Apple Inc.

Patrick H Kelly

Eric Circlaeys

Kevin Bessiere

Zachary H Smith

Nicholas James Woods

Justin S Titi

Christopher John Moulios

Guillaume Vergnaud

Daniel Cartoon

Sabrine Rekik

Betim Deva

Matthias Mauch

Nathaniel B Chapman

Bruno J Conejo

Bruno Di Giorgi

Jeremy P Bogle

Hao Yang

Neil C Foley

Menelaos D Kokolios

Lewis Kaneshiro

Jorge A Herrera Soto

James E Hewitt

Casey W Baker

Alexis H Durocher

Trevor M Slaton

Cristina N Smith

Brian J Rogosky

Alexa Rockwell

Alfredo Ramos-Alvarez

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

A memory management system which can effectively determine whether, when, and to what extent, to modify a graph database based on changes in a paired relational database storing various data like media assets, such as photos and videos. The media assets are associated with data objects, such as moments, events, and people. The memory management system also includes one or more knowledge graph metadata networks (or more simply 'knowledge graphs'). The knowledge graph(s) store information which is related to the data stored by the relational databases. The memory management system detects changes in the data and data objects stored by the relational database(s). The memory management system can translate detected changes into modification instructions for the knowledge graph. The memory management system can also determine that it is unnecessary to modify the knowledge graph due to a detected change.

Smart updates from historical database changes

In some implementations, a computing device can generate personalized music selections to associate with any collections of visual media (e.g., photos and videos) stored on the computing device. A user may prefer particular genres of music and listen to some genres more frequently than others. The computing device can create measures of the user's genre preferences and use these measures to select music that is preferred by the user and music that may be significant or relevant to the particular collection of visual media. The computing device may also determine music that was being played when and where the visual media were being created. The computing device may store the visual media and music items in association with each other. The computing device may generate composite media items that combine the visual media and music items. When the visual media are viewed, the selected music item is also played.

Music selections for personal media compositions

In some implementations, a computing device can automatically name moments (e.g., a time-based collections of images) based on the accuracy of the location data corresponding to the captured image. The computing device can, for example, send location coordinates for an image associated with a moment to a location server. The computing device can receive a hierarchical list of location strings corresponding to the location coordinates from the location server. The computing device can filter the location strings based on an accuracy metric associated with the location coordinates for the image. The computing device can suggest or select a name for the collection of images based on the filtered location strings.

Moments naming based on location accuracy

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San Francisco, CA, United States of America

Patrick H Kelly