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PTNS:基于时间网络快照的专利引用轨迹预测
Mingli Ding1, Wangke Yu2,3, Tingyu Zeng1
1Intellectual Property Information Services Center, Jingdezhen Ceramic University, Jingdezhen, 333403, China.
Scientific reports
|October 14, 2024
概括
本研究引入了一种专利引用轨迹预测模型 (PTNS),用于预测具有重大影响力的专利. 该模型有效地捕捉了专利引用行为的时间变化,提高了预测准确度.
科学领域:
- 知识产权管理知识产权管理
- 数据科学和机器学习
- 创新研究 研究 创新研究
背景情况:
- 知识经济需要对技术创新的高价值专利进行战略性识别.
- 专利引用行为是复杂的,并表现出时间变化,使准确的预测具有挑战性.
- 现有的方法很难有效地捕捉专利引用网络的动态性质.
研究的目的:
- 开发一种用于预测专利引用轨迹和识别有影响力的专利的新型模型.
- 为了有效地建模专利引用网络中的时间演变和复杂关系.
- 为了捕捉诸如专利衰老和引用模式中的"睡眠美女"效应等现象.
主要方法:
- 提出了一种使用时间网络快照的专利引用轨迹预测模型 (PTNS).
- 使用关系图卷积网络 (R-GCN) 学习复杂的专利属性关系.
- 利用双向长期短期记忆网络 (BiLSTM) 来聚合时间演变差异.
- 应用主要组件分析 (PCA) 分析引文演变特征.
主要成果:
- 与基线方法相比,PTNS模型显示出更高的性能.
- 对于新,增长和随机专利 (大约) 观察到根平均平方对数误差 (RMSLE) 的显著减少. 0.04,0.14,0.18) 的情况.
- 平均绝对对数错误 (MALE) 也在新增,增长和随机专利 (约. 0.04,0.12,0.16) 的情况.
结论:
- 通过整合时间网络分析和深度学习,PTNS模型有效预测专利引用轨迹.
- 该模型捕捉时间动态和复杂关系的能力提高了高价值专利的识别.
- 这种方法提供了一种强大的方法,用于导航专利引用行为的不确定性.
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