Multi-Persona Social Agent Using Neural Network Persona Segmentation
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Solution Overview
Problem
Conventional dialogue-based interfaces lack character and naturalness, as they are typically transactional and fail to incorporate nuanced and varied forms of communication, including non-verbal and idiosyncratic expressions, which are characteristic of human interactions.
Innovation Solution
A multi-persona social agent system that uses neural networks and persona-specific conversational databases to generate sentiment-driven, personified responses based on learned speech, sentiment, and personality characteristics, enabling automated and naturalistic interactions by projecting various personas with unique expressions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a single synthesized persona is used in dialogue interfaces, then the system is simple to implement, but the interaction lacks character and naturalness
Solution Approach 1:
The patent segments the single persona into multiple distinct personas, each with unique personality characteristics, speech patterns, and expression styles. This allows the system to maintain implementation simplicity through modular persona designs while dramatically increasing character variety and interaction naturalness by selecting appropriate personas based on context and user preference.
2Productivity
If transactional dialogue responses are used, then the system operates efficiently, but the interaction lacks nuance and human-like communication
Solution Approach 1:
The patent implements dynamic response generation that adapts to conversational context, user sentiment, and persona characteristics. Rather than static transactional responses, the system dynamically adjusts tone, style, and content to mimic human-like communication while maintaining operational efficiency through optimized selection and generation processes.
Solution Approach 2:
The system changes multiple parameters including sentiment polarity, formality level, speech patterns, and expression types based on contextual analysis. This allows efficient generation of nuanced responses that adapt to different conversational situations while maintaining productivity through parameter-based adjustment rather than complete response regeneration.
3Device complexity
If conventional dialogue interfaces are used, then the system structure is simple, but the responses lack idiosyncratic expressions and personality characteristics
Solution Approach 1:
The patent creates a universal framework that can project multiple different personas through a single system architecture. The core system remains relatively simple while gaining the ability to embody various personalities, speech patterns, and expression styles, effectively achieving multi-functionality without proportional increases in overall system complexity.
Data Source
AI summary
A system providing a multi-persona social agent includes a computing platform having a hardware processor, a system memory storing a software code, and multiple neural network (NN) based predictive models accessible by the software code. The hardware processor executes the software code to receive input data corresponding to an interaction with a user, determine a generic expression for use in the interaction, and identify one of the character personas as a persona to be assumed by the multi-persona social agent. The software code also generates, using the generic expression and one of the NN based predictive models corresponding to the persona to be assumed by the multi-persona social agent, a sentiment driven personified response for the interaction with the user based on a vocabulary, phrases, and one or more syntax rules idiosyncratic to the persona to be assumed, and renders the sentiment driven personified response using the multi-persona social agent.