PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2022 | z. 162 Contemporary Management | 9--34
Tytuł artykułu

The Analysis of Polish Patent Applications in the Solar Energy Technology With the Use of Text Mining Methodology

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Purpose: Knowledge management belongs to the most important elements of organisational management, including manufacturing enterprises. Patent information plays an increasingly important role in this area. Identification of the main directions of invention activity may inspire new product and process ideas, and can help to improve existing solutions. The above is particularly important in the energy sector, which is currently struggling with increasing problems. In this context, solar energy is the subject of interest to inventive communities. The paper discusses patent applications related to solar energy, taking up the task of discovering the main tendencies of technological solutions in this area. Design/methodology/approach: In the work, a pilot study of the research aimed to indicate the directions of technological development in the field in Poland was undertaken. Shortened descriptions of selected patent documents from the Polish Patent Office (PPO) were the subject of the investigation. The descriptions were reduced to the form of a vector space model by using text mining tools. The exploration of such prepared data was done applying unsupervised text mining techniques. Hierarchical cluster analysis enabled the identification of groups of similar inventions. An algorithm to detect outliers within individual patent groups was also developed and applied. Findings: Five patent clusters were identified covering the following thematic areas: PV panel designs, PV panel component designs, the improvement of solar-heat conversion device performance, and solar collector designs. Six patent applications stood out thematically in four of the five clusters. Research limitations/implications: The research is limited to a selected number of patent documents form PPO. However, the presented method and research area are promising. It is planned to extend the analyses to a larger set of patent documents and solve the problem related to the language uniformity of patent applications along with merging data from various sources. In this aspect, a full patent description will be consider as well. Originality/value: In relation to solar energy issues, main patent areas and patent outliers that may be indicators of special interests of inventors were identified. In relation to methodology issues, new solutions within consecutive research steps were proposed.(original abstract)
Twórcy
  • Kielce University of Technology
  • Kielce University of Technology
Bibliografia
  • 1. Act of 30 June 2000. Industrial Property Law, Journal of Law 2000, No. 49 item 508 with later amendments (in Polish) (2000).
  • 2. Albright, R. (2004). Taming Text with the SVD. Cary, NC: SAS Institute Inc.
  • 3. Banerjee, A., Dave, R.N. (2004). Validating Clusters Using the Hopkins Statistic. Proceedings of the 2004 IEEE International Conference on Fuzzy Systems, Vol. 1. Budapest, Hungary: IEEE, pp. 149-153.
  • 4. Bęben, K. (2020). Znaczenie Wyboru Reprezentacji Dokumentów Tekstowych i Miar Podobieństwa w Rankingu Wniosków Patentowych. Inżynieria zarządzania. Cyfryzacja produkcji. Aktualności badawcze, 2, Vol. 2. Warszawa: PWE, pp. 1-11.
  • 5. Binz, C., Tang, T., Huenteler, J. (2017). Spatial Lifecycles of Cleantech Industries - The Global Development History of Solar Photovoltaics. Energy Policy, Vol. 101, pp. 386-402, doi:10.1016/J.ENPOL.2016.10.034.
  • 6. Breyer, C., Birkner, C., Meiss, J., Goldschmidt, J.C., Riede, M. (2013). A top-down analysis: Determining photovoltaics R&D investments from patent analysis and R&D headcount. Energy Policy, Vol. 62, pp. 1570-1580, doi: 10.1016/j.enpol.2013.07.003.
  • 7. Chiu, Y.-J., Ying, T.-M. (2012). A Novel Method for Technology Forecasting and Developing R&D Strategy of Building Integrated Photovoltaic Technology Industry. Mathematical Problems in Engineering, Vol. 2012, p. 24, doi:10.1155/2012/273530.
  • 8. De Maesschalck, R., Jouan-Rimbaud, D., Massart, D.L. (2000). The Mahalanobis Distance. Chemometrics and Intelligent Laboratory Systems, Vol. 50, Iss. 1, pp. 1-18, doi:10.1016/S0169-7439(99)00047-7.
