Machine Learning for Configurable Component Parameter Determination

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Solution Overview

Problem

Conventional component management techniques are resource-intensive and error-prone when determining parameters for highly configurable components, such as laptops and cars, making it challenging to forecast demand and value effectively.

Innovation Solution

The use of machine learning techniques to forecast demand data for components over temporal periods, determine modification information, and automatically calculate configurable component parameter values using designated algorithms, thereby enabling automated actions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If conventional component management techniques are used to determine parameters for highly configurable components, then manual processes can be followed, but the processes become resource-intensive and error-prone

Engineering Contradiction:

Improveaccuracy of parameter determinationVSAvoidresource intensity

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The patent replaces manual mechanical processes with machine learning algorithms and automated computing systems. The machine learning model automatically processes component configuration data, historical sales data, and market information to determine optimal parameters, eliminating the need for manual analysis and reducing human error while decreasing resource consumption.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service parameter determination where the machine learning model autonomously analyzes data, forecasts demand, and recommends component parameters without requiring manual intervention. The automated system serves itself by continuously learning from data and making independent decisions about component configuration.

Inventive Principle:

Principle #25Self-service

2Productivity

If manual processes are used to forecast demand and determine component parameters, then flexibility in analysis is maintained, but the processes are error-prone and time-consuming

Engineering Contradiction:

Improvespeed of parameter determinationVSAvoidtime for demand forecasting

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The machine learning model performs preliminary processing of large datasets, pre-calculates forecasts, and prepares parameter recommendations in advance. By pre-processing historical data and training models beforehand, the system can quickly determine parameters for new configurations without time-consuming manual analysis during decision-making moments.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

Manual time-consuming forecasting processes are replaced with automated machine learning algorithms that rapidly process data and generate predictions. The computational system executes demand forecasts and parameter determinations instantly, eliminating the time loss associated with manual analysis while maintaining analytical flexibility.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Adaptability or versatility

If conventional techniques are used for component parameter determination, then simple processes can be maintained, but they become resource-intensive when dealing with highly configurable components

Engineering Contradiction:

Improvecapability to handle configurable componentsVSAvoidresource consumption

Core Design Contradiction:

Adaptability or versatilityVSUse of energy by moving object

Solution Approach 1:

The system changes the approach by using machine learning models that dynamically adjust their processing based on data complexity and configuration requirements. The model adapts its computational intensity to match the complexity of the component configuration, using fewer resources for simple components and allocating more computational power only when needed for highly configurable components with complex parameter relationships.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20250124461A1Determining configurable component parameters using machine learning techniques

Publication Date: 2025.04.17 DELL PROD LP

AI summary

Methods, apparatus, and processor-readable storage media for determining configurable component parameters using machine learning techniques are provided herein. An example computer-implemented method includes forecasting demand data for at least one component in connection with one or more temporal periods by processing component-related data using one or more machine learning techniques; determining information pertaining to one or more modifications associated with the at least one component; determining, by processing at least a portion of the demand data and at least a portion of the information pertaining to the one or more modifications using at least one designated algorithm, one or more configurable component parameter values attributed to the at least one component and at least a portion of the one or more modifications; and performing one or more automated actions based at least in part on at least one of the one or more configurable component parameter values.