Apple Inc.

Stephen Cosman

Alexis Hugo Louis Durocher

Gaurav Kapoor

Vignesh Jagadeesh

Julien F Freudiger

Abhishek Bhowmick

Sowmya Gopalan

Ryan M Rogers

Guillaume Tartavel

Kalu Onuka Kalu

John Duchi

Marcelo Lotif Araujo

Tai Ying Chiang

Michael Chatzidakis

Andrey Leonov

Thi Hai Van Do

Embodiments described herein provide for a non-transitory machine-readable medium storing instructions to cause one or more processors to receive, at a client device, a machine learning model from a server, detect a usage pattern for a content item, store an association between the content item and the detected usage pattern in local data, train the machine learning model using local data for the content item with the detected usage pattern to generate a trained machine learning model, generate an update for the machine learning model, privatize the update for the machine learning model, and transmit the privatized update for the machine learning model to the server.

User behavior model development with private federated learning

Embodiments described herein provide for a non-transitory machine-readable medium storing instructions to cause one or more processors to select a set of content items from a content item collection based upon a temporal relevance and a contextual relevance to a period of time, rank the set of content items based on at least one of a content item category or a content item predefined relevance score, partition the period of time into a set of time slots to schedule for rendering content in an application, rank the set of time slots based on device usage analysis for the period of time, and schedule the set of content items into the set of time slots in accordance with the rankings.

Timeline generation

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San Jose, CA, United States of America

Stephen Cosman