Discovering new applications: Cross-domain exploration of patent documents using causal extraction and similarity analysis

Abstract

Determining a technology’s potential applications is crucial in assessing its level of innovation and evaluating its commercial viability. However, patent documents offer limited insights into a technology’s full potential. As a solution, this study suggests an approach to explore a technology’s applicability beyond what is explicitly stated in patents. The approach employs causal extraction to extract sentences expressing technologies and their applications from patents, followed by deep learning-based similarity analysis to compare the similarity of these sentences. Experimental results show its effectiveness in extracting sentences about technologies and applications and its superiority in terms of F1 score compared to benchmark models. This study enables cross-domain comparisons of technologies and applications, identifies multiple prospective applications for a given technology, and offers new opportunities for patent value analysis and intellectual property management in the industry. A cross-domain application network of the proposed method demonstrates how to find all potential cross-domain connections of a given data and we provide open access to the code.