Bayesian inference of spatial and temporal relations in AI patents for EU countries

TL;DR

This paper introduces Bayesian models to analyze spatial and temporal patterns of AI patents in EU countries, revealing interaction strengths and predicting a slowdown in patenting growth.

Contribution

It develops novel Bayesian inference models for spatial and temporal analysis of AI patents, highlighting cooperation gaps and growth trends in the EU.

Findings

Identified significant lack of cooperation between certain EU country pairs.

Estimated interaction strengths between countries using Bayesian inference.

Predicted an upcoming slowdown in AI patenting growth in the EU.

Abstract

In the paper, we propose two models of Artificial Intelligence (AI) patents in European Union (EU) countries addressing spatial and temporal behaviour. In particular, the models can quantitatively describe the interaction between countries or explain the rapidly growing trends in AI patents. For spatial analysis Poisson regression is used to explain collaboration between a pair of countries measured by the number of common patents. Through Bayesian inference, we estimated the strengths of interactions between countries in the EU and the rest of the world. In particular, a significant lack of cooperation has been identified for some pairs of countries. Alternatively, an inhomogeneous Poisson process combined with the logistic curve growth accurately models the temporal behaviour by an accurate trend line. Bayesian analysis in the time domain revealed an upcoming slowdown in patenting intensity.