Automated Error Remediation Recommendations from Logs and Chat Data

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Solution Overview

Problem

Identifying and remediating errors in computing systems is laborious and time-consuming, often requiring administrators to search multiple data sources with conflicting solutions, and can risk security due to the lack of clear error labeling and efficient remediation processes.

Innovation Solution

A computing device processes multiple data sources using natural language processing and machine learning to identify and display error remediation recommendations, incorporating a browser extension to scrape the Document Object Model (DOM) for error identification and leveraging trained models to provide quick and accurate remediation suggestions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If an administrator manually searches multiple data sources (instruction manuals, support forums, knowledgebases) to find error solutions, then the administrator can potentially find comprehensive solutions, but the process becomes laborious, time-consuming, and frustrating

Engineering Contradiction:

Improveerror remediation speedVSAvoidtime spent searching data sources

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system automatically performs error identification and remediation recommendation without requiring manual intervention. The error identification module autonomously processes log entries, and the remediation recommendation module autonomously searches multiple data sources and generates solutions, allowing the system to serve itself rather than requiring administrator manual searching.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical searching processes with automated computational processes. Natural language processing algorithms automatically analyze error messages, machine learning models automatically identify error patterns, and automated recommendation systems generate solutions, substituting human manual search operations with automated digital processing.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Adaptability or versatility

If different data sources provide conflicting solutions to errors, then the administrator has more potential solutions to choose from, but the administrator must guess which solution is correct, increasing complexity and time

Engineering Contradiction:

Improvenumber of potential solutionsVSAvoidcomplexity of evaluating multiple solutions

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system incorporates feedback mechanisms where the error identification module continuously monitors log entries, the remediation recommendation module evaluates multiple data sources, and the interface module presents refined solutions to administrators. This feedback loop allows the system to learn from previous errors and improve recommendation accuracy over time.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent introduces an intermediary automated recommendation system that mediates between multiple conflicting data sources and the administrator. Rather than presenting raw conflicting information, the intermediary process analyzes, compares, and synthesizes solutions from multiple sources, filtering out contradictions and presenting consolidated recommendations.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Reliability

If the administrator tries multiple solutions to resolve errors, then the error can be fixed, but the process is time-consuming and can risk security of computing resources

Engineering Contradiction:

Improveerror resolution accuracyVSAvoidtime spent testing solutions

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary analysis of error messages and log entries before presenting solutions. The error identification module pre-processes and categorizes errors, and the remediation recommendation module pre-evaluates potential solutions from multiple data sources, so that when the administrator receives recommendations, they are already prepared and ranked, eliminating the need to test multiple solutions blindly.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20250321824A1Automated error identification and remediation based on a plurality of data sources

Publication Date: 2025.10.16 CAPITAL ONE SERVICES LLC

AI summary

The present disclosure describes a method and apparatus for an error remediation recommendation process based on multiple data sources. Aspects of the disclosure may provide for processing of a knowledgebase to identify error remediation recommendations, processing of a chat log to identify error remediation recommendations, identification of errors from log data, determination of a corresponding error remediation recommendation from the knowledgebase and/or the chat log, and causing display of output indicating a corresponding error remediation recommendation. Further aspects described may also provide for training of an artificial neural network to determine a corresponding error remediation recommendation. Further aspects described may also provide for a browser extension which scrapes the Document Object Model (DOM) of a web page to identify errors.