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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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    1. Xinhai Liu & Shi Yu & Frizo Janssens & Wolfgang Glänzel & Yves Moreau & Bart De Moor, 2010. "Weighted hybrid clustering by combining text mining and bibliometrics on a large-scale journal database," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(6), pages 1105-1119, June.
    2. Karvonen, Matti & Kässi, Tuomo, 2013. "Patent citations as a tool for analysing the early stages of convergence," Technological Forecasting and Social Change, Elsevier, vol. 80(6), pages 1094-1107.
    3. Adam B. Jaffe & Manuel Trajtenberg & Rebecca Henderson, 1993. "Geographic Localization of Knowledge Spillovers as Evidenced by Patent Citations," The Quarterly Journal of Economics, Oxford University Press, vol. 108(3), pages 577-598.
    4. Chen, Ssu-Han & Huang, Mu-Hsuan & Chen, Dar-Zen, 2012. "Identifying and visualizing technology evolution: A case study of smart grid technology," Technological Forecasting and Social Change, Elsevier, vol. 79(6), pages 1099-1110.
    5. Gilsing, Victor & Nooteboom, Bart & Vanhaverbeke, Wim & Duysters, Geert & van den Oord, Ad, 2008. "Network embeddedness and the exploration of novel technologies: Technological distance, betweenness centrality and density," Research Policy, Elsevier, vol. 37(10), pages 1717-1731, December.
    6. Ansgar Moeller & Martin G. Moehrle, 2015. "Completing keyword patent search with semantic patent search: introducing a semiautomatic iterative method for patent near search based on semantic similarities," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(1), pages 77-96, January.
    7. Kevin W. Boyack & Richard Klavans, 2010. "Co‐citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately?," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(12), pages 2389-2404, December.
    8. von Wartburg, Iwan & Teichert, Thorsten & Rost, Katja, 2005. "Inventive progress measured by multi-stage patent citation analysis," Research Policy, Elsevier, vol. 34(10), pages 1591-1607, December.
    9. Janghyeok Yoon & Kwangsoo Kim, 2011. "Identifying rapidly evolving technological trends for R&D planning using SAO-based semantic patent networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(1), pages 213-228, July.
    10. Lee, Changyong & Cho, Yangrae & Seol, Hyeonju & Park, Yongtae, 2012. "A stochastic patent citation analysis approach to assessing future technological impacts," Technological Forecasting and Social Change, Elsevier, vol. 79(1), pages 16-29.
    11. Jan M. Gerken & Martin G. Moehrle, 2012. "A new instrument for technology monitoring: novelty in patents measured by semantic patent analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(3), pages 645-670, June.
    12. J. Gower & P. Legendre, 1986. "Metric and Euclidean properties of dissimilarity coefficients," Journal of Classification, Springer;The Classification Society, vol. 3(1), pages 5-48, March.
    13. S. Phineas Upham & Lori Rosenkopf & Lyle H. Ungar, 2010. "Positioning knowledge: schools of thought and new knowledge creation," Scientometrics, Springer;Akadémiai Kiadó, vol. 83(2), pages 555-581, May.
    14. Kevin W. Boyack & Richard Klavans, 2010. "Co-citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately?," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(12), pages 2389-2404, December.
    15. Martin G. Moehrle & Jan M. Gerken, 2012. "Measuring textual patent similarity on the basis of combined concepts: design decisions and their consequences," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(3), pages 805-826, June.
    16. Osmo Kuusi & Martin Meyer, 2007. "Anticipating technological breakthroughs: Using bibliographic coupling to explore the nanotubes paradigm," Scientometrics, Springer;Akadémiai Kiadó, vol. 70(3), pages 759-777, March.
    17. Martin G. Moehrle, 2010. "Measures for textual patent similarities: a guided way to select appropriate approaches," Scientometrics, Springer;Akadémiai Kiadó, vol. 85(1), pages 95-109, October.
    18. Ernst, Holger, 2003. "Patent information for strategic technology management," World Patent Information, Elsevier, vol. 25(3), pages 233-242, September.
    19. Rost, Katja, 2011. "The strength of strong ties in the creation of innovation," Research Policy, Elsevier, vol. 40(4), pages 588-604, May.
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    3. 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).
    4. 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.
    5. 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.
    6. Xia Cao & Chuanyun Li & Jinqiu Li & Yunchang Li, 2022. "Modeling and simulation of knowledge creation and diffusion in an industry-university-research cooperative innovation network: a case study of China’s new energy vehicles," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(7), pages 3935-3957, July.
    7. Xu, Haiyun & Yue, Zenghui & Pang, Hongshen & Elahi, Ehsan & Li, Jing & Wang, Lu, 2022. "Integrative model for discovering linked topics in science and technology," Journal of Informetrics, Elsevier, vol. 16(2).
    8. 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).
    9. 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.
    10. Xi, Xi & Ren, Feifei & Yu, Lean & Yang, Jing, 2023. "Detecting the technology's evolutionary pathway using HiDS-trait-driven tech mining strategy," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    11. 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).
    12. Chung, Park & Sohn, So Young, 2020. "Early detection of valuable patents using a deep learning model: Case of semiconductor industry," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    13. 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.
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