The field of natural language processing is witnessing significant advancements in text embeddings and patent analysis. Researchers are exploring innovative methods to improve the efficiency and accuracy of text embeddings, including the use of hybrid query rewriting frameworks and unsupervised fine-tuning of dense embeddings. Additionally, there is a growing focus on developing specialized benchmarks and models for patent text embeddings, which enable prior art search, technology landscaping, and patent analysis. Noteworthy papers in this area include: AdaQR, which reduces reasoning cost by 28% while preserving or improving retrieval performance by 7%. CustomIR, which consistently improves retrieval effectiveness with small models gaining up to 2.3 points in Recall@10. PatenTEB, which introduces a comprehensive benchmark comprising 15 tasks across retrieval, classification, paraphrase, and clustering, with 2.06 million examples. GigaEmbeddings, which achieves state-of-the-art results on the ruMTEB benchmark spanning 23 multilingual tasks. PANORAMA, which constructs a dataset of 8,143 U.S. patent examination records that preserves the full decision trails, including original applications, all cited references, Non-Final Rejections, and Notices of Allowance. SwiftEmbed, which achieves 1.12 ms p50 latency for single text embeddings while maintaining 60.6 MTEB average score across 8 representative tasks. Towards Automated Quality Assurance of Patent Specifications, which proposes a multi-dimensional LLM framework to evaluate patents using regulatory compliance, technical coherence, and figure-reference consistency detection modules.