Disney Enterprises, Inc.

Matthew C Petrillo

Anthony M Accardo

Miquel Angel Farre Guiu

Marc Junyent Martin

Avner Swerdlow

Monica Alfaro Vendrell

Katharine S Ettinger

Pablo Beltran Sanchidrian

Evan A Binder

Nimesh C Narayan

Alexis J Lindquist

Lena Volodarsky Bareket

Daniel Fojo

Jack Luu

Alan Pao

Albert Aparicio Isarn

Katharine S Navarre

Jordi Badia Pujol

Miquel Angel Farré Guiu

Francesc Josep Guitart Bravo

Katharine (Kaki) S Ettinger

Monica Alfaro

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 automating inferential content annotation includes a computing platform having a hardware processor and a system memory storing a software code including a set of rules trained to annotate content inferentially. The hardware processor executes the software code to utilize one or more feature analyzer(s) to apply labels to features detected in the content, access one or more knowledge base(s) to validate at least one of the applied labels, and to obtain, from the knowledge base(s), descriptive data linked to the validated label(s). The software code then infers, using the set of rules, one or more label(s) for the content based on the validated label(s) and the descriptive data, and outputs tags for annotating the content, where the tags include the validated label(s) and the inferred label(s).

Automated content validation and inferential content annotation

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 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

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

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

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.

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Sandy Hook, CT, United States of America

Matthew C Petrillo