Google can use a strategy that predicts what users will click.

Cited by documentation

Sources

• US8938463B1 - Modifying search result ranking based on implicit user feedback and a model of presentation bias

  Google Patents / USPTO (assignee Google Inc.; inventors Hyung-Jin Kim, Adrian D. Corduneanu) · primary source · retrieved 2026-08-20

  “use a comparison of the predicted click through rate and the click fraction to adjust the respective ranking scores” — Claims (re machine-readable storage claim; claim family around claim 26)

• US8938463B1 - Modifying search result ranking based on implicit user feedback and a model of presentation bias

  Google Patents · primary source · retrieved 2026-08-20

  “the model is trained to predict a click through rate” — Claims, lines 714-715

• Trial Exhibit - UPXD104: U.S. and Plaintiff States v. Google LLC

  U.S. Department of Justice · primary source · retrieved 2026-08-20

  “Exploiting user feedback, principally clicks, has been the major theme of ranking work for the past decade” — PDF page 48

• United States v. Google LLC - opinion excerpts (Picker, Antitrust Fall 2025 supplement)

  University of Chicago Law School (reproducing federal court opinion text) · retrieved 2026-08-20

  “RankEmbed and its later iteration RankEmbedBERT are ranking models that rely on two main sources of data” — Remedies opinion excerpt on Glue and RankEmbed data

• Yes, Clicks and Engagement Impact SEO

  Practical Ecommerce · retrieved 2026-08-20

  “a ranking system called "Navboost" (also called "Glue") that analyzes user interactions on search results” — 'Organic Clicks' section

• Mapping of Google Updates to Leaked Ranking Signals

  Hobo Web (Shaun Anderson) · retrieved 2026-08-20

  “a system that uses a rolling 13-month window of aggregated user click data to refine and re-rank search results” — NavBoost / leaked-signals discussion

Related claims

• NARROWSSearch Engine Land reported former Googler Eric Lehman testifying in 2023 that Google was using clicks in rankings.Under evaluation