From Rules to Neural Graphs: Scalable Structured Prediction for Patent Prior Art Search

Abstract:Patent search requires processing documents routinely exceeding tens of thousands of tokens. Most neural retrieval approaches operate on truncated inputs, limiting their effectiveness. Graph-based retrieval addresses this by representing each patent as a structured invention graph, but constructing these graphs relies on brittle rule-based parsers. We present the neural parser, which adapts biaffine attention from dependency parsing to predict invention graphs directly from patent text. Our local biaffine attention restricts pairwise scoring to a sliding window, reducing complexity from $O(n^2)$ to $O(n \cdot w)$. Since local and global scoring share the same weights, the model trains on short sequences and deploys on documents exceeding 40,000 tokens without retraining. Distilled from 1 million rule-parsed documents, it surpasses its teacher at 3$\times$ lower inference cost: neural graphs improve citation recall by 0.5% on short queries and 1.1% on full documents in a downstream Graph Transformer retrieval system.

Information Retrieval (cs.IR); Computation and Language (cs.CL)

arXiv:2610.01553 [cs.IR]

(or arXiv:2610.01553v1 [cs.IR] for this version)

https://doi.org/10.48550/arXiv.2610.01553 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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