https://doi.org/10.1140/epjds/s13688-025-00598-y
Research
Development of a fundamental patent exploration methodology based on technology knowledge flow
Department of Industrial & Systems Engineering, Dongguk University, 3-26, Pil-dong 3-ga, Chung-gu, 100-715, Seoul, Republic of Korea
Received:
21
February
2025
Accepted:
27
October
2025
Published online:
20
November
2025
This study proposes a methodology for identifying fundamental patents based on their technical content. To overcome the limitations of existing methods that rely on citation networks and bibliographic information, this study extracts the subject-action-object (SAO) structure from patent claims to define the technical knowledge flow. A technical knowledge network is then constructed by identifying technical problems through modularity analysis. Fundamental patents are assessed using five characteristics: pioneering nature, applicability, originality, marketability, and path-dependency within the network. The fundamental patent is identified by calculating a fundamental score through entropy-based weights. A case study of CO2 capture technology and wearable watch technologies demonstrates that this approach can more accurately identify fundamental patents from a technical and problem-solving perspective compared to traditional methods. Notably, the methodology effectively tracks the foundation of technological innovation by reflecting the semantic flow between technical problems and their solutions. This study integrates and redefines the characteristics of fundamental patents, overcoming the limitations of previous patent analysis by reducing dependence on expert input by classifying technical problems and analyzing technical knowledge flow. This methodology highlights how rapidly growing patent data can be effectively analyzed to identify fundamental patents, contributing to R&D strategy and technology management decision-making.
Key words: Fundamental Patent Identification / Semantic Patent analysis / Patent Network Analysis / Technology Knowledge Flow
© The Author(s) 2025
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