Authors:

L.V. Matraeva, E.S. Vasiutina

MIREA – Russian Technological University, Moscow, Russia

Abstract:

Industrial companies’ strategic management increasingly faces a gap between declared technology priorities and the practical capability to convert external technological signals into decisions on R&D portfolios, investment allocation, and technology roadmaps. This study develops a comprehensive tech mining approach that turns patent analytics into an operational tool for strategic forecasting. The methodological basis combines the principles of the tech mining concept as an instrument for integrating text analysis with strategic technology management, technology roadmapping structured according to the multi-level architecture of “market–product–technology”, as well as the technology readiness level (TRL) model used to assess the maturity of technological solutions. Among the research methods are topic modelling (LDA) of a patent corpus, qualitative interpretation, multi-criteria ranking of technological niches, and content analysis of patent wording to infer technology maturity when direct stage information is unavailable. The evidence base comprises patent documents from the WIPO PatentScope database related to microalgae-based technologies. The study identifies 11 thematic clusters structured around the value creation core (biomass and biofuel production) and establishes infrastructure areas, growth points, and blind spots of the industry. Of five areas of the roadmap, the greatest maturity and commercial potential are characteristic of integrated solutions (bioremediation, CO₂ utilization). The proposed map links the results of patent analytics with R&D prioritization and investment decisions over short, medium, and long terms.

Keywords: strategic management; tech mining; patent analytics; technology foresight; technology roadmap; innovation management; R&D prioritization; technology maturity.

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For citation: Matraeva L.V., Vasiutina E.S. (2026). Topic modelling of patent landscapes as a tool for strategic forecasting in industrial companies. Upravlenets / The Manager, vol. 17, no. 1, pp. 31–46. DOI: 10.29141/2218-5003-2026-17-1-3. EDN: MDNZPI.