Prediction of University Patent Transfer Cycle Based on Random Survival Forest

Abstract

Taking the invention patents of the C9 League from 2002 to 2020 as samples, a random survival forest model is established to predict the dynamic time-point of patent transfer cycle. By ranking the variables based on importance, it is found that the countries citing, the non-patent citations and the backward citations have significant impacts on the patent transfer cycle. C-index, Brier score and integrated Brier score are used to measure the discrimination and calibration ability of the four different survival models respectively. It is found that the prediction accuracy of the random survival forest model is higher than that of the Cox proportional risk model, Cox model based on lasso penalty and random forest model. In addition, the survival function and cumulative risk function under the random survival forest are adopted to predict and analyze the individual university patent transfer cycle, which shows that the random survival forest model has good prediction performance and is able to help universities as well as enterprises to identify the patent transfer opportunities effectively, thereby shortening the patent transfer cycle and improving the patent transfer efficiency.