Disney Enterprises, Inc.

Avner Swerdlow

Anthony M Accardo

Miquel Angel Farre Guiu

Marc Junyent Martin

Monica Alfaro Vendrell

Matthew C Petrillo

Katharine S Ettinger

Evan A Binder

Pablo Beltran Sanchidrian

Albert Aparicio Isarn

Katharine S Navarre

Jordi Badia Pujol

Alexis J Lindquist

Daniel Fojo

John M Solaro

Gunter D Niemeyer

Miquel Angel Farré Guiu

Michael Breymann

Jason Roeckle

Monica Alfaro

Sean Kallas

Meredith Bailey Antonia Scheff-King

Bradley Drew Anderson

Grace Lu

Samuel C Anderson

Meredith Bailey Antonia Scheff King

A content recommendation system includes a computing platform having a hardware processor and a system memory storing a content surfacing software code providing a user interface. The hardware processor executes the content surfacing software code to receive an initiation signal identifying a user, and, in response, identify content items as desirable content items to the user based on the user and metadata describing each of the desirable content items. In addition, the content surfacing software code generates a networked map of nodes corresponding respectively to the desirable content items, where the distances between nodes are based on the similarity of the metadata describing the desirable content items corresponding to the nodes. The content surfacing software code further outputs the networked map for display to the user via the user interface, the nodes being displayed as thumbnail images depicting the desirable content and selectable by the user.

Automated content recommendation using a metadata based content map

According to one implementation, a system for performing graph-based media content evaluation includes a computing platform having a hardware processor, and a system memory storing a media content evaluation software code and a graph database. The hardware processor is configured to execute the media content evaluation software code to receive a query from a system user, and to identify one or more media content evaluation metrics corresponding to the query. In addition, the hardware processor is configured to execute the media content evaluation software code to search the graph database for a media content data relevant to the one or more media content evaluation metrics, and to retrieve the media content data from the graph database. The hardware processor is further configured to execute the media content evaluation software code to generate a report responsive to the query using the media content data.

Method and system for generating a visual representation of media content for performing graph-based media content evaluation

According to one implementation, an automated audio mapping system includes a computing platform having a hardware processor and a system memory storing an audio mapping software code including an artificial neural network (ANN) trained to identify multiple different audio content types. The hardware processor is configured to execute the audio mapping software code to receive content including multiple audio tracks, and to identify, without using the ANN, a first music track and a second music track of the multiple audio tracks. The hardware processor is further configured to execute the audio mapping software code to identify, using the ANN, the audio content type of each of the multiple audio tracks except the first music track and the second music track, and to output a mapped content file including the multiple audio tracks each assigned to a respective one predetermined audio channel based on its identified audio content type.

Automated audio mapping using an artificial neural network

Various embodiments of the invention disclosed herein provide techniques for automatically exposing 3D production assets to an editorial workstation in a content creation pipeline. A production asset management system transmits, to an editorial workstation, a first content library that includes first metadata associated with a 3D production asset that is included in an editorial cut. The first metadata allows a user to see what versions of the 3D production asset are available for incorporation into the editorial cut. The production asset management system retrieves second metadata associated with a second version of the 3D production asset from a production database. The production asset management system retrieves, based on the second metadata, the second version of the 3D production asset from the production database. The production asset management system transmits the second version of the 3D production asset to the editorial workstation for incorporation into the editorial cut.

Techniques for automatically exposing 3D production assets to an editorial workstation in a content creation pipeline

According to one implementation, a quality control (QC) system for annotated content includes a computing platform having a hardware processor and a system memory storing an annotation culling software code. The hardware processor executes the annotation culling software code to receive multiple content sets annotated by an automated content classification engine, and obtain evaluations of the annotations applied by the automated content classification engine to the content sets. The hardware processor further executes the annotation culling software code to identify a sample size of the content sets for automated QC analysis of the annotations applied by the automated content classification engine, and cull the annotations applied by the automated content classification engine based on the evaluations when the number of annotated content sets equals the identified sample size.

Quality control systems and methods for annotated content

According to one implementation, a system for programmatic generation of media content digests includes a computing platform having a hardware processor and a system memory storing a media content digest software code. The hardware processor executes the media content digest software code to identify a media content for use in generating a content digest, the media content including a timecode of the media content, to access a metadata describing the media content and indexed to the timecode, and to identify one or more constraints for the content digest. In addition, the hardware processor executes the media content digest software code to programmatically extract content segments from the media content using the metadata indexed to the timecode and based on the one or more constraints, and to generate the content digest based on the media content from the content segments.

Programmatic generation of media content digests

According to one implementation, a system for automating content annotation includes a computing platform having a hardware processor and a system memory storing an automation training software code. The hardware processor executes the automation training software code to initially train a content annotation engine using labeled content, test the content annotation engine using a first test set of content obtained from a training database, and receive corrections to a first automatically annotated content set resulting from the test. The hardware processor further executes the automation training software code to further train the content annotation engine based on the corrections, determine one or more prioritization criteria for selecting a second test set of content for testing the content annotation engine based on the statistics relating to the first automatically annotated content, and select the second test set of content from the training database based on the prioritization criteria.

