| Issue |
E3S Web Conf.
Volume 409, 2023
International Conference on Management Science and Engineering Management (ICMSEM 2023)
|
|
|---|---|---|
| Article Number | 02005 | |
| Number of page(s) | 11 | |
| Section | Decision Support Systems | |
| DOI | https://doi.org/10.1051/e3sconf/202340902005 | |
| Published online | 01 August 2023 | |
Construction and Application of University Patent Evaluation Model based on Machine Learning
1 Business School, Sichuan University, Chengdu 610065, People’s Republic of China
2 Literature and Journalism School, Sichuan University, Chengdu 610064, People’s Republic of China
3 National University Science Park, Sichuan University, Chengdu 610065, People’s Republic of China
4 GSAS of Columbia University, New York City 10027, United States
* e-mail: xjhang0711@163.com
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.
Key words: Patents and inventions / Machine learning / Intellectual property
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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