Firmware Failure Prediction Using ML Log Analysis

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Solution Overview

Problem

Existing methods fail to accurately distinguish between hardware and software issues during firmware installation failures, leading to inefficient diagnostics and potential hardware replacements for issues that can be resolved by software reinstallation.

Innovation Solution

A method using machine learning models trained on historical data to predict whether firmware installation failures are hardware or software related, based on extracted features from log data, allowing for automated remedial actions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If hardware diagnostics and repairs are performed for all firmware installation failures, then hardware issues can be identified, but time and resources are wasted on software-related failures that can be resolved by reinstallation

Engineering Contradiction:

Improveaccuracy of failure cause identificationVSAvoiddiagnostics and repair time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent introduces log analysis as an intermediary step between firmware installation failure and hardware diagnostics. The system analyzes installation logs to predict whether a failure is hardware or software-related before committing to hardware diagnostics, thereby filtering out software cases that can be resolved through reinstallation

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system performs preliminary log analysis and failure prediction before initiating hardware diagnostics. By predicting the failure cause in advance based on log patterns, the system avoids unnecessary hardware diagnostics for software-related failures, saving time and resources

Inventive Principle:

Principle #10Preliminary action

2Loss of time

If hardware diagnostics are skipped for firmware installation failures, then time and resources are saved, but hardware issues may be missed leading to repeated failures

Engineering Contradiction:

Improvediagnostics timeVSAvoidaccuracy of failure cause identification

Core Design Contradiction:

Loss of timeVSReliability

Solution Approach 1:

Log analysis serves as an intermediary screening mechanism that determines whether hardware diagnostics are necessary. The system uses log patterns to predict failure causes, allowing selective application of hardware diagnostics only when hardware failure is predicted, thus balancing time savings with diagnostic accuracy

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system changes the diagnostic approach based on log analysis results. Instead of a fixed diagnostic process, the system dynamically adjusts whether to perform hardware diagnostics or proceed with software reinstallation based on the predicted failure cause, optimizing the diagnostic pathway

Inventive Principle:

Principle #35Parameter changes

3Measurement precision

If manual analysis of log data is performed to determine failure causes, then detailed investigation is possible, but the process is time-consuming and labor-intensive

Engineering Contradiction:

Improvefailure analysis accuracyVSAvoidfailure analysis speed

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The patent replaces manual log analysis with an automated machine learning-based prediction system. The model automatically processes log data and predicts failure causes, eliminating the need for manual analysis while maintaining or improving accuracy and significantly increasing processing speed

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system performs self-service failure analysis by automatically analyzing logs and predicting failure causes without human intervention. The machine learning model independently processes log data and provides predictions, freeing up human resources for more complex tasks

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS11556409B2Firmware failure reason prediction using machine learning techniques

Publication Date: 2023.01.17 DELL PROD LP

AI summary

Techniques are provided for predicting firmware installation failure reasons using machine learning techniques. One method comprises obtaining log data for a user device, wherein the log data is obtained following a failure of a firmware installation on the user device; extracting a plurality of features from the obtained log data; applying the extracted features to a trained machine learning model to obtain a prediction of whether the firmware installation failure is caused by a hardware-related failure or a software-related failure; and performing an automated remedial action based on a result of the prediction. The trained machine learning model can be trained using historical data for multiple user devices that experienced a firmware installation failure, where the historical data comprises a label indicating whether a given failure comprises a hardware-related failure or a software-related failure. The trained machine learning model can be trained and tested using cross-validation techniques.