Classification of Technological Development Trends Based on the Analysis of US Patent Time Series. An Empirical Approach | Vestnik Tomskogo gosudarstvennogo universiteta. Ekonomika – Tomsk State University Journal of Economics. 2021. № 53. DOI: 10.17223/19988648/53/18

Classification of Technological Development Trends Based on the Analysis of US Patent Time Series. An Empirical Approach

The forecasting of development trends and the timely revealing of new technical (technological) fields are the key prerequisite for an effective development of modern economy. Only reliable results of technological analysis (forecast) allow identifying new technologies, understanding the evolution of entire industries, carrying out strategic investment planning at the state level, and also planning R&D correctly. The aim of this work is to justify one of the possible approaches to the classification of technical (technological) fields in terms of assessing their relevance, novelty and short-term prospects. This approach is based on patent analysis, in particular, on the study of the time series features of US invention patents (1976-2018) for more than seventy-three thousand main groups (subgroups) of the 17th edition of the International Patent Classification (IPC17). The United States Patent and Trademark Office (USPTO) has been selected as the primary source of information because it is one of the world’s largest and constantly updated patent resources, providing direct access to full-text descriptions. In the authors’ opinion, a feature analysis of the US patent issue dynamics at time intervals (1976-2015, 2009-2018 and 2016-2018) allows dividing the IPC groups (subgroups) into the following three main clusters: “unpromising”, “promising” and “breakthrough”. In terms of the timely revealing of new, previously unknown, technologies or solutions in the technical field, or of the steadily growing technological trends, the “breakthrough” and “promising” subgroups are of the greatest practical interest. The article presents the results of an empirical classification of 71,266 subgroups (with a non-zero number of the issued patents since 1976 to 2018) in eight sections of the IPC17. These data may be useful for developers, researchers and R&D planners in solving complex scientific and technical problems, as well as for making short-term forecast estimates of a specific technical (technological) field development.

Download file
Counter downloads: 46

Keywords

patent analysis, IPC, US patents, USPTO, empirical approach, automatic classification, technological trends, forecasting, R&D planning, investments, economy progress

Authors

NameOrganizationE-mail
Karnyshev Vladimir I.Tomsk State University of Control Systems and Radioelectronicskarnychev@/yandex.ru
Avdzeyko Vladimir I.Tomsk State University of Control Systems and Radioelectronicsavdzeykovi@yandex.ru
Paskal Evgenia S.Tomsk State University of Control Systems and Radioelectronicsevgeniapascal@gmail.com
Всего: 3

