Automated Help Desk Using NLP and Graph Database for IT Incident Resolution
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Solution Overview
Problem
The increasing number of IT service tickets due to remote working, where employees neglect initial device issues, leading to extended device downtime and loss of productivity, is exacerbated by the need for manual investigation of root causes, which is time-consuming and inefficient.
Innovation Solution
An automated IT help desk system using Natural Language Processing (NLP) and similarity detection algorithms to analyze historical tickets and device telemetry data, coupled with a graph database, to detect similar issues and propose solutions, reducing the volume of tickets and resolution time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual investigation of root cause is performed for each IT service ticket, then accurate problem diagnosis is achieved, but resolution time increases and productivity decreases
Solution Approach 1:
The system performs preliminary analysis by automatically detecting similar incidents from historical data before human intervention. The automated help desk system pre-processes incoming tickets by comparing them against a knowledge base of previously resolved incidents, preparing potential solutions in advance so that technicians only need to verify and implement pre-identified solutions rather than investigating from scratch.
Solution Approach 2:
The system creates and maintains a knowledge base by copying and storing resolved incident patterns, solutions, and diagnostic information from historical tickets. This knowledge base serves as a reusable repository where proven solutions are documented and automatically matched against new incidents, eliminating the need to reinvestigate common problems.
2Measurement precision
If manual investigation of root cause is performed for each IT service ticket, then accurate problem diagnosis is achieved, but support professional workload increases and productivity decreases
Solution Approach 1:
The system enables self-service by automatically resolving common incidents through the automated help desk. When an incoming ticket matches a pattern in the knowledge base with a confirmed solution, the system can automatically implement the fix (such as restarting services or applying patches) without requiring human technician intervention, thereby reducing overall workload.
Solution Approach 2:
The automated help desk acts as an intermediary between incoming incidents and human support professionals. It filters, categorizes, and pre-processes tickets by identifying similar historical incidents and preparing diagnostic information, so that technicians receive only the most relevant cases requiring human expertise rather than handling every ticket manually.
3Object-generated harmful factors
If employees neglect initial device issues, then minor problems are avoided, but device downtime increases and productivity is lost
Solution Approach 1:
The system performs preliminary detection of device issues by continuously monitoring telemetry data and comparing it against known incident patterns. When early signs of problems are detected (such as unusual error codes or performance degradation), the system proactively identifies and alerts users before these minor issues escalate into complete device failures requiring manual intervention.
4Reliability
If the number of IT service tickets increases due to remote working, then more issues are reported and tracked, but the volume of tickets to manage increases and resolution efficiency decreases
Solution Approach 1:
The automated help desk system provides universal functionality by handling multiple types of incidents through a single unified platform. It can process various categories of IT issues (hardware problems, software errors, network connectivity issues) using the same automated detection and matching mechanisms, allowing the system to scale efficiently as ticket volume increases without proportionally increasing manual workload.
Data Source
AI summary
Disclosed are various embodiments for automating IT help desk services by utilizing historical IT help desk data to identify potential solutions for incidents. First, a ticket associated with an incident on a client device can be received. Next, a ticket record can be generated by extracting information from the ticket using natural language processing techniques. Next, using the ticket record as a key, a graph database can be queried for one or more similar ticket records. In response to obtaining similar ticket records from the graph database, the graph database can be further queried for solution records related to the similar ticket records by edges. The solution can comprise a set of machine-readable instructions which can resolve the incident when executed.