AI Agent for IT Ticket Classification and Resolution
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Solution Overview
Problem
Current IT ticket systems are inefficient due to manual classification of IT issues, lack of opportunity to override previous categories, and limited access to historical knowledge, leading to suboptimal solution selection and minimal learning from IT personnel experiences.
Innovation Solution
An AI agent with a visualization interface, translation module, solutions module, and feedback module that classifies IT issues, selects relevant solutions, and updates based on user feedback, allowing for intelligent addressing of IT tickets and continuous knowledge improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual classification of IT issues is used, then IT personnel can categorize issues, but the process is time-consuming and prone to misclassification
Solution Approach 1:
The patent replaces the manual mechanical classification process with an automated natural language processing system. The NLP model automatically analyzes issue descriptions, extracts key features, and classifies issues into appropriate categories without human intervention, thereby eliminating time loss while maintaining or improving classification accuracy.
Solution Approach 2:
The system enables self-service classification where the IT ticketing system automatically performs categorization using embedded AI models. The system serves itself by autonomously processing and classifying incoming tickets based on their content, freeing IT personnel from manual classification tasks while ensuring consistent and accurate categorization.
2Adaptability or versatility
If a fixed category menu is provided, then classification is standardized, but IT personnel cannot override or adjust categories based on experience
Solution Approach 1:
The patent implements a dynamic classification system where the rigid category menu is replaced with a flexible NLP-based categorization approach. The system dynamically adapts to different issue types and contexts by analyzing semantic meaning rather than forcing predefined categories, allowing IT personnel to override classifications when necessary while maintaining overall consistency through learned patterns from historical data.
Solution Approach 2:
The system incorporates feedback mechanisms where IT personnel can provide corrections to automated classifications. This feedback is used to continuously train and improve the NLP models, creating a closed-loop system that balances adaptability with consistency. The feedback loop allows the system to learn from human expertise while maintaining standardized classification outcomes.
3Loss of information
If historical knowledge is accumulated in ticket systems, then organizational learning occurs, but the knowledge is not accessible to guide future IT personnel decisions
Solution Approach 1:
The patent introduces an NLP-based knowledge intermediary that automatically extracts, structures, and stores relevant information from historical tickets. This intermediary layer processes unstructured ticket data, identifies patterns and solutions, and makes them accessible through natural language queries. IT personnel can easily access relevant historical knowledge without manually searching through ticket databases, thereby improving both knowledge retention and accessibility.
Solution Approach 2:
The system replaces manual knowledge retrieval processes with automated NLP-based information extraction and retrieval. Instead of IT personnel manually searching through historical tickets, the system automatically queries the knowledge base using natural language, extracts relevant information, and presents it to the user, making historical knowledge easily accessible while maintaining comprehensive retention.
4Quantity of substance
If IT personnel manually document solutions, then a knowledge base is built, but the process increases workload and reduces productivity
Solution Approach 1:
The patent implements self-service knowledge base population where the system automatically documents solutions extracted from resolved tickets. The NLP models analyze resolution notes, categorize solutions, and store them in the knowledge base without requiring IT personnel to manually document each solution. This automated approach builds a comprehensive knowledge base while maintaining high IT personnel productivity.
Solution Approach 2:
The system replaces the manual mechanical process of knowledge documentation with automated NLP-based extraction and storage. The system automatically processes resolution information from tickets, structures it appropriately, and adds it to the knowledge base, eliminating the need for IT personnel to spend time on documentation tasks while continuously expanding the knowledge base.
Data Source
AI summary
An apparatus, system and method of providing an artificially intelligent (AI) agent capable of assisting information technology (IT) personnel in addressing an IT ticket. The AI agent may include a visualization interface for display capable of receiving dialog from the IT personnel related to the IT ticket; a translation module capable of translating the received dialog to at least one machine language translation; a solutions module capable of receiving the machine language translation, comparing the machine language to a plurality of prospective solutions for IT issues, and selecting a subset of the plurality of prospective solutions deemed most relevant to the IT ticket and returning that subset to the visualization interface; and a feedback module capable of receiving explicit and implied feedback from the IT personal regarding the machine language translation and the selected subset, wherein the selecting of the subset varies upon subsequent received dialog according to the feedback.