Disney Enterprises, Inc.

Hulu, LLC

Lifespan Extension Advocacy Foundation

Keith Comito

Dirk Edward Van Dall

James R Bowers

Kevin John Prince

James A Corrigan-Colville

Nathan Lefler

Daniel Cahall

Anthony Waters

Thomas Engel

Samuel Joseph Reisner

Dumene Comploi

Justin Ali Kennedy

Joseph A Inzerillo

Sanchita Tiwari

Bryce Horwood

Steve Sang-Hyuck Lee

Nicholas M Arciero

Mark R Brashear

Oliver Medvedik

Gad Rouache

Ryan McGarry

Frederik S Boll

David S Resnick

Embodiments provide systems and techniques for automated generation of haptic effects based on haptics data. An example technique includes obtaining a set of haptic data associated with media content being streamed to a computing device. The media content is displayed on the first computing device. One or more haptic effects are generated using one or more haptic devices while displaying the media content, based on the set of haptic data. One or more parameters of the one or more haptic effects is adjusted when a predetermined condition(s) is satisfied.

Automated generation of haptic effects based on haptics data

A system for performing synergistic object tracking and pattern recognition for event representation includes a computing platform having processing hardware and a system memory storing a software code. The processing hardware is configured to execute the software code to receive event data corresponding to one or more propertie(s) of an object, to generate, using the event data, a location data estimating a location of each of multiple predetermined landmarks of the object, and to predict, using one or both of the event data and the location data, a pattern corresponding to the propertie(s) of the object. The processing hardware is further configured to execute the software code to update, using the predicted pattern, the location data, and to merge the updated location data and the predicted pattern to provide merged data.

Synergistic object tracking and pattern recognition for event representation

A system includes a hardware processor and a memory storing software code and one or more machine learning (ML) model(s) trained to transform content. The hardware processor executes the software code to ingest content components each corresponding respectively to a different feature of multiple features included in a content file, receive sensor data describing at least one of an action or an environment of a system user, and identify, using the sensor data, at least one of the content components as content to be transformed. The hardware processor further executes the software code to transform, using the ML model(s), that identified content to provide at least one transformed content component, combine a subset of the ingested content components with the at least one transformed content component to produce a dynamically transformed content, and output the dynamically transformed content in real-time with respect to ingesting the content components.

User responsive dynamic content transformation

According to one implementation, a method of guiding an interaction between a companion module and a user includes identifying a media content for playout by a media player device, transmitting, to a remote server, an interaction schema request identifying the media content and the companion module, and receiving, from the remote server, behavioral manifest data including an instruction for guiding the interaction between the companion module and the user. Such a method also includes obtaining a play head state of the media player device and a playout timecode state of the media content, and identifying a user interaction behavior for the companion module based on the behavioral manifest data, the play head state of the media player device, and the playout timecode state of the media content.

Guided interaction between a companion device and a user

A system for producing conversation-driven character animation includes a computing platform having processing hardware and a system memory storing software code, the software code including multiple trained machine learning (ML) models. The processing hardware executes the software code to obtain a conversation understanding feature set describing a present state of a conversation between a digital character and a system user, and to generate an inference, using at least a first trained ML model of the multiple trained ML models and the conversation understanding feature set, the inference including labels describing a predicted next state of a scene within the conversation. The processing hardware further executes the software code to produce, using at least a second trained ML model of the multiple trained ML models and the labels, an animation stream of the digital character participating in the predicted next state of the scene within the conversation.

Conversation-driven character animation

Methods, systems and apparatus for inducing and verifying the level of neural entrainment at any target frequency, through a combination of biofeedback mechanisms, data analysis, and modulation of synergistic combinations of adaptable stimuli.

Inducement, verification and optimization of neural entrainment through biofeedback, data analysis and combinations of adaptable stimulus delivery

A system for synchronizing media content playout 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 state message from a first media player playing a first media content and a second state message from a second media player playing a second media content, the first media content and the second media content being the same media content. The software code further determines a coordination state for playout of the first media content and the second media content based on one or more of the first and second state messages, and transmits a first coordination message including the coordination state to the first media player and a second coordination message including the coordination state to the second media player to synchronize playout of the first media content and the second media content.

Coordination of media content delivery to multiple media players

Growing community of inventors

Seaford, NY, United States of America

Keith Comito