LAUSR

Technology convergence can trigger technological innovation and change. Therefore, it is required to develop an approach to predict the convergence between technology fields that did not exist in the past. It will allow a frim to preoccupy a completely new competitive advantage that is different from that of its competitors. The timely anticipation of converging technology fields allows the innovating firms to recognize the changing business developments associated with the technology convergence. A variety of researchers have presented supervised learning-based approaches to predict potential technology fields where technology convergence is taking place using patents. They have developed machine learning models which capture the associations between the past and future connections between technology classes. Although their contributions are absolutely significant, they have a limitation in that they do not consider in depth the technological properties that are outputs of technological activities performed in each technology field. To ensure that the predicted future connections between technology fields are reasonable, technological properties that can specifically imply technology convergence should be clearly reflected in the process of the supervised learning. Motivated to remedy this problem, this study proposes a supervised learning-based approach to anticipating potential technology convergence by using the link prediction results, the technological influence relationships, and the technological relevance between technology classes. Using these as input features, several classification models that predict new technology convergence are trained and a voting classifier is developed to ensemble all the models. This study is expected to contribute to identifying new technology opportunities that can be realized through technology convergence. Furthermore, this study will assist firms to reflect the identified opportunities on their technology roadmap and make business decisions to penetrate the relevant market in a timely manner.