Qualitative analysis of information recorded in patents
##plugins.themes.bootstrap3.article.sidebar##
##plugins.themes.bootstrap3.article.main##
https://orcid.org/0000-0001-6058-3883
Isabel Pinho
https://orcid.org/0000-0003-1714-8979
Helga Cristina Hedler
https://orcid.org/0000-0003-3895-5200
Abstract
Patent is a valuable source of information to help evaluate trends in research, since it reveals the areas of innovation that inventors are focused on. Another relevant point is that it is public and reliable information. This paper aims to present an approach to generate subsidies for the qualitative interpretation of patents using the support of the conceptual structure of the R-Bibliometrix package. The methodology comprised data collection, detailing how the search was conducted in the Lens Database, and data analysis, which presents an overview of the R-Bibliometrix package and the conversion of the data to be imported and analyzed. This database allows preliminary analysis of technological outlooks using the resources available for visualization. The results show the 10 digrams extracted from the patent abstracts that have the highest frequencies; the dynamics of the technologies from the IPC codes, focusing on the evolution of the technology over time and the trend topics; the thematic map of the evolution of the concepts that were extracted from the abstracts of the granted patents and patent applications; and the microstructure of the technological routes of the researched topic. It is concluded that patents offer opportunities for information analysis with a focus on technology and innovation. Despite not meeting all the requirements of a qualitative analysis, the R-Bibliometrix package offers several resources that support the analysis of the conceptual structure with the visualization of conceptual and technological routes, as well as allowing other possibilities for analysis of the intellectual and social structure.
Downloads
##plugins.themes.bootstrap3.article.details##
How to Cite
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
openAccess
openAccess
Abraham, B. & Morita, S. (2001). Innovation assessment through patent analysis. Technovation, 21(4), 245-252. https://doi.org/10.1016/S0166-4972(00)00040-7
Aria, M. & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11 (4), 959-975.
Aria, M. & Cuccurullo, C. (2020). A brief introduction to bibliometrix. Disponível em: https://www.bibliometrix.org/vignettes/Introduction_to_bibliometrix.html. Acesso: 10 jul 2020.
Aria, M. & Cuccurullo, C. (2021). Package ‘bibliometrix’. Disponível em: https://cran.r-project.org/web/packages/bibliometrix/bibliometrix.pdf. Acesso: 10 jul 2021.
Almeida, F. L. C., Castro, M. P. J., Travália, B. M. & Forte, M. B. S. (2021). Trends in lipase immobilization: Bibliometric review and patent analysis. Process Biochemistry, 110, 37–51. https://doi.org/10.1016/j.procbio.2021.07.005
Bengisu, M. & Nekhili, R. (2006). Forecasting emerging technologies with the aid of science and technology databases. Technological Forecasting & Social Change, 73, 835-844. http://dx.doi.org/10.1016/j.techfore.2005.09.001
Camargo, B. V. & Justo, A. M. (2018). IRAMUTEQ: Tutorial para uso do software de análise textual IRAMUTEQ. Florianópolis: Laboratório de Psicologia Social da Comunicação e Cognição; LACCOS, Universidade Federal de Santa Catarina.
Chen, Y. H., Chen, C. Y., & Lee, S. C. (2010). Technology forecasting of new clean energy: The example of hydrogen energy and fuel cell. African Journal of Business Management, 4 (7), 1372–1380.
CORE. The world’s largest aggregator of open access research papers. 2022. Disponível em: https://core.ac.uk. Acesso em: 15 mar. 2022.
Daim, T. U., Rueda, G., Martin, H. & Gerdsri, P. (2006). Forecasting emerging technologies: Use of bibliometrics and patent analysis. Technological Forecasting and Social Change, 73(8), 981–1012. https://doi.org/10.1016/j.techfore.2006.04.004
Daim, T. U., Van Blommestein, K., Islam, N., Ozcan, S., Hillegas, J. & Estep, J. (2014). Integrating data mining into technology roadmapping. In: 2014 Portland International Center for Management of Engineering and Technology, PICMET 2014, 2972–2985.
European Patent Office. Cooperative Patent Classification (CPC), 2022. Disponível em: https://www.cooperativepatentclassification.org/home. Acesso em: 15 mar. 2022.
Lai, K.-K., Lin, M.-L., Chang, S.-B. & Hsu, C.-F. (2007). The study of taxonomy and evolutional trends of relevant literatures on patent analysis. In: PICMET ’07 - Portland International Center for Management of Engineering and Technology - Management of Converging Technologies, 22–30. https://doi.org/10.1109/PICMET.2007.4349314
Lens. Search, Analyze and Manage Patent and Scholarly Data. 2022. Disponível em: https://www.lens.org. Acesso em: 10 jan. 2022.
Liu, X. & Yu, X. (2017). Patent analysis for guiding technology transfer from EU/EEA to China: The case of CO2 compressor in CCUS cooperation. In: 2016 Portland International Conference on Management of Engineering and Technology, PICMET 2016, p. 1659–1671. https://doi.org/10.1109/PICMET.2016.7806765
Morris, S., DeYong, C., Wu, Z., Salman, S., Yemenu, D. (2002). DIVA: a visualization system for exploring document databases for technology forecasting. Computers and Industrial Engineering, v. 43, n. 4, p. 841–862. https://doi.org/10.1016/S0360-8352(02)00143-2
Norton, M. J. (2010). Introductory Concepts in Information Science. 2nd Ed. New Jersey: Information Today Inc.
ORCID. Connecting research and researchers. 2022. Disponível em: https://orcid.org. Acesso em 10jan2022.
Piwowar, H., Priem, J., Larivière, V., Alperin, J. P., Matthias, L., Norlander, B., Farley, A., West, J., Haustein, S. (2018). The state of OA: a large-scale analysis of the prevalence and impact of Open Access articles. PeerJ, 6, e4375. https://doi.org/10.7717/peerj.4375
Sheikh, N. J. & Sheikh, O. (2017). Forecasting of biosensor technologies for emerging point of care and medical IoT applications using bibliometrics and patent analysis. In: 2016 Portland International Conference on Management of Engineering and Technology, PICMET 2016, 3082–3093. https://doi.org/10.1109/PICMET.2016.7806585
Wantanabe, C., Tsuji, Y. & Brown, C. (2001). Patent statistics: deciphering a ‘real’ versus a ‘pseudo’ proxy of innovation. Technovation, 21(12), 783-790. https://doi.org/10.1016/S0166-4972(01)00025-6
World Intellectual Property Organization. IPC Publication. 2022. Disponível em: https://ipcpub.wipo.int/?notion=scheme&version=20220101&symbol=none&menulang=en&lang=en&viewmode=f&fipcpc=no&showdeleted=yes&indexes=no&headings=yes¬es=yes&direction=o2n&initial=A&cwid=none&tree=no&searchmode=smart. Acesso em: 15 mar. 2022.
Wu, F.-S., Hsu, C.-C., Lee, P.-C. & Su, H.-N. (2011). A systematic approach for integrated trend analysis-The case of etching. Technological Forecasting and Social Change, 78(3), 386–407. https://doi.org/10.1016/j.techfore.2010.08.006
Xi, Y. & Xiang, Y. (2020). Patent analysis for graphene technology in ICT industry. In: 26th International Association for Management of Technology Conference, IAMOT 2017, p. 1125–1141.