How Music Apps Learn What You Don't Want in Playlists
This patent describes how a music streaming service learns what kinds of songs or artists a user dislikes for their playlists by tracking what they repeatedly ignore, then uses that information to avoid recommending similar things in the future.
Original patent title: “Media content item recommendation system”
This patent describes how a music streaming service learns what kinds of songs or artists a user dislikes for their playlists by tracking what they repeatedly ignore, then uses that information to avoid recommending similar things in the future. Granted to Spotify AB in 2025 with 23 claims, and it is expected to expire in 2043.
Key facts
Coverage
What does this patent actually cover?
The system first shows a user a playlist and offers recommended songs to add to it (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1). It then keeps track of how often songs with specific traits, called 'attributes' (like a certain artist, per Claim 3 and 4), are suggested for a playlist but the user doesn't pick them (Claim 1). If a user consistently ignores songs with a particular attribute, the system gives that attribute a lower score (Claim 1). This score is then used to reduce the chance of recommending other songs with that same attribute for playlist additions in the future (Claim 5). For example, if you keep skipping recommendations for songs by 'Artist X' when building a playlist, the system learns to show you fewer songs by 'Artist X' or similar artists for that playlist.
The gap
What does this patent NOT cover?
- Does not cover recommendations based solely on what a user likes or plays frequently, without considering ignored suggestions.
- Does not cover general content recommendations outside the specific context of adding to an existing playlist.
- Does not cover systems that track ignored individual songs without linking that behavior to broader 'attributes' like artist or genre.
- Does not cover adjusting recommendations based on explicit 'dislike' buttons, only implicit ignoring by not selecting.
- Does not cover recommendations where the user is not presented with a 'set of recommended media content items for addition to the playlist'.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
What made this novel
The noveltynoveltyThe requirement that an invention be different from anything publicly known before its priority date.Read more → lies in using repeated *non-selection* of recommended items for *playlist inclusion*, tied to specific *attributes*, to actively reduce the likelihood of future recommendations with those attributes. Most recommendation systems heavily weigh positive signals; this patent focuses on leveraging implicit negative feedback.
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
Spotify's 'Enhance' feature for playlists
Playlist creation tools in music streaming applications
Personalized radio stations that adapt based on skipped tracks
Why it matters
The bigger picture
Personalization is critical for media streaming services. This patent describes a method for refining recommendations by learning from negative signals—specifically, what users repeatedly choose *not* to add to their playlists. This approach is crucial for improving user satisfaction and engagement by reducing irrelevant suggestions. Better recommendations can lead to users spending more time on a platform and discovering new content they genuinely enjoy, while avoiding content they dislike.
Filed
May 23, 2023
Granted
April 15, 2025
Market context
Who's building on this
Companies in this space
Spotify, as the assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →, is directly building on this technology to enhance its recommendation algorithms. Other major streaming services like Apple Music, YouTube Music, and Amazon Music also invest heavily in advanced recommendation systems to personalize user experiences and improve content discovery, likely incorporating similar principles.
Market impact
This patent supports the continuous improvement of personalized content discovery in streaming services. By refining recommendations based on implicit negative feedback, it helps reduce user frustration with irrelevant suggestions, potentially increasing user retention and engagement across the music and podcast streaming industry. It contributes to the competitive landscape where superior personalization is a key differentiator for attracting and keeping subscribers.
Claim 1 — Plain English
What this patent covers
The system first shows a user a playlist and offers recommended songs to add to it (Claim 1). It then keeps track of how often songs with specific traits, called 'attributes' (like a certain artist, per Claim 3 and 4), are suggested for a playlist but the user doesn't pick them (Claim 1). If a user consistently ignores songs with a particular attribute, the system gives that attribute a lower score (Claim 1). This score is then used to reduce the chance of recommending other songs with that same attribute for playlist additions in the future (Claim 5). For example, if you keep skipping recommendations for songs by 'Artist X' when building a playlist, the system learns to show you fewer songs by 'Artist X' or similar artists for that playlist.
The clever bit
The novelty lies in using repeated *non-selection* of recommended items for *playlist inclusion*, tied to specific *attributes*, to actively reduce the likelihood of future recommendations with those attributes. Most recommendation systems heavily weigh positive signals; this patent focuses on leveraging implicit negative feedback.
What it does not cover
- Does not cover recommendations based solely on what a user likes or plays frequently, without considering ignored suggestions.
- Does not cover general content recommendations outside the specific context of adding to an existing playlist.
- Does not cover systems that track ignored individual songs without linking that behavior to broader 'attributes' like artist or genre.
- Does not cover adjusting recommendations based on explicit 'dislike' buttons, only implicit ignoring by not selecting.
- Does not cover recommendations where the user is not presented with a 'set of recommended media content items for addition to the playlist'.
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.
The original legal language
Original claims
23 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
Westraeus, C., Johansson, M., Waller, J. C., Lindstrand, P., Ackerman, M., Marmor, J., Öhman, M., Carvalho, F. O., Lamere, P., & Söderlund, O. (2025). How Music Apps Learn What You Don't Want in Playlists (U.S. Patent No. 12,277,178). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/12277178/media-content-item-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 Music Apps Learn What You Don't Want in Playlists cover?
This patent describes how a music streaming service learns what kinds of songs or artists a user dislikes for their playlists by tracking what they repeatedly ignore, then uses that information to avoid recommending similar things in the future.
Who owns patent US 12277178?
Spotify AB owns this patent, granted in 2025.
When does this patent expire?
This patent is expected to expire on May 23, 2043, when the invention enters the public domain.
What problem does this patent solve?
Personalization is critical for media streaming services. This patent describes a method for refining recommendations by learning from negative signals—specifically, what users repeatedly choose *not* to add to their playlists. This approach is crucial for improving user satisfaction and engagement by reducing irrelevant suggestions. Better recommendations can lead to users spending more time on a platform and discovering new content they genuinely enjoy, while avoiding content they dislike.
What does this patent NOT cover?
Does not cover recommendations based solely on what a user likes or plays frequently, without considering ignored suggestions.
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