How Facebook Uses Deep Learning to Predict What You Might Like
A method for training AI models to recommend new content by comparing a user's past interactions with unseen items in a social network.
Original patent title: “Identifying entities using a deep-learning model”
A method for training AI models to recommend new content by comparing a user's past interactions with unseen items in a social network. Granted to Facebook Inc in 2019 with 24 claims and 8 forward citations.
Key facts
Coverage
What does this patent actually cover?
This patent describes a way to teach a computer model to better understand user preferences. It takes a list of things a user has already interacted with, like posts or pages, and turns them into mathematical lists called vectors. By temporarily hiding one of those items and using the rest to build a profile of the user, the system can test if it correctly predicts the hidden item. It then compares this user profile against items the user has never seen, updating the model's math so that relevant items move closer to the user in a virtual space. This helps the system learn to suggest content that is more likely to interest the user.
The gap
What does this patent NOT cover?
- Does not cover non-deep-learning recommendation methods like simple keyword matching.
- Does not cover systems that do not use vector-based embedding spaces for entity comparison.
- Does not cover methods that do not involve removing a target entity from the interaction set to perform the training feedback loop.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
What made this novel
The system treats the user's own history as a training ground by hiding a known interaction to see if the model can 're-discover' it, effectively creating a self-supervised feedback loop to tune the model's accuracy.
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 News Feed content ranking
Instagram Explore tab suggestions
Targeted advertising systems based on user interest graphs
Why it matters
The bigger picture
This technology is foundational to the modern social media experience. By refining how AI models learn from user behavior, platforms can keep users engaged by showing them content they are statistically likely to enjoy. This specific approach of using 'negative sampling' or 'target entity removal' is a standard technique in modern recommendation engines.
Filed
December 30, 2015
Granted
September 3, 2019
Market context
Who's building on this
Companies in this space
Meta (formerly Facebook) continues to iterate on these recommendation architectures. Other major tech companies like Google, ByteDance, and Amazon utilize similar deep-learning embedding techniques to power their own massive-scale recommendation systems.
Market impact
This patent reflects the industry-wide shift toward deep learning for personalization. It helped codify the transition from manual, rule-based recommendation systems to automated, self-learning models that can scale to billions of users and trillions of content interactions.
Claim 1 — Plain English
What this patent covers
This patent describes a way to teach a computer model to better understand user preferences. It takes a list of things a user has already interacted with, like posts or pages, and turns them into mathematical lists called vectors. By temporarily hiding one of those items and using the rest to build a profile of the user, the system can test if it correctly predicts the hidden item. It then compares this user profile against items the user has never seen, updating the model's math so that relevant items move closer to the user in a virtual space. This helps the system learn to suggest content that is more likely to interest the user.
The clever bit
The system treats the user's own history as a training ground by hiding a known interaction to see if the model can 're-discover' it, effectively creating a self-supervised feedback loop to tune the model's accuracy.
What it does not cover
- Does not cover non-deep-learning recommendation methods like simple keyword matching.
- Does not cover systems that do not use vector-based embedding spaces for entity comparison.
- Does not cover methods that do not involve removing a target entity from the interaction set to perform the training feedback loop.
Patent timeline
Application submitted to the patent office
Application published, typically 18 months after filing
Patent officially issued
PatentBrief Score
Impact Score
Strong
Citation count
19/40
Early citations
Claim breadth
16/20
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
$100K – $319K
Midpoint $200K · 9.5 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
24 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
Adams, K., Weston, J. E., & Chopra, S. (2019). How Facebook Uses Deep Learning to Predict What You Might Like (U.S. Patent No. 10,402,750). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/10402750/automl-neural-architecture-search
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 Deep Learning to Predict What You Might Like cover?
A method for training AI models to recommend new content by comparing a user's past interactions with unseen items in a social network.
Who owns patent US 10402750?
Facebook Inc owns this patent, granted in 2019.
When does this patent expire?
This patent is expected to expire on September 3, 2039, when the invention enters the public domain.
What is patent US 10402750 cited by?
This patent has been cited by 8 later patents that build on its ideas.
What problem does this patent solve?
This technology is foundational to the modern social media experience. By refining how AI models learn from user behavior, platforms can keep users engaged by showing them content they are statistically likely to enjoy. This specific approach of using 'negative sampling' or 'target entity removal' is a standard technique in modern recommendation engines.
What does this patent NOT cover?
Does not cover non-deep-learning recommendation methods like simple keyword matching.
Same assignee
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