Research on High-Value Patent Identification Model from Perspective of Patent Transfer

Abstract Accurately identifying high-value patents can be difficult with the dramatic increase in the number of patent applications. This leads to a low rate of commercialization of patent achievements. Whether a patent is transferred or not is an important reflection of the value of the patent. In order to solve above problems, we proposed a high-value patent identification model that combines hybrid sampling technology and ensemble learning algorithm. First, we add technical capacity of patentees based on traditional high-value patent identification indicators to reconstruct the indicator system. Then we reduce the identification indicator system for high-value patents to eliminate redundant indicators. Second, we use Adaptive Synthetic Sampling - Local Outlier Factor (ADASYN-LOF) to expand minority samples to balance the data. Finally, we use Genetic Algorithm (GA) to optimise the parameters of AdaBoost. For clarity, this model is called the ADASYN-LOF-GA-AdaBoost model. To test the effectiveness of above model, we use patent data in field of scientific instruments. The results demonstrate that the proposed model achieves ACC of 94.47%, AUC of 94.87%, recall of 97.54%, and F1-score of 95.23%. The results show that ADASYN-LOF-GA-AdaBoost model performs better than other models. Therefore, this model can effectively identify high-value patents with transfer potential.