Coupled-NeuralHP: Directional Temporal Coupling Between AI Innovation Exposure and Public Response

TL;DR

This paper introduces Coupled-NeuralHP, a hybrid model linking AI patent publication streams to public response, improving forecast accuracy and recovering innovation-response links better than traditional methods.

Contribution

The paper presents a novel hybrid event-plus-state model that captures directional coupling between AI innovation exposure and public response, outperforming existing approaches.

Findings

The model achieves better innovation count forecasts than baseline methods.

Structured forecast head primarily carries the response signal.

Coupled family recovers innovation-response links more effectively than VARX.

Abstract

Artificial intelligence innovation exposure and public response co-evolve, but innovation arrives as irregular event streams while response is observed monthly. We introduce Coupled-NeuralHP, a hybrid event-plus-state model linking eight-domain USPTO AI patent publication streams to a train-only Google Trends response index. Under the cleaned response protocol, the validation-selected one-way real-data variant gives the best held-out innovation count forecasts in the registered comparison set (pseudo-log-likelihood -30.4 vs. -34.7; root mean squared error (RMSE) 471 vs. 532) while matching the stronger multi-lag factor-family baseline on response RMSE (0.295). Ablations show that the real-data response signal is carried mainly by the structured forecast head, whereas the reverse response-to-innovation block is not supported on held-out count prediction. Across 60 semi-synthetic replications with known structure, the broader coupled family recovers innovation-to-response links much better than vector autoregression with exogenous inputs (VARX) (F1 = 0.734 vs. 0.386). A placebo-controlled 2022 split-date analysis finds no robust milestone-specific regime break.