A study on the monitoring of technology innovation through patent analysis

These days, innovation cycles are becoming shorter, and market competition has grown increasingly intense. Therefore, it is essential for companies to adopt strategic R&D approaches to remain competitive. This study proposes a systematic approach to technology monitoring (TM). While previous TM studies have employed scientific analysis methods based on different types of patent information, their interpretations have often relied heavily on the subjective judgments of analysts. To mitigate such subjectivity, several methods have been proposed using patent classification schemes; however, they still remain dependent on expert interpretation. The present study introduces a technology monitoring methodology based solely on the Cooperative Patent Classification (CPC), the most recent and fine-grained patent classification system established in 2013. To demonstrate the utility of the proposed methodology, we apply it to the domain of electric vehicle (EV) battery charging technologies. Patent data were collected from WIPS ON, a major commercial patent database in Korea, and 2498 CPC-classified patents filed between 2013 and 2023 were analyzed. The Bass diffusion model was used to identify stages of technological innovation. To identify core technology clusters, UMAP (with cosine similarity, min dist = 0.5, and n neighbors = 50) and DBSCAN (with Euclidean distance, eps = 15, and min samples = 4) were employed, achieving a silhouette score of 0.781 for three clusters. Association Rule Mining (ARM) was then conducted to identify the technological patterns within each cluster, thereby revealing temporal shifts in EV charging technologies. Notably, this study finds that patent data not only capture technological characteristics but also reflect strategic business directions, suggesting that structured patent metadata, such as CPC codes, can serve as an insightful resource for future-oriented decision-making in innovation management.