Quality, trust and E-E-A-T: patents and papers
Quality and trust patents and papers describe ways a search engine could score a whole site’s quality, relate content to its authors, propagate trust from reviewed pages, or judge a source by the accuracy of its facts. E-E-A-T itself comes from Google’s rater guidelines, not from a patent. Four Google patents and three papers are listed, all verified.
What does this theme cover?
E-E-A-T (experience, expertise, authoritativeness, trust) is a concept in Google’s Search Quality Rater Guidelines; Google added the extra “E” for experience in December 2022.[1][2] Officially documented Google says “rater data is not used directly in our ranking algorithms” and that “E-E-A-T itself isn’t a specific ranking factor”, although its systems look for signals that align with it.[3] The patents and papers below describe possible mechanisms for site quality and trust. Practical guidance is on E-E-A-T and author entities and AEO.wiki’s E-E-A-T page.
Which patents describe site quality and authorship?
Ranking search results
What it describes: Ranking with a group-level (for example site-level) modification factor. For each group of resources the system counts independent incoming links and “reference queries” that refer to the group, and uses them to compute a factor applied to the group’s pages.
Why it matters for SEO: Widely called the Panda patent by SEO commentators because inventor Navneet Panda shares his name with Google’s 2011 Panda update (the patent itself makes no such link). The idea is that a page’s ranking can be adjusted by the quality of the whole site, measured partly by independent links and by people searching for the site by name. Practitioner practice
Site quality score
What it describes: A site quality score computed from two counts: unique queries categorised as referring to a particular site, and unique queries associated with the site because searchers selected one of its results.
Why it matters for SEO: Brand demand, meaning people searching for your site or entity by name, can act as a quality signal in this design. Building a recognisable entity helps. See why entities matter. Practitioner practice
Predicting site quality
What it describes: Predicting quality for new sites. A phrase model maps how often phrases (n-grams) appear on sites to baseline quality scores of previously scored sites, and is applied to a new site’s phrases.
Why it matters for SEO: Language patterns on a site can correlate with quality. Thin, templated or spammy phrasing is a poor pattern to share with low-quality sites.
Agent rank
What it describes: “Agent rank”: content items are linked to agents (authors or publishers) by digital signatures, and each agent receives a score based on the content associated with it.
Why it matters for SEO: The clearest patent precedent for author reputation. Clear bylines, author pages and consistent author entities (see Person schema) are the practical equivalent. It does not prove Google scores authors. Practitioner practice
Which research papers study trust and spam?
Knowledge-based trust: estimating the trustworthiness of web sources
What it describes: A Google research method that estimates a source’s trustworthiness from the correctness of the facts it states, rather than from links pointing to it.
Why it matters for SEO: Accuracy can be measured. Keep facts about your entity correct and consistent with authoritative sources; errors are visible to systems that cross-check facts.
Combating Web Spam with TrustRank
What it describes: Separating reputable pages from spam by having experts review a small seed set of pages and propagating trust outward through links.
Why it matters for SEO: Links from sites close to trusted seeds are worth more than volume from unknown sites. The same logic appears in Google’s seed-distance patent on the ranking page.
Detecting spam web pages through content analysis
What it describes: Content features (such as keyword stuffing, compressibility and unusual word statistics) that help classify spam pages.
Why it matters for SEO: Keyword-stuffed or machine-spun text is statistically detectable. Write for readers; Google’s spam policies cover these tactics.[4]
What should you do with this?
- Build real brand demand: an entity people search for by name.
- Make authorship explicit with bylines, author pages and consistent author entities.
- Keep facts accurate and consistent with authoritative sources.
- Remove or improve thin and templated pages that drag down a site’s overall quality.
Frequently asked questions
Is E-E-A-T a ranking factor?
Google says E-E-A-T itself isn't a specific ranking factor, but its systems use a mix of factors that identify content with good E-E-A-T. No patent here is described by Google as E-E-A-T.
What is the Panda patent?
US 8,682,892 B1, 'Ranking search results', by Navneet Panda and Vladimir Ofitserov, granted March 25, 2014. It describes a group-level modification factor based on independent links and reference queries.
Does Google score authors?
Google has not confirmed an author score. The Agent rank patent (US 7,565,358 B2) describes one way it could be done.
See also
References
Pages accessed September 29, 2026 unless a date is given. See all sources and our editorial policy.
- ^ "Our latest update to the quality rater guidelines: E-A-T gets an extra E for Experience". Google Search Central Blog. Published December 15, 2022.
- ^ "Search Quality Rater Guidelines (PDF)". Google.
- ^ "Creating helpful, reliable, people-first content". Google Search Central.
- ^ "Spam policies for Google web search". Google Search Central. Updated May 2026 to cover generative AI responses.