Social Agent Expression Determination via Contextual ANN and Remapping
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional dialogue-based interfaces lack character and naturalness, as they are transactional and primarily verbal, failing to incorporate nonverbal cues in interactions with users.
Innovation Solution
The development of automated systems and methods that interpret contextual inputs to determine naturalistic expressions for interactive social agents, using a combination of training content standardization software, expression determining artificial neural networks, and character remapping to generate nuanced and multi-modal responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated systems use transactional dialogue interfaces, then they can process user requests efficiently, but they lack character and naturalness in communication
Solution Approach 1:
The system dynamically adapts its communication style by selecting from multiple expression types (verbal, nonverbal, emotional) based on the conversation context. The expression determining ANN receives contextual inputs and dynamically determines appropriate expressions, allowing the agent to transition between transactional efficiency and natural communication as needed.
Solution Approach 2:
The system combines multiple types of expressions (verbal responses, nonverbal cues, emotional expressions) into a composite communication output. The expression determining module integrates various input types and generates multi-modal responses that combine different expression types, creating a richer communication pattern that mimics natural human interaction.
2Ease of operation
If dialogue interfaces respond only to affirmative user requests, then they maintain clear transactional boundaries, but they fail to incorporate nonverbal and emotional cues
Solution Approach 1:
The system segments the response generation process into distinct modules: input processing, contextual analysis, expression determination, and output generation. The expression determining ANN separately processes different types of inputs (verbal, nonverbal) and determines appropriate expression types independently, allowing clear operational boundaries while incorporating diverse expression modes.
Solution Approach 2:
The expression determining module serves multiple functions by processing various input types (verbal requests, nonverbal cues) and generating multiple output types (verbal responses, nonverbal expressions, emotional cues). This multi-functional capability allows the system to maintain clear transactional boundaries while incorporating diverse expression modes in a unified framework.
3Reliability
If conventional systems use standardized response protocols, then they ensure consistent processing, but they produce robotic and unnatural communication
Solution Approach 1:
The system changes the parameters of expression selection based on contextual inputs. Rather than using fixed response protocols, the expression determining ANN adjusts expression parameters (type, intensity, timing) dynamically based on the analyzed context, maintaining processing consistency through systematic parameter adjustment while achieving natural communication through parameter variability.
Data Source
AI summary
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.