# FullRecall: A Semantic Search-Based Ranking Approach for Maximizing Recall in Patent Retrieval

arXiv: https://arxiv.org/abs/2507.14946 · HTML: https://mlsanity.com/paper/2507.14946/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 52 references that are in the map.
- First posted on arXiv 20 Jul 2025.

## Builds on (most cited first, 9 of 9)

- [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://mlsanity.com/paper/2004.09813/) (2020)
- [Document Expansion by Query Prediction](https://mlsanity.com/paper/1904.08375/) (2019)
- [Graph Learning based Recommender Systems: A Review](https://mlsanity.com/paper/2105.06339/) (2021)
- [Efficient Document Re-Ranking for Transformers by Precomputing Term Representations](https://mlsanity.com/paper/2004.14255/) (2020)
- [PatentSBERTa: A Deep NLP based Hybrid Model for Patent Distance and Classification using Augmented SBERT](https://mlsanity.com/paper/2103.11933/) (2021)
- [Patent Retrieval: A Literature Review](https://mlsanity.com/paper/1701.00324/) (2017)
- [High-Precision Extraction of Emerging Concepts from Scientific Literature](https://mlsanity.com/paper/2006.06877/) (2020)
- [Query Generation for Patent Retrieval with Keyword Extraction based on Syntactic Features](https://mlsanity.com/paper/1906.07591/) (2019)
- [Embedding Meta-Textual Information for Improved Learning to Rank](https://mlsanity.com/paper/2010.16313/) (2020)

## Abstract (arXiv)

Patent examiners and inventors face significant pressure to verify the originality and non-obviousness of inventions, and the intricate nature of patent data intensifies the challenges of patent retrieval. Therefore, there is a pressing need to devise cutting-edge retrieval strategies that can reliably achieve the desired recall. This study introduces FullRecall, a novel patent retrieval approach that effectively manages the complexity of patent data while maintaining the reliability of relevance matching and maximising recall. It leverages IPC-guided knowledge to generate informative phrases, which are processed to extract key information in the form of noun phrases characterising the query patent under observation. From these, the top k keyphrases are selected to construct a query for retrieving a focused subset of the dataset. This initial retrieval step achieves complete recall, successfully capturing all relevant documents. To further refine the results, a ranking scheme is applied to the retrieved subset, reducing its size while maintaining 100% recall. This multi-phase process demonstrates an effective strategy for balancing precision and recall in patent retrieval tasks. Comprehensive experiments were conducted, and the results were compared with baseline studies, namely HRR2 [1] and ReQ-ReC [2]. The proposed approach yielded superior results, achieving 100% recall in all five test cases. However, HRR2[1] recall values across the five test cases were 10%, 25%, 33.3%, 0%, and 14.29%, while ReQ-ReC [2] showed 50% for the first test case, 25% for the second test case, and 0% for the third, fourth, and fifth test cases. The 100% recall ensures that no relevant prior art is overlooked, thereby strengthening the patent pre-filing and examination processes, hence reducing potential legal risks.