How Your Device Learns Your Habits for Personalized Content Recommendations
This patent describes a system where your device creates a personal profile of your behavior, combines it with a vast online knowledge base, and uses both to give you super-personalized content recommendations.
Original patent title: “Method and apparatus for providing content based on knowledge graph”
This patent describes a system where your device creates a personal profile of your behavior, combines it with a vast online knowledge base, and uses both to give you super-personalized content recommendations. Granted to Samsung Electronics Co in 2024 with 18 claims and 4 forward citations, and it is expected to expire in 2040.
Coverage
What does this patent actually cover?
This patent outlines a method for a device, like a smartphone, to generate highly personalized content recommendations. It starts by collecting "log history information" about how you use the device, such as apps opened or settings changed (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1). Based on this history, the device builds a "device knowledge graph" – essentially a map of your interactions, structured in a specific data format called an ontology (Claim 1). It also creates a "pattern knowledge graph" that captures your typical behavior patterns, connecting your actions to specific situations (Claim 3). This local knowledge is then combined with a larger "server knowledge graph," which is updated regularly with vast amounts of data from the internet (Claim 1). The device updates its own knowledge graph by adding your behavior patterns and connects its local insights to the server's broad knowledge. Finally, the device uses this combined, updated knowledge to provide you with content recommendations tailored to your current situation, or "context data," on its screen (Claim 1). For example, if your device learns you often listen to podcasts while commuting (a behavior pattern in a specific context), it might recommend new podcasts when it detects you're on your morning bus ride.
The gap
What does this patent NOT cover?
- Does not cover content recommendation systems that rely solely on a server-side knowledge graph without generating or updating a device-specific knowledge graph.
- Does not cover systems where the device knowledge graph is not structured in an 'ontology form comprising a first entity expressed in a resource description framework (RDF)'.
- Does not cover systems that do not generate a 'pattern knowledge graph associated with a behavior pattern of a user' based on device usage.
- Does not cover systems where the server's knowledge graph is not 'updated periodically by the server based on big data collected through web crawling' and does not 'support a search engine'.
- Does not cover recommendation systems that do not connect device-generated recommendation knowledge to server-generated recommendation knowledge to update the device's graph.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
Key facts
What made this novel
The innovation lies in the device's ability to create and continually update a detailed map of a user's behavior patterns (a 'pattern knowledge graph') locally, and then intelligently merge this personal understanding with a massive, general knowledge graph maintained on a server. This dual-layer, self-improving system allows for highly context-aware and personalized recommendations that go beyond simple preferences, adapting to how and when a user typically interacts with their device.
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
Samsung Bixby recommendations
Google Discover feed
Netflix content suggestions
Amazon product recommendations
Spotify music playlists
TikTok video feed
Why it matters
The bigger picture
Personalized content recommendations are a cornerstone of modern digital experiences, influencing everything from what videos you watch to what products you buy. This patent matters because it describes a sophisticated way to make these recommendations even smarter by combining a user's unique, on-device behavior patterns with a vast, general knowledge base from a server. This hybrid approach allows for highly relevant suggestions that adapt to individual habits and real-time context, potentially leading to more engaging user experiences across various devices and services.
Filed
July 14, 2020
Granted
January 16, 2024
Market context
Who's building on this
Companies in this space
Samsung Electronics Co., the assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →, is actively building on this technology for its various devices and services, such as Bixby and SmartThings. Other major technology companies like Google, Amazon, and Meta are also heavily invested in developing sophisticated recommendation engines that leverage user data and knowledge graphs to enhance user experience across their platforms.
Market impact
This type of technology has a significant impact on the market by enabling more effective and engaging personalized experiences. It allows companies to better predict user needs and preferences, driving increased content consumption, product sales, and user retention. The ability to combine local, real-time user behavior with broad, server-side knowledge creates a competitive advantage for platforms that can deliver highly relevant suggestions, influencing how users interact with their devices and the digital world.
