Dynamic technology impact analysis: A multi-task learning approach to patent citation prediction

TL;DR

This paper introduces a multi-task learning approach to predict patent citation impacts over time, capturing dynamic changes and interdependencies to improve accuracy and understanding of technological influence.

Contribution

It presents a novel multi-task learning framework that models the evolving nature of technology impact across different periods using patent citation data.

Findings

Enhanced prediction accuracy for patent citations over time

Identification of impact patterns through citation analysis

Guidelines for interpreting multi-task learning model results

Abstract

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.