@inproceedings{yoo-etal-2026-patentmind,
title = "{P}atent{M}ind: A Multi-Aspect Reasoning Graph for Patent Similarity Evaluation",
author = "Yoo, Yongmin and
Xu, Qiongkai and
Cao, Longbing",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.735/",
pages = "14947--14964",
ISBN = "979-8-89176-395-1",
abstract = "Patent similarity evaluation is essential for intellectual property analysis, yet existing methods struggle to capture the multifaceted structure of patent documents encompassing technical specifications, legal boundaries, and application contexts. We propose PatentMind, a framework that performs patent similarity assessment through a Multi-Aspect Reasoning Graph (MARG). PatentMind decomposes patent documents into three dimensions: technical features, application domains, and claim scopes, and computes dimension-specific similarity scores, which are then integrated via a context-aware dynamic weighting mechanism that emulates expert-level judgment. To facilitate evaluation, we introduce PatentSimBench, an expert-annotated benchmark comprising 500 patent pairs. Experiments show that PatentMind achieves a Pearson correlation of $r=0.938$ with expert annotations, substantially outperforming embedding-based, patent-specific, and prompt engineering baselines. Our framework offers interpretable, multi-dimensional assessment applicable to downstream tasks such as prior art search and infringement risk analysis."
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<abstract>Patent similarity evaluation is essential for intellectual property analysis, yet existing methods struggle to capture the multifaceted structure of patent documents encompassing technical specifications, legal boundaries, and application contexts. We propose PatentMind, a framework that performs patent similarity assessment through a Multi-Aspect Reasoning Graph (MARG). PatentMind decomposes patent documents into three dimensions: technical features, application domains, and claim scopes, and computes dimension-specific similarity scores, which are then integrated via a context-aware dynamic weighting mechanism that emulates expert-level judgment. To facilitate evaluation, we introduce PatentSimBench, an expert-annotated benchmark comprising 500 patent pairs. Experiments show that PatentMind achieves a Pearson correlation of r=0.938 with expert annotations, substantially outperforming embedding-based, patent-specific, and prompt engineering baselines. Our framework offers interpretable, multi-dimensional assessment applicable to downstream tasks such as prior art search and infringement risk analysis.</abstract>
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%0 Conference Proceedings
%T PatentMind: A Multi-Aspect Reasoning Graph for Patent Similarity Evaluation
%A Yoo, Yongmin
%A Xu, Qiongkai
%A Cao, Longbing
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F yoo-etal-2026-patentmind
%X Patent similarity evaluation is essential for intellectual property analysis, yet existing methods struggle to capture the multifaceted structure of patent documents encompassing technical specifications, legal boundaries, and application contexts. We propose PatentMind, a framework that performs patent similarity assessment through a Multi-Aspect Reasoning Graph (MARG). PatentMind decomposes patent documents into three dimensions: technical features, application domains, and claim scopes, and computes dimension-specific similarity scores, which are then integrated via a context-aware dynamic weighting mechanism that emulates expert-level judgment. To facilitate evaluation, we introduce PatentSimBench, an expert-annotated benchmark comprising 500 patent pairs. Experiments show that PatentMind achieves a Pearson correlation of r=0.938 with expert annotations, substantially outperforming embedding-based, patent-specific, and prompt engineering baselines. Our framework offers interpretable, multi-dimensional assessment applicable to downstream tasks such as prior art search and infringement risk analysis.
%U https://aclanthology.org/2026.findings-acl.735/
%P 14947-14964
Markdown (Informal)
[PatentMind: A Multi-Aspect Reasoning Graph for Patent Similarity Evaluation](https://aclanthology.org/2026.findings-acl.735/) (Yoo et al., Findings 2026)
ACL