Patent Value Prediction Method Based on Bibliographic Item Concatenation, Legal Value Calculation, and Deep Learning Fusion

Citation analysis in high-value patent identification faces challenges such as regional bias, time lag, and insufficient legal event analysis. This paper adopts a strategy combining multi-source data fusion and deep learning techniques to enhance the accuracy and comprehensiveness of patent value assessment. The dataset is sourced from the IncoPat patent database. Patent validity duration serves as the key metric for categorization. The data is divided into three value classes: low, medium, and high after value calculation.. Oversampling is applied to address imbalanced sample distribution, laying the groundwork for subsequent model research. The study introduces a patent value assessment model built on BERT and BiLSTM. The BERT embedding layer captures word semantics. The BiLSTM encoder deeply encodes the semantic structure of the text. The value prediction layer outputs classification probabilities. The BERT-BiLSTM model is compared with the BERT model. Experimental results on the test set show that the BERT-BiLSTM model achieves a lower test loss of 0.53 and a higher test accuracy of 79.40 , surpassing the BERT model's 77.31 . For the high value class, the BERT-BiLSTM model outperforms the BERT model in recall and F1 scores. The results demonstrate the superior performance of the BERT-BiLSTM model in patent text value classification tasks. This method exhibits significant advantages in patent value prediction.