Dynamic Training Dataset Aggregation via Virtual Behavioral Agents
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Solution Overview
Problem
The process of developing and implementing predictive models is labor-intensive and costly due to the complexity of collecting and annotating large datasets, and ensuring quality control, particularly in machine learning applications where data collection and model refinement require significant manual effort and resources.
Innovation Solution
A computer system configures a multi-layer hierarchy of behavioral agents to dynamically generate and refine a virtual representation of individual attributes, using input stimuli to adapt and improve the model through reinforcement learning, thereby simplifying the aggregation of training datasets and enhancing model performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data collection and annotation methods are used to train predictive models, then model accuracy can be improved, but the process becomes labor-intensive and costly
Solution Approach 1:
The patent creates virtual copies of real users (virtual users) that can be generated from minimal real user data. These virtual users replicate the behavior patterns, preferences, and characteristics of real users, allowing the system to generate training data through simulation rather than manual collection. This copying approach maintains model accuracy while eliminating the labor-intensive manual annotation process.
Solution Approach 2:
The system enables training data generation to be self-service by allowing virtual users to autonomously generate training datasets through simulated interactions. The virtual users automatically perform tasks, make decisions, and generate labeled data without human intervention, transforming the manual process into an automated self-generating system that reduces both labor and cost.
2Reliability
If large amounts of training data are collected manually, then model performance improves, but the time required for data collection increases
Solution Approach 1:
The patent performs preliminary action by pre-generating virtual users and their behavioral patterns before actual training data collection is needed. The system creates a library of virtual users with established characteristics and behaviors that can immediately generate training data through simulation, eliminating the time-consuming process of manual data collection while ensuring sufficient training data volume for model performance.
Solution Approach 2:
The system implements continuous useful action by enabling virtual users to continuously generate training data through ongoing simulated interactions. Rather than discrete manual collection events, the virtual users operate continuously to produce streams of labeled training data, significantly accelerating the data collection process while maintaining consistent quality for model performance.
3Manufacturing precision
If manual processing and insertion of data into computer systems is performed, then data quality can be controlled, but the cost and complexity increase
Solution Approach 1:
The patent replaces the mechanical manual processing system with an automated computational system. Virtual users programmatically generate and insert training data into computer systems through automated workflows, eliminating manual data entry and processing operations. This substitution maintains data quality through controlled generation processes while dramatically improving implementation ease by removing manual labor requirements.
4Measurement precision
If feedback mechanisms are implemented to optimize predictive models, then model accuracy improves, but system complexity increases
Solution Approach 1:
The patent implements feedback mechanisms where virtual users generate training data based on performance metrics and model predictions. The system continuously monitors model accuracy, identifies areas for improvement, and directs virtual users to generate targeted training data that addresses specific weaknesses. This feedback loop improves model accuracy while managing complexity by focusing data generation on specific performance gaps rather than requiring comprehensive system redesign.
Data Source
AI summary
A system receives information associated with an interaction with an individual in a context. Then, the system analyzes the information to extract features associated with one or more attributes of the individual. Moreover, the system generates, based at least in part on the extracted features, a group of behavioral agents in a multi-layer hierarchy that automatically mimics the one or more attributes. Next, the system calculates one or more performance metrics associated with the group of behavioral agents and the one or more attributes. Furthermore, the system determines, based at least in part on the one or more performance metrics, one or more deficiencies in the extracted features. Additionally, the system selectively acquires second information associated with additional interaction with the individual in the context based at least in part on the one or more deficiencies to at least in part correct for the one or more deficiencies.