Natural Language Database Querying With Dynamic Dialogue Flow
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Solution Overview
Problem
Conventional chatbot systems are inflexible and time-consuming, producing fixed predefined responses and lacking the ability to dynamically adapt to user queries, necessitating a more robust and flexible system for generating database queries based on natural language inputs.
Innovation Solution
A method and system utilizing a conversational AI editing module to configure and parse database schema, combined with a natural language question-answering system, to generate dynamic database queries and dialogue flows, enabling interaction with databases and user engagement through iterative query refinement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional chatbot systems use fixed dialogue flows and static responses, then the system structure is simple and easy to implement, but the system lacks flexibility and adaptability to dynamic user queries
Solution Approach 1:
The patent transforms the static dialogue flow into a dynamic system by introducing a natural language question-answering module that generates database queries in real-time. The dialogue flow is no longer fixed but adapts dynamically based on user inputs and database results, allowing the system to flexibly respond to diverse queries while maintaining a structured approach through the underlying database schema.
Solution Approach 2:
The patent introduces a natural language question-answering system as an intermediary between the user and the database. This intermediary translates natural language queries into database queries, retrieves relevant information, and constructs appropriate responses, thereby bridging the gap between simple fixed responses and complex adaptive behavior without requiring complete system redesign.
2Productivity
If conventional chatbot systems use fixed predefined responses, then the development time is short and implementation is quick, but the system requires extensive manual configuration of dialogue flows which is time-consuming
Solution Approach 1:
The patent enables the system to automatically generate dialogue flows and responses through the natural language question-answering module. Instead of requiring manual configuration of every dialogue path, the system self-generates appropriate responses by querying the database and constructing answers based on retrieved information, significantly reducing the time spent on dialogue flow configuration while improving productivity.
Solution Approach 2:
The patent performs preliminary action by pre-configuring the database schema and establishing the relationship between database tables and natural language queries. This upfront preparation allows the system to quickly generate responses during runtime without requiring extensive real-time configuration, thereby reducing development time and improving implementation efficiency.
3Adaptability or versatility
If conventional chatbot systems route intents to static responses, then the system is easy to operate and maintain, but the system cannot dynamically retrieve and adapt information from databases
Solution Approach 1:
The patent makes the natural language question-answering module a universal component that handles multiple types of queries across different database tables. This single module can adapt to various user intents and generate appropriate database queries and responses, providing dynamic adaptation capabilities while maintaining operational simplicity through a unified interface and consistent processing approach.
Data Source
AI summary
Provided herein are systems, methods, and computer-readable media for generating one or more database queries based on a natural language input data. An example method comprises configuring, using a conversational artificial intelligence (AI) editing module, information for a task. Moreover, the method may further comprise parsing the configured information based on a database schema to obtain a parsed database schema. Further, the method may further comprise generating, using a natural language question-answering system, one or more database queries based on the parsed database schema and natural language input data.