Natural-Language Synthetic Database Query Generation for Private AI Training
Solution Overview
Problem
Existing methods for creating synthetic data are slow and error-prone, and do not effectively replicate the statistical characteristics of original data, limiting their utility for training and testing artificial intelligence systems, particularly in sensitive data domains like customer financial records and patient healthcare data.
Innovation Solution
A system and method for generating synthetic database query results using natural language processing and generative adversarial networks to create data models that replicate the structure and statistics of original datasets, while ensuring privacy by using synthetic data to train and test AI systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing methods are used to create synthetic data, then data can be generated for testing purposes, but the process is slow and error-prone and does not replicate statistical characteristics accurately
Solution Approach 1:
The patent replaces traditional mechanical data generation methods (manual creation, basic algorithms) with neural network-based synthetic data generation. The neural networks learn statistical patterns from real data and generate synthetic data that accurately replicates these characteristics, significantly improving accuracy while maintaining efficient generation speeds through automated processing.
2Reliability
If real data is used to train AI systems, then training accuracy is improved, but sensitive information is exposed
Solution Approach 1:
The patent creates synthetic copies of real data that replicate statistical characteristics and patterns without containing actual sensitive information. These synthetic copies serve as safe training data that maintains training accuracy while eliminating privacy risks, as they are generated representations rather than actual personal or sensitive data.
3Adaptability or versatility
If algorithms are used for database testing, then testing can be performed, but the methods are limited in size and scope
Solution Approach 1:
The patent creates a universal synthetic data generation system that can produce diverse datasets for multiple testing scenarios and database types. The neural networks are trained on various data types and can generate synthetic data applicable to different testing needs, expanding testing scope while managing complexity through a unified approach rather than multiple specialized systems.
Data Source
AI summary
A system for returning synthetic database query results. The system may include a memory unit for storing instructions, and a processor configured to execute the instructions to perform operations comprising: receiving a query input by a user at a user interface; determining, based on natural language processing, a type of the query input; determining, based on the received query input and a database language interpreter, an output data format; returning, based on a generation model and the output data format, a result of the query input; providing, to a plurality of training models and based on the determined query type, the query input and the result; and training the training models, based on the query input and the result.