Machine Learning Chatbot Query Translation Across Database Formats
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Solution Overview
Problem
Current bot systems are not adapted to handle data requests across databases with different formats, and require extensive training data and retraining for each new type of query, making them impractical for environments with varied data requests.
Innovation Solution
A chatbot system that uses intent detection and entity recognition to generate format-agnostic queries, reducing the need for extensive training data by normalizing search parameters across any vehicle dealership database.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current bot systems are used to handle data requests across databases with different formats, then query capability is limited to specific formats, but adaptability to different database formats deteriorates
Solution Approach 1:
The patent introduces a natural language processing intermediary that translates user queries into database-specific query languages. This mediator layer handles the complexity of format conversion, allowing the bot to adapt to different database formats without requiring complex format-specific processing logic in the core system.
Solution Approach 2:
The system dynamically changes query parameters and syntax based on the target database format. By detecting the database type and automatically adjusting query language parameters (SQL, NoSQL, etc.), the bot achieves adaptability across different formats without manual reconfiguration or extensive retraining.
2Measurement precision
If extensive training data is used to train chat bot for specific query types, then query accuracy for that type improves, but training time and data requirements increase
Solution Approach 1:
The patent implements a universal natural language processing model that can handle multiple query types and database formats with a single training process. This multi-functional approach eliminates the need for separate training datasets for each query type, significantly reducing training time and data requirements while maintaining high accuracy across diverse queries.
Solution Approach 2:
The system employs dynamic query generation that adapts to different database formats and query types in real-time based on the detected intent and entities. This dynamic approach allows the bot to maintain high accuracy without requiring static training data for every possible query scenario, reducing the overall training burden.
3Adaptability or versatility
If chat bot is retrained for new types of queries, then query capability for new types improves, but retraining resource requirements increase
Solution Approach 1:
The system performs preliminary intent detection and entity recognition to identify the type of query and required database operations before generating the actual query. This preliminary classification allows the bot to handle new query types by mapping them to existing templates and patterns, reducing the need for extensive retraining data for each new query type.
Solution Approach 2:
The patent uses query template copying and adaptation mechanisms where new query types are handled by copying existing successful query patterns and modifying them to fit the new context. This approach allows the bot to extend its capability to new query types without requiring completely new training datasets, significantly reducing training data requirements.
Data Source
AI summary
A device receives a query from a user associated with a car dealership and applies the query to a first trained machine learning model configured to predict an intent, and to a second trained machine learning model to predict a set of entities. The device generates a normalized representation of the first query that is database language agnostic based on the predicted intent and the predicted set of entities, and translates the normalized representation into a second query having a format compatible with a language of a database of the car dealership. The device fetches data from the database of the car dealership using the second query, and provides the data for display to the user.