Machine Learning Product Configuration Anomaly Prediction

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Solution Overview

Problem

Current systems lack proactive methods to detect and correct errors in product configuration and support offerings in information processing systems, leading to incorrect order fulfillment and inadequate product support, as they are reactive and unable to handle textual and contextual analysis for large datasets.

Innovation Solution

A product configuration validation platform using machine learning algorithms, natural language processing, and natural language generation to analyze product selection data, predict anomalous combinations, and generate accurate product descriptions, thereby flagging issues before they impact customers.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If conventional reactive systems are used to detect product configuration errors, then system complexity is low, but error detection capability and reliability are insufficient

Engineering Contradiction:

Improveerror detection capabilityVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system performs preliminary analysis of product configuration data using machine learning models before errors occur or impact customers. The anomaly detection model proactively identifies potential configuration errors in advance, allowing preventive action rather than reactive response.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

A machine learning-based anomaly detection model serves as an intermediary between raw product configuration data and error detection. This intermediary layer processes and analyzes the data, transforming unstructured configuration information into actionable anomaly detections without requiring complex rule-based systems.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If machine learning algorithms are implemented for proactive anomaly detection, then error detection precision and reliability improve, but computational resources and processing time increase

Engineering Contradiction:

Improveanomaly detection precisionVSAvoidcomputational resource consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The system applies machine learning anomaly detection selectively to product configuration data that requires analysis, rather than processing all possible data uniformly. The model focuses computational resources on identifying anomalies in relevant configuration patterns, achieving high precision without excessive resource consumption across the entire system.

Inventive Principle:

Principle #16Partial or excessive action

3Extent of automation

If natural language processing is used to analyze product configuration data, then analysis accuracy and automation level improve, but processing time and computational complexity increase

Engineering Contradiction:

Improveautomation levelVSAvoidprocessing time

Core Design Contradiction:

Extent of automationVSLoss of time

Solution Approach 1:

The system replaces manual or rule-based analysis mechanisms with machine learning-based natural language processing. The ML model automatically analyzes product configuration data and generates anomaly detections without requiring manual intervention or complex rule engines, achieving high automation while optimizing processing efficiency.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS12124801B2Product configuration analysis and anomaly prediction using machine learning

Publication Date: 2024.10.22 DELL PROD LP

AI summary

A method comprises receiving product selection data, wherein the product selection data characterizes at least one combination of at least two products. In the method, the product selection data is analyzed using one or more machine learning algorithms. The method further comprises predicting based, at least in part, on the analyzing, whether the at least one combination is anomalous. One or more alerts are generated in response to predicting that the at least one combination is anomalous.