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GEO 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 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 GEO 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 GEO 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.
  • Patents and research papers from Google, Microsoft, OpenAI … summarized from a SEO / GEO perspective.
  • New documents and summaries every month.
  • All patents tagged by topic and important authors for quick and targeted research.
  • Get GEO expert knowledge for optimizing your visibility in AI Search via the  GEO assistant
  • Use all AI Research Tools to gain insights in seconds from all documents in the taining databases, Google Leaks and DOJ trials via the Patent & Paper Analyzer and Google Leak Analyzer
  • more tools to come …
  • 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 GEO Research Suite now!

440 Patents & Papers in this database now

E-E-A-T (62)
Semantic Search (85)
Knowledge Graph (65)
Probably in use (126)
Marc Najork (11)
Local Search (12)
Image Search (15)
Retrieval Augmented Generation (RAG) (79)
LLMO / GEO (161)
OpenAI / ChatGPT (16)
AI Mode (61)
Chunk Relevance (43)
Query Fan Out (54)
Krisztian Balog (4)
Microsoft (51)
Reranking (41)
LLM Readability (59)
Brand Context (9)
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AI Mode (61)AIOverviews (54)Anna Lynn Patterson (6)Backlinks (29)Brand Context (9)Chunk Relevance (43)Dan Popovici (4)Data Mining (37)Deepseek (1)Document Classification (46)E-E-A-T (62)Entity based search (74)Featured Snippets (7)Freshness (16)Gemini (3)Graph RAG (19)Image Search (15)Indexing (41)Information Gain (7)Jeff Dean (11)Knowledge Graph (65)Krisztian Balog (4)Learning-to-rank (7)LLM Readability (59)LLMO / GEO (161)Local Search (12)Marc Najork (11)Microsoft (51)Navboost (10)Navneet Panda (7)News and Discover (7)Nitin Gupta (6)OpenAI / ChatGPT (16)Passage based retrieval (38)Paul Haahr (10)Personalization (39)Phrase based Indexing (10)Probably in use (126)Prompt Engineering (6)Query Fan Out (54)Ranking (150)Reranking (41)Retrieval Augmented Generation (RAG) (79)Scoring (73)Search Intent (61)Search Query Processing (114)Semantic Search (85)SERP Serving (Tangram/Glue) (8)SERP-Features (24)Shopping (10)User Signals (77)Video Search (7)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-Aspect Dense Retrieval

Dense Retrieval models typically encode queries and documents using single-vector representations for retrieval in the embedding space, which can miss different aspects of relevance, especially in specific domains. This patent read more

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Identifying Product Variants

The patent “Identifying Product Variants” describes a system and method for identifying and grouping product variants in an electronic product catalog. This invention aims to improve the user interface and read more

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Mining for product classification structures for internet-based product searching

This patent describes a system and method for improving internet-based product searching by automatically mining product classification structures. The core innovation is using web-crawling software to collect and analyze product read more

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Web-scale entity relationship extraction that extracts pattern(s) based on an extracted tuple

The patent describes methods and systems for extracting entity relationships at web scale. This involves creating large-scale entity relationship graphs from various data corpora. These graphs help identify previously undiscoverable read more

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Distantly supervised wrapper induction for semi-structured documents

The patent describes systems and methods for distantly supervised wrapper induction for semi-structured documents. It includes generating and annotating training documents for the wrapper and training the wrapper in two read more

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Answering entity-seeking queries

This patent outlines a system designed to enhance how queries are processed to identify relevant entities more efficiently. The system utilizes a complex method of mapping queries to a dependency read more

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Document ranking based on entity frequency

The patent, titled “Document Ranking Based on Entity Frequency,” describes methods, systems, and computer programs for ranking documents, such as news articles, blogs, or social media updates, by considering the read more

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Learning Dense Representations for Entity Retrieval

This academic research paper discusses a dual encoder model for entity retrieval in large text databases, specifically focusing on linking text mentions to the correct entities in a knowledge base.

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Systems and methods for measuring the semantic relevance of keywords

This patent describes a system designed to enhance keyword relevance measurements by using a semantic relationship graph. The system receives a seed keyword and generates additional keywords based on their read more

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Identifying central entities

The patent describes methods, systems, and apparatuses, including computer programs encoded on computer storage mediums, for identifying central entities in digital data processing, specifically within search systems.

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