. The developed model formalizes the procedure for selecting and combining intelligent
algorithms from a variety of those acceptable for specific business conditions, thereby improving the validity of decisions, including in the presence of incomplete data. The methodological foundation for the
developed algorithms includes graph neural networks with a knowledge graph-based model and the GAT algorithm, genetic programming and particle swarm methods, a gradient boosting algorithm combined with BERT contextual embeddings, and the K-means method with contrastive loss.
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Neretin O.P. Intellectual Sovereignty of the Economy of Russia. Moscow: Federal Institute of Industrial Property, 2022, 166 p. (in Russian)
Bogomolov A.V., Chuikov D.S., Zaporozhskiy Y.A. Means of Ensuring Information Security in Modern Automated Systems. Information Technologies, 2003, no. 1, pp. 2–8. (in Russian)
Prudnikov S.I. Quality Assessment of Automated Detection of Malicious Information. Cybernetics Bulletin, 2023, no. 22(3), pp. 60–65.DOI: 10.35266/1999-7604-2023-3-60-65 (in Russian)
Novikov D.A., Gubanov D.A. Analysis of the Terminological Structure of Control Theory. Large Systems Management: collection of works, 2024, no. 110, pp. 181–210. (in Russian)
Its A.E., Surina A.V. Management of Innovative Projects Using W.R. Ashby’s Approach. Scientific and Technical Reports of Saint Petersburg State Polytechnic University, 2013, no. 4-2(183), pp. 77–82. (in Russian)
Prudnikov S.I. Expert-Analytical Substantiation of the Structure of Decision Support Systems for Intellectual Property Management. Russian Science, Innovation, and Education (ROSNIO-IV-2025), Krasnoyarsk, 2025, pp. 107–115. (in Russian)
Shcheglov A.S. Results of Intellectual Activity Directly Related to Ensuring Defense and Security. Academic Bulletin of the National Guard Troops of the Russian Federation, 2022, no. 4, pp. 4–8. (in Russian)
Prokhorov M.A., Prudnikov S.I., Aseev A.O., Tobin D.S. Methodology for Assessing the Performance Quality of Specialized Systems for Information Support of Scientific Research. Automation. Modern technologies, vol. 78, no. 7, 2024, pp. 332–336. DOI: 10.36652/0869-4931-2024-78-7-332-336 (in Russian)
Prudnikov S.I. Big Data Processing in Special-Purpose Decision Support Systems. Bulletin of Tula State University. Technical sciences, 2023, no. 5, pp. 48–52. DOI: 10.24412/2071-6168-2023-5-48-49 (in Russian)
Prokhorov M.A., Tobin D.S. Computer Technologies for Information Support of Automated Systems. Journal of Computational and Engineering Mathematics. 2024, vol. 11, no. 2, pp. 51–61. DOI 10.14529/jcem240206
Bogomolov A.V., Larkin E.V., Privalov A.N. Mathematical Models of Software Failures of Digital Control Systems. Automatic Documentation and Mathematical Linguistics, 2025, vol. 59, no. 1, pp. 1–8. DOI: 10.3103/S0005105524700419
Tobin D.S., Bogomolov A.V., Golosovskiy M.S. Organization of Software Testing for Various Models of Its Life Cycle. Mathematical Methods in Technology and Engineering, 2021, no. 7, pp. 132–135. DOI 10.52348/2712-8873_MMTT_2021_7_132 (in Russian)
Ben-Tal A., El Ghaoui L., Nemirovski A. Robust Optimization. Princeton, Princeton University Press, 2009, 564 p.
Presman E.L., Sonin I.M. Sequential Control Under Incomplete Data. Moscow, Nauka, 1982. 255 p. (in Russian)
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