Predictive modeling for technology convergence: A patent data-driven approach through technology topic networks

Abstract

Anticipating technology convergence is crucial for driving innovation and gaining a competitive advantage. Developing a method for forecasting the convergence of previously unrelated technology fields is necessary. Previous studies explored technology convergence using patents but focused on technology classes. However, these prior studies lacked clear insights because they relied on ambiguous technology classes. Therefore, this study seeks to define technology topics with precise technological meanings and develop model predictions using machine learning through technology topic networks to anticipate technology convergence potential. Specifically, it explores the use of text mining techniques to gain insights into technology topics, highlighting best practices and our research findings. This study constructs a network of technology topics derived from patent documents and maps their interactions using a topic network approach. Technology topic networks are constructed through dynamic topic modeling, and features extracted from these networks are used as input for training multiple classification models. By incorporating new link results and leveraging technological influence, the proposed approach can accurately anticipate technology convergence. The results of this research can have significant implications for firms seeking to gain a competitive advantage through innovation and technology convergence and contribute to the establishment of a next-generation R&D planning system.