APPLICATION OF PATENT ANALYSIS FOR FORECASTING THE DEVELOPMENT OF NANOMATERIALS

Authors

  • M. Dubinina Central Economics and Mathematics Institute RAS

DOI:

https://doi.org/10.31618/nas.2413-5291.2021.3.74.532

Keywords:

patent; nanotechnology; nanomaterials; the technology maturity ratio

Abstract

In this article, in order to identify the most promising nanomaterials, the method of patent analysis is applied, dynamic series of the number of issued patents in specific classes and IPC groups, as well as patents containing the given names of nanomaterials on the title page, are constructed. Models of the life cycle of the studied nanomaterials were constructed, the coefficients of their maturity and the expected remaining service life were calculated.

Author Biography

M. Dubinina, Central Economics and Mathematics Institute RAS

Researcher

References

Avdzejko V.I., Karnyshev V.I., Meshherjakov R.V. Patentnyj analiz. Vyjavlenie perspektivnyh i proryvnyh tehnologij // Voprosy innovacionnoj jekonomiki. – 2018. – Tom 8. – № 1. – S. 79-90. doi: 10.18334/vinec.8.1.38890.

Varshavskij A.E., Dubinina M.G., Nikonova M.A. Ocenka izmenenija prioritetov razvitija otdel'nyh napravlenij nanotehnologii po dannym o grantah i patentah // Glava v knige «Jekonomicheskie problemy razvitija revoljucionnyh tehnologij. Nanotehnologii», - M., Nauka, 2012, S. 302-350.

Dubinina M.G. Analiz i modelirovanie diffuzii oblachnyh vychislenij v Rossii i za rubezhom // Trudy ISA RAN. 2017. T. 67. vyp. 4. S.22-34.

Fomenkova M.A., Korobkin D.M., Fomenkov S.A., Kolesnikov S.G. Metod analiza innovacionnyh tendencij na osnove dannyh patentnogo massiva // Modelirovanie, optimizacija i informacionnye tehnologii. 2019. T. 7. № 2. S. 149-161. DOI: 10.26102/23106018/2019.25.2.018.

Aharonson B.S., Schilling M.A. Mapping the technological landscape: Measuring technology distance, technological footprints, and technology evolution // Research Policy, 45(2016), pp. 81–96. https://doi.org/10.1016/j.respol.2015.08.001.

Baumann M., Domnik T., Haase M., Wulf C., Emmerich Ph., Rösch C., Zapp P., Naegler T., Weil M. Comparative patent analysis for the identification of global research trends for the case of battery storage, hydrogen and bioenergy // Technological Forecasting and Social Change, Volume 165, 2021, 120505, ISSN

-1625, https://doi.org/10.1016/j.techfore.2020.120505.

Clancy M.S. Inventing by combining preexisting technologies: Patent evidence on learning and fishing out // Research Policy 47 (2018) 252–265. https://doi.org/10.1016/j.respol.2017.10.015.

Espacenet patent search. URL: https://www.epo.org/searching-forpatents/technical/espacenet.html (data obrashhenija: 18.12.2021).

Inshakova E., Inshakova A., Goncharov A. Engineered nanomaterials for energy sector: market trends, modern applications and future prospects // IOP Conf. Series: Materials Science and Engineering, 971, 2020, 032031. doi:10.1088/1757-899X/971/3/032031.

Kim J.-M., Im D.M., Jun S. Factor analysis and structural equation model for patent analysis: a case study of Apple’s technology // Technology Analysis & Strategic Management, 2017, 29:7, 717-734, DOI: 10.1080/09537325.2016.1227067

Lacerda J.S. Linking scientific knowledge and technological change: Lessons from wind turbine evolution and innovation // Energy Research & Social Science, Volume 50, 2019, Pages 92-105. https://doi.org/10.1016/j.erss.2018.11.012.

Mao G., Han Y., Liu X., Crittenden J., Huang N., Ahmad M.U. Technology status and trends of industrial wastewater treatment: A patent analysis // Chemosphere, Volume 288, Part 2, 2022, 132483, ISSN 0045-6535, https://doi.org/10.1016/j.chemosphere.2021.132483.

Milanez D.H., de Faria L.I.L., Morato do Amaral R., Gregolin J.A.R. Claim-based patent indicators: A novel approach to analyze patent content and monitor technological advances // World Patent Information, Volume 50. 2017. P. 64-72. https://doi.org/10.1016/j.wpi.2017.08.008.

Park I., Yoon B. Technological opportunity discovery for technological convergence based on the prediction of technology knowledge flow in a citation network // Journal of Informetrics, Volume 12, Issue 4, 2018, Pages 1199-1222. DOI: 10.1016/j.joi.2018.09.007.

Park, S., Lee, SJ. & Jun, S. Patent Big Data Analysis using Fuzzy Learning // Int. J. Fuzzy Syst. 19, 1158–1167 (2017). https://doi.org/10.1007/s40815016-0192-y.

PATENTOSCOPE. URL: https://patentscope.wipo.int/search/en/search.jsf (data obrashhenija: 17.12.2021).

Sinigaglia T., Martins M.E.S., Siluk J.C.M. Technological evolution of internal combustion engine vehicle: A patent data analysis // Applied Energy, Volume 306, Part A, 2022, 118003, ISSN 0306-2619, https://doi.org/10.1016/j.apenergy.2021.118003.

StatNano. Average citation per nano-article. URL: https://statnano.com/report/s55/3 (data obrashhenija: 17.12.2021).

Yoon J., Jeong B., Lee W.H., J. Kim J. Tracing the Evolving Trends in Electronic Skin (e-Skin) Technology Using Growth Curve and Technology Position-Based Patent Bibliometrics. in IEEE Access, vol. 6, pp. 26530-26542, 2018, doi: 10.1109/ACCESS.2018.2834160.

Published

2021-12-30

Issue

Section

Статьи