Papers with patent claims
Structural Patent Classification Using Label Hierarchy Optimization (2025.findings-emnlp)
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| Challenge: | Existing methods for patent classification ignore key technical content claims and citation relationships . existing methods treat labels as independent targets, failing to exploit semantic and structural information within the label taxonomy. |
| Approach: | They propose a Claim Structure based Patent Classification model with Label Awareness . structural graph learning is used to mine the internal logic of patent claims . |
| Outcome: | The proposed method is more effective than state-of-the-art classification models. |
PatentVision: A multimodal method for drafting patent applications (2026.eacl-industry)
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| Challenge: | PatentVision integrates textual and visual inputs to generate patent specifications . existing systems fail to capture the nuanced interplay between textual, visual components . |
| Approach: | They propose a multimodal framework that integrates textual and visual inputs to generate patent specifications. |
| Outcome: | The proposed framework surpasses text-only methods in patent writing, the authors show . it integrates visual data to better represent intricate design features and functional connections . |
Patentformer: A Novel Method to Automate the Generation of Patent Applications (2024.emnlp-industry)
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| Challenge: | Patentformer is a novel method for generating patent specification by fine-tuning the generative models with diverse sources of information, e.g., patent claims, drawing text, and brief descriptions of the drawings. |
| Approach: | They propose a method for generating patent specification by fine-tuning generative models with diverse sources of information, e.g., patent claims, drawing text, and brief descriptions of the drawings. |
| Outcome: | The proposed method generates patent specification in legal writing style and human-like quality may be better than the actual specification. |
Towards Better Evaluation for Generated Patent Claims (2025.acl-long)
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| Challenge: | Existing studies highlight inconsistencies between automated evaluation metrics and human expert assessments for patent claims. |
| Approach: | They propose a multi-dimensional evaluation method specifically designed for patent claims that incorporates features annotated by patent experts. |
| Outcome: | The proposed method achieves highest correlation with human expert evaluations across all assessment criteria across all tested metrics. |
PatentScore: Multi-dimensional Evaluation of LLM-Generated Patent Claims (2025.emnlp-main)
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| Challenge: | Existing natural language generation (NLG) metrics fail to capture domain-specific nuances . patent claims require precise assessment of structural elements such as antecedent consistency and claim dependency. |
| Approach: | They propose a multi-dimensional evaluation framework specifically designed for patent claims . PatentScore integrates hierarchical decomposition of claim elements, validation patterns and scoring across structural, semantic, and legal dimensions. |
| Outcome: | The proposed evaluation framework outperforms existing evaluation frameworks on patent claims . patentScore achieved highest correlation with expert annotations on 400 patent claims dataset . |