NLP Virtual Assistant for Technical Support Query Resolution
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Solution Overview
Problem
Conventional methods for providing technical support are inefficient due to the inability to leverage past queries, leading to delays and reduced productivity.
Innovation Solution
A natural language processing (NLP) based virtual assistant that receives queries, analyzes them using a trained model, retrieves relevant past queries and their resolutions from a database, and displays them to the user, along with the ability to receive feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual query resolution by support team is used, then human expertise can be applied, but query resolution time increases and productivity decreases
Solution Approach 1:
An NLP-based virtual assistant is introduced as an intermediary between the employee and the technical support team. The virtual assistant automatically analyzes queries, retrieves relevant solutions from a database, and provides resolutions without requiring manual intervention from the support team for routine queries, thereby reducing resolution time while maintaining quality through systematic processing
Solution Approach 2:
A database of previously resolved queries and their solutions is prepared in advance. When a new query is received, the system performs preliminary analysis to match it with existing resolved queries in the database, retrieving relevant solutions before involving human support staff, thus eliminating the need for manual resolution of repetitive queries
2Ease of operation
If manual classification and context gathering is performed, then appropriate support teams can be identified, but additional time is consumed in query resolution
Solution Approach 1:
The manual mechanical process of query classification and context gathering by support staff is replaced with an automated NLP-based system. The virtual assistant uses natural language processing to automatically analyze query content, classify it into appropriate categories, and retrieve relevant context from the database, eliminating the time-consuming manual classification step while maintaining accurate routing
3Productivity
If repetitive queries are handled manually, then each query can be addressed individually, but the same issues recur and productivity is impacted
Solution Approach 1:
The system creates copies of previously resolved queries and their solutions in a structured database. When similar queries are received, the system retrieves these copied solutions and applies them to new queries, ensuring consistent handling of repetitive issues. This allows the support team to focus on unique, complex problems while routine queries are resolved using copied proven solutions
Solution Approach 2:
The system enables self-service by allowing employees to interact with the virtual assistant that automatically analyzes their queries, searches the database for relevant solutions, and provides resolutions without requiring support team intervention. This empowers employees to resolve common issues independently, improving overall productivity while maintaining consistent resolution quality through systematic database retrieval
Data Source
AI summary
A method and a system for providing a natural language processing based virtual assistant for a technical support are disclosed. The method includes receiving at least one query from at least one entity. Next, the method includes analyzing the at least one query. Next, the method includes retrieving, from a database, a plurality of queries along with a corresponding resolution based on the analysis of the at least one query. Next, the method includes displaying, via a display, the plurality of queries along with the corresponding resolution to the at least one entity. Thereafter, the method includes receiving feedback on the plurality of queries displayed to the at least one entity along with the corresponding resolution.