Predicting and analyzing technology convergence for exploring technological opportunities in the smart health industry

Abstract

Technology convergence (TC) represents a prevailing innovation pattern in various industries, as it enables the integration of two or more existing technologies to create new hybrid technologies or even a new technological domain, transforming the way firms compete with each other. The advent of smart health can be attributed to the convergence of various technologies, including wireless communication, embedded systems, artificial intelligence (AI), medical technologies, and information security. It is essential for smart health firms to systematically comprehend TC patterns and anticipate emerging convergent technologies at an early stage to leverage potential technological opportunities. This paper proposes a machine learning methodology that integrates network analysis to predict and analyze changing TC patterns from patent data related to smart health. A machine learning-based link prediction method is developed to forecast convergent technologies from a comprehensive set of link prediction measures. Network centrality analysis is then used to understand and evaluate the convergent patterns in the predicted technological knowledge interaction network and identify critical convergent technologies that may play significant roles in the future. The research not only demonstrates that convergent innovation is a continually growing trend in the smart health industry but also identifies several important emerging convergent patterns.