Synthetic Personas with Parameterized Conversational Threads
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Solution Overview
Problem
Existing user interactive systems, including IVR and human-agent interactions, often face limitations such as repetitive conversations due to random agent selection, restricted information access for privacy reasons, and accent-related communication difficulties, leading to frustrating user experiences.
Innovation Solution
Implementing AI/ML-driven personas that analyze user interactions to identify goals, determine conversational structures, and generate parameterized conversational threads to steer conversations effectively, while adapting to user characteristics and accessing comprehensive user data for seamless interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If human agents are randomly selected to interact with users, then system simplicity is maintained, but user experience deteriorates due to repetitive conversations and lack of continuity
Solution Approach 1:
The patent creates synthetic persona copies of human agents that replicate their communication styles, knowledge, and interaction patterns. These digital twins maintain conversation history and user preferences, eliminating the need for users to repeat information when switching agents while preserving the original agent's expertise and personality.
Solution Approach 2:
The system pre-processes and stores conversation history, user preferences, and agent expertise during initial interactions. This preliminary action enables synthetic personas to immediately continue conversations without requiring users to repeat themselves, as all necessary context is already captured and organized for rapid retrieval.
2Adaptability or versatility
If human agents are restricted from accessing user information for privacy reasons, then user privacy is protected, but interaction scope is limited
Solution Approach 1:
The synthetic persona acts as an intermediary layer between the user and the underlying AI/ML models. This intermediary manages and controls access to user information, allowing comprehensive data processing for personalized interactions while maintaining privacy through controlled information disclosure and selective data access.
Solution Approach 2:
The system dynamically adjusts information access parameters based on user preferences, conversation context, and privacy settings. By changing accessibility parameters rather than implementing rigid restrictions, the system enables comprehensive interaction scope while adapting to varying privacy requirements across different user scenarios.
3Adaptability or versatility
If multiple human agents are used to interact with users, then diverse expertise is provided, but conversation continuity is lost requiring users to repeat themselves
Solution Approach 1:
The patent merges multiple agent expertise domains into a unified synthetic persona framework. The system combines knowledge from various specialized agents while maintaining a centralized conversation history that all personas can access, allowing users to switch between different expertise areas without repeating themselves.
Solution Approach 2:
The synthetic persona system creates multi-functional agents that can handle diverse conversation topics and user needs within a single unified interface. Each synthetic persona is designed to perform multiple functions across different domains while maintaining consistent conversation context, eliminating the need for users to adapt to different communication styles when switching agents.
4Productivity
If AI/ML-driven personas with parameterized behaviors are implemented, then user interaction efficiency is enhanced, but system complexity increases
Solution Approach 1:
The patent segments the AI/ML system into modular components including separate modules for conversation analysis, goal identification, parameter determination, and thread generation. This segmentation allows each component to be independently optimized and managed, reducing overall system complexity while maintaining high interaction efficiency through specialized functional modules.
Data Source
AI summary
Novel tools and techniques are provided for implementing tailored synthetic personas with parameterized behaviors. In various embodiments, a computing system may cause an AI/ML-driven persona(s) to interact with a user via a UI, the interaction including a conversation between the AI/ML-driven persona(s) and the user. Using at least one AI/ML model, the computing system may analyze the conversation to identify a goal(s) of the conversation, may determine a structure of the interaction, may determine one or more first parameters for the determined structure of the interaction (the one or more first parameters defining conversational guardrails for steering the interaction away from conversational tangents), may generate one or more first conversational threads configured to achieve the goal(s) of the conversation, and may cause the AI/ML-driven persona(s) to continue the conversation with the user using the one or more first conversational threads to work toward achieving the goal(s) of the conversation.