Virtual Agent NLP for IT Incident Summarization and Response
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Solution Overview
Problem
Large enterprises face challenges in efficiently processing and analyzing large volumes of IT incident requests, which are often repetitive and resource-intensive, with substantial overlap between similar queries, leading to inefficiencies in response times and resource allocation.
Innovation Solution
A machine natural language processing architecture is implemented to automate incident-related communication, utilizing a virtual agent that processes IT tickets, updates a knowledge base, and generates prescriptive solutions using natural language processing and machine learning, enabling real-time interaction and proactive incident management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processing of IT incident requests is used, then human judgment and flexibility are maintained, but response times are slow and resource allocation is inefficient
Solution Approach 1:
The system enables self-service through virtual agents that automatically process incident requests using natural language processing. The virtual agent autonomously analyzes incoming requests, retrieves relevant information from the knowledge base, and provides resolutions without requiring manual human intervention for each incident, thereby improving productivity and reducing response time
Solution Approach 2:
The patent replaces manual mechanical processing with automated computational systems. Natural language processing algorithms and machine learning models substitute human analysts, automatically parsing incident descriptions, matching them with known solutions in the knowledge base, and generating responses, thus eliminating the time loss associated with manual review and acceleration incident resolution
2Ease of operation
If all incident requests are processed individually by human analysts, then each case receives personalized attention, but resource allocation becomes inefficient due to substantial overlap between similar queries
Solution Approach 1:
The virtual agent system provides universal functionality by handling multiple types of incident requests through a single automated platform. The natural language processing engine can interpret various user queries and the knowledge base contains diverse solutions, allowing one system to serve multiple functions and maintain personalized response quality across different incident types without requiring separate human analysts for each case
Solution Approach 2:
The system uses copying by retrieving and reusing proven solutions from the knowledge base for similar incidents. When a new incident is processed, the natural language processing system searches for matching patterns in previously resolved incidents and copies their solutions, adapting them to the current context. This eliminates redundant analysis while maintaining personalized attention, significantly improving resource allocation efficiency
3Reliability
If a comprehensive knowledge base is maintained for all possible incidents, then solution accuracy improves, but system complexity increases
Solution Approach 1:
The system applies preliminary action by pre-processing and structuring incident data during the knowledge base population phase. Natural language processing techniques are used in advance to parse, categorize, and index incident descriptions and solutions, creating a structured repository that can be efficiently queried. This preliminary organization maintains high solution accuracy while reducing the operational complexity of managing the knowledge base
4Loss of time
If natural language processing is implemented to automate incident responses, then response time decreases, but the complexity of the processing system increases
Solution Approach 1:
The natural language processing system is segmented into distinct functional modules: text preprocessing, entity recognition, intent classification, knowledge base querying, and response generation. Each module performs a specific task independently, which reduces overall system complexity by making each component manageable and maintainable while still achieving rapid automated incident resolution through their coordinated operation
Data Source
AI summary
A virtual agent can implement a chatbot to provide output based on predictive/prescriptive models for incidents. The virtual agent can integrate with natural language processor for text analysis and summary report generation. The virtual agent can integrate with cognitive search to enable processing of search requests and retrieval of search results. The virtual agent uses computing processes with self-learning systems that use data mining, pattern recognition and natural language processing to mimic the way the human brain works. The virtual agent provides an automated IT system that is capable of resolving incidents without requiring human assistance. The virtual agent can display condensed summaries of a large amount of data and can link the summaries to predictive models and operational risk models to identify risk events and provide summaries of those events.