Artificial Intelligence Reducing the Intricacies of Patent Prior Art Search

Digitalization has a significant impact on various fields, including business, research, and community. One of the areas influenced by automation is patent administration, specifically in searching for and assessing patents. Traditional methods of patent prior art search involve using Boolean logic, keywords, synonym-selection, classifiers, multilingualism, and other techniques. However, this manual process can be time-consuming and inefficient. This study explores how artificial intelligence (AI) can aid patent assessors in their prior art search by suggesting search keywords, retrieving relevant documents, ranking them, and visualizing their content. The findings reveal that AI can reduce the time and cost involved in sifting through many patents. The study highlights the importance of human-in-the-loop methods and the need for better tools that support human-centered decision-making in prior art searches.