Google Inc.

Brian Potetz

Shinko Yuanhsien Cheng

Lily Berg

Keegan Mosley

Joe Agajanian

Hartwig Adam

Matthew E Kulick

Taehee Lee

Kristina Bohl

A media application segments a library of media associated with a user account into episodes, wherein each episode is associated with a corresponding time period. The media application generates, using an event machine-learning model, an event signal that indicates a likelihood that an event occurred in each episode, wherein the event machine-learning model is a classifier that receives the media as input. The media application generates an event significance score for each episode. The media application determines one or more events from the episodes based on the event signal and a corresponding event significance score exceeding a threshold event significance value. The media application provides a user interface that includes corresponding media from the one or more events.

Automatic generation of events using a machine-learning model

One or more devices may receive multiple images; determine one or more classifications for each of the multiple images; generate a confidence score for each classification of the one or more classifications for each of the multiple images. The confidence score may include a value between two numbers and may identify the likelihood that that a particular classification, of the one or more classifications, correctly corresponds to a particular image, of the multiple images. The confidence score may be based on information, associated with a user, and pixel-based classification information. The one or more devices may sort the multiple images by the one or more classifications and by corresponding confidence scores to form sorted multiple images; and provide the sorted multiple images.

Associating classifications with images

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Irvine, CA, United States of America

Brian Potetz