Disney Enterprises, Inc.

James R Kennedy

Douglas A Fidaleo

Komath Naveen Kumar

Raymond J Scanlon

Lanny Starkes Smoot

Quinn Smithwick

David Loyal Christensen

Alfredo Medina Ayala

Alexis Paul Wieland

Mubbasir Kapadia

Jill Fain Lehman

Günter D Niemeyer

John Wiseman

Robert P Michel

Dawson McLeod Dill

Salvator D Lombardo

Kyna P McIntosh

Matthew K Pan

Sungjoon Choi

Wojciech Witon

Daniel De Jesus Campos Zamora

Ashutosh Modi

Kevin El Haddad

Pierre Colombo

Prashanth Gurunath Shivakumar

Ashley N Girdich

Victor R Martinez Palacios

A system for performing context-based management of social agent interactions includes processing hardware and a memory storing a software code. The processing hardware executes the software code to detect the presence of an interaction partner, identify a present state of an interaction with the interaction partner, and to determine, based on the present state, a first score for each of multiple interactive expressions for use in initiating or continuing the interaction. The processing hardware further executes the software code to predict a state change of the interaction based on each of the interactive expressions to provide multiple predicted state changes corresponding respectively to the multiple interactive expressions, to determine, using the predicted state changes, a second score for each of the interactive expressions, and to select, using the first scores and the second scores, at least one of the interactive expressions to initiate or continue the interaction.

Context-based social agent interaction

A system for monitoring and improving social agent interaction quality 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, from a social agent, interaction data describing an interaction of the social agent with a user, and to perform an assessment of the interaction, using the interaction data, as one of successful or including a flaw. When the assessment indicates that the interaction includes the flaw, the processing hardware is further configured to execute the software code to identify an interaction strategy for correcting the flaw, and to deliver, to the social agent, one or both of the assessment and the interaction strategy to correct the flaw in the interaction.

Automated social agent interaction quality monitoring and improvement

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

A multi-party conversational agent includes a computing platform having a hardware processor and a memory storing a software code. The hardware processor is configured to execute the software code to identify a first predetermined expression for conversing with a group of people, and to have a group conversation, using the first predetermined expression, with at least some members of the group. The hardware processor is configured to further execute the software code to identify, while having the group conversation, a second predetermined expression for having a dialogue with at least one member of the group, and to interrupt the group conversation to have the dialogue, using the second predetermined expression, with the at least one member of the group.

Multi-party conversational agent

A system for incremental natural language understanding includes a media module, a memory storing a software code, and a hardware processor communicatively coupled to the media module. The hardware processor is configured to execute the software code to receive an audio stream including a first utterance, and generate a first and second incremental speech recognition outputs based on first and second portions of the first utterance. In addition, the hardware processor is configured to execute the software code to determine, prior to generating the second incremental speech recognition output, a first intent of the first utterance based on the first incremental speech recognition output. The hardware processor is further configured to execute the software code to retrieve a first resource based on the determined first intent, and incorporate the first resource in the media content to be played by the media module.

Systems and methods for incremental natural language understanding

A system for sensing and controlling eye contact for a robot. The system includes a robotic figure with a movable eye. The system includes a light source positioned in the robotic figure to output light through a light outlet of the eye. A light sensor is included that senses light striking surfaces in a physical space in which the robotic figure is positioned including the output light from the light source. The system includes an image processor processing output of the light sensor to identify a location of a target formed by the output light striking surfaces in the physical space and to identify a location of a face of a human. Further, the system includes a robot controller generating eye movement control signals based on the location of the target and the location of the face to position the eye to provide eye contact with the human observer.

Eye contact sensing and control for robotic characters

According to one implementation, an affect-driven dialog generation system includes a computing platform having a hardware processor and a system memory storing a software code including a sequence-to-sequence (seq2seq) architecture trained using a loss function having an affective regularizer term based on a difference in emotional content between a target dialog response and a dialog sequence determined by the seq2seq architecture during training. The hardware processor executes the software code to receive an input dialog sequence, and to use the seq2seq architecture to generate emotionally diverse dialog responses based on the input dialog sequence and a predetermined target emotion. The hardware processor further executes the software code to determine, using the seq2seq architecture, a final dialog sequence responsive to the input dialog sequence based on an emotional relevance of each of the emotionally diverse dialog responses, and to provide the final dialog sequence as an output.

Affect-driven dialog generation

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Glendale, CA, United States of America

James R Kennedy