Abstract
Abstract This article investigates the creation and integration of artificial intelligence (AI) patents in Europe. We create a panel of AI patents over time, mapping them into regions at the NUTS2 level. We then proceed by examining how AI is integrated into the knowledge space of each region. In particular, we find that those regions where AI is most embedded into the innovation landscape are also those where the number of AI patents is largest. This suggests that, to increase AI innovation, it may be necessary to integrate it with industrial development, a feature central to many recent AI-promoting policies.
Highlights
Artificial Intelligence (AI) has become one of the most high-profile areas for technological development and potential regional economic growth opportunities
We find approximately 5,300 AI patents, which tend to be clustered in twelve four-digit Cooperative Patent Classification (CPC) codes out of the over 650 codes that make up the entire classification scheme
Utilizing the knowledge space methodology of Kogler et al (2013, 2017), which takes advantage of the co-occurrence network of CPC codes found on individual patent documents, the objective is to construct a series of indicators that describe the inventive network of a region. We estimate how these indicators change when omitting the AI patents, which gives us insight into how central those patents are to the region’s knowledge space. We find that those regions that have the most AI patents tend to be those where AI is most connected to the overall knowledge space, i.e. in their absence there is a significant shift in the inventive network structure
Summary
Artificial Intelligence (AI) has become one of the most high-profile areas for technological development and potential regional economic growth opportunities. Taking advantage of the fact that patents are considered the best option for intellectual property protection in AI technology development, one way forward is to investigate the technological codes which underpin development of an invention (Office, 2018) Investigating these codes that are based on the Cooperative Patent Classification (CPC) scheme has proven useful in previous studies where the identification and subsequent diffusion of a novel product or process of economic value has been the object of inquiry (Feldman et al, 2015). We find that those regions that have the most AI patents tend to be those where AI is most connected to the overall knowledge space, i.e. in their absence there is a significant shift in the inventive network structure This suggests that in order to emerge as an AI superstar it may be important to ensure that those patents are well-connected to other research and development activities.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
