Literature Review: Advanced Computational Tools for Patent Analysis

The integration of Artificial Intelligence (AI)-Powered Technologies is becoming increasingly pervasive across manufacturing and operational domains, offering a valuable avenue for informed decision-making and the exploration of cutting-edge technologies. Notably, AI incorporates a variety of advanced tools, such as machine learning, deep learning, natural language processing, data visualization, clustering etc., that play a prominent role in patent analysis. These adaptable methodologies enable researchers and industries to partake in a wide array of patent-related endeavors, encompassing the anticipation of forthcoming technological patterns and strategic technology mapping, as well as the evaluation of patent quality, recognizing infringements, and recognizing emerging technology domains and gaps in patent coverage. In this comprehensive review, our objective is to present the current advancements in the utilization of advanced computational tools for patent analysis. We achieve this by analyzing 61 articles sourced from the Web of Science database. Additionally, we offer an overview of various patent analysis techniques through a detailed taxonomy. We anticipate that this review will prove invaluable to both scholars and practitioners seeking the latest developments in the realm of patent analytics, fostering innovation and informed decision-making in the process.