mlsanityData to 7 Oct 2026

A Survey on Patent Analysis: From NLP to Multimodal AI

arXiv 2404.08668 ↗First posted 2 Apr 2024cs.IR cs.AIcited by 10

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Abstract (arXiv)

Recent advances in Pretrained Language Models (PLMs) and Large Language Models (LLMs) have demonstrated transformative capabilities across diverse domains. The field of patent analysis and innovation is not an exception, where natural language processing (NLP) techniques presents opportunities to streamline and enhance important tasks -- such as patent classification and patent retrieval -- in the patent cycle. This not only accelerates the efficiency of patent researchers and applicants, but also opens new avenues for technological innovation and discovery. Our survey provides a comprehensive summary of recent NLP-based methods -- including multimodal ones -- in patent analysis. We also introduce a novel taxonomy for categorization based on tasks in the patent life cycle, as well as the specifics of the methods. This interdisciplinary survey aims to serve as a comprehensive resource for researchers and practitioners who work at the intersection of NLP, Multimodal AI, and patent analysis, as well as patent offices to build efficient patent systems.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 23 of its 77 references that are in the map. First posted on arXiv 2 Apr 2024.

Builds on

23 of 77 references.

References · 23

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11 more
  1. 2019OpenNRE: An Open and Extensible Toolkit for Neural Relation Extractioncited by 62
  2. 2019Patent Claim Generation by Fine-Tuning OpenAI GPT-2cited by 49
  3. 2022The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applicationscited by 32
  4. 2021PatentSBERTa: A Deep NLP based Hybrid Model for Patent Distance and Classification using Augmented SBERTcited by 22
  5. 2020PatentMatch: A Dataset for Matching Patent Claims & Prior Artcited by 14
  6. 2021Linguistically Informed Masking for Representation Learning in the Patent Domaincited by 7
  7. 2023Event-based Dynamic Graph Representation Learning for Patent Application Trend Predictioncited by 6
  8. 2022Enhancing Patent Retrieval using Text and Knowledge Graph Embeddings: A Technical Notecited by 5
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 4; 2026: 5.

10 papers.

  1. 2024Pap2Pat: Benchmarking Outline-Guided Long-Text Patent Generation with Patent-Paper Pairscited by 8
  2. 2025PatenTEB: A Comprehensive Benchmark and Model Family for Patent Text Embeddingcited by 4
  3. 2026Multi-Disciplinary Dataset Discovery from Citation-Verified Literature Contextscited by 4
  4. 2026Is It Novel and Why? Fine-Grained Patent Novelty Prediction Based on Passage Retrievalcited by 1
  5. 2025DesignCLIP: Multimodal Learning with CLIP for Design Patent Understandingcited by 0
  6. 2025A Hybrid Ai Framework For Strategic Patent Portfolio Pruning: Integrating Learning To-Rank And Market Need Analysis For Technology Transfer Optimizationcited by 0
  7. 2025ERA-IT: Aligning Semantic Models with Revealed Economic Preference for Real-Time and Explainable Patent Valuationcited by 0
  8. 2026Benchmarking Patent Embeddings: A Multi-Task Evaluation of 22 Models Across Retrieval, Classification, and Clusteringcited by 0
  9. 2026From Patent Expiry to Business Pathways: AI Workflows for Activating Innovation Archivescited by 0
  10. 2026LogicTree-RAG: Logic Tree-guided Retrieval-Augmented Generation for Long-form Patent Draftingcited by 0