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Development of technology opportunity analysis based on technology landscape by extending technology elements with BERT and TRIZ

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  • Wang, Jinfeng
  • Zhang, Zhixin
  • Feng, Lijie
  • Lin, Kuo-Yi
  • Liu, Peng

Abstract

Technology opportunity analysis (TOA) has been the subject of many prior studies, most of which have focused on deconstructing and restructuring the original knowledge structure in a single domain. This study suggests a method by extending technology elements with BERT and TRIZ that endeavors to address these issues. First, patents collected from the Derwent database were used as data sources. Second, BERT was employed to construct a technology landscape as a vector space model where similar technology elements are classified into the same technology topic. Meanwhile, TEMPEST was employed to cluster technology topics and elements according to different functions and other dimensions. Third, technology elements were extended by function-oriented search (FOS), which is a useful method of TRIZ. It includes extracting new technology elements from newly retrieved patents about implementing a specific function in other domains. Fourth, technology opportunities were identified by recombining original and new technology elements and then verifying their feasibility. Finally, the proposed approach was employed in empirical analysis for unmanned ships and 10 technology opportunities generated through knowledge migration. The process designed in this study combines quantitative modeling and qualitative analysis, which realizes accurate search and efficient innovation among different domains.

Suggested Citation

  • Wang, Jinfeng & Zhang, Zhixin & Feng, Lijie & Lin, Kuo-Yi & Liu, Peng, 2023. "Development of technology opportunity analysis based on technology landscape by extending technology elements with BERT and TRIZ," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
  • Handle: RePEc:eee:tefoso:v:191:y:2023:i:c:s004016252300166x
    DOI: 10.1016/j.techfore.2023.122481
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    1. Serkan Altuntas & Zulfiye Erdogan & Turkay Dereli, 2020. "A clustering-based approach for the evaluation of candidate emerging technologies," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 1157-1177, August.
    2. Teng, Fei & Sun, Yuling & Chen, Fang & Qin, Aning & Zhang, Qi, 2021. "Technology opportunity discovery of proton exchange membrane fuel cells based on generative topographic mapping," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    3. Nailong Wu & Xinyuan Chen & Shaonan Chen & Haodong Yuan & Jie Qi & Yueying Wang & Chih Chiang, 2021. "Inertial Gyro Wave Energy Conversion Nonlinear Modeling and Power-Index Predictive Control for Autonomous Ship," Complexity, Hindawi, vol. 2021, pages 1-13, November.
    4. Zhang, JingJing & Yan, Yan & Guan, JianCheng, 2019. "Recombinant distance, network governance and recombinant innovation," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 260-272.
    5. Mariotti, Francesca & Haider, Sajjad, 2020. "Managing institutional diversity and structural holes: Network configurations for recombinant innovation," Technological Forecasting and Social Change, Elsevier, vol. 160(C).
    6. Sam Arts & Lee Fleming, 2018. "Paradise of Novelty—Or Loss of Human Capital? Exploring New Fields and Inventive Output," Organization Science, INFORMS, vol. 29(6), pages 1074-1092, December.
    7. Ren, Haiying & Zhao, Yuhui, 2021. "Technology opportunity discovery based on constructing, evaluating, and searching knowledge networks," Technovation, Elsevier, vol. 101(C).
    8. Kwon, Heeyeul & Park, Yongtae & Geum, Youngjung, 2018. "Toward data-driven idea generation: Application of Wikipedia to morphological analysis," Technological Forecasting and Social Change, Elsevier, vol. 132(C), pages 56-80.
    9. Kim, Juram & Kim, Seungho & Lee, Changyong, 2019. "Anticipating technological convergence: Link prediction using Wikipedia hyperlinks," Technovation, Elsevier, vol. 79(C), pages 25-34.
    10. Ardito, Lorenzo & D'Adda, Diego & Messeni Petruzzelli, Antonio, 2018. "Mapping innovation dynamics in the Internet of Things domain: Evidence from patent analysis," Technological Forecasting and Social Change, Elsevier, vol. 136(C), pages 317-330.
    11. Changyong Lee & Gyumin Lee, 2019. "Technology opportunity analysis based on recombinant search: patent landscape analysis for idea generation," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 603-632, November.
    12. Wang, Zhinan & Porter, Alan L. & Wang, Xuefeng & Carley, Stephen, 2019. "An approach to identify emergent topics of technological convergence: A case study for 3D printing," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 723-732.
    13. Yoon, Byungun & Magee, Christopher L., 2018. "Exploring technology opportunities by visualizing patent information based on generative topographic mapping and link prediction," Technological Forecasting and Social Change, Elsevier, vol. 132(C), pages 105-117.
    14. Tiziano Fagni & Fabrizio Falchi & Margherita Gambini & Antonio Martella & Maurizio Tesconi, 2021. "TweepFake: About detecting deepfake tweets," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-16, May.
    15. Lee, Changyong & Jeon, Daeseong & Ahn, Joon Mo & Kwon, Ohjin, 2020. "Navigating a product landscape for technology opportunity analysis: A word2vec approach using an integrated patent-product database," Technovation, Elsevier, vol. 96.
    16. Albino, Vito & Ardito, Lorenzo & Dangelico, Rosa Maria & Messeni Petruzzelli, Antonio, 2014. "Understanding the development trends of low-carbon energy technologies: A patent analysis," Applied Energy, Elsevier, vol. 135(C), pages 836-854.
    17. Noh, Heeyong & Kim, Kyuwoong & Song, Young-Keun & Lee, Sungjoo, 2021. "Opportunity-driven technology roadmapping: The case of 5G mobile services," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    18. 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.
    19. 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.
    20. Guo, Junfang & Wang, Xuefeng & Li, Qianrui & Zhu, Donghua, 2016. "Subject–action–object-based morphology analysis for determining the direction of technological change," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 27-40.
    21. Sunhye Kim & Inchae Park & Byungun Yoon, 2020. "SAO2Vec: Development of an algorithm for embedding the subject–action–object (SAO) structure using Doc2Vec," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-26, February.
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