With the Google monopoly remedies ruling from the other day, we have more documents from the court mentioning more about Google's usage of user interactions, user data, Chrome data and more.
This is all in addition to all the DOJ documents we covered earlier and that big search leak, which Google did end up responding to. We also covered yesterday the Google FastSearch bit on grounding for Gemini. Plus, earlier today on Google's search index, spam score, PageRank, page quality, Glue, and more.
Most of these were spotted by Marie Haynes, but I dug maybe a bit deeper to pull out more references that I found.
I should note, just because these court documents have these statements, it doesn't mean these are used in Google Search today and these statements were also given by non-Googlers:
User Data
Mentions of "user data" in the ruling PDF:
User data determines which websites Google crawls and how often. pic.twitter.com/Y7WUdsrKqI
— Marie Haynes (@Marie_Haynes) September 3, 2025
Here are more mentions:
Page 87:
Google utilizes user data “[a]t every stage of the search process,” from crawling and indexing to retrieval and ranking. User data further helps Google understand which ads capture users’ attention, enabling it to better evaluate ad quality and serve more relevant ads in the future. (finding that users’ sessions data “helps to tailor the advertisements that Google delivers to [them]”). These improvements in search quality and ad monetization ultimately translate into higher revenue, as superior search results attract additional users and more targeted ads generate more clicks.
Page 87-88:
(1) More user data allows a GSE to improve search quality, (2) better search quality attracts more users and improves monetization, (3) more users and better monetization attract more advertisers, (4) more advertisers mean higher ad revenue, and (5) more ad revenue enables a GSE to expend more resources on traffic acquisition costs (i.e., revenue share payments) and investments, which enable the continued acquisition of scale.”
Page 93-94:
Google incorporates user data into every step of the search process. As the court’s liability-phase findings made clear, Google’s vast collection of user data has not gathered proverbial dust on Google’s servers over the past decade. Just the opposite—Google has continuously deployed user data to, among other things, determine which websites to crawl, in what order, and at what frequency; construct and organize its search index to ensure that it covers a wide range of subject matter and sources (and thus a diverse array of queries); enhance the “freshness” of results (i.e., bring them up to date); create signals and models that assess results’ relevance and establish their ranking; and run large-format experiments to develop new features. Google, (observing that queries issued on mobile devices tend to have “more location-focused intents” than those issued on desktop devices and therefore “one of the signals that does go into Google Search is . . . is it a desktop query or is it a mobile query”); (“Google continues to maintain significant volumes of data—despite the expense of storing it— because its value outweighs that cost.”). In the words of one Google presentation, “Search can look like magic . . . . But really it’s just about building signals . . . to identify user intent and match it to relevant documents.” Because the knowledge derived from users’ data provides “a strong proxy for users’ intent,” such data supplies “a critical input” for GSEs, including Google.
Page 129:
Google put that additional query volume to good use. It “deploy[ed] user data to, among other things, crawl additional websites, expand the index, re-rank the SERP, and improve the ‘freshness’ of results (i.e., bring them up to date).”
Page 150:
The Knowledge Graph is not, however, directly derived from user data. Its underlying data comes from over data feeds and pipelines, including from third parties.
Page 152:
In simple terms, User-side Data is data that Google collects from the pairing of a user query and the returned response. It also can be thought of as user-interaction data or “click- and query” data. Examples of such data include the web link or vertical information the user clicks on, how long a user hovers over a link, and whether the user clicks back from a web page and how quickly. User-interaction data is the raw material that Google uses to improve search services. (“At every stage of the search process, user data is a critical input that directly improves [search] quality.”)(describing “feeding in the clicks and other things” into algorithms to improve search results).
Page 157:
Recall, Google trains Navboost on 13 months of user data, which is the equivalent of over 17 years of data received by Bing. (explaining that training on 13 months of user data means the “queries and clicks” collected from “all users” worldwide); UPX0005 at -811 (“Glue Cache (13 months)”).
User Interactions
Mentions of "user interactions" in the ruling PDF:
Google says the key to their improvement is learning from what users interact with.
— Marie Haynes (@Marie_Haynes) September 3, 2025
"Learning from this user feedback is perhaps the central way that web ranking has improved for 15 years."
"Every [user] interaction gives us another example, another bit of training data: for… pic.twitter.com/HQ1CStLPEH
Page 157:
The sharing of the dataset underlying the Glue statistical models, on the other hand, presents a stronger case for inclusion in the final judgment. Again, the data in question is largely raw user-interaction data that associates queries and results with user interactions, such as clicks, hovers, and other aspects of a user’s journey on and from the SERP. This is the bread and butter of Google’s scale advantage.
Chrome Data
Mentions of "Chrome data" in the ruling PDF:
There is very little in the document about exactly how Chrome data is used for ranking.
— Marie Haynes (@Marie_Haynes) September 3, 2025
"Two exhibits suggest that popularity is based on 'Chrome visit data' and 'the number of anchors'."
That sentence right there could be discussed for hours. 🤔 pic.twitter.com/s2rsA9HQfg
Page 143 -144:
Two exhibits suggest that popularity is based on “Chrome visit data” and “the number of anchors,” which is a measure that quantifies the number of links between pages and is used to promote well-linked documents. (popularity signal (P*) “uses Chrome data”). The former appears to be a type of user-interaction data—albeit from Chrome visits, not through key default distribution channels—but the court can say no more, as Plaintiffs offered no testimony on the matter. The court will not force data sharing based on an uncertain record.
What do you take from these?
Forum discussion at X.
