Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Bayesian inference of spatial and temporal relations in AI patents for
  EU countries   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.

19
The categories of this paper are: eess
4
The categories of this paper are: cond-mat
25
The categories of this paper are: patt-sol
27
The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
13
The categories of this paper are: quant-ph
6
The categories of this paper are: astro-ph
15
The categories of this paper are: nucl-ex
24
The categories of this paper are: dg-ga
14
The categories of this paper are: math-ph
21
The categories of this paper are: adap-org
36
The categories of this paper are: cmp-lg
28
The categories of this paper are: alg-geom
2
The categories of this paper are: math
3
The categories of this paper are: physics
32
The categories of this paper are: atom-ph