PAGE: Patent Assessment with LLM-Extracted Technical Feature Graphs

General-purpose large language models (LLMs) have demonstrated remarkable capabilities in language understanding, reasoning and generation across a wide range of domains. Patent assessment, however, remains difficult for them, because they struggle to understand the complex structural information embedded in patent claims. We define patent assessment as three critical tasks that an examiner performs on a patent: novelty evaluation, prior-art retrieval and patent classification. Novelty evaluation is the most demanding of the three, because establishing a novelty conflict requires that every technical feature of the patent under examination, together with its properties, is covered by the prior art. Considering that patent claims are inherently written with a hierarchical structure, we propose PAGE (Patent Graph Examiner), a two-stage framework that addresses all three tasks using a unified representation. PAGE first devises an LLM to convert claims and prior-art texts into directed technical feature graphs, whose nodes represent technical features and typed edges encode composition, interaction, taxonomy and property relations. A lightweight graph neural network then encodes each graph using frozen text embeddings of features and relations, edge-aware graph attention, and attentive readout. Task-specific heads map the resulting graph embeddings to predictions for the three tasks: a Siamese interaction head for novelty evaluation, a contrastively trained dual encoder for prior-art retrieval, and a multilayer perceptron classifier for patent classification. Experiments on PatentMatch and CLEF-IP datasets show that PAGE effectively improves patent novelty evaluation and is equally effective for prior-art retrieval and patent classification.