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

M.Y. Sheresheva ()
Additional contact information
M.Y. Sheresheva: 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:

GATR Journals from Global Academy of Training and Research (GATR) Enterprise

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"

Keywords: Innovation activity; patent analytics; machine learning technology; a gravity-driven power generator. (search for similar items in EconPapers)
JEL-codes: D80 D81 (search for similar items in EconPapers)
Pages: 12
Date: 2025-12-31
New Economics Papers: this item is included in nep-inv and nep-ipr
References: Add references at CitEc
Citations:

Published in Journal of Business and Economics Review, Volume 10, Issue3

Downloads: (external link)
https://gatrenterprise.com/GATRJournals/JBER/pdf_f ... eresheva,%20M.Y..pdf (application/pdf)
http://gatrenterprise.com/GATRJournals/online_submission.html

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gtr:gatrjs:jber267

DOI: 10.35609/jber.2025.10.3(5)

Access Statistics for this paper

More papers in GATR Journals from Global Academy of Training and Research (GATR) Enterprise
Bibliographic data for series maintained by Prof. Dr. Abd Rahim Mohamad ().