Claim 1 — Plain English
What this patent covers
This patent outlines a method for a device, like a smartphone, to generate highly personalized content recommendations. It starts by collecting "log history information" about how you use the device, such as apps opened or settings changed (Claim 1). Based on this history, the device builds a "device knowledge graph" – essentially a map of your interactions, structured in a specific data format called an ontology (Claim 1). It also creates a "pattern knowledge graph" that captures your typical behavior patterns, connecting your actions to specific situations (Claim 3). This local knowledge is then combined with a larger "server knowledge graph," which is updated regularly with vast amounts of data from the internet (Claim 1). The device updates its own knowledge graph by adding your behavior patterns and connects its local insights to the server's broad knowledge. Finally, the device uses this combined, updated knowledge to provide you with content recommendations tailored to your current situation, or "context data," on its screen (Claim 1). For example, if your device learns you often listen to podcasts while commuting (a behavior pattern in a specific context), it might recommend new podcasts when it detects you're on your morning bus ride.
The clever bit
The innovation lies in the device's ability to create and continually update a detailed map of a user's behavior patterns (a 'pattern knowledge graph') locally, and then intelligently merge this personal understanding with a massive, general knowledge graph maintained on a server. This dual-layer, self-improving system allows for highly context-aware and personalized recommendations that go beyond simple preferences, adapting to how and when a user typically interacts with their device.
What it does not cover
- Does not cover content recommendation systems that rely solely on a server-side knowledge graph without generating or updating a device-specific knowledge graph.
- Does not cover systems where the device knowledge graph is not structured in an 'ontology form comprising a first entity expressed in a resource description framework (RDF)'.
- Does not cover systems that do not generate a 'pattern knowledge graph associated with a behavior pattern of a user' based on device usage.
- Does not cover systems where the server's knowledge graph is not 'updated periodically by the server based on big data collected through web crawling' and does not 'support a search engine'.
- Does not cover recommendation systems that do not connect device-generated recommendation knowledge to server-generated recommendation knowledge to update the device's graph.
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
Strong
Citation count
14/40
Early citations
Claim breadth
12/20
Broad claimsclaimsThe numbered statements at the end of a patent that legally define what the inventor owns.Read more →
Recency
20/20
Granted within 5 years
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
$75K – $240K
Midpoint $150K · 13.7 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.
Claim text not yet imported for this patent
The original legal language
Original claims
18 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
LEE, Y., KANG, J., LEE, H., Hwang, T., Jeong, M., Park, J., & Lee, J. (2024). How Your Device Learns Your Habits for Personalized Content Recommendations (U.S. Patent No. 11,874,885). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/11874885/method-and-apparatus-for-providing-content-based-on-knowledge-graph
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 Your Device Learns Your Habits for Personalized Content Recommendations cover?
This patent describes a system where your device creates a personal profile of your behavior, combines it with a vast online knowledge base, and uses both to give you super-personalized content recommendations.
Who owns patent US 11874885?
Samsung Electronics Co owns this patent, granted in 2024.
When does this patent expire?
This patent is expected to expire on July 14, 2040, when the invention enters the public domain.
What is patent US 11874885 cited by?
This patent has been cited by 4 later patents that build on its ideas.
What problem does this patent solve?
Personalized content recommendations are a cornerstone of modern digital experiences, influencing everything from what videos you watch to what products you buy. This patent matters because it describes a sophisticated way to make these recommendations even smarter by combining a user's unique, on-device behavior patterns with a vast, general knowledge base from a server. This hybrid approach allows for highly relevant suggestions that adapt to individual habits and real-time context, potentially leading to more engaging user experiences across various devices and services.
What does this patent NOT cover?
Does not cover content recommendation systems that rely solely on a server-side knowledge graph without generating or updating a device-specific knowledge graph.
Same assignee
More from Samsung Electronics Co
Patent monitoring