Patent protection of biological genetic resources based on deep learning and artificial intelligence
8.4
来源：
Nature
关键字：
ASO
发布时间：
2025-11-21 23:52
摘要：
The paper proposes an optimized Recurrent Convolutional Neural Network (RCNN) model for the classification of patents related to biological genetic resources, achieving an accuracy of 90.20% and an F1 score of 89.00%. This model integrates deep learning techniques to enhance the efficiency and accuracy of patent protection, addressing critical issues in biodiversity and intellectual property management. The findings highlight the model's applicability across various sectors, including agriculture and medicine, providing significant insights for future research and practical applications in intellectual property protection.
原文：
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价值维度分析
domain_focus
1.0分+1.0分
business_impact
0.5分+0.5分
scientific_rigor
1.5分+1.5分
timeliness_innovation
1.5分+1.5分
investment_perspective
2.5分+2.5分
market_value_relevance
1.0分+1.0分
team_institution_background
0.5分+0.5分
technical_barrier_competition
1.0分+1.0分
关键证据
The optimized RCNN model achieves an accuracy of 90.20% and an F1 score of 89.00%.
The model demonstrates strong adaptability in classifying patents across agriculture, medicine, and biotechnology.
The research provides new technical support for the intellectual property protection of biological genetic resources.
真实性检查
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AI评分总结
The paper proposes an optimized Recurrent Convolutional Neural Network (RCNN) model for the classification of patents related to biological genetic resources, achieving an accuracy of 90.20% and an F1 score of 89.00%. This model integrates deep learning techniques to enhance the efficiency and accuracy of patent protection, addressing critical issues in biodiversity and intellectual property management. The findings highlight the model's applicability across various sectors, including agriculture and medicine, providing significant insights for future research and practical applications in intellectual property protection.