Here you can find a database of hundreds of search related active patents and papers. The patents and papers are tagged by SEO and GEO related topics, steps of the information retrieval process and the probabilty they could be used nowadays or in the future.
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A patent application does not mean that the methods described there will find its way into practice in the search engines. An indication of whether a methodology/technology is so interesting for Google that it could find its way into practice can be obtained by checking whether the patent is pending only in the US or other countries. The claim for a patent priority for other countries must be made 12 months after the first filing.Regardless of whether a patent finds its way into practice, it makes sense to deal with Google patents, as you get an indication of the topics and challenges that product developers at Google and other search engines are dealing with.
Researchers at Google have developed a technique called Pairwise Ranking Prompting (PRP) to enhance document ranking using Large Language Models (LLMs). PRP simplifies the task by comparing document pairs, achieving read more
This research paper by Google presents a novel approach to improve zero-shot Large Language Model (LLM) document ranking systems by introducing fine-grained relevance labels instead of simple binary (yes/no) relevance read more
The document introduces a novel zero-shot document ranking method using large language models (LLMs) called the Setwise prompting approach. This method aims to improve the efficiency and effectiveness of LLM-based read more
The paper titled “Can Query Expansion Improve Generalization of Strong Cross-Encoder Rankers?” explores the impact of query expansion on the performance of second-stage, cross-encoder rankers. The study finds that while read more
The paper “Attribute First, then Generate: Locally-attributable Grounded Text Generation” proposes a novel method to address hallucinations in Large Language Models (LLMs) by ensuring concise and precise attributions in text read more
The mechanism of this Google patent described provides a method and system for interpreting search and voice queries using entity information derived from metadata. It optimizes search results by identifying read more
The paper “From Local to Global: A Graph RAG Approach to Query-Focused Summarization” presents a new method that enhances the capabilities of large language models (LLMs) for query-focused summarization (QFS) read more
The patent describes technology for creating search filters from content found on the web. Essentially, when a search query is made, the system identifies relevant resources (like web pages) and read more
“Click Models for Web Search” by Aleksandr Chuklin, Ilya Markov, and Maarten de Rijke is a comprehensive guide on click models used to analyze and predict user behavior in web read more
The patent describes various technologies relating to constructing an answer to a query are described, where a deep model receives the content of a webpage to create an answer in read more
