Machine Learning Solution Generation with Feedback Loop Control

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Solution Overview

Problem

Current machine learning methods face challenges in accurately generating solutions due to divergence from expected outcomes, requiring iterative adjustments of coefficients and data values to minimize deviations and improve performance.

Innovation Solution

The system employs an electronic controller to receive requests, extract data, determine coefficients, and adjust them through feedback loops to enhance solution accuracy, using artificial neural networks structured with corrective weights to minimize deviations and directly adjust data values when divergence exceeds a margin.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If machine learning methods are used to generate solutions, then productivity is improved, but measurement precision deteriorates due to divergence from expected outcomes

Engineering Contradiction:

Improvesolution generation efficiencyVSAvoidsolution accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent implements feedback loops that compare generated solutions against expected outcomes and use the divergence information to adjust coefficients and re-generate solutions, thereby improving accuracy while maintaining automated efficiency

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system dynamically adjusts coefficients and parameters based on measured divergence from expected solutions, changing the parameters iteratively to improve solution accuracy while maintaining high productivity through automated parameter optimization

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If coefficients are adjusted iteratively to improve solution accuracy, then measurement precision is improved, but loss of time increases due to multiple adjustment cycles

Engineering Contradiction:

Improvesolution accuracyVSAvoiditeration time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary actions by pre-defining coefficient adjustment strategies and divergence thresholds before the iterative process begins, allowing faster convergence and reducing the time lost in trial-and-error iterations

Inventive Principle:

Principle #10Preliminary action

3Productivity

If data values are extracted and coefficients determined to generate solutions, then productivity is improved, but manufacturing precision deteriorates due to deviation from input values

Engineering Contradiction:

Improvesolution generation efficiencyVSAvoiddata value accuracy

Core Design Contradiction:

ProductivityVSManufacturing precision

Solution Approach 1:

The patent replaces manual data verification and coefficient tuning with automated electronic controller-based systems that systematically extract data, determine coefficients, and adjust parameters, maintaining high precision through algorithmic control rather than manual processes

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20250005366A1System and method for generating a solution using machine learning

Publication Date: 2025.01.02 PROGRESS INC

AI summary

A method of machine learning includes receiving a request for a solution and defining a set of input values correlated to the received request. The method also includes extracting, via an electronic controller, data values from a data repository in response and correlated to the defined set of input values. The method further includes generating, via the electronic controller, the solution using the received request and the extracted data values. A system for machine learning includes a data repository configured to store data values and an electronic controller configured to manage a request for a solution according to the method.