Disney Enterprises, Inc.

Katharine S Ettinger

Anthony M Accardo

Miquel Angel Farre Guiu

Avner Swerdlow

Matthew C Petrillo

Marc Junyent Martin

Monica Alfaro Vendrell

Pablo Beltran Sanchidrian

Evan A Binder

Alexis J Lindquist

Skarphedinn S Hedinsson

Nimesh C Narayan

Miquel Angel Farré Guiu

Lena Volodarsky Bareket

Christopher Eich

John M Solaro

Jack Luu

Alan Pao

Grace Lu

Monica Alfaro

Craig R Ferguson

Danton S Miller

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

There is provided a system including a non-transitory memory storing an executable code and a hardware processor executing the executable code to receive a media content including a plurality of frames, divide the media content into a plurality of shots, each of the plurality of shots including a plurality of frames of the media content based on a first similarity between the plurality of frames, determine a plurality of sequential shots of the plurality of shots to be part of a first sub-scene of a plurality of sub-scenes of a scene based on a timeline continuity of the plurality of sequential shots, identify each of the plurality of shots of the media content and each of the plurality of sub-scenes with a corresponding beginning time code and a corresponding ending time code.

Systems and methods for intelligent media content segmentation and analysis

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 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

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

There is provided a media content ontology system and method. The system includes a computing platform having a processor and a memory, a content genome database stored in the memory, and ontology software for execution by the processor. The ontology software is configured to map a media content asset to first and second content classifications based on respective first and second data, and to generate first and second content genome database entries associating the media content asset with other, correspondingly mapped, media content assets. In addition, the ontology software is configured to cross-index the first and second content genome database entries to enable identification of each of the first and second content classifications and the media content asset based on any one of the first or second content classifications or the media content asset.

Media content ontology

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.

Semiautomatic machine learning model improvement and benchmarking

According to one implementation, a content classification system includes a computing platform having a hardware processor and a system memory storing a video asset classification software code. The hardware processor executes the video asset classification software code to receive video clips depicting video assets and each including images and annotation metadata, and to preliminarily classify the images with one or more of the video assets to produce image clusters. The hardware processor further executes the video asset classification software code to identify key features data corresponding respectively to each image cluster, to segregate the image clusters into image super-clusters based on the key feature data, and to uniquely identify each of at least some of the image super-clusters with one of the video assets.

Video asset classification

There is provided a system including a non-transitory memory storing a media library including an ordered plurality of media contents, each including a plurality of attribute tags, and a hardware processor configured to provide a user interface for display on a user device for navigating the media contents, receive a user input from the user device for playing one or more of the ordered media contents based on a first attribute tag of the plurality of attribute tags, stream a first portion of a first media content to the user device, based on the first attribute tag of the media content selected by the user input, and stream a second portion of a second media content to the user device following the first portion of the first media content, based on the first attribute tag of the media content selected by the user input.

Systems and methods for streaming media contents based on attribute tags

According to one implementation, a media content annotation system includes a computing platform including a hardware processor and a system memory storing a model-driven annotation software code. The hardware processor executes the model-driven annotation software code to receive media content for annotation, identify a data model corresponding to the media content, and determine a workflow for annotating the media content based on the data model, the workflow including multiple tasks. The hardware processor further executes the model-driven annotation software code to identify one or more annotation contributors for performing the tasks included in the workflow, distribute the tasks to the one or more annotation contributors, receive inputs from the one or more contributors responsive to at least some of the tasks, and generate an annotation for the media content based on the inputs.

Growing community of inventors

Santa Monica, CA, United States of America

Katharine S Ettinger