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

Zhenfeng Liu, Jian Feng and Lorna Uden

Technological Forecasting and Social Change, 2023, vol. 192, issue C

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.

Keywords: Patent analysis; Technology opportunity analysis; Idea generation; Generative topographic mapping; Link prediction (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:192:y:2023:i:c:s0040162523002500

DOI: 10.1016/j.techfore.2023.122565

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