Disney Enterprises, Inc.

Jill Fain Lehman

Douglas A Fidaleo

Mubbasir Kapadia

Steven Poulakos

Rita Singh

Jakob Joachim Buhmann

John Wiseman

Maayan Shvo

Sarah K Wulfeck

Rylan Gibbens

Ali Israr

James R Kennedy

Boyang Li

Siyan Zhao

Samer Al Moubayed

Oliver Stirling Schneider

Roberta Klatzky

Nikolas Wolfe

Andre Pereira

Andre Tiago Abelho Pereira

Iolanda M Dos Santos Caravalho Leite

Kevin El Haddad

Ming Sun

Eli Pincus

Pallavi N Baljekar

A system for simulating human-like affect-driven behavior includes a hardware processor and a system memory storing software code providing a virtual agent. The hardware processor executes the software code to identify a character assumed by the virtual agent and having a personality, a target state of motivational fulfillment, a baseline mood, and emotions. The software code identifies current physical and motivational fulfillment states, and currently active emotions of the character, and determines a current mood of the character based on the baseline mood and the currently active emotions. The software code further detects an experience by the character and plans multiple behaviors including a first behavior based on the experience and the current physical state, the second behavior based on the experience, the personality, the current mood, and the currently active emotions, and a third behavior based on the target and current states of motivational fulfillment.

Simulated human-like affect-driven behavior by a virtual agent

A system providing an interactive social agent can include a computing platform having a hardware processor and a memory storing a training content standardization software code configured to receive content depicting human expressions and including annotation data describing the human expressions from multiple content annotation sources, generate a corresponding content descriptor for each content annotation source to translate the annotation data into a standardized data format, and transform the annotation data into the standardized data format using the corresponding content descriptor. The content and the annotation data in the to standardized format are stored as training data for use in training expressions for the interactive social agent. The memory may also store a character remapping software code configured to receive data identifying an expression for the interactive social agent, identify a character persona of the interactive social agent, and determine a modified expression based on expressive idiosyncrasies of the character persona.

Automated determination of expressions for an interactive social agent

There is provided a system including a microphone configured to receive an input speech, an analog to digital (A/D) converter configured to convert the input speech to a digital form and generate a digitized speech including a plurality of segments having acoustic features, a memory storing an executable code, and a processor executing the executable code to extract a plurality of acoustic feature vectors from a first segment of the digitized speech, determine, based on the plurality of acoustic feature vectors, a plurality of probability distribution vectors corresponding to the probabilities that the first segment includes each of a first keyword, a second keyword, both the first keyword and the second keyword, a background, and a social speech, and assign a first classification label to the first segment based on an analysis of the plurality of probability distribution vectors of one or more segments preceding the first segment and the probability distribution vectors of the first segment.

Classifying segments of speech based on acoustic features and context

There is provided a system comprising a microphone, configured to receive an input speech from an individual, an analog-to-digital (A/D) converter to convert the input speech to digital form and generate a digitized speech, a memory storing an executable code and an age estimation database, a hardware processor executing the executable code to receive the digitized speech, identify a plurality of boundaries in the digitized speech delineating a plurality of phonemes in the digitized speech, extract a plurality of formant-based feature vectors from each phoneme in the digitized speech based on at least one of a formant position, a formant bandwidth, and a formant dispersion, compare the plurality of formant-based feature vectors with age determinant formant-based feature vectors of the age estimation database, determine the age of the individual when the comparison finds a match in the age estimation database, and communicate an age-appropriate response to the individual.

Systems and methods for estimating age of a speaker based on speech

A dialog knowledge acquisition system includes a hardware processor, a memory, and hardware processor controlled input and output modules. The memory stores a dialog manager configured to instantiate a persistent interactive personality (PIP), and a dialog graph having linked dialog state nodes. The dialog manager receives dialog initiation data, identifies a first state node on the dialog graph corresponding to the dialog initiation data, determines a dialog interaction by the PIP based on the dialog initiation data and the first state node, and renders the dialog interaction. The dialog manager also receives feedback data corresponding to the dialog interaction, identifies a second state node based on the dialog initiation data, the dialog interaction, and the feedback data, and utilizes the dialog initiation data, the first state node, the dialog interaction, the feedback data, and the second state node to train the dialog graph.

Dialog knowledge acquisition system and method

A device includes a plurality of components, a memory having a keyword recognition module and a context recognition module, a microphone configured to receive an input speech spoken by a user, an analog-to-digital converter configured to convert the input speech from an analog form to a digital form and generate a digitized speech, and a processor. The processor is configured to detect, using the keyword recognition module, a keyword in the digitized speech, initiate, in response to detecting the keyword by the keyword recognition module, an action to be taken one of the plurality of components, wherein the keyword is associated with the action, determine, using the context recognition module, a context for the keyword, and execute the action if the context determined by the context recognition module indicates that the keyword is a command.

Speech-controlled actions based on keywords and context thereof

There is provided a system and method for producing a haptic effect. In one implementation, such a system includes a system processor, a system memory, and a haptic engine stored in the system memory. The system processor is configured to execute the haptic engine to receive a media content, to map an event contained in the media content to a predetermined haptic effect, and to display an interface enabling a system user to produce a customized haptic effect based on the predetermined haptic effect. The system processor is further configured to generate an output data for causing one of the predetermined haptic effect and the customized haptic effect to occur.

Customized haptic effects

Systems, methods, and computer program products to detect a keyword in speech, by generating, from a sequence of spectral feature vectors generated from the speech, a plurality of blocked feature vector sequences, and analyzing, by a neural network, each of the plurality of blocked feature vector sequences to detect the presence of the keyword in the speech.

Sectioned memory networks for online word-spotting in continuous speech

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Pittsburgh, PA, United States of America

Jill Fain Lehman