References

Ruotsalainen L. Data Mining Tools for Technology and Competitive Intelligence. VTT Tiedotteita - Research Notes 2451. Espoo 2008. 63 p.
Кортов С.В., Шульгин Д.Б., Толмачев Д.Е., Егармина А.Д. Анализ технологических трендов на основе построения патентных ландшафтов // Экономика региона. 2017. Т. 13, вып. 3. С. 935-947.
Liu Z., Jia Z., Vong C.-M., Han J., Yan C., Pecht M. A patent analysis of prognostics and health management (PHM) innovations for electrical systems // IEEE Access. 2018. Vol. 6. Р. 18088-18107.
Kim D., Lee B., Lee H.J., Lee S.P., Moon Y., Jeong M.K. Automated detection of influential patents using singular values // IEEE Transactions on Automation Science and Engineering. 2012. Vol. 9, is. 4. Р. 723-733.
Duan H., Li M., You H., Chen F., Jiang J., Wang Q. Tendency determining of knowledge-transfer evolution based on patent citation network // 13th International Conference on Natural Computation. Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), 2017. Р. 1757-1763. DOI: 10.1109/ICE/ITMC39735.2016.9025846
Hao X., Xie N., Sun W. Analyzing technology development trend based on patent data // 3rd International Conference on Systems and Informatics (ICSAI). 2016. Р. 1056-1061. DOI: 10.1109/ICSAI.2016.7811107
Nam S., Kim K. Monitoring Newly Adopted Technologies Using Keyword Based Analysis of Cited Patents // IEEE Access. 2017. Vol. 5. Р. 23086-23091. DOI: 10.1109/ACCESS.2017.2764478
Seo W., Kim N., Choi S. Big Data Framework for Analyzing Patents to Support Strategic R&D Planning // IEEE 14th Intl Conf on Dependable, Autonomic and Secure Computing, 14th Intl Conf on Pervasive Intelligence and Computing, 2nd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech). 2016. Р. 746-753. DOI: 10.1109/DASC-PICom-DataCom-CyberSciTec.2016.131
Gui B., Liu Y., Bai X., Zhang J. Longitudinal Patent Analysis for Big Data Technology. // Portland International Conference on Management of Engineering and Technology (PIC-MET). 2017. Р. 1-8. DOI: 10.23919/PICMET.2017.8125461
Shin J., Lee S., Wang T. Semantic Patent Analysis System Based on Big Data // IEEE 11th International Conference on Semantic Computing (ICSC). 2017. Р. 284-285. DOI: 10.1109/ICSC.2017.20
Sun B., Wang H. Comparative study on chinese and global OLED industry based on patent data // IEEE Access. 2018. Vol. 6. Р. 72381-72391.
Li X., Xie Q., Huang L. Identifying the Development Trends of Emerging Technologies Using Patent Analysis and Web News Data Mining: The Case of Perovskite Solar Cell Technology // IEEE Transactions on Engineering Management. 2019. № 99. Р. 1-16. DOI: 10.1109/TEM.2019.2949124
Ki W., Kim K. Generating information relation matrix using semantic patent mining for technology planning: a case of nano-sensor // IEEE Access. 2017. Vol. 5. Р. 2678326797.
Alksher M.A., Azman A., Yaakob R., Kadir R.A., Mohamed A., Alshari E.M. A review of methods for mining idea from text // Third International Conference on Information Retrieval and Knowledge Management (CAMP). 2016. Р. 88-93. DOI: 10.1109/ IN-FRKM.2016.7806341
Zhu L., Lu X., Xu L. Patent Subject Words Extraction Based on Integrated Strategy Method // 15th International Symposium on Parallel and Distributed Computing (ISPDC). 2016. Р. 401-405. DOI: 10.1109/ISPDC.2016.68
Takano K., Tanaka M., Sakai H., Kitajima R., Ota T., Tanabe C., Sakaji H. Extraction of Characteristic Terms from Patent Documents for Technical Trend Analysis // 8th International Congress on Advanced Applied Informatics (IIAI-AAI). 2019. Р. 667-672. DOI: 10.1109/IIAI-AAI.2019.00138
Авдзейко В.И., Карнышев В.И., Мещеряков Р.В., Паскаль Е.С. Патентный анализ: выявление перспективных направлений развития радиоэлектронных систем, использующих отражение и вторичное излучение радиоволн // Радиопромышленность. 2019. Т. 29, № 1. С. 53-61.
Yoon J., Kim K. Identifying rapidly evolving technological trends for R&D planning using SAO-based semantic patent networks // Scientometrics. 2011. Vol. 88, № 1. Р. 213228.
Lee C., Jeon J., Park Y. Monitoring trends of technological changes based on the dynamic patent lattice: A modified formal concept analysis approach // Technological Forecasting Social Change. 2011. Vol. 78, № 4. Р. 690-702.
Rodriguez A., Ali T., Kim B., Choi J., Lee J.-M., Coh B.-Y., Jeong M.K. Patent clustering and outlier ranking methodologies for attributed patent citation networks for technology opportunity discovery // IEEE Transactions on Engineering Management. 2016.Vol. 63, iss. 4. Р. 426-437.
Yoon J., Jeong B., Lee W.H., Kim J. Tracing the evolving trends in electronic skin (e-Skin) technology using growth curve and technology position-based patent bibliometrics // IEEE Access. 2018. Vol. 6. Р 26530-26542.
Авдзейко В.И., Карнышев В.И., Мещеряков Р.В. Патентный анализ. Выявление перспективных и прорывных технологий // Вопросы инновационной экономики. Январь-март 2018. Т. 8, № 1. С. 79-90. ISSN 2222-0372 (Russian Journal of Innovation Economics).
Авдзейко В.И., Карнышев В.И., Мещеряков Р.В. Прогнозирование направлений развития силовой электроники на основе временных рядов по данным Международной патентной классификации // Электротехнические и информационные комплексы и системы. 2016. Т. 12, № 2. С. 23-28.
Авдзейко В.И., Карнышев В.И., Мещеряков Р.В., Шелупанов А.А., Парнюк Л.В. Анализ динамики выдачи патентов для выявления перспективных направлений развития в области силовой электроники // Вестник Томского государственного университета. 2015. № 394. С. 159-169.
Паскаль Е.С., Авдзейко В.И., Захаров Ф.Н., Мещеряков А.А., Карнышев В.И., Парнюк Л.В. Программа формирования баз данных описаний патентов США в подгруппах МПК G01S13/00, G01S15/00, G01S17/00 // Свидетельство о регистрации ПрЭВМ № 2018662422. Зарегистрировано в Реестре программ для ЭВМ 08.10.2018 г. Заявка № 2018619965 от 17.09.2018.
Паскаль Е.С., Авдзейко В.И., Захаров Ф.Н., Мещеряков А.А., Карнышев В.И., Парнюк Л.В. Программа автоматической генерации временных рядов патентов США в интервале 1976-2017. Свидетельство о регистрации ПрЭВМ № 2018662921. Зарегистрировано в Реестре программ для ЭВМ 17.10.2018 г. Заявка № 2018619897 от 17.09.2018.
 Classification of Technological Development Trends Based on the Analysis of US Patent Time Series. An Empirical Approach | Vestnik Tomskogo gosudarstvennogo universiteta. Ekonomika – Tomsk State University Journal of Economics. 2021. № 53. DOI: 10.17223/19988648/53/18

Classification of Technological Development Trends Based on the Analysis of US Patent Time Series. An Empirical Approach | Vestnik Tomskogo gosudarstvennogo universiteta. Ekonomika – Tomsk State University Journal of Economics. 2021. № 53. DOI: 10.17223/19988648/53/18

Download full-text version
Counter downloads: 267