Predictive Model Training with Resource Capacity Monitoring
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Solution Overview
Problem
Artificial intelligence models are trained primarily for accuracy without considering resource constraints and cost-benefit factors, leading to suboptimal performance and decreased compliance when deployed in real-world scenarios.
Innovation Solution
Monitoring user behavior and feedback to estimate and update resource capacity and cost-benefit values, allowing for more accurate decision-making within predictive systems, and training models to optimize predictions based on these values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If AI models are trained purely for accuracy, then prediction accuracy is improved, but compliance with real-world constraints and cost-benefit considerations deteriorates
Solution Approach 1:
The patent changes the training parameters of AI models from purely accuracy-based to include resource capacity and cost-benefit factors. The system monitors resource capacity (e.g., number of outputs assigned to a class over time) and uses this information to retrain models, allowing them to adapt to real-world constraints while maintaining predictive accuracy.
Solution Approach 2:
The patent implements a feedback loop where user compliance data and resource capacity information are continuously monitored and fed back into the model training process. This feedback mechanism allows the model to learn from actual usage patterns and adjust its predictions to better align with real-world constraints and cost-benefit considerations.
2Device complexity
If models are trained without resource capacity information, then training simplicity is maintained, but decision-making effectiveness in resource-constrained environments deteriorates
Solution Approach 1:
The patent performs preliminary monitoring and estimation of resource capacity before model training. By collecting data on resource capacity (e.g., number of outputs assigned to a class over a period of time) in advance, the system prepares this information for use in model training, enabling more effective decision-making without significantly complicating the training process.
3Ease of manufacture
If capacity estimates are manually provided, then system setup is simplified, but accuracy of capacity and cost-benefit estimates deteriorates
Solution Approach 1:
The patent implements self-service capacity estimation where the system automatically monitors and collects data on resource capacity from actual usage patterns. Instead of requiring manual input, the system autonomously gathers information about outputs assigned to classes, user compliance behavior, and other relevant metrics, then uses this data to generate accurate capacity and cost-benefit estimates through automated processing.
Data Source
AI summary
Data characterizing inputs to a prediction process that classifies events, an output of the prediction process, and feedback data characterizing a performance of the outcome is monitored. A resource capacity affecting the outcome of the prediction process, and/or a cost-benefit affecting the outcome of the prediction process is determined from the monitoring. The determined resource capacity and/or the determined cost-benefit is provided. Related apparatus, systems, techniques, and articles are also described.