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| Research on Patent Tradability Prediction Based on Weight Balancing Algorithm |
| Ran Congjing1, Ding Qunzhe1, Li Wang1, Song Yonghui1, Liu Shuang2 |
1.School of Information Management, Wuhan University, Wuhan 430072
2.Center for Scientific Research and Development in Higher Education Institutes, Ministry of Education, Beijing 100080 |
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Abstract As a crucial link between innovation and the realization of market value, patent transactions play a significant role in supporting national strategic goals, driving technological progress, and facilitating technology transfer and collaboration between enterprises and research institutions. Therefore, considering the importance of identifying patents with high market potential to foster innovation and collaboration, predicting patent tradability is crucial. To address this, this paper proposes a patent tradability prediction method based on a weighted balancing algorithm. To support the prediction of patent tradability, an initial dataset was constructed by integrating data from the incoPat patent database and China Patent Information Service Platform. The initial patent transaction dataset was further refined based on patent transfer records, transfer and assignee addresses, and stakeholder information, using a series of rules and algorithms to construct the final patent transaction dataset. Using this dataset, patent tradability prediction is framed as a supervised binary classification task. The input variables include the multidimensional technical features of the patents prior to the transaction, while the target variable indicates whether the patent is transacted before its expiration. Using this methodology, the proposed patent tradability prediction model based on the weighted balancing algorithm outperformed baseline models in terms of overall performance, and its effectiveness was validated through empirical results. In addition, model interpretability techniques revealed that key technical features, such as applicant country, applicant type, number of family patents, and number of family countries, significantly influence patent transactions. Despite these advancements, predicting patent transactions remains challenging. Future research could explore the incorporation of multidimensional features, such as patent text and images, to further enhance the predictive performance of the model.
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Received: 27 September 2024
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