SEO Research Suite database: Hundreds of search related papers and patents (Google, Microsoft) ... every SEO should know!
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.
You can navigate and filter the analysis by the internal search or by the tags. It is possible to combine the tags.
To read the full patent and paper analysis and full usage of the SEO Research Suite including AI research assistants you have to sign up for a monthly or yearly membership.
I am very grateful if you support and motivate me and this project with a paid membership, recommendation, reference …
Your advantages as a SEO Thought Leader Member:
Insights of hundreds active Microsoft and Google patents and resesearch papers about how search engines and LLMs work.
Save a lot of time and get insights in just a few minutes, without having to spend hours analyzing the documents.
Get quick exclusive insights about how search engines and Google could work with easy to understand summaries and analysis.
Google patents and research papers summarized from a SEO perspective.
New documents and summaries every month.
All patents tagged by topic and important authors for quick and targeted research.
Use the AI Research Tools to gain insights in seconds from all documents in the database, the Google API Leak, Quality Rater Guidelines, Antitrust trial, Google developer documentation …
Gain fundamental insights for your SEO work and become a real thought leader.
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 methods and systems for ranking search results by combining both global and local (onsite) ranking factors. The technology uses a two-step approach: first calculating a global read more
This Microsoft patent describes a computing system that integrates a search engine with a generative AI model to provide more accurate and contextual responses to user queries. The system allows read more
This Google patent describes a method for classifying sequences of user interactions with digital content as either valid or invalid. The system analyzes interaction data including event types and time read more
The paper investigates the performance of two approaches in automatic prompt optimization (APO) for large language models (LLMs): instruction optimization (IO) and exemplar optimization (EO). While much research has focused read more
MIDAS is a system designed to address the bottleneck in knowledge base augmentation by automating the selection of high-quality web sources. It introduces “web source slices” to efficiently extract relevant read more
The document introduces GEAR, a Graph-enhanced Agent for Retrieval-augmented Generation designed to improve retrieval and reasoning in multi-hop question-answering tasks. By leveraging graph-based retrieval with an LLM-based agent, GEAR surpasses read more
This Microsoft patent describes techniques for detecting and compensating for bias in search engine results. It outlines methods to identify situations where search results deviate significantly from authoritative sources and read more
This Google patent describes a system for generating answers to queries by accessing a user’s browsing history. The technology allows users to find previously accessed information through natural language queries, read more
This paper provides an introduction to Graph Neural Networks (GNNs) for machine learning engineers. It explains GNNs through an encoder-decoder framework and demonstrates their applications across various graph analysis tasks. read more
This paper introduces “Head-to-Tail,” a comprehensive benchmark designed to evaluate how well Large Language Models (LLMs) retain and accurately recall factual knowledge. The researchers created 18,000 question-answer pairs covering various read more