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.

翻译：机器学习模型是利用专利引用信息分析技术影响的重要工具。然而，现有的基于机器学习的方法往往难以处理技术影响随时间变化的动态特性，以及不同时期技术影响之间的相互依赖关系。本研究提出一种多任务学习方法，通过知识共享和同时监测技术影响的演变，来增强对不同时间范围内技术影响的预测能力。首先，我们通过不同时间段的引用分析量化技术影响并识别其模式。其次，我们开发多任务学习模型，利用随时间变化的多项专利指标来预测引用次数。最后，我们使用SHapley Additive exPlanation方法研究关键输入指标及其模式在不同时期的变化。我们还通过采用统计方法和自然语言处理技术，为验证和解释结果提供指导原则。一项关于电池技术的案例研究表明，我们的方法不仅深化了对技术影响的理解，而且提高了预测准确性，为学术界和工业界提供了有价值的见解。