There’s been a lot of speculation of what Navboost is but to my knowledge nobody has pinpointed an adequate patent that could be the original Navboost patent. This patent from 2004 closely aligns with Navboost
So I took the few clues we have about it and identified a couple likely patents.
The clues I was working with are that Google Software Engineer Amit Singhal was involved with Navboost and had a hand in inventing it. Another clue is that Navboost dated to 2005. Lastly, the court documents indicate that Navboost was updated later on so there may be other patents in there about that, which we’ll get to at some point but not in this article.
So I deduced that if Amit Singhal was the inventor then there would be a patent with his name on it and indeed there is, dating from 2004.
Out of all the patents I saw, the two most interesting were these:
- Systems and methods for correlating document topicality and popularity 2004
- Interleaving Search Results 2007
This article will deal with the first one, Systems and methods for correlating document topicality and popularity dating from 2004, which aligns with the known timeline of Navboost dating to 2005.
Patent Does Not Mention Clicks
An interesting quality of this patent is that it doesn’t mention clicks and I suspect that people looking for the Navboost patent may have ignored it because it doesn’t mention clicks.
But the patent discusses concepts related to user interactions and navigational patterns which are references to clicks.
Instances Where User Clicks Are Implied In The Patent
Document Selection and Retrieval:
The patent describes a process where a user selects documents (which can be inferred as clicking on them) from search results. These selections are used to determine the documents’ popularity.
Mapping Documents to Topics:
After documents are selected by users (through clicks), they are mapped to one or more topics. This mapping is a key part of the process, as it associates documents with specific areas of interest or subjects.
User Navigational Patterns:
The patent frequently refers to user navigational patterns, which include how users interact with documents, such as the documents they choose to click on. These patterns are used to compute popularity scores for the documents.
It’s clear that user clicks are a fundamental part of how the patent proposes to assess the popularity of documents.
By analyzing which documents users choose to interact with, the system can assign popularity scores to these documents. These scores, in combination with the topical relevance of the documents, are then used to enhance the accuracy and relevance of search engine results.
Patent: User Interactions Are A Measure Of Popularity
The patent US8595225 makes implicit references to “user clicks” in the context of determining the popularity of documents. Heck, popularity is so important to the patent that it’s in the name of the patent: Systems and methods for correlating document topicality and popularity
User clicks, in this context, refers to the interactions of users with various documents, such as web pages. These interactions are a critical component in establishing the popularity scores for these documents.
The patent describes a method where the popularity of a document is inferred from user navigational patterns, which can only be clicks.
I’d like to stop here and mention that Matt Cutts has discussed in a video that Popularity and PageRank are two different things. Popularity is about what users tend to prefer and PageRank is about authority as evidenced by links.
Matt defined popularity:
“And so popularity in some sense is a measure of where people go whereas PageRank is much more a measure of reputation.”
That definition from about 2014 fits what this patent is talking about in terms of popularity being about where people go.
See Matt Cutts Explains How Google Separates Popularity From True Authority
Watch the YouTube Video: How does Google separate popularity from authority?
How The Patent Uses Popularity Scores
The patent describes multiple ways that it uses popularity scores.
Assigning Popularity Scores:
The patent discusses assigning popularity scores to documents based on user interactions such as the frequency of visits or navigation patterns (Line 1).
Per-Topic Popularity:
It talks about deriving per-topic popularity information by correlating the popularity data associated with each document to specific topics (Line 5).
Popularity Scores in Ranking:
The document describes using popularity scores to order documents among one or more topics associated with each document (Line 13).
Popularity in Document Retrieval:
In the context of document retrieval, the patent outlines using popularity scores for ranking documents (Line 27).
Determining Popularity Based on User Navigation:
The process of determining the popularity score for each document, which may involve using user navigational patterns, is also mentioned (Line 37).
These instances demonstrate the patent’s focus on incorporating the popularity of documents, as determined by user interaction (clicks), into the process of ranking and correlating them to specific topics.
The approach outlined in the patent suggests a more dynamic and user-responsive method of determining the relevance and importance of documents in search engine results.
Navboost Assigns Scores To Documents
I’m going to stop here to also mention that this patent mentions assigning scores to documents, which is how Google executive Eric Lehman described in the trial how Navboost worked:
Speaking about the situation where there wasn’t a lot of click data, Lehman testified:
“And so I think Navboost does kind of the natural thing, which is, in the face of that kind of uncertainty, you take gentler measures. So you might modify the score of a document but more mildly than if you had more data.”
That’s another connection to Navboost in that the trial description and the patent describe using User Interaction for scoring webpages.
The more this patent is analyzed, the more it looks like what the trial documents described as Navboost.
Read the patent here:
Systems and methods for correlating document topicality and popularity
Featured Image by Shutterstock/Sabelskaya
