Context-Aware Chatbot Engine for Multi-Turn Query Resolution
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Solution Overview
Problem
Current chatbot systems lack the ability to effectively handle multi-turn context-aware conversations, particularly in scenarios where implicit references and domain-specific keywords are involved, leading to challenges in extracting and resolving criteria from user queries.
Innovation Solution
A multi-turn context-aware chatbot engine that utilizes a text-to-SQL parser, intent recognition engine, table detection engine, and implicit reference resolution criteria extraction engine to generate context-aware responses by parsing user queries and deriving criteria from explicit and implicit information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current chatbot systems are used, then basic query handling is possible, but multi-turn context-aware conversations with implicit references cannot be effectively handled
Solution Approach 1:
The system performs preliminary action by maintaining a context window that stores previous conversation turns and extracted criteria before processing new queries. This allows the chatbot to pre-process and retain contextual information, enabling more accurate resolution of implicit references in subsequent turns without requiring complex real-time analysis.
Solution Approach 2:
The patent introduces an intermediary mechanism - a context management system that acts as a mediator between user queries and the database schema. This intermediary maintains conversation state, tracks implicit references, and manages criteria extraction across multiple turns, bridging the gap between simple query handling and complex context-aware conversations.
2Measurement precision
If domain-specific keywords are used in queries, then precise information retrieval is possible, but the system lacks ability to resolve implicit references to database schemas
Solution Approach 1:
The system applies universality by creating a multi-functional processing pipeline that handles both domain-specific keyword extraction and database schema reference resolution within the same chatbot framework. The context-aware processing engine performs multiple functions: extracting criteria from keywords, resolving implicit schema references, and maintaining conversation state, making the system adaptable to various query types without requiring separate specialized systems.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously refines its understanding of user intent by analyzing previous turns and using extracted criteria to inform subsequent processing. The context window provides feedback about conversation history, and the schema resolution process feeds back into improving keyword interpretation, creating a loop that enhances both precision and adaptability over time.
3Speed
If simple query processing is used, then response speed is maintained, but context-awareness and implicit reference resolution are lost
Solution Approach 1:
The system extracts only the essential contextual information needed for understanding implicit references, rather than processing entire conversation histories. By extracting key criteria, entities, and schema references from previous turns and storing them in a compact context window, the system maintains response speed while preserving necessary context information for accurate query processing.
Data Source
AI summary
A multi-turn context-aware chatbot is disclosed. A framework to create a multi-turn context-aware chatbot can make use of a database schema. A system that includes a multi-turn context-aware chatbot can include a criteria tuple extractor. A method for carrying out multi-turn context-aware chat can include implicit reference resolution.