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Inter-cluster connectivity analysis for technology opportunity discovery

Author

Listed:
  • Byunghoon Kim

    (Rutgers, The State University of New Jersey)

  • Gianluca Gazzola

    (Rutgers, The State University of New Jersey)

  • Jae-Min Lee

    (Korea Institute of Science and Technology Information)

  • Dohyun Kim

    (Korea Institute of Science and Technology Information)

  • Kanghoe Kim

    (Korea Institute of Science and Technology Information)

  • Myong K. Jeong

    (Rutgers, The State University of New Jersey)

Abstract

In today’s competitive business environment, the timely identification of potential technology opportunities is becoming increasingly important for the strategic management of technology and innovation. Existing studies in the field of technology opportunity discovery (TOD) focus exclusively on patent textual information. In this article, we introduce a new method that tackles TOD via technology convergence, using both patent textual data and patent citation networks. We identify technology groups with high convergence potential by measuring connectivity between clusters of patents. From such technology groups we select pairs of core patents based on their technological relatedness, on their past involvement in convergence, and on the impact of their new potential convergence. We finally carry out TOD by extracting representative keywords from the text of the selected patent pairs and organizing them into the basic description of a new invention, which the potential convergence of the patent pair might produce. We illustrate our proposed method using a data set of U.S. patents in the field of digital information and security.

Suggested Citation

  • Byunghoon Kim & Gianluca Gazzola & Jae-Min Lee & Dohyun Kim & Kanghoe Kim & Myong K. Jeong, 2014. "Inter-cluster connectivity analysis for technology opportunity discovery," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(3), pages 1811-1825, March.
    • Handle: RePEc:spr:scient:v:98:y:2014:i:3:d:10.1007_s11192-013-1097-2
    DOI: 10.1007/s11192-013-1097-2
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    References listed on IDEAS

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    Cited by:

    1. Jinho Choi & Yong Sik Chang, 2020. "Development of a New Methodology to Identity Promising Technology Areas Using M&A Information," Sustainability, MDPI, vol. 12(14), pages 1-25, July.
    2. Andrew Rodriguez & Byunghoon Kim & Mehmet Turkoz & Jae-Min Lee & Byoung-Youl Coh & Myong K. Jeong, 2015. "New multi-stage similarity measure for calculation of pairwise patent similarity in a patent citation network," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(2), pages 565-581, May.
    3. Lijie Feng & Yuxiang Niu & Zhenfeng Liu & Jinfeng Wang & Ke Zhang, 2019. "Discovering Technology Opportunity by Keyword-Based Patent Analysis: A Hybrid Approach of Morphology Analysis and USIT," Sustainability, MDPI, vol. 12(1), pages 1-35, December.
    4. Juite Wang & Tzu-Yen Hsu, 2023. "Early discovery of emerging multi-technology convergence for analyzing technology opportunities from patent data: the case of smart health," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(8), pages 4167-4196, August.
    5. Lee, Jiho & Ko, Namuk & Yoon, Janghyeok & Son, Changho, 2021. "An approach for discovering firm-specific technology opportunities: Application of link prediction to F-term networks," Technological Forecasting and Social Change, Elsevier, vol. 168(C).
    6. Jungpyo Lee & So Young Sohn, 2021. "Recommendation system for technology convergence opportunities based on self-supervised representation learning," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(1), pages 1-25, January.
    7. Guannan Xu & Weijie Hu & Yuanyuan Qiao & Yuan Zhou, 2020. "Mapping an innovation ecosystem using network clustering and community identification: a multi-layered framework," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 2057-2081, September.
    8. Xuefeng Wang & Pingping Ma & Ying Huang & Junfang Guo & Donghua Zhu & Alan L. Porter & Zhinan Wang, 2017. "Combining SAO semantic analysis and morphology analysis to identify technology opportunities," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 3-24, April.
    9. Xiao Zhou & Lu Huang & Yi Zhang & Miaomiao Yu, 2019. "A hybrid approach to detecting technological recombination based on text mining and patent network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 699-737, November.
    10. Byunghoon Kim & Gianluca Gazzola & Jaekyung Yang & Jae-Min Lee & Byoung-Youl Coh & Myong K. Jeong & Young-Seon Jeong, 2017. "Two-phase edge outlier detection method for technology opportunity discovery," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(1), pages 1-16, October.
    11. Wenjing Wang & Yiwei Liu, 2022. "Does University-industry innovation community affect firms’ inventions? The mediating role of technology transfer," The Journal of Technology Transfer, Springer, vol. 47(3), pages 906-935, June.
    12. Christian Mühlroth & Michael Grottke, 2018. "A systematic literature review of mining weak signals and trends for corporate foresight," Journal of Business Economics, Springer, vol. 88(5), pages 643-687, July.
    13. Jee, Jeonghun & Park, Sanghyun & Lee, Sungjoo, 2022. "Potential of patent image data as technology intelligence source," Journal of Informetrics, Elsevier, vol. 16(2).
    14. 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).
    15. Park, Youngjin & Yoon, Janghyeok, 2017. "Application technology opportunity discovery from technology portfolios: Use of patent classification and collaborative filtering," Technological Forecasting and Social Change, Elsevier, vol. 118(C), pages 170-183.

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