摘要：

Abstract:

【Purpose/significance】 Before the occurrence of patent litigation, it is of great significance to identify high-risk patents that

are easy to cause litigation, which will help China's relevant patent subjects to take prevention and control measures as soon as pos⁃

sible to avoid patent risks.【Method/process】 This study collects litigation patent data and non-litigation patent data from the autho⁃

rized patents published by the USPTO as the research object. Based on the characteristic variables such as patent application, patent

examination and patent value, this study uses machine learning technology to build the identification model of potential litigation risk

patents, the performance of the model is evaluated and compared.【Result/conclusion】 The results show that the identification model

based on Random Forest is the best in Accuracy, Recall, and AUC, which is more suitable for patent litigation risk pre-warning. Com⁃

pared with the potential litigation risk patent recognition model based on a single classifier, the recognition model based on ensemble

learning has not shown significant advantages in the process of identifying potential litigation risk patents.【Innovation/limitation】 This

article avoids the shortcomings of existing research in patent data collection, key feature selection, and core algorithm selection. At the

same time, it provides a preliminary attempt of machine learning technology in the field of potential litigation risk patent identification,

further enriching the theory and methods of potential litigation risk patent identification.