Disney Enterprises, Inc.

Yazmaliza Yaacob

Nimesh C Narayan

Gregory S Head

Lee Bombard

Kari M Grubin

Andrew J Wahlquist

Alexander C Chen

A process receives, with a receiver, health and wellness data of a user that is sensed by one or more sensors during consumption by the user of media content. Further, the process receives, with the receiver, health and wellness data of the user that is determined by a healthcare provider during an event that is distinct from the consumption of the media content. In addition, the process aggregates, with a processor, the health and wellness data of the user that is sensed and the health and wellness data of the user that is determined by the health care provider into an aggregated health and wellness data model. The process also determines, with the processor, one or more optimal audio/visual device settings based on the aggregated health and wellness data model.

System for adjusting an audio/visual device based on health and wellness data

In one implementation, a system providing remote access set-up for electronic devices includes a mobile device and a profile server communicatively coupled to the mobile device via a network. The profile server includes a hardware processor, and a server memory storing a profile management software code and a profile information associated with a user of the mobile device. The hardware processor executes the profile management software code to receive, via the network, a platform ID of a configurable hardware platform communicatively coupled to the mobile device, and to determine, based on the platform ID, platform assets including a default configuration associated with the configurable hardware platform. The hardware processor further executes the profile management software code to determine, based on the profile information and the default configuration, a user specific configuration for the configurable hardware platform, and to transmit the user specific configuration to configure the configurable hardware platform.

Standardized remote access and set-up for electronic devices

Techniques are disclosed for converting image frames from one color space to another while predicting artistic choices that a director, colorist, or others would make. In one configuration, a color conversion application receives image frames, an indication of color spaces to convert between, and metadata associated with the image frames and/or regions therein. The conversion application determines a global, base color conversion for the image frames using a predefined color space transformation. Then, the conversion application (optionally) extracts image regions depicting objects of interest in the image frames, after which the color conversion application processes each of the extracted image regions and the remaining image frames (after the extracted regions have been removed) using one or more functions determined using machine learning. The processed extracted regions and remainders of the image frames are then combined by the color conversion application for output.

Machine learning color science conversion

Techniques are disclosed for converting image frames, such as the image frames of a motion picture, from one aspect ratio to another while predicting the pan and scan framing decisions that a human operator would make. In one configuration, one or more functions for predicting pan and scan framing decisions are determined, at least in part, via machine learning using training data that includes historical pan and scan conversions. The training data may be prepared by extracting features indicating visual and/or audio elements associated with particular shots, among other things. Function(s) may be determined, using machine learning, that take such extracted features as input and output predicted pan and scan framing decisions. Thereafter, the image frames of a received video may be converted between aspect ratios on a shot-by-shot basis, by extracting the same features and using the function(s) to make pan and scan framing predictions.

Aspect ratio conversion with machine learning

Techniques described herein are directed to adaptive virtual reality and augmented reality viewing based on a user's eye condition data. In a first implementation, a software application renders video content based on the user's eye condition data by mapping the user's eye condition data to video rendering parameters. The video content rendered based on the user's eye condition data may be made available to a virtual reality/augmented reality player and played using a head mounted display. The video content rendered based on the user's eye condition data may be pre-rendered before video content playback or rendered in real time during video content playback. In an additional implementation, the head mounted display may be calibrated and adjusted based on the eye condition data prior to video playback.

Adaptive VR/AR viewing based on a users eye condition profile

Methods and systems of archiving media files are provided. Media files may be archived such that only the difference between a media file and a base media file is stored. The base media file is archived. The media file to be archived and the base media file may have common attributes such as video codec, resolution, frame rate, and/or color space. A media file to be archived may be compared to the base media file to determine any difference. Bit-to-bit analysis or frame-to-frame analysis may be performed to identify the differences between a media file to be archived and a base media file. The differences may be extracted from the media file to be archived. A difference media file may be created to store the difference of a media file with respect to the base media file. A record may be created to store the actual location where the difference is extracted from a media file to be archived.

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Burbank, CA, United States of America

Yazmaliza Yaacob