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Deriving technology intelligence from patents: Preposition-based semantic analysis

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  • An, Jaehyeong
  • Kim, Kyuwoong
  • Mortara, Letizia
  • Lee, Sungjoo

Abstract

Patents are one of the most reliable sources of technology intelligence, and the true value of patent analysis stems from its capability of describing the content of technology based on the relationships between keywords. To date a number of techniques for analyzing the information contained in patent documents that focus on the relationships between keywords have been suggested. However, a drawback of the existing keyword approaches is that they cannot yet determine the types of relationships between the keywords. This study proposes a novel approach based on preposition semantic analysis network which overcomes the limitations of the existing keywords-based network analysis and demonstrates its potential through an application. A preposition is a word that defines the relationship between two neighboring words, and, in the case of patents, prepositions aid in revealing the relationships between keywords related to technologies. To demonstrate the approach, patents regarding an electric vehicle were employed. 13 prepositions were identified which could be used to define 5 relationships between neighboring technological terms: “inclusion (utilization),” “objective (purpose),” “effect,” “process,” and “likeness.” The proposed approach is expected to improve the usability of keyword-based patent analyses and support more elaborate studies on patent documents.

Suggested Citation

  • An, Jaehyeong & Kim, Kyuwoong & Mortara, Letizia & Lee, Sungjoo, 2018. "Deriving technology intelligence from patents: Preposition-based semantic analysis," Journal of Informetrics, Elsevier, vol. 12(1), pages 217-236.
    • Handle: RePEc:eee:infome:v:12:y:2018:i:1:p:217-236
    DOI: 10.1016/j.joi.2018.01.001
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    4. Park, Inchae & Yoon, Byungun, 2018. "Technological opportunity discovery for technological convergence based on the prediction of technology knowledge flow in a citation network," Journal of Informetrics, Elsevier, vol. 12(4), pages 1199-1222.
    5. An, Xin & Li, Jinghong & Xu, Shuo & Chen, Liang & Sun, Wei, 2021. "An improved patent similarity measurement based on entities and semantic relations," Journal of Informetrics, Elsevier, vol. 15(2).
    6. Font-Julián, Cristina I & Ontalba-Ruipérez, José-Antonio & Orduña-Malea, Enrique & Thelwall, Mike, 2022. "Which types of online resource support US patent claims?," Journal of Informetrics, Elsevier, vol. 16(1).
    7. Chen, Liang & Xu, Shuo & Zhu, Lijun & Zhang, Jing & Yang, Guancan & Xu, Haiyun, 2022. "A deep learning based method benefiting from characteristics of patents for semantic relation classification," Journal of Informetrics, Elsevier, vol. 16(3).
    8. Trappey, Amy J.C. & Wei, Ann Y.E. & Chen, Neil K.T. & Li, Kuo-An & Hung, L.P. & Trappey, Charles V., 2023. "Patent landscape and key technology interaction roadmap using graph convolutional network – Case of mobile communication technologies beyond 5G," Journal of Informetrics, Elsevier, vol. 17(1).
    9. Jee, Jeonghun & Park, Sanghyun & Lee, Sungjoo, 2022. "Potential of patent image data as technology intelligence source," Journal of Informetrics, Elsevier, vol. 16(2).
    10. Liang Chen & Shuo Xu & Lijun Zhu & Jing Zhang & Xiaoping Lei & Guancan Yang, 2020. "A deep learning based method for extracting semantic information from patent documents," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 289-312, October.
    11. Jiang, Cuiqing & Zhou, Yiru & Chen, Bo, 2023. "Mining semantic features in patent text for financial distress prediction," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
    12. Jinho Choi & Nina Shin & Yong Sik Chang, 2021. "Strategic Investment Decisions for Emerging Technology Fields in the Health Care Sector Based on M&A Analysis," Sustainability, MDPI, vol. 13(7), pages 1-20, March.
    13. Percia David, Dimitri & Maréchal, Loïc & Lacube, William & Gillard, Sébastien & Tsesmelis, Michael & Maillart, Thomas & Mermoud, Alain, 2023. "Measuring security development in information technologies: A scientometric framework using arXiv e-prints," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    14. Lijie Feng & Yilang Li & Zhenfeng Liu & Jinfeng Wang, 2020. "Idea Generation and New Direction for Exploitation Technologies of Coal-Seam Gas through Recombinative Innovation and Patent Analysis," IJERPH, MDPI, vol. 17(8), pages 1-21, April.
    15. Mun, Changbae & Yoon, Sejun & Raghavan, Nagarajan & Hwang, Dongwook & Basnet, Subarna & Park, Hyunseok, 2021. "Function score-based technological trend analysis," Technovation, Elsevier, vol. 101(C).
    16. Moehrle, Martin G. & Frischkorn, Jonas, 2021. "Bridge strongly or focus – An analysis of bridging patents in four application fields of carbon fiber reinforcements," Journal of Informetrics, Elsevier, vol. 15(2).

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