Construction and Application of University Patent Evaluation Model based on Machine Learning

As the frontier of scientific and technological innovation, universities will produce a large number of patents based on their talent, technology and resource advantages. How to evaluate the value of university patents in a more scientific and efficient manner is of great significance in improving the scientific research and innovation capability of universities and promoting the transfer and transformation of university patents. Firstly, combined with the characteristics of universities and the definition of “high-value patents”, we constructed a scientific evaluation index system of university patent value. Secondly, machine learning algorithms were used to build patent value evaluation models. Finally, we conducted an empirical study with invention patent data from 134 universities in Sichuan Province, and then tested six evaluation models for their performances. The XGB model and GBDT model are found to have better accuracy and reliability. In addition, the number of IPC classifications, patent family citations and independent claims are of higher importance in patent value evaluation, university characteristics are less important to the value of university patents.