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SEO Research Suite database: Hundreds of SEO and LLMO related papers and patents (Google, Microsoft, OpenAI) ... 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 and LLMO/GEO 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.


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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 Research Suite member:

  • Access to the full exclusive paid articles in the blog.
  • Insights of hundreds active Microsoft, OpenAI 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 / LLMO 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 and LLMO / GEO work and become a real thought leader.
Get the monthly or yearly SEO thought leader membership and get full access to the SEO Research Suite now!

298 Patents & Papers in this database now

E-E-A-T (52)
Semantic Search (72)
Knowledge Graph (45)
Probably in use (82)
Marc Najork (11)
Paul Haahr (10)
User Signals (60)
Local Search (12)
Image Search (11)
Retrieval Augmented Generation (RAG) (36)
LLMO / GEO (53)
OpenAI / ChatGPT (3)
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AI (Deep Learning) (110)AI Mode (7)AIOverviews (17)Anna Lynn Patterson (6)Backlinks (27)Dan Popovici (4)Data Mining (24)Deepseek (1)Document Classification (37)E-E-A-T (52)Entity based search (60)Featured Snippets (5)Freshness (11)Gemini (2)Graph RAG (9)Image Search (11)Indexing (24)Information Gain (6)Jeff Dean (11)Know (1)Knowledge Graph (45)Krisztian Balog (3)Learning-to-rank (5)LLMO / GEO (53)Local Search (12)Marc Najork (11)Microsoft (14)Navboost (9)Navneet Panda (7)News and Discover (3)Nitin Gupta (6)OpenAI / ChatGPT (3)Paul Haahr (10)Personalization (17)Phrase based Indexing (6)Probably in use (82)Prompt Engineering (5)Query Fan Out (2)Ranking (128)Reranking (34)Retrieval Augmented Generation (RAG) (36)Scoring (61)Search Intent (34)Search Query Processing (75)Semantic Search (72)SERP Serving (Tangram/Glue) (5)SERP-Features (20)Shopping (4)User Signals (60)Video Search (4)Xin Luna Dong (5)

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.

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Multi source extraction and scoring of short query answers

The patent aims to improve the quality of short answers provided by search engines. The patent focuses on generating short answers for search queries. It involves a training operation on a read more

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Combining parameters of multiple search queries that share a line of inquiry

The patent focusses on enhancing the way search engines handle multiple, related search queries from a user.

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Predictive Query Completion and Predictive Search Results

The patent focuses on enhancing search query suggestions and the delivery of search results. It introduces a system where search results are provided based on prediction criteria, independent of user read more

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Embedding Based Retrieval for Image Search

The patent describes a method for retrieving image search results using a trained embedding neural network model.

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Twiddler Quick Start Guide

The Twiddler Quick Start Guide outlines the functionalities and usage of the Twiddler framework within Google’s Superroot system, focusing on re-ranking search results from a single corpus. It supports two read more

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FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation

The FRESHLLMS project demonstrates the potential of enhancing LLMs with search engine results to dynamically adapt to new information. The introduction of FRESHQA and the development of FRESHPROMPT offer promising read more

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GEO: Generative Engine Optimization

The document discusses the advent of large language models (LLMs) and their integration into search engines, forming a new paradigm known as Generative Engines (GEs). These engines use generative models read more

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Locally significant search queries

The patent describes methods, systems, and apparatus for servicing search queries by determining whether a general search query is locally significant for a user’s location. This involves generating a local read more

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Triggering local extensions based on inferred intent

The patent describes methods, systems, and apparatus for triggering local content features based on the inferred intent of search queries. The process involves determining whether a search query indicates local read more

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Determining relevance scores for locations

This patent describes methods, systems, and apparatus for determining relevance scores for geographic locations based on the relevance of various entities associated with a category. The system uses pre-computed relevance read more

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