Social Agent Interaction Using Context and Predicted Response States
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Solution Overview
Problem
Conventional dialogue interfaces lack character and naturalness, are transactional, and unable to engage in nuanced, dynamic interactions, process multiple statements concurrently, or repair miscommunications, with limited user personalization.
Innovation Solution
A context-based social agent interaction system that utilizes an interaction manager software to select interactive expressions based on past interactions, present states, and predicted responses, employing machine learning models to determine relevant and goal-oriented responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single synthesized persona is used in conventional dialogue interfaces, then the system structure is simple, but the character and naturalness of the interaction deteriorates
Solution Approach 1:
The patent implements dynamic persona projection by continuously adjusting the synthesized persona's characteristics based on real-time interaction context, user feedback, and conversation state. The system transitions from static pre-defined personas to dynamic adaptive personas that evolve during interactions, enabling naturalistic conversation flow and contextual appropriateness while maintaining manageable system architecture through parameter-based adaptation rather than complex reconfiguration
2Productivity
If conventional transactional dialogue interfaces are used, then the interaction model is simple, but the ability to process multiple statements concurrently and engage in fluid interactions deteriorates
Solution Approach 1:
The patent implements continuous processing of multiple statements by maintaining an active conversation context window that continuously accumulates and processes user inputs. The system continuously generates responses based on the evolving conversation state rather than processing statements in discrete sequential turns, enabling fluid naturalistic interactions and concurrent statement processing through persistent contextual analysis and generation
Solution Approach 2:
The system performs preliminary processing of conversation context, user preferences, and interaction history before generating responses. This preliminary action includes analyzing multiple statements in advance, understanding contextual nuances, and preparing appropriate responses that address multiple user inputs simultaneously, thereby improving processing efficiency without requiring overly complex interaction models
3Reliability
If conventional transactional design is used, then the system is simple to implement, but the ability to repair miscommunication and provide personalized responses deteriorates
Solution Approach 1:
The patent implements feedback mechanisms that continuously monitor interaction quality, detect miscommunications, and adjust responses accordingly. The system analyzes user responses, conversation flow, and contextual cues to identify breakdowns in communication and automatically repairs them through clarifying questions, rephrasing, or contextual adjustments. This feedback-driven approach enables reliable miscommunication repair while maintaining personalization through adaptive response generation based on individual user patterns and preferences
Data Source
AI summary
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.