[Research purpose]Accurately and efficiently identifying high-value invention patents among numerous patents not only pro-motes the implementation of China's intellectual property strategy,but also helps to encourage the technological transformation of high-value invention patents.[Research method]Firstly,in response to the issue of insufficient utilization of domain patent texts,Bert is pre-trained through contrastive learning of wireless communication network domain patent texts to obtain a domain adapted Bert model.Then,a domain adapted Bert model is used to train a high-value invention patent recognition model,and an oversampling strategy is used in the training process of the high-value invention patent recognition model to alleviate the problem of imbalanced positive and negative samples and improve the effectiveness of the model.[Research conclusion]The experimental results on a dataset containing 62 000 Chi-nese invention patents for wireless communication networks show that the models trained using contrastive learning and oversampling strate-gies achieve 97％and 0.93 Accuracy and Macro-Fl index values respectively,increased by 9.77％and 0.19 respectively compared to the direct use of Bert.
关键词
高价值发明专利/专利识别/专利文本/专利价值评估/对比学习/无线通信网络
Key words
high-value patents/patent identification/patent texts/patent value evaluation/contrastive learning/wireless communication network
万方数据