SAO 기반 기능–문제 매칭을 통한 기술 기회 탐색

초록

As technological change accelerates, the systematic identification of technology opportunities across industries is becoming increasingly important. However, existing approaches to Technology Opportunity Analysis (TOA) have primarily focused on statistical and network-based methods within single domains, which fail to sufficiently capture the semantic relationships between technical functions and problems. To overcome this limitation, this study proposes a novel Technology Opportunity Discovery (TOD) framework that combines patent text-based Subject–Action–Object (SAO) analysis with semantic similarity evaluation. The proposed framework consists of three main steps: (1) data collection and preprocessing, (2) AO-based function-problem extraction, and (3) identification of cross-industry applicability. Functional AO structures are extracted from patent abstracts, while problem AO structures are extracted from the background sections, and their potential linkages across technological domains are identified using cosine similarity. The proposed methodology is applied to hologram technology to identify potential technology opportunities with heterogeneous industries. Furthermore, it is verified that technology opportunities derived from past patent data were subsequently realized in later patent applications. This study demonstrates the potential of hologram technology to address critical challenges in other industrial domains, thereby providing a practical basis for guiding future R&D investments and commercialization strategies.

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