Papers with patent applications

5 papers
Towards Comprehensive Patent Approval Predictions:Beyond Traditional Document Classification (2022.acl-long)

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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.
Enriching Patent Claim Generation with European Patent Dataset (2025.findings-emnlp)

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Challenge: Existing work on large language models to assist inventors in writing patent claims relies on datasets from the United States Patent and Trademark Office.
Approach: They propose a European patent dataset that provides rich textual data and structured metadata to support multiple patent-related tasks.
Outcome: The proposed dataset outperforms existing datasets and GPT-4o in claim quality and cross-domain generalization.
PAP2PAT: Benchmarking Outline-Guided Long-Text Patent Generation with Patent-Paper Pairs (2025.findings-acl)

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Challenge: In patents, the description constitutes more than 90% of the document on average, yet its automatic generation remains understudied.
Approach: They propose a method to generate patent documents using a research paper as an invention specification.
Outcome: The proposed model can generate 1.8k patent-paper pairs describing the same inventions, but it's difficult to provide the level of detail required.
DesignCLIP: Multimodal Learning with CLIP for Design Patent Understanding (2025.findings-emnlp)

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Challenge: patent images often lack comprehensive visual context and semantic information, authors say . recent advances in vision-language models offer promising opportunities for patent analysis .
Approach: They develop a framework for design patent analysis using large-scale patent dataset . they validate the effectiveness of DesignCLIP across various downstream tasks .
Outcome: The proposed framework outperforms baseline and SOTA models on all tasks.
JaParaPat: A Large-Scale Japanese-English Parallel Patent Application Corpus (2024.lrec-main)

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Challenge: a recent study has demonstrated that patent translation accuracy improves as the amount of training data or the number of model parameters increases.
Approach: They construct a bilingual corpus of Japanese-English patent application data from 2000 to 2021 . they extracted 1.4M Japanese- English document pairs and extracted 350M sentence pairs .
Outcome: The proposed method improves translation accuracy by 20 bleu points . it is the first publicly available large-scale Japanese-English patent corpus .

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