Guided training for automation of content annotation

According to one implementation, a media content annotation system includes a computing platform having a hardware processor and a system memory storing a software code. The hardware processor executes the software code to receive a first version of media content and a second version of the media content altered with respect to the first version, and to map each of multiple segments of the first version of the media content to a corresponding one segment of the second version of the media content. The software code further aligns each of the segments of the first version of the media content with its corresponding one segment of the second version of the media content, and utilizes metadata associated with each of at least some of the segments of the first version of the media content to annotate its corresponding one segment of the second version of the media content.

Media content metadata mapping

According to one implementation, a cloud-based system for performing cloud-based image rendering for video stream enrichment includes a video forwarding unit and a video enrichment unit. The video forwarding unit is configured to detect one or more non-interactive video player(s) linked to the video forwarding unit over a communication network, forward a video stream to the non-interactive video player(s), and forward the video stream to the video enrichment unit. The video enrichment unit is configured to receive the video stream, detect one or more interactive video player(s) linked to the video enrichment unit over the communication network, identify a video enhancement corresponding to one or more customizable video segment(s) in the video stream, insert a rendered video enhancement into the one or more customizable video segment(s) to produce an enriched video stream, and distribute the enriched video stream to one or more of the interactive video player(s).

Cloud-based image rendering for video stream enrichment

A content annotation system includes a computing platform having a hardware processor and a memory storing a tagging software code including an artificial neural network (ANN). The hardware processor executes the tagging software code to receive content having a content interval including an image of a generic content feature, encode the image into a latent vector representation of the image using an encoder of the ANN, and use a first decoder of the ANN to generate a first tag describing the generic content feature based on the latent vector representation. When a specific content feature learned by the ANN corresponds to the generic content to feature described by the first tag, the tagging software code uses a second decoder of the ANN to generate a second tag uniquely identifying the specific content feature based on the latent vector representation, and tags the content interval with the first and second tags.

Bi-level specificity content annotation using an artificial neural network

A content promotion system includes a computing platform having a hardware processor and a system memory storing a conversational agent software code. The hardware processor executes the conversational agent software code to receive user identification data, obtain user profile data including a content consumption history of a user associated with the user identification data, and identify a first predetermined phrase for use in interacting with the user based on the user profile data. In addition, the conversational agent software code initiates a dialog with the user based on the first predetermined phrase, detects a response or non-response to the dialog, updates the user profile data based on the response or non-response, resulting in updated user profile data, identifies a second predetermined phrase for use in interacting with the user based on the updated user profile data, and continues the dialog with the user based on the second predetermined phrase.

Content promotion using a conversational agent

A media content tagging system includes a computing platform having a hardware processor, and a system memory storing a tag selector software code configured to receive media content having segments, each segment including multiple content elements each associated with metadata tags having respective pre-computed confidence scores. For each content element, the tag selector software code assigns each of the metadata tags to at least one tag group, determines a confidence score for each tag group based on the pre-computed confidence scores of its assigned metadata tags, discards tag groups having less than a minimum number of assigned metadata tags, and filters the reduced number of tag groups based on the second confidence score to identify a further reduced number of tag groups. The tag selector software code then selects at least one representative tag group for a segment from among the further reduced number of tag groups.

Automation of media content tag selection

In various embodiments, a broker application automatically allocates tasks to application programming interfaces (APIs) in microservice architectures. After receiving a task from a client application, the broker application performs operation(s) on content associated with the task to compute predicted performance data for multiple APIs. The broker application then determines that a first API included in the APIs should process the first task based on the predicted performance data. The broker application transmits an API request associated with the first task to the first API for processing. After receiving a result associated with the first task from the first API, the client application performs operation(s) based on the result.

Techniques for automatically allocating tasks to application programming interfaces

An embodiment provides a monopod jib for cameras, including: a pole; a multi-axis gimbal disposed at one end of the pole; and a user interface comprising: a first user interface element having a first plurality of camera controls; and a second user interface element having a second plurality of camera controls. Other embodiments are shown and described.

User interface aspects for a motorized monopod jib for cameras

Systems, methods, and articles of manufacture to perform an operation comprising processing, by a machine learning (ML) algorithm and a ML model, a plurality of images in a first dataset, wherein the ML model was generated based on a plurality of images in a training dataset, receiving user input reviewing a respective set of tags applied to each image in the first data set as a result of the processing, identifying, based on a first confusion matrix generated based on the user input and the sets of tags applied to the images in the first data set, a first labeling error in the training dataset, determining a type of the first labeling error based on a second confusion matrix, and modifying the training dataset based on the determined type of the first labeling error.

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Los Angeles, CA, United States of America

Avner Swerdlow