Learning and Predicting Patent Technology Reuse Trajectories from Emergence-Time Signals

Recent research introduces a novel methodology for predicting the future reuse trajectories of newly emerging patent technologies. By analyzing 201,710 patent technologies from 2002-2022, the study utilizes a GRU autoencoder to learn 20-year reuse patterns and subsequently classifies these patterns using k-means clustering. The key innovation is the ability to forecast these complex trajectories using only seven observable features from a technology's first year, achieving a high predictive accuracy without requiring pre-defined reuse labels.

This research is strategically important because it provides a data-driven method for early forecasting of technological evolution and adoption patterns. This capability allows for more informed decision-making regarding investment, R&D focus, and market positioning in emerging technology domains.

A new methodology uses a GRU autoencoder to learn and represent 20-year patent technology reuse trajectories.

Forward consideration, not a verified fact.