Abstract
Technological developments in nanomaterials can be tracked using patent indicators. However, the traditional International Patent Classification indicators cannot be considered conclusive, since nanotechnology is not easily defined as a field of research as well as there are different types of nanomaterials not well delineated into hierarchical codes. Therefore, text mining approaches can be used to enhance patent analysis and provide insightful trends to support research and development, competitive intelligence, and policy making. This study aims at proposing a method to classify nanomaterials into main types and mapping technological developments using an advanced text mining-based method to compile patent indicators. Patent records were provided by Derwent Innovations Index database, which indexes an enhanced bibliographic data of patents filed worldwide. A comparison between the IPC indicators and those developed here by text mining is presented. We concluded that the proposed method provides useful outcomes for decision-making, technological forecasting, and material selection process.
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Acknowledgements
The authors are thankful to the Graduate Program in Materials Science and Engineering and the Centre for Technological Information in Materials (NIT/Materiais) for all support to this study.
Funding
This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001.
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Milanez, D.H., de Faria, L.I.L. & Leiva, D.R. A text mining-based approach for the evaluation of patenting trends on nanomaterials. J Nanopart Res 23, 212 (2021). https://doi.org/10.1007/s11051-021-05304-3
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DOI: https://doi.org/10.1007/s11051-021-05304-3