Summary of Dynamic Technology Impact Analysis: a Multi-task Learning Approach to Patent Citation Prediction, by Youngjin Seol et al.

Main: Machine Learning (cs.LG)
Secondary: Artificial Intelligence (cs.AI)

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

by Youngjin Seol, Jaewoong Choi, Seunghyun Lee, Janghyeok Yoon

First submitted to arxiv on: 14 Nov 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary The proposed multi-task learning (MTL) approach leverages knowledge sharing to predict the evolution of technology impact across various time frames using patent citation information. The method quantifies technology impacts through citation analysis over distinct periods, develops MTL models to predict citation counts based on multiple patent indicators, and examines changes in key input indicators and patterns using SHapley Additive exPlanation (SHAP). This approach improves prediction accuracy and provides valuable insights for academia and industry.
Low	GrooveSquid.com (original content)	Low Difficulty Summary This study uses machine learning to understand how technology affects the world. It looks at how patents are connected over time and develops a new way to predict these connections. The method helps us see how technology is changing and makes predictions more accurate. This is useful for both researchers and companies.