ABSTRACTS OF ARTICLES OF THE JOURNAL "INFORMATION TECHNOLOGIES".
No. 4. Vol. 29. 2023

DOI: 10.17587/it.29.215-223

D. M. Korobkin, Cand. Tech. Sc., Associate Professor, S. A. Fomenkov, Dr. Tech. Sc., Professor,
A. R. Zlobin, Master, G. A. Vereshchak, Postgraduate,
Volgograd State Technical University, Volgograd, 400005, Russian Federation

The Formation of Metrics of Innovative Potential and Prospects for the Task of Technological Forecasting

In today's rapidly developing technological world, new ideas, inventions and developments appear daily. At the same time, individual technologies may have common features, through the use of similar methods, modification and expansion of existing technologies, or solving a common problem for which developments are being created. Patenting is often used to preserve these ideas and protect intellectual property. During the development of the program for the analysis of the patent array to obtain criteria assessments of innovation potential and prospects, enshrined in patent high—tech technical systems and technologies, the subject area — patent array was investigated, methods for analyzing texts in natural language and various options for determining the criteria of innovation potential were considered. As a result, algorithms were developed to obtain the following criteria for assessing the innovative potential of a patent: the mass nature of the subject of this technology for the current year and the estimated frequency of occurrence for the next, the economic characteristics of the patent holder's company, the potential citation of the patent. These criteria are determined based on the analysis of texts and patent data by means of clustering, classification, regression analysis, normalization of the name of the patent holder. The developed algorithms have been tested on patents issued by the US Patent and Trademark Office, as well as on Google Patents.
Keywords: technical systems, patents, fact extraction

DOI: 10.17587/it.29.215-223

References

1. De Prato G., Nepelski D., Piroli G. Innovation Radar: Identifying Innovations and Innovators with High Potential in ICT FP7, CIP and H2020 Projects, Publications Office, 2016, available at: http://dx.doi.org/10.2791/61591.
2. Roh T., Jeong Y., Jang H., Yoon B. Technology opportu­nity discovery by structuring user needs based on natural language processing and machine learning PLoS one, 2019, 14(10), available at: https://doi.org/10.1371/journal.pone.0223404
3. Yang C., Huang C., Su J. An improved SAO network-based method for technology trend analysis: A case study of gra-phene, Journal of informetrics, 2018, vol. 12(1), pp. 271—286.
4. Kim S., Park I., Yoon B. SAO2Vec: Development of an algorithm for embedding the subject—action—object (SAO) structure using Doc2Vec, Plos one, 2020, vol. 15(2), available at: https://doi.org/10.1371/journal.pone.0227930
5. Guo J., Wang X., Li Q., Zhu D. Subject—action—object-based morphology analysis for determining the direction of tech­nological change, Technological Forecasting and Social Change, 2016, vol. 105, pp. 27—40.
6. Wang X., Qiu P., Zhu D., Mitkova L., Lei M., Porter A. Identification of technology development trends based on sub­ject—action—object analysis: The case of dye-sensitized solar cells, Technological forecasting and social change, 2015, vol. 98, pp. 24—46.
7. Yang C., Zhu D., Wang X. SAO semantic information identification for text mining, International Journal of Computational Intelligence Systems, 2017, vol. 10(1), pp. 593—604.
8. Korobkin D. M., Fomenkov S. A., Kravets A. G., Kolesnikov S. G. Prior art candidate search on base of statistical and semantic patent analysis. Proceedings of the International Conferences on Computer Graphics, Visualization, Computer Vision and Image Processing 2017 and Big Data Analytics, Data Mining and Computa­tional Intelligence 2017 — Part of the Multi Conference on Computer Science and Information Systems 2017, 2017, pp. 231—238.
9. Kolesnikova V., Korobkin D., Fomenkov S., Rayushkin E., Glushkin V. The analysis of technology development trends based on the network semantic structure "subject-action-object", Studies in Systems, Decision and Control, 2022, vol. 417, pp. 43—53.
10. Souili A., Cavallucci D., Rousselot F., Zanni C. Starting from patents to find inputs to the problem graph model of IDM-TRIZ. Procedia Engineering, 2015, vol. 131, pp. 150—161.
11. Feng L., Niu Y., Liu Z., Wang J., Zhang K. Discovering technology opportunity by keyword-based patent analysis: A hybrid approach of morphology analysis and USIT. Sustainability, 2019, vol. 12(1), pp. 136, available at: https://doi.org/10.3390/su12010136.
12. Brownlee J. How to Create an ARIMA Model for Time Series Forecasting in Python, available at: https://machinelearn-ingmastery.com/arima-for-time-series-forecasting-with-python/ (accessed on 27.04.2022).
13. The Global Innovation 1000 study, available at: https:// www.strategyand.pwc.com/gx/en/insights/innovation1000.html (accessed on 27.04.2022).
14. Global 2000, available at: https://www.forbes.com/lists global2000/#4e109b4a5ac0 (accessed on 27.04.2022).
15. National Bureau of Economic Research, available at: https://www.nber.org/research (accessed on 27.04.2022).

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