Coupled-NeuralHP: Directional Temporal Coupling Between AI Innovation Exposure and Public Response
For researchers studying the co-evolution of AI innovation and public attention, this work provides a novel modeling framework that improves forecasting accuracy and causal link recovery.
The paper introduces Coupled-NeuralHP, a hybrid model that links AI patent streams to public response indices, achieving better innovation count forecasts (pseudo-log-likelihood -30.4 vs. -34.7, RMSE 471 vs. 532) and matching response RMSE (0.295) compared to baselines, while recovering innovation-to-response links more effectively than VARX (F1=0.734 vs. 0.386).
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.