AI Model Training with Resource Capacity and Cost-Benefit Constraints
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Solution Overview
Problem
Artificial intelligence models are trained primarily for accuracy without considering resource capacity and cost-benefit factors, leading to suboptimal performance and decreased compliance in decision-making processes, particularly in resource-constrained environments.
Innovation Solution
Monitoring user behavior and feedback to estimate and update resource capacity and cost-benefit values, which are then used to train and deploy models within enterprise resource management systems, ensuring that predictions take into account the ability to act on predictions and their practical impact.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If models are trained purely for accuracy, then prediction accuracy is improved, but resource capacity utilization and cost-benefit optimization deteriorate
Solution Approach 1:
The patent changes the training parameters of the model from purely accuracy-based to include resource capacity and cost-benefit factors. By modifying the objective function to incorporate these additional parameters, the model learns to optimize multiple criteria simultaneously, resolving the contradiction between accuracy and resource utilization
Solution Approach 2:
The patent introduces dynamic adjustment of model predictions based on real-time resource capacity and cost-benefit conditions. The model adapts its output decisions dynamically according to current operational constraints, allowing it to maintain accuracy while optimizing resource utilization under varying conditions
2Reliability
If models are trained purely for accuracy, then prediction correctness is improved, but compliance with resource constraints deteriorates
Solution Approach 1:
The patent modifies the training parameters to include resource constraint compliance as a explicit objective. By changing the optimization criteria to incorporate constraint satisfaction alongside correctness, the model learns to produce predictions that are both reliable and compliant with operational limitations
Solution Approach 2:
The patent creates a multi-functional model that simultaneously optimizes for prediction correctness and resource constraint compliance. The unified model performs multiple functions - accurate classification and constraint satisfaction - resolving the contradiction between reliability and adaptability
3Measurement precision
If traditional accuracy-focused training is used, then model performance on test data is improved, but practical business impact deteriorates
Solution Approach 1:
The patent changes the training parameters to include business impact metrics such as profit and revenue optimization. By incorporating these practical business parameters into the loss function, the model learns to balance test performance with real-world business outcomes
Solution Approach 2:
The patent implements feedback mechanisms where business performance metrics are continuously monitored and used to refine model training. The feedback loop allows the model to learn from actual business impacts rather than just test accuracy, improving practical utility while maintaining performance
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.