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Two-phase edge outlier detection method for technology opportunity discovery

Author

Listed:
  • Byunghoon Kim

    (Korea Institute of Science and Technology Information)

  • Gianluca Gazzola

    (Rutgers Business School)

  • Jaekyung Yang

    (Chonbuk National University)

  • Jae-Min Lee

    (Korea Institute of Science and Technology Information)

  • Byoung-Youl Coh

    (Korea Institute of Science and Technology Information)

  • Myong K. Jeong

    (Rutgers University)

  • Young-Seon Jeong

    (Chonnam National University)

Abstract

This article introduces a method for identifying potential opportunities of innovation arising from the convergence of different technological areas, based on the presence of edge outliers in a patent citation network. Edge outliers are detected via the assessment of their centrality; pairs of patents connected by edge outliers are then analyzed for technological relatedness and past involvement in technological convergence. The pairs with the highest potential for future convergence are finally selected and their keywords combined to suggest new directions of innovation. We illustrate our method on a data set of US patents in the field of digital information and security.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:scient:v:113:y:2017:i:1:d:10.1007_s11192-017-2472-1
    DOI: 10.1007/s11192-017-2472-1
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    References listed on IDEAS

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    1. Yongho Lee & So Young Kim & Inseok Song & Yongtae Park & Juneseuk Shin, 2014. "Technology opportunity identification customized to the technological capability of SMEs through two-stage patent analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(1), pages 227-244, July.
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    4. Janghyeok Yoon & Kwangsoo Kim, 2012. "Detecting signals of new technological opportunities using semantic patent analysis and outlier detection," Scientometrics, Springer;Akadémiai Kiadó, vol. 90(2), pages 445-461, February.
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    8. 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.
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    11. Sungchul Choi & Janghyeok Yoon & Kwangsoo Kim & Jae Yeol Lee & Cheol-Han Kim, 2011. "SAO network analysis of patents for technology trends identification: a case study of polymer electrolyte membrane technology in proton exchange membrane fuel cells," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(3), pages 863-883, September.
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    2. Huayan Shen & Zhiyong Ou & Kexin Bi & Yu Gao, 2023. "Impact of Customer Predictive Ability on Sustainable Innovation in Customized Enterprises," Sustainability, MDPI, vol. 15(13), pages 1-18, July.
    3. Jinzhu Zhang & Wenqian Yu, 2020. "Early detection of technology opportunity based on analogy design and phrase semantic representation," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 551-576, October.
    4. 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).
    5. Li, Xin & Wu, Yundi & Cheng, Haolun & Xie, Qianqian & Daim, Tugrul, 2023. "Identifying technology opportunity using SAO semantic mining and outlier detection method: A case of triboelectric nanogenerator technology," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    6. Wenjing Zhu & Bohong Ma & Lele Kang, 2022. "Technology convergence among various technical fields: improvement of entropy estimation in patent analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(12), pages 7731-7750, December.
    7. 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.

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