How Facebook Uses User Feedback to Improve Search Results
A method for improving search engine accuracy by letting users manually rate search results, then using those ratings to automatically adjust how the search algorithm ranks future results.
Original patent title: “Ranking test framework for search results on an online social network”
A method for improving search engine accuracy by letting users manually rate search results, then using those ratings to automatically adjust how the search algorithm ranks future results. Granted to Facebook Inc in 2016 with 21 claims.
Key facts
Coverage
What does this patent actually cover?
This patent describes a feedback loop for search algorithms within a social network. When a user performs a search, the system presents results that are personalized based on the user's connections in a social graph. The user then manually assigns scores to these results. The system uses these user-provided scores to calculate a 'discounted cumulative gain'—a mathematical metric that measures how well the search algorithm ordered the most relevant items at the top. The algorithm then updates its internal ranking logic to better prioritize similar results for future queries.
The gap
What does this patent NOT cover?
- Does not cover search ranking systems that rely solely on automated click-through rates rather than explicit user-provided scores.
- Does not cover general search engines that operate without a social graph or user-connection data.
- Does not cover the specific machine learning models used to perform the ranking, only the method of using user feedback to modify those models.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
What made this novel
It treats the user's manual rating of a search result as a direct input to re-weight the social graph's influence on future search rankings, effectively turning the user into a real-time trainer for the search algorithm.
Schematic visualization of the patent's claim structure. Hand-drawn diagrams in progress for each landmark patent.
Where you've seen this
Real-world examples
Facebook search result feedback prompts
Internal search quality evaluation tools for social platforms
Personalized recommendation engines in social media apps
Why it matters
The bigger picture
As social networks grew, search became a primary way to navigate massive amounts of user-generated content. This patent represents the industry shift toward 'human-in-the-loop' optimization, where the platform treats its own users as data labelers to refine search relevance without needing expensive manual quality assurance teams.
Filed
December 20, 2012
Granted
July 19, 2016
Market context
Who's building on this
Companies in this space
Meta (formerly Facebook) continues to utilize this framework for its internal search and discovery products. Other major platforms like LinkedIn and Pinterest employ similar human-feedback loops to tune their search algorithms for personalized content discovery.
Market impact
This approach helped standardize the practice of using explicit user feedback to train ranking algorithms in social environments. It enabled platforms to scale search relevance improvements rapidly by leveraging the collective intelligence of their user base rather than relying on static, developer-defined ranking rules.
Claim 1 — Plain English
What this patent covers
This patent describes a feedback loop for search algorithms within a social network. When a user performs a search, the system presents results that are personalized based on the user's connections in a social graph. The user then manually assigns scores to these results. The system uses these user-provided scores to calculate a 'discounted cumulative gain'—a mathematical metric that measures how well the search algorithm ordered the most relevant items at the top. The algorithm then updates its internal ranking logic to better prioritize similar results for future queries.
The clever bit
It treats the user's manual rating of a search result as a direct input to re-weight the social graph's influence on future search rankings, effectively turning the user into a real-time trainer for the search algorithm.
What it does not cover
- Does not cover search ranking systems that rely solely on automated click-through rates rather than explicit user-provided scores.
- Does not cover general search engines that operate without a social graph or user-connection data.
- Does not cover the specific machine learning models used to perform the ranking, only the method of using user feedback to modify those models.
Patent timeline
Application submitted to the patent office
Application published, typically 18 months after filing
Patent officially issued
PatentBrief Score
Impact Score
Moderate
Citation count
0/40
No citations yet
Claim breadth
14/20
Broad claimsclaimsThe numbered statements at the end of a patent that legally define what the inventor owns.Read more →
Recency
10/20
Granted 5–10 years ago
Assignee scale
20/20
Major company or institution
PatentBrief Impact Score — based on citation count, claim breadth, recency, and assignee scale. Not a legal assessment.
Heuristic Value Estimate
What this patent might be worth
$27K – $87K
Midpoint $55K · 6.5 yr remaining · industry ×1.6
Heuristic only — blends forward/backward citation counts, claim scope, time remaining, litigation history, and CPC-derived industry baseline. Real valuations need a professional appraisal.
The original legal language
Original claims
21 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
Sankar, S., & Hong, K. (2016). How Facebook Uses User Feedback to Improve Search Results (U.S. Patent No. 9,398,104). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/9398104/facebook-ads-manager
Auto-generated from the patent record. Double-check author order and the issue date against the official USPTO document before submitting.
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Common Questions
Frequently Asked Questions
What does How Facebook Uses User Feedback to Improve Search Results cover?
A method for improving search engine accuracy by letting users manually rate search results, then using those ratings to automatically adjust how the search algorithm ranks future results.
Who owns patent US 9398104?
Facebook Inc owns this patent, granted in 2016.
When does this patent expire?
This patent is expected to expire on July 19, 2036, when the invention enters the public domain.
What problem does this patent solve?
As social networks grew, search became a primary way to navigate massive amounts of user-generated content. This patent represents the industry shift toward 'human-in-the-loop' optimization, where the platform treats its own users as data labelers to refine search relevance without needing expensive manual quality assurance teams.
What does this patent NOT cover?
Does not cover search ranking systems that rely solely on automated click-through rates rather than explicit user-provided scores.
Same assignee
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