By NizamUdDeen · · Reviewed by the Nizam SEO War Room editorial team.
First, the short version. Below is the AIO-eligible passage and the question-format primer for Using Categorical Metadata to Rank Search Results.
First, read the definition above — it's the answer most search and AI engines extract first.
Second, scan the question-format H2s to find the specific facet you came for.
Third, follow the patent + related-entry links at the bottom to map the dependency graph around Using Categorical Metadata to Rank Search Results.
What is Using Categorical Metadata to Rank Search Results?
Patent overview Inventor Susan T.
Patent overview Inventor Susan T.
NizamUdDeen, Nizam SEO War Room
Patent overview
Inventor
Susan T. Dumais, others
Assignee
Microsoft Corporation
Patent number
US 9,020,936
Filing or grant year
April 28, 2015
Patent family
categorical-metadata-ranking
Track
Susan Dumais, Microsoft IR & Search Patents
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What this patent covers
61 search and IR patents by Susan Dumais, Microsoft Technical Fellow and Gerard Salton Award winner. Co-inventor on the foundational Latent Semantic Indexing patent (US 4,839,853, Bellcore 1989) — the conceptual ancestor of every dense-embedding retrieval system. At Microsoft Research her work covers automated SERP satisfaction measurement, preference-judgment click models, activity-based-context ranking, time-aware ranking with temporal dynamics, personalized navigation and search results, per-user domain-expertise determination, web-page-change × revisitation freshness, implicit device-related query reformulation, authority ranking. Spans 1989 to 2026. Co-authors include Jaime Teevan, Eric Horvitz, Ryen White, Adam Fourney, Joshua Goodman, Eric Brill.
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Why Using Categorical Metadata to Rank Search Results matters
This patent is part of the Susan Dumais, Microsoft IR & Search Patents research track inside the Nizam SEO War Room patents archive. It describes a piece of the search-engine machinery that working SEOs need to understand to optimize against modern ranking and retrieval systems. A deeper annotated walkthrough of this patent — covering the claims, the disclosure, the prior art it cites, and the algorithms it influences — is queued for the next archive expansion pass.
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Related research
Patents in the Susan Dumais, Microsoft IR & Search Patents track are cross-linked to neighboring tracks where the same inventor or research lineage continues. Read this patent alongside the other entries in the track to recover the full research arc — the original disclosure, its continuations and divisional applications, and any follow-up patents that branched from the same line of work.
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For example, a working SEO consultant uses Using Categorical Metadata to Rank Search Results when diagnosing a ranking drop, planning a content calendar, or briefing a client on why a tactic shifted. However, the concept only compounds when paired with the surrounding entries in the encyclopedia and patents archive. In addition, the platform connects this concept to live SERP data so the theory carries through to execution.
How does Using Categorical Metadata to Rank Search Results work in modern search?
The full breakdown is in the article body above. In short: Using Categorical Metadata to Rank Search Results ties into how search engines and AI answer engines weigh signals — every detail (definition, ranking impact, related patents, related signals) is captured in this article and cross-linked to neighboring entries in the encyclopedia and patents archive.
Working SEOs reach for Using Categorical Metadata to Rank Search Results when diagnosing why a page ranks where it does, when planning a content strategy that aligns with the surfaces search engines and answer engines weigh, and when explaining ranking moves to non-technical stakeholders. The concept is one piece of the broader Semantic SEO + AEO operating system; the Nizam SEO War Room platform ties it to live SERP data, the patent lineage that introduced it, and the strategy moves that compound across projects.
Where Using Categorical Metadata to Rank Search Results fits in the Semantic SEO + AEO stack
Search engines have moved from keyword matching toward semantic understanding, entity reasoning, and AI-mediated answer generation. Using Categorical Metadata to Rank Search Results sits inside that shift — its weight, its measurement, and its downstream effects all changed when the underlying ranking and retrieval systems changed. Read the related encyclopedia entries linked above for the surrounding context.
The concept of Using Categorical Metadata to Rank Search Results is grounded in the search-engine research lineage tracked in the Nizam SEO War Room platform. Primary sources:
Related encyclopedia entries and patent walkthroughs are linked inline above. The Strategy Brain inside the platform connects these sources to live project state so the research has a direct execution surface.
Finally, to summarize. Using Categorical Metadata to Rank Search Results matters because it intersects directly with the signals search engines and AI answer engines use to rank and surface results. The full article above covers the mechanism in depth, the patents it derives from, and the related encyclopedia entries to read next.