# Patent Retrieval: A Literature Review

arXiv: https://arxiv.org/abs/1701.00324 · HTML: https://mlsanity.com/paper/1701.00324/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jan 2017.

## Built on by (most cited first, 17 of 17)

- [PatentSBERTa: A Deep NLP based Hybrid Model for Patent Distance and Classification using Augmented SBERT](https://mlsanity.com/paper/2103.11933/) (2021)
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- [Case law retrieval: problems, methods, challenges and evaluations in the last 20 years](https://mlsanity.com/paper/2202.07209/) (2022)
- [Pap2Pat: Benchmarking Outline-Guided Long-Text Patent Generation with Patent-Paper Pairs](https://mlsanity.com/paper/2410.07009/) (2024)
- [Summarization, Simplification, and Generation: The Case of Patents](https://mlsanity.com/paper/2104.14860/) (2021)
- [Event-based Dynamic Graph Representation Learning for Patent Application Trend Prediction](https://mlsanity.com/paper/2308.09780/) (2023)
- [Patent Data for Engineering Design: A Critical Review and Future Directions](https://mlsanity.com/paper/2111.08500/) (2021)
- [Enriching Patent Claim Generation with European Patent Dataset](https://mlsanity.com/paper/2505.12568/) (2025)
- [PatentMind: A Multi-Aspect Reasoning Graph for Patent Similarity Evaluation](https://mlsanity.com/paper/2505.19347/) (2025)
- [Creating a silver standard for patent simplification](https://mlsanity.com/paper/2310.15689/) (2023)
- [PATENTWRITER: A Benchmarking Study for Patent Drafting with LLMs](https://mlsanity.com/paper/2507.22387/) (2025)
- [Large Language Model Informed Patent Image Retrieval](https://mlsanity.com/paper/2404.19360/) (2024)
- [Advances in Electron Microscopy with Deep Learning](https://mlsanity.com/paper/2101.01178/) (2021)
- [Retrieval-Augmented Generation Systems for Intellectual Property via Synthetic Multi-Angle Fine-tuning](https://mlsanity.com/paper/2506.00527/) (2025)
- [Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test?](https://mlsanity.com/paper/2507.10576/) (2025)
- [FullRecall: A Semantic Search-Based Ranking Approach for Maximizing Recall in Patent Retrieval](https://mlsanity.com/paper/2507.14946/) (2025)
- [AI Prior Art Search: Semantic Clusters and Evaluation Infrastructure](https://mlsanity.com/paper/2512.18384/) (2025)

## Abstract (arXiv)

With the ever increasing number of filed patent applications every year, the need for effective and efficient systems for managing such tremendous amounts of data becomes inevitably important. Patent Retrieval (PR) is considered the pillar of almost all patent analysis tasks. PR is a subfield of Information Retrieval (IR) which is concerned with developing techniques and methods that effectively and efficiently retrieve relevant patent documents in response to a given search request. In this paper we present a comprehensive review on PR methods and approaches. It is clear that, recent successes and maturity in IR applications such as Web search cannot be transferred directly to PR without deliberate domain adaptation and customization. Furthermore, state-of-the-art performance in automatic PR is still around average in terms of recall. These observations motivate the need for interactive search tools which provide cognitive assistance to patent professionals with minimal effort. These tools must also be developed in hand with patent professionals considering their practices and expectations. We additionally touch on related tasks to PR such as patent valuation, litigation, licensing, and highlight potential opportunities and open directions for computational scientists in these domains.