数字图书馆论坛2024，Vol.20Issue(10)：53-62,10.DOI:10.3772/j.issn.1673-2286.2024.10.006
基于专利主体特征的专利权维持期预测研究
Patent Maintenance Period Prediction Based on Patent Subject Features
摘要
Abstract
This paper proposes a patent maintenance period prediction method based on patent subject characteristics to address the current issues of feature lag and neglect of patent subject information in predicting features.The proposed method uses a patent dataset to construct patent subject features including patent inventors,patent owners,and agencies,and uses a correlation-based ensemble learning model to predict the patent maintenance period.Finally,the SHAP model is used to interpret the obtained prediction model to enhance understanding.Empirical research based on patent data in the field of wind energy conversion demonstrates the feasibility and effectiveness of the proposed method in this paper.The model achieves evaluation metrics with mean absolute error of 0.469 2,mean squared error of 0.933 1,and R2 of 0.936 8.Compared to existing methods,the model achieves more ideal predictive results,demonstrating that the features of the patent subject can effectively predict the maintenance period of patent rights,thereby enhancing the accuracy of the predictions.关键词
专利权维持期/预测/专利主体特征/集成学习/可解释性Key words
Patent Maintenance Period/Prediction/Feature of Patent Subject/Ensemble Learning/Interpretability分类
社会科学引用本文复制引用
俞琰,马昕远,刘攀..基于专利主体特征的专利权维持期预测研究[J].数字图书馆论坛,2024,20(10):53-62,10.基金项目
本研究得到国家社会科学基金一般项目"数据驱动的高校技术转移供需信息挖掘模式构建研究"(编号:23BTQ098)资助. （编号:23BTQ098）
