Disney Enterprises, Inc.

Immersive Media Company

Anthony M Accardo

Miquel Angel Farre Guiu

Marc Junyent Martin

Avner Swerdlow

Monica Alfaro Vendrell

Matthew C Petrillo

Katharine S Ettinger

Mark Arana

Edward C Drake

Evan A Binder

Albert Aparicio Isarn

Gunter D Niemeyer

Skarphedinn S Hedinsson

Nimesh C Narayan

Pablo Beltran Sanchidrian

Alexis J Lindquist

Thabo D Beeler

Aljosa Aleksej Andrej Smolic

Katharine S Navarre

Vincent H Roberts

Miquel Angel Farré Guiu

Daniel Fojo

Leonid Sigal

Erika Varis Doggett

Aljoscha Smolic

Kenneth D Salter

Pablo Pernias

Mara Idai Lucien

Michael Holton

Jordi Badia Pujol

Maged Boctor

Jordi Pont-Tuset

Francesc Josep Guitart Bravo

Lena Volodarsky Bareket

Michael Breymann

Miquel À Farré Guiu

Walter P Chatfield, Jr

Christopher Abbey

Jack Luu

Alan Pao

Jillian Foster

John M Solaro

Pablo Beltran

Vince Roberts

Alexander Marshall

Tracia Ord

Stephanie Glover

Patrick Hovsepian

Jocelyn McDowell

Anna Singer

Michael Paul Goslin

Markus H Gross

Robert Walker Sumner

Bogdan Frusina

Alexandre Chapiro

Lori L Huck

Noel Brandon Vasquez

Christopher Eich

Seth Frey

Steven S Shapiro

Chris Thomes

Samuel C Anderson

Katharine (Kaki) S Ettinger

Gino Guzzardo

Jason Roeckle

Marc Junyet Martin

Melina Ovanessian

Monica Alfaro

Marcel Porta Valles

Miguel Angel Farre Guiu

Craig R Ferguson

Danton S Miller

Grace Lu

Amber Brown

Joe Marks

Bradley Drew Anderson

Myles McGovern

Scott Lance Van Nostrand

Rafael Huber

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

A system for assessing performer density includes a computing platform having processing hardware and a memory storing software code. The processing hardware is configured to execute the software code to receive content and content data, the content data identifying one or more performer(s) depicted or referenced in the content, and determine, using the content and the content data, one or more segment(s) of the content in which the performer(s) is/are depicted or referenced. The processing hardware is further configured to execute the software code to infer, for each determined segment(s) of the content, a respective importance of the performer(s) in a respective context of each determined segment(s), and calculate, based on the determined segment(s) of the content and the respective importance of the performer(s), a respective density score of each performer with respect to the content.

Performer density assessment

A system for performing authentication of content based on intrinsic attributes includes a computing platform having a hardware processor and a memory storing a content authentication software code. The hardware processor executes the content authentication software code to receive a content file including digital content and authentication data created based on a baseline version of the digital content, to generate validation data based on the digital content, to compare the validation data to the authentication data, and to identify the digital content as baseline digital content in response to determining that the validation data matches the authentication data based on the comparison. The hardware processor is also configured to execute the content authentication software code to identify the digital content as manipulated digital content in response to determining that the validation data does not match the authentication data based on the comparison.

Content authentication based on intrinsic attributes

A system includes a computing platform having processing hardware, and a memory storing software code. The processing hardware is configured to execute the software code to receive an image having a plurality of image regions, determine a boundary of each of the image regions to identify a plurality of bounded image regions, and identify, within each of the bounded image regions, one or more image sub-regions to identify a plurality of image sub-regions. The processing hardware is further configured to execute the software code to identify, within each of the bounded image regions, one or more first features, respectively, identify, within each of the image sub-regions, one or more second features, respectively, and provided an annotated image by annotating each of the bounded image regions using the respective first features and annotating each of the image sub-regions using the respective second features.

