View source for Sirius XM Radio Inc. (20240346072). SYSTEMS, METHODS AND APPARATUS FOR GENERATING MUSICRECOMMENDATIONS BASED ON COMBINING SONG AND USER INFLUENCERS WITH CHANNEL RULE CHARACTERIZATIONS simplified abstract

=SYSTEMS, METHODS AND APPARATUS FOR GENERATING MUSICRECOMMENDATIONS BASED ON COMBINING SONG AND USER INFLUENCERS WITH CHANNEL RULE CHARACTERIZATIONS=


==Organization Name==

[[:Category:Sirius XM Radio Inc.|Sirius XM Radio Inc.]]


[[Category:Sirius XM Radio Inc.]]

==Inventor(s)==

[[:Category:Raymond Lowe of North Caldwell NJ (US)|Raymond Lowe of North Caldwell NJ (US)]][[Category:Raymond Lowe of North Caldwell NJ (US)]]

[[:Category:Christopher Ward of Bloomfield NJ (US)|Christopher Ward of Bloomfield NJ (US)]][[Category:Christopher Ward of Bloomfield NJ (US)]]

==SYSTEMS, METHODS AND APPARATUS FOR GENERATING MUSICRECOMMENDATIONS BASED ON COMBINING SONG AND USER INFLUENCERS WITH CHANNEL RULE CHARACTERIZATIONS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240346072 titled 'SYSTEMS, METHODS AND APPARATUS FOR GENERATING MUSICRECOMMENDATIONS BASED ON COMBINING SONG AND USER INFLUENCERS WITH CHANNEL RULE CHARACTERIZATIONS

The patent application describes systems, methods, and apparatus for generating music recommendations based on combining song and user influencers with channel rule characterizations. These systems output playlists that can be delivered as audio streams on various devices.

* User and song influencers are combined to generate influencer weightings for each audio clip, which are then used to create a combined play weighting for a given user.
* Rules are applied to generate a set of candidates to play to the user in different time slots.
* Real-time playlists are generated by applying a set of rules, such as channel rules, to the sets of candidates.
* Data used for generating influencer weightings includes user-specific preferences, listening history, audio clip specifics, and data from internet sources like social media.
* A feedback loop may be implemented to modify influencer weightings based on playlist accuracy.

**Potential Applications:**
- Music streaming services
- Personalized radio stations
- Content recommendation platforms

**Problems Solved:**
- Providing personalized music recommendations
- Enhancing user experience in music streaming
- Optimizing playlist generation based on user preferences

**Benefits:**
- Improved user engagement
- Enhanced music discovery
- Tailored listening experiences

**Commercial Applications:**
Title: Personalized Music Recommendation System for Streaming Services
This technology can revolutionize the way music streaming platforms deliver content to users, leading to increased user satisfaction and retention. It can also open up new revenue streams through targeted advertising and premium subscription models.

**Questions about the Technology:**
1. How does this technology differ from traditional music recommendation algorithms?
   This technology combines user and song influencers with channel rule characterizations to create personalized playlists, offering a more tailored listening experience.
   
2. Can this system adapt to changing user preferences over time?
   Yes, the feedback loop allows for modifications to influencer weightings based on playlist accuracy, enabling the system to adapt to evolving user preferences.


==Original Abstract Submitted==

systems, methods and apparatus for generating music recommendations based on combining song and user influencers with channel rule characterizations are presented. such systems and methods output a playlist, which may be delivered as an information stream of audio on a user or client device, such as a telephone or smartphone, tablet, computer or mp3 player, or any consumer device with audio play capabilities. the playlist may comprise various individual audio clips of one genre or type, such as songs, or of multiple types, such as music, talk, sports and comedy. the individual audio clips may be ordered by a sequencer, which, using large amounts of data, generates both (i) user independent and (i) user dependent influencer weightings for each clip, and then combines all of such influencer weightings into a combined play weighting w for a given audio clip, for a given user. taking the various play weightings w(ui, sj), a set of rules may be applied to generate a set of candidates c(ui, sj, tk) to play to user j in each of time slots k through k+m. real time playlists may then be generated from the m sets of candidates by application of a set of rules, which may be channel rules, for example. the data used to generate influencer weightings may include user-specific data including preferences and detailed listening history, audio clip specific data, and data gleaned from various internet accessible sources, including social media. in some embodiments a feedback loop may be implemented to gauge the accuracy of the dynamically generated playlists and modify the influencer weightings in response.



[[Category:G06F16/638]]
[[Category:G06F16/68]]
[[Category:G06N5/046]]
[[Category:CPC_G06F16/638]]