Ranking Documents Based on Large Data Sets (continuation)
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 Ranking Documents Based on Large Data Sets (continuation).
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 Ranking Documents Based on Large Data Sets (continuation).
What is Ranking Documents Based on Large Data Sets (continuation)?
Patent overview Inventor Jeremy Bem, Georges Harik, Simon Tong, Noam Shazeer, others Assignee Google LLC Patent number US 9,116,976 Filing or grant year August 25, 2015 Patent family large-dataset-ran
Patent overview Inventor Jeremy Bem, Georges Harik, Simon Tong, Noam Shazeer, others Assignee Google LLC Patent number US 9,116,976 Filing or grant year August 25, 2015 Patent family large-dataset-ran
NizamUdDeen, Nizam SEO War Room
Patent overview
Inventor
Jeremy Bem, Georges Harik, Simon Tong, Noam Shazeer, others
Assignee
Google LLC
Patent number
US 9,116,976
Filing or grant year
August 25, 2015
Patent family
large-dataset-ranking
Track
Simon Tong, Google Click-Driven Ranking, Population Signals & Anti-Spam Patents
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What this patent covers
5 new canonical articles plus 4 cross-listings from the Kim, Shazeer, and 65 Google Patents sections. Tong is co-inventor with Marc Pearson and Sergey Brin on the related-query ranking patent (US 7,505,964), with Pearson on the population-information ranking patent (US 7,454,417), solo on country biasing (US 20040254932), with Bem/Harik/Levenberg/Shazeer on the pre-Transformer large-scale ML ranking infrastructure (US 7,222,127), and with Ghemawat/Piscitello/Cutts on the user-document-removal patent (US 8,417,697, the structural ancestor of Personal Blocklist and crowd-sourced spam signals). Cross-listings cover the Navboost implicit-feedback family, the CTR-as-ranking-factor patent, the historical-data patent, and the large-dataset-ranking continuation. Spans 2003 to 2017+.
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Why Ranking Documents Based on Large Data Sets (continuation) matters
This patent is part of the Simon Tong, Google Click-Driven Ranking, Population Signals & Anti-Spam 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 Simon Tong, Google Click-Driven Ranking, Population Signals & Anti-Spam 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 Ranking Documents Based on Large Data Sets (continuation) 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 Ranking Documents Based on Large Data Sets (continuation) work in modern search?
The full breakdown is in the article body above. In short: Ranking Documents Based on Large Data Sets (continuation) 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 Ranking Documents Based on Large Data Sets (continuation) 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 Ranking Documents Based on Large Data Sets (continuation) fits in the Semantic SEO + AEO stack
Search engines have moved from keyword matching toward semantic understanding, entity reasoning, and AI-mediated answer generation. Ranking Documents Based on Large Data Sets (continuation) 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 Ranking Documents Based on Large Data Sets (continuation) 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. Ranking Documents Based on Large Data Sets (continuation) 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.