Machine Learning Feedback for Accurate Model Optimization
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Solution Overview
Problem
Existing data analysis systems lack adequate feedback mechanisms to identify and correct inaccuracies in iterative processes, leading to inefficiencies in model optimization.
Innovation Solution
A method and apparatus that utilize optimal and suboptimal machine-learning models to generate positive and negative feedback functions, respectively, for parameter adjustments based on measured subsystem data, enabling the correction of suboptimal measurements within a continuum range.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If iterative analysis of data is performed to improve computational efficiency, then processing speed and output are improved, but accuracy and reliability deteriorate due to lack of feedback mechanisms to identify inaccuracy
Solution Approach 1:
The patent implements feedback mechanisms by comparing measurements to predetermined ranges and generating feedback functions that identify deviations. The system continuously monitors subsystem measurements, compares them against thresholds, and provides corrective feedback to adjust parameters, thereby maintaining accuracy while preserving computational efficiency through iterative optimization.
Solution Approach 2:
The patent replaces traditional mechanical or manual verification processes with automated machine learning models that perform iterative analysis. These models automatically identify patterns, compare measurements to ranges, and generate optimization recommendations, substituting human-in-the-loop verification with automated computational systems that maintain both speed and accuracy.
2Reliability
If feedback mechanisms are added to identify inaccuracy in iterative processes, then reliability and accuracy are improved, but device complexity increases
Solution Approach 1:
The patent introduces intermediary components in the form of machine learning models that act as mediators between raw measurements and optimization decisions. These models serve as intelligent intermediaries that automatically process measurements, compare them to predetermined ranges, and generate feedback without requiring complex manual intervention systems, thereby adding reliability while managing complexity through automated intermediary processing.
3Measurement precision
If multiple machine-learning models are used to generate feedback functions, then measurement accuracy is improved, but computational resources and time increase
Solution Approach 1:
The patent applies partial action by using multiple machine learning models selectively rather than exhaustively. The system employs an ensemble approach where multiple models contribute to feedback generation only when needed for complex measurements, while simpler measurements may use fewer models. This partial application of multiple models maintains measurement precision while reducing unnecessary computational overhead and time loss.
Data Source
AI summary
An apparatus and method for model optimization. is disclosed. The apparatus comprises at least a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to generate a positive feedback function of an optimal measurement, wherein generating the positive feedback function further comprises identifying, using an optimal machine-learning model, a first set of parameter changes to a subsystem corresponding to the optimal measurement, and generate a negative feedback function of the suboptimal measurement, wherein generating the negative feedback function comprises identifying, using a suboptimal machine-learning model, a second set of parameter changes to a subsystem corresponding to the suboptimal measurement.