특허 출원 정보와 정형적 개념 분석을 활용한 유망기술 및 융합기술 탐지 방법론에 관한 연구

Rapid technological change and accelerating convergence have made early detection of emerging technologies a strategic imperative for nations and enterprises. This study proposes a methodology for detecting emerging and convergence technologies by applying Formal Concept Analysis (FCA) to patent application data. Unlike existing studies relying on simple frequency analysis or static classification, we introduce new quantitative indicators—Average Novelty and Convergence Degree—to structurally analyze the evolution of technology groups. We applied this methodology to patents in the 6T (six key future technologies) fields from 2005 to 2024. The results visualize dynamic changes in technological patterns and identify specific convergence points over time. Specifically, we calculated average novelty from patent concepts by year and redefined the concept with the highest average novelty in each year as a promising technology. Furthermore, we analyzed the characteristics of convergent technologies in the 6T fields using patent application information and FCA results. This study demonstrates that the proposed FCA-based framework serves as an effective analytical tool for establishing R&D strategies and predicting future technological trends. With future cross-validation through industry expert evaluations, we anticipate this framework will be more widely utilized as a methodology for predicting promising and convergent technologies.