@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://preview.aclanthology.org/ingest-acl/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."
}