High-Value Invention Patent Identification Based on Contrastive Learning:Taking the Field of Wireless Communication Network as an Example
[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.
high-value patentspatent identificationpatent textspatent value evaluationcontrastive learningwireless communication network
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