ML Automation Recommendation for Infrastructure Support Tickets
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Solution Overview
Problem
Conventional methods for identifying automated solutions for technology infrastructure issues are time-consuming, subjective, and lack standardization, leading to inefficiencies and increased costs due to manual assessments.
Innovation Solution
A system and method utilizing machine learning to automatically classify and predict automation solutions for technology infrastructure issues, reducing bias and subjectivity by analyzing support tickets and estimating cost-savings, allowing for selective application of automation to maximize efficiency and allocate human resources effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual assessment methods are used to identify automated solutions, then human expertise and judgment can be applied, but the process becomes time-consuming and subjective
Solution Approach 1:
The patent replaces manual mechanical assessment processes with an automated machine learning system that uses natural language processing and classification algorithms to evaluate support tickets and recommend automation solutions, eliminating the time-consuming human review process while maintaining assessment quality
Solution Approach 2:
The system creates a digital model of the assessment process using trained machine learning classifiers that replicate human expert judgment patterns, allowing automated evaluation of support tickets without requiring actual human reviewers for each case
2Productivity
If manual assessment of support tickets is performed, then detailed analysis can be conducted, but the process lacks standardization and consistency
Solution Approach 1:
The patent implements a universal automated assessment system that applies consistent classification rules and criteria across all support tickets, ensuring standardized evaluation regardless of which tickets are being reviewed, thereby eliminating variability between different human assessors
Solution Approach 2:
The system transforms subjective human judgment into objective measurable parameters through machine learning classifiers that evaluate tickets based on consistent features and criteria, converting qualitative assessments into quantifiable results that can be standardized and replicated
3Speed
If automation solutions are implemented without proper selection, then deployment speed increases, but extraneous costs are incurred from wrong solutions
Solution Approach 1:
The patent performs preliminary classification and prediction of automation solution suitability before actual deployment, using machine learning models to identify which support tickets are appropriate for automation and predict potential cost savings, allowing selective implementation that avoids wasted resources on unsuitable cases
Solution Approach 2:
The system incorporates feedback mechanisms that evaluate the performance and cost-effectiveness of implemented automation solutions, using this information to refine classification models and improve future selection accuracy, thereby reducing extraneous costs from poor automation choices
Data Source
AI summary
A system and method to intelligently formulate automation strategies for technology infrastructure operations are disclosed. The system and method include analyzing infrastructure issue data from support tickets and predicting automation solutions. A cost-benefit analysis is then performed on the automation solutions. Solutions can be ranked and recommended according to the cost-benefit analysis.