  • 9. De Paulo, A.F., Ribeiro, E.M.S., Porto, G.S. (2018). Mapping Countries Cooperation Networks in Photovoltaic Technology Development Based on Patent Analysis. Scientometrics, 117, pp. 667-686, doi:10.1007/s11192-018-2892-6.
  • 10. European Patent Office. PATSTAT. Worldwide Patent Statistical Database. Retrived from https://www.epo.org/searching-for-patents/business/patstat.html, 15 May 2022.
  • 11. Halkidi, M., Batistakis, Y., Vazirgiannis, M. (2001). On Clustering Validation Techniques. Journal of Intelligent Information Systems 17, pp. 107-145, doi: 10.1023/A:1012801612483.
  • 12. Hopkins, B., Skellam, J.G. (1954). A New Method for Determining the Type of Distribution of Plant Individuals. Annals of Botany, Vol. 18, No. 70, pp. 213-227, doi:10.1093/oxfordjournals.aob.a083391.
  • 13. Kadhim, A.I., Cheah, Y.-N., Ahamed, N.H. (2014). Text Document Preprocessing and Dimension Reduction Techniques for Text Document Clustering. Proceedings of the 2014 4-th International Conference on Artificial Intelligence with Applications in Engineering and Technology (pp. 69-73). Kota Kinabalu, Malaysia: IEEE.
  • 14. Lan, M., Tan, C.L., Su, J.; Lu, Y. (2009). Supervised and Traditional Term Weighting Methods for Automatic Text Categorization. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 31, No. 4, pp. 721-735, doi:10.1109/TPAMI.2008.110.
  • 15. Li, X., Xie, Q., Jiang, J., Zhou, Y., Huang, L. (2019). Identifying and Monitoring the Development Trends of Emerging Technologies Using Patent Analysis and Twitter Data Mining: The Case of Perovskite Solar Cell Technology. Technological Forecasting and Social Change, Vol. 146, pp. 687-705, doi:10.1016/j.techfore.2018.06.004.
  • 16. Liu, J.S., Kuan, C., Cha, S.-C., Chuang, W.-L., Gau, G.J., Jeng, J. (2011). Photovoltaic Technology Development: A Perspective from Patent Growth Analysis. Solar Energy Materials and Solar Cells, Vol. 95, Iss. 11, pp. 3130-3136, doi:10.1016/J.SOLMAT. 2011.07.002.
  • 17. Lizin, S., Leroy, J., Delvenne, C., Dijk, M., Schepper, E., Passel, S. (2013). A Patent Landscape Analysis for Organic Photovoltaic Solar Cells: Identifying the Technology's Development Phase. Renewable Energy, Vol. 57, pp. 5-11, doi:10.1016/ j.renene.2013.01.027.
  • 18. Rousseeuw, P.J. (1987). Silhouettes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis. Journal of Computational and Applied Mathematics, Vol. 20, pp. 53-65, doi:10.1016/0377-0427(87)90125-7.
  • 19. Sampaio, P.G.V., González, M.O.A., de Vasconcelos, R.M., dos Santos, M.A.T., de Toledo, J.C., Pereira, J.P.P. (2018). Photovoltaic Technologies: Mapping from Patent Analysis. Renewable and Sustainable Energy Reviews, Vol. 93, pp. 215-224, doi:10.1016/ j.rser.2018.05.033.
  • 20. SAS Institute Inc. (2012). SAS Text Miner 12.1 Reference Help. Cary, NC.
  • 21. SAS Institute Inc. (2016). SAS/STAT®14.2 User's Guide. The CLUSTER Procedure. Cary, NC.
  • 22. Trappey, A.J.C., Chen, P.P.J., Trappey, C.V., Ma, L. (2019). A Machine Learning Approach for Solar Power Technology Review and Patent Evolution Analysis. Applied Sciences, 9(7), 1478, p. 25, doi:10.3390/app9071478.
  • 23. Venugopalan, S., Rai, V. (2015). Topic Based Classification and Pattern Identification. Technological Forecasting and Social Change, Vol. 94, pp. 236-250. doi:10.1016/ j.techfore.2014.10.006.
  • 24. Vijaya, Sharma, S., Batra, N. (2019). Comparative Study of Single Linkage, Complete Linkage, and Ward Method of Agglomerative Clustering. Proceedings of the 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon). Faridabad, India: IEEE, pp. 568-573.
  • 25. WIPO (2015). WIPO Guide to Using PATENT INFORMATION; Patent brochures. Geneva: World Intellectual Property Organization.
  • 26. WIPO (2021). World Intellectual Property Indicators 2021. Geneva: World Intellectual Property Organization.
  • 27. WIPO (2022). Guide to the International Patent Classification. Geneva: World Intellectual Property Organization.
  • 28. Yoon, J., Kim, K. (2012). Detecting Signals of New Technological Opportunities Using Semantic Patent Analysis and Outlier Detection. Scientometrics, 90, pp. 445-461, doi:10.1007/s11192-011-0543-2.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171661662

Zgłoszenie zostało wysłane

Zgłoszenie zostało wysłane

Musisz być zalogowany aby pisać komentarze.
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.