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From technology opportunities to ideas generation via cross-cutting patent analysis: Application of generative topographic mapping and link prediction

Author

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  • Liu, Zhenfeng
  • Feng, Jian
  • Uden, Lorna

Abstract

Technology opportunity analysis (TOA) with ideas generation has been recognized an important activity to remain competitive and lead the industry in the future. However, there are several issues with existing TOA, such as an unclear path from technology opportunities to ideas generation, a fuzzy integration between automated TOA techniques and expert-based methods, and a lack of detailed schemes for technology opportunities. This study proposes a new systematic approach to show the way from technology opportunities to ideas generation via cross-cutting patent analysis. The proposed approach is comprised of three stages: 1) establishing a cross-cutting relationship between the target and reference technologies through the results of F-terms; 2) collecting and processing patents to construct patent-keyword vector matrices of the target and reference technologies, respectively; and 3) migrating corresponding ideas via cosine similarity and link prediction for the target technology opportunities that are discovered based on generative topographic mapping (GTM). The feasibility and effectiveness of the proposed approach is demonstrated by empirical research on the exploitation technology in both the natural gas hydrate (NGH) and the coal bed methane (CBM) fields. This study represents a contribution to expand the existing TOA research into generating creative ideas by providing more detailed schemes for technology opportunities.

Suggested Citation

  • Liu, Zhenfeng & Feng, Jian & Uden, Lorna, 2023. "From technology opportunities to ideas generation via cross-cutting patent analysis: Application of generative topographic mapping and link prediction," Technological Forecasting and Social Change, Elsevier, vol. 192(C).
    • Handle: RePEc:eee:tefoso:v:192:y:2023:i:c:s0040162523002500
    DOI: 10.1016/j.techfore.2023.122565
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    Citations

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    Cited by:

    1. Liao, Haojie & Chen, Yuqiang & Tan, RongYong & Chen, Yuling & Wei, Xiaoyu & Yang, Hongmei, 2023. "Can natural resource rent, technological innovation, renewable energy, and financial development ease China's environmental pollution burden? New evidence from the nonlinear-autoregressive distributive lag model," Resources Policy, Elsevier, vol. 84(C).
    2. Liu, Zhenfeng & Feng, Jian & Uden, Lorna, 2023. "Technology opportunity analysis using hierarchical semantic networks and dual link prediction," Technovation, Elsevier, vol. 128(C).
    3. Mi Zou & Peng Liu & Xuan Wu & Wei Zhou & Yuan Jin & Meiqi Xu, 2023. "Cognitive Characteristics of an Innovation Team and Collaborative Innovation Performance: The Mediating Role of Cooperative Behavior and the Moderating Role of Team Innovation Efficacy," Sustainability, MDPI, vol. 15(14), pages 1-23, July.
    4. Ma, Binfeng & Wang, Xiaofang, 2023. "How does green floating bond and financial sector readiness promote green economic growth evidence from China," Resources Policy, Elsevier, vol. 85(PB).
    5. Yu-Hsiang Hsiao & Jing-Cih Wu, 2026. "Exploring the fintech advancements in banking of Taiwan and the U.S. using patent-based topic modeling and generative topographic mapping," Scientometrics, Springer;Akadémiai Kiadó, vol. 131(1), pages 609-645, January.
    6. Seo, Wonchul & Afifuddin, Mokh, 2024. "Developing a supervised learning model for anticipating potential technology convergence between technology topics," Technological Forecasting and Social Change, Elsevier, vol. 203(C).
    7. Xiang, Bo & Pan, Zhuoya & Yu, Dejian & Zuo, Wenjin, 2025. "Detecting technology opportunities appropriate for enterprise R&D: The synthesis analysis of industrial technical windows and enterprise competition relations," Technology in Society, Elsevier, vol. 82(C).
    8. Lamei, Yin & Zhou, Yue & Shan, Liu, 2023. "Environmental efficiency, climate innovation, and resource rent in Chinaʼs SDGs: Insights from quantile regressions," Resources Policy, Elsevier, vol. 86(PA).
    9. Park, Sanghyun & Kim, Giyun & Lee, Sungjoo, 2026. "Evaluating the value of LLMs in patent-based technology intelligence: Toward increasing efficiency and reducing expert dependency," Technological Forecasting and Social Change, Elsevier, vol. 222(C).

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