A Machine-Learning Analysis of 50 Years of Trends In Wildlife Trade Patents

Identifying potential emerging human rights implications in Chinese smart cities via machine-learning aided patent analysis

“…The approach presented here represents a new approach to deriving insights from patents -an almost entirely ignored, hugely valuable and freely-available largescale data source concerning developments in technological innovation -and has significant potential for broader application. The methods shown here were originally developed in the context of understanding commercial interest in products derived from endangered wildlife (Hinsley et al, 2023) and their implications for the wildlife trade. There are clear applications of these approaches for general investigation of emerging technologies and commercial developments, as well as for deeper analysis of smart cities and their implications for human rights.…”

“…To understand the landscape of patenting concerning smart cities, we scrape public patent data via the Google Patents search engine and apply both a time series frequency analysis to identify key changepoints in the rate of patent filing, representing interest in particular innovations; as well as a topical analysis of the contents of patents. This approach draws on previous work performing large-scale machine-assisted assessment of developments in technological innovation in the wildlife trade (Hinsley et al, 2023). Topic modelling has also recently been applied…”

“…We make use of patent repositories to reach a better understanding of the public security aspects of smart cities, using data scraping and machine learning to process our initial list of 67,130 patents, which was reduced to a final list of 5,989 patents that were identified, with high probability, as directly related to smart cities, and that were filed in China. The process of scraping and machine learning is refined and analysed for quality purposes by a 'human in the loop' to remove false positive search results caused by colliding terms that match different search topics (Hinsley et al, 2023).…”