# PatentMind: A Multi-Aspect Reasoning Graph for Patent Similarity Evaluation

arXiv: https://arxiv.org/abs/2505.19347 · HTML: https://mlsanity.com/paper/2505.19347/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 36 references that are in the map.
- First posted on arXiv 25 May 2025.

## Builds on (most cited first, 14 of 14)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [PatentBERT: Patent Classification with Fine-Tuning a pre-trained BERT Model](https://mlsanity.com/paper/1906.02124/) (2019)
- [Patent Retrieval: A Literature Review](https://mlsanity.com/paper/1701.00324/) (2017)
- [A Text-Embedding-based Approach to Measure Patent-to-Patent Technological Similarity -- Workflow, Code, and Applications](https://mlsanity.com/paper/2003.12303/) (2020)
- [Automating the search for a patent's prior art with a full text similarity search](https://mlsanity.com/paper/1901.03136/) (2019)
- [Summarization, Simplification, and Generation: The Case of Patents](https://mlsanity.com/paper/2104.14860/) (2021)
- [A Survey on Sentence Embedding Models Performance for Patent Analysis](https://mlsanity.com/paper/2206.02690/) (2022)
- [Patents Phrase to Phrase Semantic Matching Dataset](https://mlsanity.com/paper/2208.01171/) (2022)
- [A Novel Patent Similarity Measurement Methodology: Semantic Distance and Technological Distance](https://mlsanity.com/paper/2303.16767/) (2023)
- [A comparative analysis of embedding models for patent similarity](https://mlsanity.com/paper/2403.16630/) (2024)
- [Can AI Examine Novelty of Patents?: Novelty Evaluation Based on the Correspondence between Patent Claim and Prior Art](https://mlsanity.com/paper/2502.06316/) (2025)

## Built on by (most cited first, 5 of 5)

- [Self-Filtered Distillation with LLMs-generated Trust Indicators for Reliable Patent Classification](https://mlsanity.com/paper/2510.05431/) (2025)
- [FlowPlan-G2P: A Structured Generation Framework for Transforming Scientific Papers into Patent Descriptions](https://mlsanity.com/paper/2601.02589/) (2026)
- [ERA-IT: Aligning Semantic Models with Revealed Economic Preference for Real-Time and Explainable Patent Valuation](https://mlsanity.com/paper/2512.12869/) (2025)
- [Heterogeneous Dependency Graph-Guided Attentionfor Patent Representation Learning](https://mlsanity.com/paper/2605.10073/) (2026)
- [Benchmarking Patent Drafting from Inventor-Style Disclosures](https://mlsanity.com/paper/2608.21249/) (2026)

## Abstract (arXiv)

Patent similarity evaluation plays a critical role in intellectual property analysis. However, existing methods often overlook the intricate structure of patent documents, which integrate technical specifications, legal boundaries, and application contexts. We introduce PatentMind, a novel framework for patent similarity assessment based on a Multi-Aspect Reasoning Graph (MARG). PatentMind decomposes patents into their three dimensions of technical features, application domains, and claim scopes, then dimension-specific similarity scores are calculated over the MARG. These scores are dynamically weighted through a context-aware reasoning process, which integrates contextual signals to emulate expert-level judgment. To support evaluation, we construct a human-annotated benchmark PatentSimBench, comprising 500 patent pairs. Experimental results demonstrate that the PatentMind-generated scores show a strong correlation (r=0.938) with expert annotations, significantly outperforming embedding-based models, patent-specific models, and advanced prompt engineering methods. Beyond computational linguistics, our framework provides a structured and semantically grounded foundation for real-world decision-making, particularly for tasks such as infringement risk assessment, underscoring its broader impact on both patent analytics and evaluation.