Apple Inc.

Mark Hilary Levy

Daniel Cartoon

Arvind S Shenoy

Thomas M Alsina

Matthias Mauch

Jeffrey L Robbin

Drew Rocky Domm

Taylor Gerard Carrigan

Venkat Sundaranatha

Fredric R Vinna

Rabi Chakraborty

Paul C Irvine

Erik Lindholm

Chandrasekar Venkataraman

Jayasimha R Katukuri

Priyo Mustafi

Jan Markus Harrie Van Balen

Thomas S Plagenhoef

Simon C Janpanah

Bekir B Dundar

Denise L Chen

The present technology pertains to automatically context labeling media items with relevant contexts, and further for algorithmically generating high quality playlists built around a context that are personalized to a profile of an account. This is accomplished by combining data from observed playlists, and data representing intrinsic properties of media items to predict contexts for media items.

Automatically predicting relevant contexts for media items

Generating a customized playlist may include identifying a user account for which a playlist is to be generated, where the user account is associated with a user listening history, a taste profile, and a social profile, identifying one or more friend accounts linked to the user account based on the social profile, obtaining an indication of a plurality of songs associated with one or more of the friend accounts, determining a listening history for the one or more plurality of songs based on the friend accounts, obtaining a subset of the plurality of songs based on the listening history of the plurality of songs, and generating a playlist from the subset of the plurality of songs.

Generating a customized social-driven playlist

A content management system and/or client device can enable a user to initiate a quick play mode where a content category and content medium are selected for the user. A client device and/or a content management system can select a content medium for a user based on one or more factors, such as the content category. Certain content categories of content can be preferably delivered in certain content mediums. In some embodiments, a content management system and/or client device can select a content medium for a user based on contextual data gathered from the user. Contextual data can be data describing the user's current state and/or environment. For example, contextual data can include data such as the time of day, geographic location, etc.

Automated content medium selection

The present technology pertains to steering a playlisting service toward media items that are likely to receive positive feedback from a user operating a client device. The present technology permits a request to play media items without requiring an input context. A playlist service can begin to receive feedback on the playback of the media items and the received playback can be utilized by a steering service in response to a steering request to identify media items for playback that are likely to receive positive feedback based on the feedback received on a sequence of previously played media items.

Steering for unstructured media stations

The present technology pertains to automatically contextlabeling media items with relevant contexts, and further for algorithmically generating high quality playlists built around a context that are personalized to a profile of an account. This is accomplished by combining data from observed playlists, and data representing intrinsic properties of media items to predict contexts for media items.

The techniques utilize a user's recommendation data to choose a presentation template. Each presentation template includes a plurality of media asset slots, each of which is associated with a certain type of media asset. For example, each media asset slot generally may be assigned to a playlist, album, composer, or music video. Further, each of these general assignments may further specify that the playlist, album, composer, or music video be from a known artist, familiar artist, recommended artist, or unknown artist, for example. Once the appropriate presentation template has been chosen along with the recommended media content for each of the plurality of media asset slots, the media asset slots are associated with the selected media content. The presentation template is then utilized to generate a graphical user interface that may be delivered to the display of an electronic device for presentation to a user, where the order in which the recommended media content is presented via the graphical user interface corresponds to the order of the media asset slots of the chosen presentation template.

Personalized media presentation templates

User created playlists can be analyzed to create a statistical language model indicating the likelihood that a particular sequence of content attributes will be found in a playlist created by a user, as well as the likelihood of any sequence of one or more content attributes following a playlist or partial playlist created by a user. The language model can be used to generate a recommended content attribute sequence based on a partial playlist of one or more content items. A recommended content item sequence that will be pleasant to a user when added to the partial playlist can be selected based on the recommended content attribute sequence.

Content item recommendations based on content attribute sequence

Growing community of inventors

Winchester, United Kingdom

Mark Hilary Levy