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Technology opportunity discovery of proton exchange membrane fuel cells based on generative topographic mapping

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  • Teng, Fei
  • Sun, Yuling
  • Chen, Fang
  • Qin, Aning
  • Zhang, Qi

Abstract

Technology opportunity discovery is a key factor in technological innovation that is closely related to the development of a country and has a great impact on promoting social progress. To find the unexplored areas of technology and present the detailed direction of technology development, this paper proposes a systematic approach to patent text data. It includes four stages. First, patent text data are collected and preprocessed by natural language process methods. Second, text mining is executed by the means of supervised machine learning methods. It is clustered with the k-mean++ algorithm after text representation as keywords vectors. Third, patents are visualized in two-dimensional space by the generative topographic mapping. Different from other work, Principal components analysis is used to transform complex multi-dimensional keyword vectors in two-dimensional space. Last, take proton exchange membrane fuel cells as an example. Discuss the meaning of each patent vacancy which be interpreted by its inverse mapping onto the original keyword vector and approved by experts. This approach not only saves time to identify patent vacancies but also increases objectivity and reliability. To some extent, it can help enterprises and researchers to identify the research and development strategy that focuses on innovation in the future.

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  • 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).
    • Handle: RePEc:eee:tefoso:v:169:y:2021:i:c:s0040162521002912
    DOI: 10.1016/j.techfore.2021.120859
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    References listed on IDEAS

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    Cited by:

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    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. Runbo Zhao & Huiying Zhang & Marina Yue Zhang & Fei Qu & Yunlong Xu, 2023. "Competitor-Weighted Centrality and Small-World Clusters in Competition Networks on Firms’ Innovation Ambidexterity: Evidence from the Wind Energy Industry," IJERPH, MDPI, vol. 20(4), pages 1-18, February.
    4. Teng, Fei & Zhang, Qi & Chen, Siyuan & Wang, Ge & Huang, Zhenyue & Wang, Lu, 2024. "Comprehensive effects of policy mixes on the diffusion of heavy-duty hydrogen fuel cell electric trucks in China considering technology learning," Energy Policy, Elsevier, vol. 185(C).
    5. Just, Julian, 2024. "Natural language processing for innovation search – Reviewing an emerging non-human innovation intermediary," Technovation, Elsevier, vol. 129(C).
    6. 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).
    7. Choi, Jaewoong & Lee, Changyong & Yoon, Janghyeok, 2023. "Exploring a technology ecology for technology opportunity discovery: A link prediction approach using heterogeneous knowledge graphs," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).

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