Can AI Examine Novelty of Patents?: Novelty Evaluation Based on the Correspondence between Patent Claim and Prior Art

TL;DR

This paper explores the potential of large language models to evaluate patent novelty by comparing claims with prior art, introducing a new dataset, and analyzing model capabilities in a task similar to patent examination.

Contribution

It presents the first dataset for patent novelty evaluation and analyzes LLMs' effectiveness in assessing patent claims against prior art.

Findings

Generative models show reasonable accuracy in novelty assessment.

Classification models struggle with effective novelty evaluation.

Explanations from models help understand relationships between patents and prior art.

Abstract

Assessing the novelty of patent claims is a critical yet challenging task traditionally performed by patent examiners. While advancements in NLP have enabled progress in various patent-related tasks, novelty assessment remains unexplored. This paper introduces a novel challenge by evaluating the ability of large language models (LLMs) to assess patent novelty by comparing claims with cited prior art documents, following the process similar to that of patent examiners done. We present the first dataset specifically designed for novelty evaluation, derived from real patent examination cases, and analyze the capabilities of LLMs to address this task. Our study reveals that while classification models struggle to effectively assess novelty, generative models make predictions with a reasonable level of accuracy, and their explanations are accurate enough to understand the relationship between the target patent and prior art. These findings demonstrate the potential of LLMs to assist in patent evaluation, reducing the workload for both examiners and applicants. Our contributions highlight the limitations of current models and provide a foundation for improving AI-driven patent analysis through advanced models and refined datasets.