Automated image analysis and indexing

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 includes a computing platform having processing hardware, a system memory storing a software code; and a machine learning model based classifier. The processing hardware is configured to execute the software code to receive tagging quality assurance (QA) data including multiple terms applied as tags and corrections to those tags, to identify, using the tagging QA data, a first problematic term, and to classify, using the machine learning model based classifier, the first problematic term as one of confusing or flawed. The processing hardware is further configured to execute the software code to obtain, when the first problematic term is classified as confusing, a comparative sample for clarifying use of the first problematic term, and to obtain, when the first problematic term is classified as flawed, modification data for editing a predetermined annotation taxonomy including the first problematic term.

Performance-based evolution of content annotation taxonomies

A graphical user interface ('GUI') personalization application automatically generates a GUI for a software application. In operation, the GUI personalization application tracks interactions between a user and a first GUI that is associated with the software application and a GUI framework. The GUI personalization application then computes a behavioral simplification for the first GUI based on the interactions and the GUI framework. Subsequently, the GUI personalization application modifies the first GUI based on the behavioral simplification to generate a second GUI for the user that is associated with the software application and the GUI framework. Advantageously, the GUI personalization application can more efficiently and reliably improve the quality of the overall user experience when interacting with the software application across a wider range of users relative to prior art approaches.

Techniques for personalizing graphical user interfaces based on user interactions

A system for authenticating digital contents includes a computing platform having a hardware processor and a memory storing a software code. According to one implementation, the hardware processor executes the software code to receive digital content, identify an image of a person depicted in the digital content, determine an ear shape parameter of the person depicted in the image, determine another biometric parameter of the person depicted in the image, and calculate a ratio of the ear shape parameter of the person depicted in the image to the biometric parameter of the person depicted in the image. The hardware processor is also configured to execute the software code to perform a comparison of the calculated ratio with a predetermined value, and determine whether the person depicted in the image is an authentic depiction of the person based on the comparison of the calculated ratio with the predetermined value.

Systems for authenticating digital contents

Techniques for machine learning optimization are provided. A video comprising a plurality of segments is received, and a first segment of the plurality of segments is processed with a machine learning (ML) model to generate a plurality of tags, where each of the plurality of tags indicates presence of an element in the first segment. A respective accuracy value is determined for each respective tag of the plurality of tags, where the respective accuracy value is based at least in part on a maturity score for the ML model. The first segment is classified as accurate, based on determining that an aggregate accuracy of tags corresponding to the first segment exceeds a predefined threshold. Upon classifying the first segment as accurate, the first segment is bypassed during a review process.

Automated machine learning tagging and optimization of review procedures

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

A recommendation engine generates contextually relevant media programs tailored to the specific context of the user. The recommendation engine interfaces with one or more Internet-of-Things (IoT) appliances to determine one or more food items stored within the IoT appliance(s) and/or one or more cooking capabilities associated with the IoT appliance(s). The recommendation engine also gathers profile data that reflects a set of preferences associated with the user. Based on the gathered data, the recommendation engine queries a database of cooking program data associated with various cooking programs. The recommendation engine obtains cooking program data that is contextually relevant to the user and then generates a contextual cooking program that describes how to perform a given recipe. During playback of the contextual cooking program, the recommendation engine can modify the contextual cooking program based on the progress of the user in following the recipe.

Techniques for generating contextually-relevant recommendations

A system for securing a content processing pipeline includes a computing platform having a hardware processor and a memory storing a software code. The hardware processor executes the software code to insert a synthesized test image configured to activate one or more neurons of a malicious neural network into a content stream, provide the content stream as an input stream to a first processing node of the pipeline, and receive an output stream including a post-processed test image. The hardware processor further executes the software code to compare the post-processed test image in the output with an expected image corresponding to the synthesized test image, and to validate at least one portion of the pipeline as secure when the post-processed test image in the output matches the expected image.

Secure content processing pipeline

Growing community of inventors

Los Angeles, CA, United States of America

Anthony M Accardo