Disney Enterprises, Inc.

Lucasfilm Entertainment Company Ltd.

Mara Idai Lucien

Rebecca Bever

Lori L Huck

Anthony M Accardo

Miquel Angel Farre Guiu

Erika Varis Doggett

Monica Alfaro Vendrell

Jo Lockman

Julianne Hope Groves

Alexander Niedt

Marc Junyent Martin

Pablo Pernias

Alexis L Johnson

Sonia Doshi

Paige Townsend

Juli Logemann

Marc Junyet Martin

Melina Ovanessian

Marcel Porta Valles

Various embodiments set forth systems and techniques for personalizing censorship of digital content. The techniques include generating, based on a user characteristic, a censorship profile associated with a user, wherein the censorship profile identifies a censorship element; determining a censorship feature associated with the censorship element, wherein the censorship feature is to be activated during playback of digital content that includes the censorship element; identifying, based on temporal boundaries data and temporal facet data associated with a digital content item, one or more portions of the digital content item that include at least one element matching the censorship element; and activating the censorship feature during playback of the one or more portions of the digital content item.

Personalized censorship of digital content

Embodiments provide for evaluation of scripts. A script for producing media content is received, and a plurality of tags related to content of the script is determined. A quality score is generated for the script by processing the plurality of tags using a first model, and one or more modifications for the script are generated based on the quality score and the plurality of tags.

Script analytics to generate quality score and report

A system includes processing hardware, a user interface (UI), and a system memory storing a software code. The processing hardware executes the software code to receive from a first user, via the UI, project data identifying a project including multiple tasks, determine, using the project data, a node-based graph of the project including the tasks, receive from a first user or a second user via the UI, task action data producing a modification to the node-based graph, and identify, based on the modification to the node-based graph, one of a conflict or a potential future conflict among at least two nodes of the node-based graph. The software code further identifies a conflict avoidance strategy for resolving the conflict or preventing the potential future conflict, and displays, via the UI, the conflict avoidance strategy to the first user or the second user, or performs the conflict avoidance strategy.

Proactive conflict resolution in node-based collaboration systems

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 includes processing hardware, and a memory storing software code including a hierarchical user interface (UI). The processing hardware executes the software code to receive from a first user, via the hierarchical UI, project data describing a project, and a project task for completion by a second user, and to generate, using the project data, a node-based graph of the project including the project task. The processing hardware further executes the software code to receive, via the hierarchical UI in response to performance of at least a portion of the project task by the second user, a task progression or task completion data, update, using the task progression or task completion data, the node-based graph, and display to the first user, via the hierarchical UI, the updated node-based graph and the task progression data or task completion data.

Node-based control and collaboration systems

A system includes a computing platform including processing hardware and a memory storing software code, a trained machine learning (ML) model, and a content thumbnail generator. The processing hardware executes the software code to receive interaction data describing interactions by a user with content thumbnails, identify, using the interaction data, an affinity by the user for at least one content thumbnail feature, and determine, using the interaction data, a predetermined business rule, or both, content for promotion to the user. The software code further provides a prediction, using the trained ML model and based on the affinity by the user, of the desirability of each of multiple candidate thumbnails for the content to the user, generates, using the content thumbnail generator and based on the prediction, a thumbnail having features of one or more of the candidate thumbnails, and displays the thumbnail to promote the content to the user.

Automated generation of personalized content thumbnails

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

Growing community of inventors

Los Angeles, CA, United States of America

Mara Idai Lucien