Automated Incident Response System Using ML Pattern Recognition
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Solution Overview
Problem
Current systems lack an efficient automated method for responding to technology incidents such as hardware failures and software bugs, leading to significant disruptions and productivity losses, as they fail to quickly identify and apply effective solutions.
Innovation Solution
An automated incident response system utilizing machine learning processes, including Term Frequency-Inverse Document Frequency and natural language processing, to identify incident patterns, tag relevant keywords, and retrieve and apply solutions from similar resolved incidents, integrating data from various sources like centralized repositories and chat transcripts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated incident response system is implemented, then response speed and productivity are improved, but system complexity increases
Solution Approach 1:
The incident response system automatically performs incident classification, pattern identification, and solution retrieval without human intervention. The system self-services by using machine learning models to autonomously analyze incident data, tag keywords, identify patterns, and recommend solutions, eliminating the need for manual incident management operations.
Solution Approach 2:
The patent replaces manual mechanical incident response processes with automated computational systems. Machine learning algorithms substitute human analysts in performing data analysis, pattern recognition, and solution retrieval tasks, transforming manual operations into automated computational processes that increase speed while managing complexity through algorithmic efficiency.
2Measurement precision
If machine learning processes are used for automated tagging and pattern identification, then measurement precision and automation extent are improved, but computational resources and time are consumed
Solution Approach 1:
The system performs preliminary action by pre-processing incident data and pre-training machine learning models with historical incident data before actual incident response is needed. The machine learning models are trained in advance on labeled training data, so when real incidents occur, the system can quickly apply the pre-trained models for rapid and accurate keyword tagging and pattern identification without performing heavy computation during the incident response itself.
3Loss of information
If comprehensive incident data from multiple sources is integrated, then information completeness is improved, but data processing complexity increases
Solution Approach 1:
The incident response system implements multi-functionality by integrating and processing data from multiple diverse sources including incident management systems, monitoring tools, logs, and support tickets through a single unified platform. The system handles various data formats and sources uniformly, applying the same machine learning processes regardless of the data origin, which manages integration complexity while achieving comprehensive information coverage.
Data Source
AI summary
Systems and methods for automated incident response are disclosed. In one embodiment, a method for managing response to an incident may include (1) receiving training incident data from a training data source; (2) identifying at plurality of incident-related training keywords in the training data; (3) receiving one of a plurality of tags for each of the plurality of training keywords from a trainer; (4) executing a machine learning process to associate the received tags with the training keywords; (5) receiving incident data related to an incident from an incident data source; (6) identifying a plurality of incident-related keywords in the incident data; (7) automatically tagging the incident-related keyword with one of the plurality of tags; (8) automatically identifying at least one incident pattern from the tags; (9) automatically retrieving a solution for the incident based on similar resolved incidents; and (10) automatically applying the solution to the incident.