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Identifying technological topics and institution-topic distribution probability for patent competitive intelligence analysis: a case study in LTE technology

Author

Listed:
  • Bo Wang

    (WISELab, Dalian University of Technology
    Dalian University of Technology
    Drexel University)

  • Shengbo Liu

    (WISELab, Dalian University of Technology
    Dalian University of Technology
    Drexel University)

  • Kun Ding

    (WISELab, Dalian University of Technology
    Dalian University of Technology
    Drexel University)

  • Zeyuan Liu

    (WISELab, Dalian University of Technology
    Dalian University of Technology
    Drexel University)

  • Jing Xu

    (Sichuan University)

Abstract

An extended latent Dirichlet allocation (LDA) model is presented in this paper for patent competitive intelligence analysis. After part-of-speech tagging and defining the noun phrase extraction rules, technological words have been extracted from patent titles and abstracts. This allows us to go one step further and perform patent analysis at content level. Then LDA model is used for identifying underlying topic structures based on latent relationships of technological words extracted. This helped us to review research hot spots and directions in subclasses of patented technology in a certain field. For the extension of the traditional LDA model, another institution-topic probability level is added to the original LDA model. Direct competing enterprises’ distribution probability and their technological positions are identified in each topic. Then a case study is carried on within one of the core patented technology in next generation telecommunication technology-LTE. This empirical study reveals emerging hot spots of LTE technology, and finds that major companies in this field have been focused on different technological fields with different competitive positions.

Suggested Citation

  • Bo Wang & Shengbo Liu & Kun Ding & Zeyuan Liu & Jing Xu, 2014. "Identifying technological topics and institution-topic distribution probability for patent competitive intelligence analysis: a case study in LTE technology," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(1), pages 685-704, October.
    • Handle: RePEc:spr:scient:v:101:y:2014:i:1:d:10.1007_s11192-014-1342-3
    DOI: 10.1007/s11192-014-1342-3
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    10. Xuan Shi & Lingfei Cai & Hongfang Song, 2019. "Discovering Potential Technology Opportunities for Fuel Cell Vehicle Firms: A Multi-Level Patent Portfolio-Based Approach," Sustainability, MDPI, vol. 11(22), pages 1-22, November.
    11. Li, Xin & Xie, Qianqian & Daim, Tugrul & Huang, Lucheng, 2019. "Forecasting technology trends using text mining of the gaps between science and technology: The case of perovskite solar cell technology," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 432-449.
    12. Koopo Kwon & Sungchan Jun & Yong-Jae Lee & Sanghei Choi & Chulung Lee, 2022. "Logistics Technology Forecasting Framework Using Patent Analysis for Technology Roadmap," Sustainability, MDPI, vol. 14(9), pages 1-30, April.
    13. Takano, Yasutomo & Mejia, Cristian & Kajikawa, Yuya, 2016. "Unconnected component inclusion technique for patent network analysis: Case study of Internet of Things-related technologies," Journal of Informetrics, Elsevier, vol. 10(4), pages 967-980.
    14. Erzurumlu, S. Sinan & Pachamanova, Dessislava, 2020. "Topic modeling and technology forecasting for assessing the commercial viability of healthcare innovations," Technological Forecasting and Social Change, Elsevier, vol. 156(C).
    15. Heng Xu & Menglu Zhang & Jun Zeng & Huihui Hao & Hao-Chiang Koong Lin & Mengyun Xiao, 2022. "Use of Latent Dirichlet Allocation and Structural Equation Modeling in Determining the Factors for Continuance Intention of Knowledge Payment Platform," Sustainability, MDPI, vol. 14(15), pages 1-25, July.
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    17. Han, Xiaotong & Zhu, Donghua & Lei, Ming & Daim, Tugrul, 2021. "R&D trend analysis based on patent mining: An integrated use of patent applications and invalidation data," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    18. Jinlou Zhao & Hongyu Gao & Yongli Li & Jiaguo Liu, 2017. "Which factors affect the duration of hot topics on social media platforms?," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(5), pages 2395-2407, September.
    19. Kim, Min Sung & Kim, Junghwan & Kim, Seongcheol, 2023. "Korea's leadership in 5G and beyond: Footprints and futures," Telecommunications Policy, Elsevier, vol. 47(8).

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