基于深度学习和人工智能的生物遗传资源的专利保护
Zichen Liu1, Lu Liu2
1Intellectual Property Convergence Department, Gyeongsang National University, Jinju, 52828, South Korea.
Scientific reports
|November 21, 2025
概括
人工智能 (AI) 和深度学习优化了对生物遗传资源的专利保护. 一个先进的反复卷积神经网络 (RCNN) 模型提高了分类的准确性和效率.
科学领域:
- 知识产权法 知识产权法 知识产权法
- 计算机科学 计算机科学
- 生物技术是生物技术.
背景情况:
- 人工智能 (AI) 的快速发展为知识产权保护提供了新的方法.
- 深度学习为在专利框架内保护生物遗传资源提供了机会.
研究的目的:
- 探索人工智能驱动的深度学习在生物遗传资源专利保护中的应用.
- 提出一种优化的技术方法,以加强该领域的专利保护.
主要方法:
- 基于深度学习的循环卷积神经网络 (RCNN) 模型的开发和优化.
- 整合自然语言处理和图像识别用于特征提取.
- 使用Top-Kmax聚合和预训练的词向量 (GloVe) 增强RCNN模型.
主要成果:
- 优化后的RCNN模型在分类生物遗传资源专利方面实现了90.20%的准确性和89.00%的F1得分.
- 子类别分类 (农业,医学,生物技术) 超过了90%的准确性和F1分数.
- 顶级Kmax聚合和GloVe显著改善了特征提取和分类性能.
结论:
- 优化的RCNN模型显示了对生物遗传资源专利保护的高度适应性和效率.
- 这种方法提高了专利文本分类的准确性和效率,降低了手动审查成本.
- 这些发现为知识产权保护提供了技术支持,并为深度学习应用提供了实际见解.
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