用多任务学习预测专利引用，看清技术影响随时间变化的规律。
Dynamic technology impact analysis: A multi-task learning approach to patent citation prediction
- 设计多任务模型，同时预测不同时间段的专利引用数。
- 在电池技术案例中，预测准确率显著提升。
- 结合解释性方法，揭示关键影响因素的变化模式。
机器学习模型在利用专利引用信息分析技术影响方面具有重要价值。然而，现有方法难以捕捉技术影响随时间动态演变的特性，以及不同时期间的影响关联性。本文提出一种多任务学习（MTL）方法，通过知识共享机制，同时建模多个时间窗口的技术影响演化过程。首先，基于不同时段的引用分析量化技术影响并识别模式；其次，构建MTL模型，利用多项专利指标预测引用数量；最后，采用SHapley Additive exPlanation（SHAP）方法分析关键输入指标随时间的变化趋势与模式。研究还结合统计方法和自然语言处理技术，提供结果验证与解读的指南。以电池技术为例的案例研究表明，该方法不仅深化了对技术影响的理解，且提升了预测准确性，为学术界与产业界提供了可操作的洞见。
原文摘要 · Abstract (English)
Machine learning (ML) models are valuable tools for analyzing the impact of technology using patent citation information. However, existing ML-based methods often struggle to account for the dynamic nature of the technology impact over time and the interdependencies of these impacts across different periods. This study proposes a multi-task learning (MTL) approach to enhance the prediction of technology impact across various time frames by leveraging knowledge sharing and simultaneously monitoring the evolution of technology impact. First, we quantify the technology impacts and identify patterns through citation analysis over distinct time periods. Next, we develop MTL models to predict citation counts using multiple patent indicators over time. Finally, we examine the changes in key input indicators and their patterns over different periods using the SHapley Additive exPlanation method. We also offer guidelines for validating and interpreting the results by employing statistical methods and natural language processing techniques. A case study on battery technologies demonstrates that our approach not only deepens the understanding of technology impact, but also improves prediction accuracy, yielding valuable insights for both academia and industry.
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