Sporting a virtual future: exploring sports and virtual reality patents using deep learning-based analysis

Abstract

We investigate the convergence of sports and emerging technologies from the Fourth Industrial Revolution, with a focus on virtual reality (VR) applications. Using patent big data, we introduce SportsBERT, a bidirectional encoder representation from trans- formers (BERT)-based algorithm tailored for enhanced natural language processing in sports-related knowledge-based documents. Through topic modeling, we extract key themes and clusters from sports-related VR patents, providing insights into the knowledge structure and technological trends in VR applications for sports. Our analysis identifies key drivers of technological advancement, including spatial hardware, tactile human-computer interactions, aerobic exercise, rehabilitation, and swing sports. Additionally, we highlight challenges such as the high cost and usability limitations of current VR devices. This study presents the first deep learning-based topic modeling approach specialized for sports patents and offers a comprehensive roadmap for current developments and future trajectories in VR sports technologies.