Using Machine Learning to Forecast Patent Quality - Take "Vehicle Networking" Industry for Example

Machine learning has become a key development target globally in recent years. An increasing number of algorithms have been applied to solve practical issues. At the present stage, machine learning technologies have progressed from a pure research topic to tools employed for solving practical issues, becoming a key development direction of practical technologies and a prominent emerging discipline. Furthermore, current machine learning technologies have transformed from tools that supplement decision-making to methods that replace manual decision making when generating optimal decisions. This transformation fundamentally changes the tasks that required relatively long workhours in the past. In addition, this may even facilitate distinctive interpretations to effectively aid researchers and operators in addressing problems from a new perspective. Therefore, this study adopted a machine learning technology, namely artificial neural networks (ANNs), to examine relevant topics in patent quality. To verify the effect and identify the characteristics of machine learning in patent quality analysis, this study focused on the fast-changing internet of vehicles (IoV) industry. tailed analyses of key patents were also performed. Finally, a model of high-quality patents in this industry was developed to serve as a reference for other researchers.