Patent protection of biological genetic resources based on deep learning and artificial intelligence

Abstract

With the rapid development of artificial intelligence (AI), deep learning has provided new ideas for the patent protection of biological genetic resources in the field of intellectual property. This paper aims to explore the application of deep learning technology driven by artificial intelligence in the patent protection of biological genetic resources. By analyzing the advantages of deep learning models in extracting patent texts and technical features, the paper proposes a technical approach to optimize the patent protection of biological genetic resources. This paper constructs and optimizes a deep learning-based Recurrent Convolutional Neural Network (RCNN) model. It combines natural language processing techniques and image recognition algorithms to efficiently extract and analyze patent texts and technical features. The model’s performance is further enhanced by introducing Top-K max pooling strategy and pre-trained word vectors (such as GloVe). Experimental results show that the optimized RCNN model performs excellently in classifying patents related to biological genetic resources. It achieves an overall accuracy of 90.20% and an F1 score of 89.00%. In subcategories such as agriculture, medicine, and biotechnology, the model’s accuracy and F1 score both exceed 90%. Additionally, the use of Top-K max pooling strategy and pre-trained word vectors (GloVe) significantly improves the model’s feature extraction ability and classification accuracy. The proposed optimized RCNN model demonstrates strong adaptability and efficiency in the field of patent protection for biological genetic resources. Compared with existing technologies, the optimized model not only improves the accuracy and efficiency of patent text classification but also reduces the time and cost of manual review through automated processing. The research results provide new technical support for the intellectual property protection of biological genetic resources and offer practical experience for the application of deep learning in the field of intellectual property. © The Author(s) 2025.