Predicting User Interests Based on Who Looks at Whose Profile
A method for predicting what a user might be interested in by analyzing the web of connections created when people view each other's social media profile pages.
Original patent title: “Detecting content on a social network using browsing patterns”
A method for predicting what a user might be interested in by analyzing the web of connections created when people view each other's social media profile pages. Granted to Google LLC in 2012 with 48 claims and 73 forward citations, and it is expected to expire in 2029.
Coverage
What does this patent actually cover?
This patent describes a way to map social network activity by tracking who visits whose profile page. It creates a graph where nodes represent users and directional links represent a user viewing another user's profile. If a user is known to like a specific topic, the system assigns them a score and then propagates that score to other users who have viewed the same profiles. This allows the system to calculate a likelihood score for other users, suggesting they might also be interested in that same topic based on their browsing behavior.
The gap
What does this patent NOT cover?
- Does not cover analyzing content based on text keywords or hashtags within a user's own posts.
- Does not cover predicting interests based on direct social connections like 'friends' or 'followers'.
- Does not cover real-time tracking of mouse movements or dwell time on a specific image.
- Does not cover interest prediction based on external search engine queries.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
Key facts
What made this novel
The system treats the act of 'viewing a profile' as a signal of shared interest, effectively using the social graph as a proxy for taste, even if the users never interact directly.
The Patent Drawing
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
Targeted advertising on social media platforms
Content recommendation feeds
Suggested user or interest discovery features
Why it matters
The bigger picture
This technology is a foundation for interest-based recommendation engines. By using behavioral data—who looks at what—rather than just profile data, companies can build more accurate models of user preferences. It was filed during the early growth of social networks, when platforms were moving from simple directories to complex, data-driven advertising engines.
Filed
October 1, 2009
Granted
November 13, 2012
Market context
Who's building on this
Companies in this space
Google continues to iterate on these graph-based recommendation methods across its search and YouTube platforms. Other major social media platforms like Meta and TikTok utilize similar behavioral propagation models to refine their content discovery algorithms.
Market impact
This approach helped shift the industry away from static user profiles toward dynamic, behavioral-based interest modeling. It enabled platforms to monetize user attention more effectively by predicting interests that users hadn't explicitly declared, becoming a standard component of modern ad-tech stacks.
Claim 1 — Plain English
What this patent covers
This patent describes a way to map social network activity by tracking who visits whose profile page. It creates a graph where nodes represent users and directional links represent a user viewing another user's profile. If a user is known to like a specific topic, the system assigns them a score and then propagates that score to other users who have viewed the same profiles. This allows the system to calculate a likelihood score for other users, suggesting they might also be interested in that same topic based on their browsing behavior.
The clever bit
The system treats the act of 'viewing a profile' as a signal of shared interest, effectively using the social graph as a proxy for taste, even if the users never interact directly.
What it does not cover
- Does not cover analyzing content based on text keywords or hashtags within a user's own posts.
- Does not cover predicting interests based on direct social connections like 'friends' or 'followers'.
- Does not cover real-time tracking of mouse movements or dwell time on a specific image.
- Does not cover interest prediction based on external search engine queries.
Patent timeline
Application submitted to the patent office
Application published, typically 18 months after filing
Patent officially issued
Patent enters public domain
PatentBrief Score
Impact Score
High impact
Citation count
37/40
Highly cited
Claim breadth
20/20
Very broad protection
Recency
5/20
Granted 10–20 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
$403K – $1.3M
Midpoint $806K · 3.2 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.
Patent Claims
0 independent claims · 1 dependent
Claims are the legal boundaries of the patent. An independent claim stands alone. A dependent claim adds limitations to its parent, narrowing — but not broadening — the scope.
The original legal language
Original claims
48 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
Panesar, K. S., Thakur, M. N., Kunal, R., & Asgekar, A. S. (2012). Predicting User Interests Based on Who Looks at Whose Profile (U.S. Patent No. 8,311,950). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/8311950/detecting-content-on-a-social-network-using-browsing-patterns
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 Predicting User Interests Based on Who Looks at Whose Profile cover?
A method for predicting what a user might be interested in by analyzing the web of connections created when people view each other's social media profile pages.
Who owns patent US 8311950?
Google LLC owns this patent, granted in 2012.
When does this patent expire?
This patent is expected to expire on October 1, 2029, when the invention enters the public domain.
What is patent US 8311950 cited by?
This patent has been cited by 73 later patents that build on its ideas.
What problem does this patent solve?
This technology is a foundation for interest-based recommendation engines. By using behavioral data—who looks at what—rather than just profile data, companies can build more accurate models of user preferences. It was filed during the early growth of social networks, when platforms were moving from simple directories to complex, data-driven advertising engines.
What does this patent NOT cover?
Does not cover analyzing content based on text keywords or hashtags within a user's own posts.
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