DOI: 10.17587/prin.15.243-253

The Predicting the Significance of Patented Technologies based on Innovation Potential Metrics

D. M. Korobkin, Associate Professor, dkorobkin80@mail.ru, A. A. Rublev, Bachelor, aarvlg@mail.ru, S. А. Fomenkov, Professor, saf@vstu.ru, Volgograd State Technical University, Volgograd, 400005, Russian Federation

Corresponding author: Dmitriy M. Korobkin, Associate Professor, Volgograd State Technical University, Volgograd, 400005, Russian Federation E-mail: dkorobkin80@mail.ru

To ensure one of the key principles of the development of the state, namely, the achievement of technological sovereignty, it is necessary to build all spheres of life at a qualitatively new technological level, own their own innovative technologies. In modern sanctions realities, the development of enterprises in the Russian Federation cannot be carried out without coordination with partners from Russia, as well as from China, India and other friendly countries. The selection of potential partners can be carried out on the basis of the identified importance of their patented technological solutions. Further ranking of potential partners can be carried out on the basis of the identified importance of their patented technologies. At the same time, in this study it is proposed to use three criteria: the mass nature of the subject of the patented invention in the current period; the predicted mass nature of the subject (technology) in the future period; the popularity of the patent in the information field. The authors have developed a method for predicting the importance of patented technologies and its software implementation.

Keywords: patent, parsing, forecast, clustering, enterprise, information, data, Python

pp. 243–253

For citation:
Korobkin D. M., Rublev A. A., Fomenkov S. А. The Predicting the Significance of Patented Technologies based on Innovation Potential Metrics, Programmnaya Ingeneria, 2024, vol. 15, no. 5, pp. 243—253. DOI: 10.17587/prin.15.243-253. (in Russian).

The study was supported by the grant of the Russian Science Foundation No. 23-21-00464, https://rscf.ru/en/project/23-21-00464/

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