Diagnostic Data Collection via Problem Description Analysis

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Solution Overview

Problem

Existing data collection processes for diagnostic data in software systems are cumbersome, resource-intensive, and can negatively impact production systems, leading to delays in problem resolution and potential violations of service level agreements (SLAs).

Innovation Solution

A computer-implemented method and system that analyzes problem descriptions to identify missing diagnostic data and employs a data collection scheme to minimize disruption of the originating system, allowing for efficient collection and communication of missing data to an issue tracking system, using a diagnostic scheduling tool to optimize data collection and reduce system impact.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional data collection methods are used to gather diagnostic data, then complete diagnostic information can be obtained, but system performance is negatively impacted and disruption to the origination system increases

Engineering Contradiction:

Improvediagnostic data completenessVSAvoidsystem disruption

Core Design Contradiction:

Measurement precisionVSObject-affected harmful factors

Solution Approach 1:

The system performs preliminary analysis of problem descriptions to identify missing diagnostic data before initiating data collection. This allows for targeted collection of only necessary data, reducing overall system disruption while ensuring diagnostic completeness.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system dynamically adjusts data collection parameters based on the specific problem type and missing data identification. Collection schemes are modified in real-time to minimize impact on system performance while gathering required diagnostic information.

Inventive Principle:

Principle #35Parameter changes

2Reliability

If comprehensive diagnostic data is collected to ensure accurate problem resolution, then problem resolution accuracy improves, but time and resources required increase significantly

Engineering Contradiction:

Improveproblem resolution accuracyVSAvoiddata collection time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system analyzes problem descriptions in advance to identify exactly which diagnostic data is missing, allowing targeted collection rather than comprehensive collection. This reduces time and resources while maintaining resolution accuracy.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system uses feedback from problem description analysis to dynamically adjust data collection requirements. Only the specific missing data identified through analysis is collected, optimizing the balance between resolution accuracy and resource consumption.

Inventive Principle:

Principle #23Feedback

3Measurement precision

If large amounts of diagnostic data are collected to thoroughly analyze the problem, then diagnostic accuracy improves, but data transfer and analysis become difficult and time-consuming

Engineering Contradiction:

Improvediagnostic accuracyVSAvoiddata processing complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system extracts and collects only the specific missing diagnostic data identified through problem description analysis, rather than collecting all possible data. This reduces data volume and processing complexity while maintaining diagnostic accuracy.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system modifies data collection parameters to gather only the necessary missing data types and volumes required for accurate diagnosis, reducing overall data complexity and processing requirements.

Inventive Principle:

Principle #35Parameter changes

4Adaptability or versatility

If manual data collection processes are used requiring user involvement, then specific diagnostic scenarios can be addressed, but ease of operation decreases and user burden increases

Engineering Contradiction:

Improvescenario-specific data collectionVSAvoiduser involvement requirement

Core Design Contradiction:

Adaptability or versatilityVSEase of operation

Solution Approach 1:

The system automatically analyzes problem descriptions and initiates data collection without requiring user intervention. The system serves itself by identifying missing data and executing collection schemes autonomously, reducing user burden while maintaining adaptability.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system accelerates the data collection process through automated analysis and scheme execution, eliminating manual user steps while maintaining the ability to handle specific diagnostic scenarios.

Inventive Principle:

Principle #38Strong oxidants (Accelerated oxidation)

Data Source

PatentUS10255127B2Optimized diagnostic data collection driven by a ticketing system

Publication Date: 2019.04.09 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

A computer implemented method and system for optimizing diagnostic data collection for a computerized issue tracking system. The method and system includes receiving a problem description from an origination system communicating with an issue tracking system. The problem description is analyzed for missing diagnostic data. A data collection scheme is identified to collect the missing diagnostic data. The data collection scheme is in accordance with a criteria for minimizing disruption of the origination system to collect the missing diagnostic data. The method and system includes communicating the missing diagnostic data to the issue tracking system.