The effective use of artificial intelligence in patent searches: A case study in using AI-based classifiers to identify AI inventions
Aleksei L. Kalinichenko, Kelvin Wayne Willoughby · World Patent Information · 2025
This study proposes a new patent search methodology for enhancing the quality and utility of patent research. The methodology focuses on techniques for effectively searching large patent datasets using artificial intelligence (AI) based classifiers to generate robust and reproducible results for subsequent statistical analysis. An extensive literature review revealed that salient approaches to patent searching fail to provide transparent, accurate and reproducible results, thereby hindering validation as well as evoking the need for manual post-processing and subjective judgments. Our proposed methodology, to enable precise, reliable and reproducible AI-enabled search queries, involves employing a novel terminological framework and formulating search regulations based on a formal definition of the technological subject matter of interest. We tested the methodology by applying it to patent searches in the field of AI technologies. In other words, we employed AI to facilitate our development of an operational technical definition of AI for patent searches. The primary results of our research are: (1) an automated patent search technique utilizing a learning algorithm guided by a formal definition of the search area; and (2) a novel terminological framework tailored for patent searches in the AI technology domain. Our approach offers enhanced transparency, reproducibility, and reliability in patent research, with applicability to both AI and other fields of technology.