A Study on Domain Diagnosis of Corporate-Owned Technologies using Text Mining

This study proposes a novel methodology for extracting technical elements emphasized by corporations in patent documents that uses compound word-based text mining rather than conventional unigram-based approaches. We hypothesize that compound words, which function as bridges between distinct technical elements, play a critical role in patent documents, and we use betweenness centrality to assess their importance. Compound words are extracted by calculating betweenness centrality within the scope of individual claims, using claim-level segmentation based on the structural characteristics unique to patent documents. To mitigate sparsity and bias caused by differences in the length of claim descriptions, compound words with high betweenness centrality are ranked on a per-patent basis, and the frequencies of the top-ranked terms are aggregated by industry and company for analysis. Furthermore, regression analysis incorporating time-series data is used to adjust for short-term fluctuations and develop a mechanism for predicting promising technological elements. The proposed framework is applied to representative Cooperative Patent Classification (CPC) categories in the automotive sector. Leading firms are identified based on the number of patents granted in the target CPC domains, and their technological trajectories are analyzed. Empirical results reveal that these firms not only drive technological advancement within their respective industries but also align their development strategies with overarching trends observed at the CPC category level.