Here you can find a database of hundreds of search related active patents and papers. The patents and papers are tagged by SEO 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.
This Google patent describes a computerized method for selecting the most effective algorithm to identify similar user identifiers based on predicted click-through rates. The system compares different algorithms by generating read more
This Google research paper examines the performance of various large language models (LLMs) in question-answering tasks, particularly focusing on their ability to handle sufficient and insufficient context scenarios. The study read more
This Google patent describes a novel approach to information retrieval using machine learning models called Differentiable Search Index (DSI). The system directly predicts relevant resources in response to queries by read more
This Google patent describes a system and method for adapting search results presented by an automated assistant during a user dialog. The technology enables users to navigate through search results read more
The paper “Generative Models are Unsupervised Predictors of Page Quality: A Colossal-Scale Study” by Google Research explores how classifiers trained to distinguish between human-written and machine-generated text can serve as read more
The Google research paper “Stochastic Retrieval-Conditioned Reranking” explores how search engines can improve ranking by optimizing both retrieval and reranking stages together. It challenges the traditional assumption that recall optimization read more
The document describes Retrieval-Enhanced Machine Learning (REML), a framework designed to improve machine learning models by integrating information retrieval (IR) techniques. Traditional ML models store knowledge within their parameters, requiring read more
This Google patent describes a method for optimizing approximate nearest neighbor (ANN) search engines using Lagrange multiplier methods. The technology automatically tunes quantization-based ANN search systems to achieve optimal speed-recall read more
The Google paper introduces ED2LM (Encoder-Decoder to Language Model), a novel approach to document re-ranking that significantly improves inference efficiency while maintaining competitive ranking performance. Traditional cross-attention-based ranking models, such read more
This Google paper investigates how generative retrieval techniques perform when scaled to millions of passages. Unlike traditional retrieval systems that rely on external indices, generative retrieval reframes retrieval as a read more
