Chatbot Knowledge Base Gap Detection and Q&A Generation
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Solution Overview
Problem
Existing knowledge bases for chatbots and customer service systems are often incomplete, requiring inefficient and time-consuming manual assessment to identify gaps, which can be automated using textual analytics tools.
Innovation Solution
The use of schemaless systems and methods that employ natural language processing (NLP) and deep learning engines to analyze knowledge bases, identify gaps, and generate new question-and-answer pairs, either manually or automatically, to update the knowledge base iteratively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual assessment is used to identify gaps in the knowledge base, then the completeness of the knowledge base can be improved, but the time and resources required increase significantly
Solution Approach 1:
The patent replaces manual assessment processes with automated textual analytics tools that use NLP and deep learning engines to detect gaps in the knowledge base. The system automatically analyzes queries against the knowledge base, identifies missing question-answer pairs, and generates candidates for addition, eliminating the need for manual review of each potential gap.
Solution Approach 2:
The system enables self-service by automatically detecting knowledge base gaps and generating candidate answers without requiring continuous human intervention. The textual analytics tools autonomously analyze query patterns, identify knowledge gaps, and propose new Q&A pairs that can be directly added to the knowledge base, allowing the system to maintain and improve itself automatically.
2Reliability
If manual processes are used to prepare and tag answers, then the accuracy of the knowledge base can be maintained, but the productivity of the system decreases
Solution Approach 1:
The patent replaces manual answer preparation and tagging with automated AI algorithms that generate answers and automatically tag them for later use. The deep learning engines process queries and generate appropriate answers, along with metadata tags, eliminating the need for manual processing while maintaining quality through automated validation mechanisms.
Solution Approach 2:
The system enables continuous operation by automatically processing queries, identifying gaps, generating answers, and updating the knowledge base without interruption. The textual analytics tools continuously monitor query patterns and knowledge base content, allowing for real-time or near-real-time updates that maintain high productivity while ensuring accuracy through automated validation.
3Productivity
If automated textual analytics tools are used to detect knowledge gaps, then the productivity of the system improves, but the complexity of the system increases
Solution Approach 1:
The patent implements a multi-functional platform that combines query processing, knowledge base management, gap detection, answer generation, and validation within a single integrated system. The textual analytics tools perform multiple functions including analyzing query patterns, identifying knowledge gaps, generating candidate answers, and validating content, thereby improving productivity while managing complexity through consolidation rather than multiplication of separate systems.
Data Source
AI summary
Schemaless systems and methods for automatically building and utilizing a chatbot knowledge base or the like. Textual analytics tools, such as natural language processing (NLP) and/or deep learning engines, are used to analyze the knowledge base and uncover and highlight gaps, which are turned into topics. Predetermined answers to these queries can then be prepared manually, or by an artificial intelligence (AI) algorithm with alternative database visibility. In this manner, new question-and-answer (Q&A) pairs are generated by the systems and methods in an automated manner, for later use by a chatbot, coaching system, or the like. These processes are iterative. Advantageously, transferring the processes from manual control to automated control greatly conserves resources.