Chat Bot Semantic Answering for Contact Center Query Resolution
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Solution Overview
Problem
In communication software, repetitive questions often go unanswered or disrupt conversations due to lack of efficient search functionality and semantic mismatch in question language, leading to frustration and wasted time for users and administrators.
Innovation Solution
A knowledgebase is built using natural language processing to store and update question-answer pairs within communication channels, allowing a chat bot to identify and provide answers to semantically similar questions, reducing the need for repeated queries and improving user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If manual searching and monitoring of communication channels is used to answer questions, then users can get answers to their questions, but it consumes excessive time and administrative resources
Solution Approach 1:
The system enables self-service by automatically monitoring communication channels, analyzing incoming questions using NLP, and providing answers autonomously without human intervention. The chatbot independently queries the knowledgebase and responds to user questions, eliminating the need for manual administrative oversight.
Solution Approach 2:
An automated chatbot serves as an intermediary between users and the knowledgebase. The chatbot receives questions from users, processes them through NLP to extract meaning and intent, queries the knowledgebase for relevant answers, and delivers responses back to users, thereby mediating the interaction and eliminating direct human involvement.
2Measurement precision
If comprehensive search functionality is implemented to find relevant answers, then answer accuracy improves, but system complexity increases
Solution Approach 1:
The patent replaces complex mechanical search systems with an intelligent NLP-based analysis system. Instead of relying on keyword matching or manual search interfaces, the system uses natural language processing to understand the semantic meaning, intent, and context of questions, thereby achieving high answer relevance without increasing system complexity.
Solution Approach 2:
The system changes the parameter of question analysis from simple keyword matching to semantic understanding through NLP. By transforming the analysis approach from surface-level text comparison to deep meaning extraction, the system achieves higher precision in finding relevant answers while maintaining manageable complexity through automated processing.
3Speed
If automated chatbot is deployed to answer questions, then response speed increases, but ability to handle complex or novel questions decreases
Solution Approach 1:
The system incorporates feedback mechanisms where user interactions, question patterns, and answer effectiveness are continuously monitored and fed back into the NLP model and knowledgebase. This allows the chatbot to learn from complex questions it encounters, improving its ability to handle novel and complicated inquiries over time while maintaining fast response speeds.
Solution Approach 2:
The knowledgebase is pre-populated with comprehensive question-answer pairs covering anticipated complex scenarios. By preparing answers in advance and structuring the knowledgebase with detailed information, the system enables the chatbot to quickly retrieve and deliver accurate responses to complex questions without real-time analysis delays.
Data Source
AI summary
A contact center server receives, during a contact center engagement and via a chat bot of the contact center server, a query from a user device. The contact center server determines that the query corresponds to a stored prompt associated with a stored response in a contact center knowledgebase. The contact center server provides, via the chat bot, the stored response to the user device.