How a Recommendation System Learns What You Really Want to Watch
This patent describes a media recommendation system that creates a unique user profile, uses it to pick a starting point, and then dynamically adjusts a list of suggestions based on what you've already watched and your real-time feedback.
Original patent title: “Dynamic feedback in a recommendation system”
This patent describes a media recommendation system that creates a unique user profile, uses it to pick a starting point, and then dynamically adjusts a list of suggestions based on what you've already watched and your real-time feedback. Granted to Malibu Entertainment in 2024 with 23 claims, and it is expected to expire in 2043.
Coverage
What does this patent actually cover?
The system first builds a "profile vector" for a user (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1). This profile is clever because it gives less weight to media items the user has watched many times, encouraging new discoveries. For example, if you've watched "The Matrix" 50 times, that movie's influence on your profile might be reduced. Next, it picks a "seed media item" based on this profile, which acts as a starting point for recommendations (Claim 1). This seed could be a trending show or something you recently liked (Claim 4). Then, the system creates a "pool" of other media items and sorts them by how similar they are to the seed item (Claim 1). Finally, it selects an item from this sorted pool for you to watch (Claim 1). Crucially, if you give feedback, like skipping a song or rating a movie, the system instantly updates the pool with new suggestions (Claim 3).
The gap
What does this patent NOT cover?
- Does not cover recommendation systems that do not reduce the influence of frequently consumed media items in the user profile.
- Does not cover systems that generate recommendations without first establishing a "seed media item" based on the user's profile.
- Does not cover systems where the pool of media items is not sorted by vector distance to a seed item.
- Does not cover systems that do not dynamically modify the recommendation pool based on user feedback.
- Does not cover systems that rely solely on explicit user ratings without considering consumption frequency.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
Key facts
What made this novel
The truly novel aspect is how the user's profile is generated: it's "inversely proportional to a total number of times that the user has consumed a specific media item" (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1). This means the system actively reduces the weight of content you've watched repeatedly, pushing it to recommend fresh, relevant items rather than endlessly suggesting your all-time favorites.
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
Netflix's "Because you watched..." suggestions
Spotify's Discover Weekly playlist
YouTube's video recommendations
TikTok's For You Page algorithm
Amazon's "Customers who bought this also bought..."
Why it matters
The bigger picture
Recommendation systems are fundamental to how users discover content across streaming platforms, social media, and e-commerce. This patent aims to refine how these systems learn user preferences, potentially leading to more engaging and less repetitive content discovery experiences. By dynamically adapting to user feedback and de-emphasizing over-consumed content, it seeks to improve user satisfaction and retention in competitive media markets.
Filed
July 19, 2023
Granted
October 15, 2024
Market context
Who's building on this
Companies in this space
Malibu Entertainment Inc., the assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →, is likely developing and integrating this technology into their own media platforms. Major streaming services like Netflix, Spotify, and YouTube continuously refine their recommendation algorithms, often incorporating similar principles of dynamic feedback and user preference modeling.
Market impact
This patent contributes to the ongoing evolution of personalized content delivery. By focusing on dynamic adaptation and preventing recommendation staleness, it aims to enhance user engagement and satisfaction, which are critical metrics in the highly competitive media and entertainment industry. Improved recommendation quality can directly impact subscriber retention and content consumption rates.
Claim 1 — Plain English
What this patent covers
The system first builds a "profile vector" for a user (Claim 1). This profile is clever because it gives less weight to media items the user has watched many times, encouraging new discoveries. For example, if you've watched "The Matrix" 50 times, that movie's influence on your profile might be reduced. Next, it picks a "seed media item" based on this profile, which acts as a starting point for recommendations (Claim 1). This seed could be a trending show or something you recently liked (Claim 4). Then, the system creates a "pool" of other media items and sorts them by how similar they are to the seed item (Claim 1). Finally, it selects an item from this sorted pool for you to watch (Claim 1). Crucially, if you give feedback, like skipping a song or rating a movie, the system instantly updates the pool with new suggestions (Claim 3).
The clever bit
The truly novel aspect is how the user's profile is generated: it's "inversely proportional to a total number of times that the user has consumed a specific media item" (Claim 1). This means the system actively reduces the weight of content you've watched repeatedly, pushing it to recommend fresh, relevant items rather than endlessly suggesting your all-time favorites.
What it does not cover
- Does not cover recommendation systems that do not reduce the influence of frequently consumed media items in the user profile.
- Does not cover systems that generate recommendations without first establishing a "seed media item" based on the user's profile.
- Does not cover systems where the pool of media items is not sorted by vector distance to a seed item.
- Does not cover systems that do not dynamically modify the recommendation pool based on user feedback.
- Does not cover systems that rely solely on explicit user ratings without considering consumption frequency.
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
Early stage
Citation count
0/40
No citations yet
Claim breadth
15/20
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
0/20
Independent or smaller assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →
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
$47K – $150K
Midpoint $94K · 16.9 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
23 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
SANDBERG, S. E., & Manning, D. F. (2024). How a Recommendation System Learns What You Really Want to Watch (U.S. Patent No. 12,118,030). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/12118030/dynamic-feedback-in-a-recommendation-system
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 a Recommendation System Learns What You Really Want to Watch cover?
This patent describes a media recommendation system that creates a unique user profile, uses it to pick a starting point, and then dynamically adjusts a list of suggestions based on what you've already watched and your real-time feedback.
Who owns patent US 12118030?
Malibu Entertainment owns this patent, granted in 2024.
When does this patent expire?
This patent is expected to expire on July 19, 2043, when the invention enters the public domain.
What problem does this patent solve?
Recommendation systems are fundamental to how users discover content across streaming platforms, social media, and e-commerce. This patent aims to refine how these systems learn user preferences, potentially leading to more engaging and less repetitive content discovery experiences. By dynamically adapting to user feedback and de-emphasizing over-consumed content, it seeks to improve user satisfaction and retention in competitive media markets.
What does this patent NOT cover?
Does not cover recommendation systems that do not reduce the influence of frequently consumed media items in the user profile.
Patent monitoring