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

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