Apple Inc.

Matthias Mauch

Daniel Cartoon

Arvind S Shenoy

Mark Hilary Levy

Eric Circlaeys

Kevin Bessiere

Zachary H Smith

Patrick H Kelly

Betim Deva

Nathaniel B Chapman

Bruno J Conejo

Neil C Foley

Hao Yang

Jan Markus Harrie Van Balen

Menelaos D Kokolios

Lewis Kaneshiro

Jorge A Herrera Soto

James E Hewitt

Casey W Baker

Alexis H Durocher

Brian J Rogosky

Trevor M Slaton

Cristina N Smith

Alfredo Ramos-Alvarez

Alexa Rockwell

Bruno Di Giorgi

Jeremy P Bogle

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

Techniques for suggesting media assets, the technique including: requesting a set of candidate media assets for a set of user media items based on a knowledge graph metadata network describing the set of user media items; receiving metadata for the set of candidate media assets; determining one or more sets of ranked media assets based on the received metadata; and outputting the determined one or more sets of ranked media assets.

Automatic media asset suggestions for presentations of selected user media items

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.

Growing community of inventors

London, United Kingdom

Matthias Mauch