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The paper introduces Coupled-NeuralHP, a hybrid event-plus-state model, to link the irregular event streams of AI patent publications from the USPTO to monthly Google Trends data representing public response. The model forecasts future AI innovation counts by incorporating public response, achieving superior performance compared to baseline models in held-out validation. Ablation studies indicate that the innovation-to-response link is more significant than the reverse, and semi-synthetic replications demonstrate the model's ability to recover innovation-to-response links better than VARX.
Knowing how the public reacts to AI breakthroughs can help predict the *next* breakthrough, but only if you model the relationship as a directional coupling between innovation and response.
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.
