Using Machine Learning With Technological Innovation Factors to Predict the Transferability of University Patents

We quantify the impact of technological innovation factors on university patent transferability, accurately identify transferable patents, and address the lack of interpretability in existing patent transferability models. Firsly, we apply the latent Dirichlet allocation (LDA) model to conduct text mining and feature extraction on abstracts of university patents in the field of artificial intelligence to obtain the technological innovation features of university patents. We then construct a patent transferability fusion index system that includes technological innovation features and quality features. Four typical machine learning algorithms, namely support vector machine (SVM), random forest (RF), artificial neural network (ANN), and extreme gradient boosting (XGBoost) are used to predict university patent transferability. We use SHapley Additive exPlanations (SHAP) to explore feature importance and interactions based on the model with the strongest performance. Our results show that (1) XGBoost outperforms the other algorithms in predicting university patent transferability; (2) fusion indicators can effectively improve prediction performance with respect to university patent transferability; (3) the importance of technological innovation features generated with XGBoost is generally high; and (4) the impact of both technology innovation and patent quality features on university patent transferability is nonlinear and there are significant positive interaction effects between them.