Intelligent Orchestration for Emotion Contagion in Agent-Human Interactions
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Solution Overview
Problem
Current software agent to human interactions lack emotional depth and nuance, leading to negative outcomes such as decreased satisfaction and emotion contagion, which existing techniques fail to address by not facilitating positive emotion contagion in multi-human to multi-software agent interactions.
Innovation Solution
An Intelligent Orchestration System that senses interactions between software agents and humans, computes mood patterns, and adapts responses to influence human emotions towards a prevalent mood pattern, using components like Mood Pattern Observation, Grouping, Orchestration of Agent Interactions, and Text Generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If software agents focus on accurately and correctly responding to human inquiries, then response accuracy is improved, but emotional depth and nuance deteriorate
Solution Approach 1:
The system segments the response generation process into multiple independent components: a cognitive processing component that handles accurate information processing, and an emotional processing component that handles mood pattern generation and emotion contagion. These segmented components work in parallel to produce responses that are both accurate and emotionally resonant.
Solution Approach 2:
The system merges previously separate cognitive and emotional processing functions into a unified response generation architecture. The mood pattern observation component, mood pattern grouping component, and orchestration component are integrated with the text generation component to create a holistic system that simultaneously delivers factual accuracy and emotional depth.
2Ease of operation
If software agents provide standard responses to human interactions, then operational simplicity is improved, but user satisfaction deteriorates due to lack of emotional engagement
Solution Approach 1:
The system dynamically adapts response generation based on real-time mood pattern analysis. Instead of static standard responses, the orchestration component continuously adjusts the emotional tone and style of responses by selecting from different mood patterns observed in human-human interactions, thereby maintaining user satisfaction while preserving operational simplicity.
Solution Approach 2:
The system implements feedback loops where mood patterns from human interactions are continuously observed, grouped, and used to refine future response generation. The mood pattern observation component monitors interactions, and this feedback is fed back into the text generation component to improve emotional engagement over time while maintaining simple operational workflows.
3Productivity
If software agents do not consider emotional patterns, then processing speed is improved, but negative emotion contagion increases
Solution Approach 1:
The system performs preliminary mood pattern analysis and grouping before generating responses. By pre-processing and categorizing mood patterns from human interactions in advance, the system prepares emotional context information that can be quickly retrieved and applied during response generation, thereby preventing negative emotion contagion without significantly impacting processing speed.
Data Source
AI summary
An embodiment senses an interaction among a software agent and a plurality of humans, responsive to the sensed interaction, computes a mood pattern in a Mood Pattern Observation Component based on the sensed interaction. The embodiment computes a prevalent mood pattern in a Mood Pattern Grouping Component based on the mood pattern. The embodiment decides by an Orchestration of Agent Interactions Component based on the prevalent mood pattern to adapt a response of the software agent to influence at least one of the plurality of humans towards the prevalent mood pattern, the deciding further comprises training a Text Generation Component to generate a text sequence based on the response wherein the software agent emits the text sequence and the sensed interaction among the software agent and the plurality of humans is updated with the text sequence.