Multi-Sourced AI Character Training with Interaction Graph Simulation
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Solution Overview
Problem
Existing AI character systems face challenges in estimating interactions with diverse participant cohorts, lack mechanisms for user feedback integration, and struggle to adapt to evolving content or user expectations, necessitating improved training and development methods.
Innovation Solution
A multi-sourced machine learning model-based approach that utilizes interaction data to generate and simulate participant behaviors, compare interaction graphs, and iteratively refine AI behavior models using qualitative and quantitative feedback to enhance AI character interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human beings manually verify all possible interaction scenarios, then interaction accuracy can be improved, but time consumption and labor requirements increase exponentially
Solution Approach 1:
The patent replaces manual verification processes with machine learning models that automatically simulate and evaluate interaction scenarios. The system uses neural networks to predict user responses and evaluate dialogue quality, substituting human cognitive verification with automated computational modeling that scales efficiently with complexity
Solution Approach 2:
The patent creates virtual copies of users through synthesized user models that replicate user behavior patterns, preferences, and response styles. These digital twins allow unlimited interaction scenarios to be simulated without requiring actual human participants for each scenario, enabling comprehensive verification through replication
2Adaptability or versatility
If AI characters are trained with diverse participant cohort data, then adaptability to different users improves, but data processing complexity increases
Solution Approach 1:
The patent segments diverse user data into distinct participant cohort profiles, each characterized by specific behavioral patterns, preferences, and demographic attributes. The system processes and stores these segmented cohorts separately, allowing the machine learning models to handle diversity through structured organization rather than monolithic processing
Solution Approach 2:
The patent develops universal machine learning models that can process multiple types of data from different participant cohorts through a single integrated framework. The model architecture is designed to handle various data formats, user types, and interaction modes simultaneously, reducing processing complexity through multi-functionality
3Reliability
If AI behavior models are continuously refined using user feedback, then interaction quality improves, but system complexity increases
Solution Approach 1:
The patent implements feedback mechanisms where user responses and interaction outcomes are automatically collected, analyzed, and used to refine the AI behavior models. The system incorporates reinforcement learning that adjusts model parameters based on observed user behavior, creating a self-improving system that enhances interaction quality over time
Solution Approach 2:
The patent performs preliminary model training and simulation before actual deployment, pre-refining the AI behavior models using synthetic data and controlled scenarios. This preliminary action allows the system to learn from hypothetical interactions and prepare optimized responses before encountering real users, reducing the complexity of real-time adaptation
Data Source
AI summary
A system includes a hardware processor configured to execute software code to receive interaction data identifying an action and personality profiles corresponding respectively to multiple participant cohorts in the action, generate, using the interaction data, an interaction graph of behaviors of the participant cohorts in the action, simulate, using a behavior model, participation of each of the participant cohorts in the action to provide a predicted interaction graph, and compare the predicted and generated interaction graphs to identify a similarity score for the predicted interaction graph relative to the generated interaction graph. When the similarity score satisfies a similarity criterion, the software code is executed to train, using the behavior model, an artificial intelligence character for interactions. When the similarity score fails to satisfy the similarity criterion, the software code is executed to modify the behavior model based on one or more differences between the predicted and generated interaction graphs.