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Updated: Jan 9, 2026

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Pavlovian Conditioned Approach Training in Rats
Published on: February 4, 2016
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L:专利预测与快速学习
IEEE transactions on neural networks and learning systems
|December 4, 2025
概括
本研究介绍了采用快速学习 (P3L) 的专利预测,这是一种使用预训练语言模型准确预测未来专利趋势的新方法,通过分析发展路径和相似性来准确预测未来的专利趋势. 通过克服现有的趋势预测技术的局限性,P3L增强了战略规划和创新.
科学领域:
- 知识产权管理知识产权管理
- 数据科学数据科学数据科学
- 技术预测技术预测
背景情况:
- 专利对于保护创新和推动工业竞争至关重要.
- 专利预测通过预测技术趋势来帮助战略规划.
- 现有的方法与专利数据的复杂性和专利相互依赖性作斗争.
研究的目的:
- 开发一种有效和准确的方法来预测未来的专利发展.
- 改进现有的科学研究趋势预测方法.
主要方法:
- 提出了一种新的方法:专利预测与快速学习 (P3L).
- 使用预训练语言模型 (PLM) 进行专利趋势预测.
- 开发了一种专利的相似性路径提取模块.
- 设计了一个快速学习方法,整合了开发路径,关键词和相似之处.
- 实施了注意力掩盖矩阵,以便快速拒绝噪音.
主要成果:
- 与现有方法相比,P3L的表现优越.
- 实现了对未来专利发展的有效和准确预测.
- 在多个专利数据集上进行了广泛的实验,其中包括一个公共数据集.
结论:
- 在专利预测准确性和有效性方面,P3L提供了显著的进步.
- 该方法成功地模拟了专利结构和依赖关系,以便更好地预测趋势.
- 开发的数据集和代码是公开可用的,以便进一步研究.
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