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Prediction of University Patent Transfer Cycle Based on Random Survival Forest

Author

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  • Disha Deng

    (School of Management, Wuhan University of Science and Technology, Wuhan 430081, China)

  • Tao Chen

    (School of Management, Wuhan University of Science and Technology, Wuhan 430081, China
    Industrial Policy and Management Research Center of Hubei, Wuhan 430081, China)

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.

Suggested Citation

  • Disha Deng & Tao Chen, 2022. "Prediction of University Patent Transfer Cycle Based on Random Survival Forest," Sustainability, MDPI, vol. 15(1), pages 1-13, December.
    • Handle: RePEc:gam:jsusta:v:15:y:2022:i:1:p:218-:d:1012553
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    References listed on IDEAS

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    3. Alessandro Muscio, 2010. "What drives the university use of technology transfer offices? Evidence from Italy," The Journal of Technology Transfer, Springer, vol. 35(2), pages 181-202, April.
    4. Tie Wei & Tingting Liu, 2020. "Evolution of High-Value Patents in Reverse Innovation: Focus on Chinese Local Enterprises," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-13, November.
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