论文标题
欧盟国家AI专利中对空间和时间关系的贝叶斯推论
Bayesian inference of spatial and temporal relations in AI patents for EU countries
论文作者
论文摘要
在本文中，我们建议在欧盟（EU）国家（EU）国家（AI）专利的两种模型，以解决空间和时间行为。特别是，这些模型可以定量描述国家之间的相互作用或解释AI专利的快速增长趋势。对于空间分析，使用泊松回归来解释通过普通专利数量来衡量的一对国家之间的合作。通过贝叶斯的推论，我们估计了欧盟和世界其他国家之间互动的优势。特别是，已经确定了某些国家的严重缺乏合作。 或者，不均匀的泊松过程与逻辑曲线的增长相结合，可以通过准确的趋势线准确地对时间行为进行建模。在时间域中的贝叶斯分析显示，专利强度即将发生的放缓。
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.
