Technological Convergence of AI Across the Industrial Sectors

The AI market has been experiencing significant growth recently and is projected to thrive. Yet, there is still a lack of comprehensive studies integrating diverse industries and technologies in AI. Furthermore, AI-related patent analysis often examines AI technologies without considering their convergence with other sectors. Therefore, to fill this gap, this study aims to explore the technological convergence of AI using a network analysis approach with patent data in finance & management, healthcare, semiconductors, games, biotechnology, and transport. This study used an IPC-based convergence network methodology to define critical industrial areas and influential technologies with the four-digit IPC codes for the AI patent group from 2000 to 2019. Moreover, this study conducted a centrality analysis using Net-Miner software to identify hubs and connected nodes and analyze the comprehensive convergence status related to AI. According to the results, we defined hub nodes based on the degree and centralities and analyzed the centrality of six sectors in the AI convergence network. In addition, the technology classification of solid ties is analyzed based on IPC network analysis. Finally, this study attempts to deliver theoretical and empirical contributions to technological convergence, providing a comprehensive framework for understanding how different technologies can converge in AI with three categories: 1) learning and reasoning, 2) natural language processing, and 3) computer vision. This study suggests that companies operating within the industrial AI space should reflect the evolution of technology as revealed in the mainstream trends of sector-specific AI integration.