| Challenge: | Graph Neural Networks (GNNs)-based methods can predict patent citations using only patent text. |
| Approach: | They propose to use Graph Neural Networks to predict citations for patents based on their semantic similarities to generate a semantic graph of patents. |
| Outcome: | The proposed methods produce 94% recall for patents with high citations and outperform baselines. |
Similar Papers
Connecting the Dots: Inferring Patent Phrase Similarity with Retrieved Phrase Graphs (2024.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods for inferring patent phrase similarity do not perform satisfactorily . et al., 2010: patents are pivotal to innovation, safeguarding novel ideas . |
| Approach: | They propose a graph-augmented approach to amplify patent phrase contextual information . they construct a phrase graph that links to patents cited by or cited in patents for each phrase . |
| Outcome: | The proposed approach significantly improves the representation of patent phrases in a self-supervised fashion. |
PatentMind: A Multi-Aspect Reasoning Graph for Patent Similarity Evaluation (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for patent similarity evaluation lack the multifaceted structure of patent documents . patent documents pose significant challenges due to specialized domain knowledge, intricate legal language, and complex structural formats. |
| Approach: | They propose a framework that performs patent similarity evaluation through a Multi-Aspect Reasoning Graph. |
| Outcome: | The proposed framework outperforms embedding-based, patent-specific, and prompt engineering benchmarks in evaluating patent similarity with expert annotations. |
A Survey on Patent Analysis: From NLP to Multimodal AI (2025.acl-long)
Copied to clipboard
| Challenge: | Recent advances in pretrained language models and large language models have demonstrated transformative capabilities across diverse domains. |
| Approach: | They propose a taxonomy for categorization based on tasks in the patent life cycle . they introduce a novel taxonomies for categorizing based upon tasks in patent life cycles . |
| Outcome: | The proposed method is based on tasks in the patent life cycle and provides a taxonomy for categorization based upon tasks in patent life cycles. |
PatentEval: Understanding Errors in Patent Generation (2024.naacl-long)
Copied to clipboard
| Challenge: | a patent is a legal instrument that grants inventors or entities exclusive rights over their invention for a designated period. |
| Approach: | They propose a typology specifically designed for evaluating two distinct tasks in machine-generated patent texts. |
| Outcome: | The proposed approach provides valuable insights into the capabilities and limitations of current language models in the specialized field of patent text generation. |
Towards Comprehensive Patent Approval Predictions:Beyond Traditional Document Classification (2022.acl-long)
Copied to clipboard
| Challenge: | a new framework for patent approval prediction is proposed to address this problem . novelty scores are based on comparing an application with millions of prior arts . |
| Approach: | They propose a framework that unifies the document classifier with handcrafted features, particularly time-dependent novelty scores. |
| Outcome: | The proposed framework unifies the document classifier with handcrafted features, particularly time-dependent novelty scores. |
Beyond Scaling: Predicting Patent Approval with Domain-specific Fine-grained Claim Dependency Graph (2024.acl-long)
Copied to clipboard
| Challenge: | Scaling up language models has demonstrated predictable improvement and unprecedented abilities in many language tasks. |
| Approach: | They propose a fine-grained cLAim depeNdency graph that captures the dependencies within the patent data and extends the embedding-based state-of-the-art (SOTA) they then explore prompt-based methods to harness proprietary LLMs' potential, but find the best results close to random guessing, underlining the ineffectiveness of model scaling-up. |
| Outcome: | The proposed graph methods outperform the standard model scaling methods in the patent approval prediction task and show that they are cost-effective. |
Structural Patent Classification Using Label Hierarchy Optimization (2025.findings-emnlp)
Copied to clipboard
| 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. |
A Label Informative Wide & Deep Classifier for Patents and Papers (D19-1)
Copied to clipboard
| Challenge: | Existing methods for classification of patents and papers are manual and limited . et al., a chinese research team has developed a label-informative classification model . |
| Approach: | They propose a label-informative classifier based on the Wide & Deep structure . they train on millions of patents and transfer to papers by developing distant-supervised training set and domain-specific features. |
| Outcome: | The proposed model performs comparable to the state-of-the-art model used in industry on patents and papers. |
PatentScore: Multi-dimensional Evaluation of LLM-Generated Patent Claims (2025.emnlp-main)
Copied to clipboard
| 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 . |
Realistic Citation Count Prediction Task for Newly Published Papers (2023.findings-eacl)
Copied to clipboard
| Challenge: | Existing studies on citation count prediction assume that future citation counts of academic papers have not had enough time pass since publication. |
| Approach: | They propose to use citation counts of newly published papers as a realistic citation count prediction task and to use them to leverage the citations of papers shortly after publication. |
| Outcome: | The proposed methods significantly improve the performance of citation count prediction for newly published papers in a realistic setting. |