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Use of a new patent text-mining and visualization method for identifying patenting patterns over time: Concept, method and test application

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  • Niemann, Helen
  • Moehrle, Martin G.
  • Frischkorn, Jonas

Abstract

Understanding the evolution of a technological field in the course of time is a key task in technology analysis. Analysts in research institutions as well as in companies need to know which topics are relevant for the respective technological field, which are the emerging topics, which traditional topics have been deepened in the course of time and which have been abandoned. For this purpose we suggest a patent lane analysis. Patent lanes can be seen as the deployment of patent clusters in the course of time. We use a method based on semantic similarities to develop patent lanes. A case study focuses on the application of carbon fibers in bicycle technology; it is used to demonstrate our method, i.e. to establish patent lanes in this case and characterize them by multiple use of a Tf idf measure. Despite some limitations, patent lanes enable deep insights into the development of patent-friendly technological fields.

Suggested Citation

  • Niemann, Helen & Moehrle, Martin G. & Frischkorn, Jonas, 2017. "Use of a new patent text-mining and visualization method for identifying patenting patterns over time: Concept, method and test application," Technological Forecasting and Social Change, Elsevier, vol. 115(C), pages 210-220.
    • Handle: RePEc:eee:tefoso:v:115:y:2017:i:c:p:210-220
    DOI: 10.1016/j.techfore.2016.10.004
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    3. Eilers, Kathi & Frischkorn, Jonas & Eppinger, Elisabeth & Walter, Lothar & Moehrle, Martin G., 2019. "Patent-based semantic measurement of one-way and two-way technology convergence: The case of ultraviolet light emitting diodes (UV-LEDs)," Technological Forecasting and Social Change, Elsevier, vol. 140(C), pages 341-353.
    4. Block, Carolin & Wustmans, Michael & Laibach, Natalie & Bröring, Stefanie, 2021. "Semantic bridging of patents and scientific publications – The case of an emerging sustainability-oriented technology," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    5. Ki Hong Kim & Young Jae Han & Sugil Lee & Sung Won Cho & Chulung Lee, 2019. "Text Mining for Patent Analysis to Forecast Emerging Technologies in Wireless Power Transfer," Sustainability, MDPI, vol. 11(22), pages 1-24, November.
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    7. Fredström, Ashkan & Wincent, Joakim & Sjödin, David & Oghazi, Pejvak & Parida, Vinit, 2021. "Tracking innovation diffusion: AI analysis of large-scale patent data towards an agenda for further research," Technological Forecasting and Social Change, Elsevier, vol. 165(C).
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    9. Kuan, Chung-Huei & Chen, Dar-Zen & Huang, Mu-Hsuan, 2019. "Bibliographically coupled patents: Their temporal pattern and combined relevance," Journal of Informetrics, Elsevier, vol. 13(4).
    10. Taeyeoun Roh & Yujin Jeong & Byungun Yoon, 2017. "Developing a Methodology of Structuring and Layering Technological Information in Patent Documents through Natural Language Processing," Sustainability, MDPI, vol. 9(11), pages 1-19, November.
    11. Kristóf Gyódi & Łukasz Nawaro & Michał Paliński & Maciej Wilamowski, 2023. "Informing policy with text mining: technological change and social challenges," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(1), pages 933-954, February.
    12. Puccetti, Giovanni & Giordano, Vito & Spada, Irene & Chiarello, Filippo & Fantoni, Gualtiero, 2023. "Technology identification from patent texts: A novel named entity recognition method," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    13. Xia Cao & Chuanyun Li & Wei Chen & Jinqiu Li & Chaoran Lin, 2020. "Research on the invulnerability and optimization of the technical cooperation innovation network based on the patent perspective—A case study of new energy vehicles," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-19, September.
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