An eXplainable AI (XAI) model for text-based patent novelty analysis

Abstract

The technology development cycle continues to accelerate, and novelty analysis is becoming increasingly important for R&D planning as well as in the patent application process. Recently, thanks to significant advances in both text mining and natural language processing, researchers started to look into AI-assisted novelty analysis of technical content including patents. However, existing language models do not take into account the unique characteristics of technical elements in patent documents nor do they provide any explanation of their decisions including which technical elements represent a novelty. Therefore, we developed an eXplainable AI (XAI) model that evaluates novelty, takes into account the claim structure of a patent, and provides an explanation. The proposed framework of an XAI model for patent novelty consists of the following three parts: (1) dataset construction, (2) model architecture, and (3) inference process. The training dataset for patent novelty is constructed using a full-text dataset provided by the European Patent Office (EPO). A self-explainable novelty classification model is proposed and investigated. Using the fitted model, the inference results are then analyzed by extracting patents in the field of vehicle communication networks. The inference process is done by applying the fitted model to patents in the vehicle communication network field and can be expanded to address potential applications. The performance of the proposed model is then verified by comparing it with the results of similar studies. We also discuss practical applications from the perspective of patent examiners and technical planning practitioners. This study involves both an academic contribution that uses a novel approach to technology management via an XAI model and a practical contribution that can be used for patent analysis.