Modular Syntactic Generators for Synthetic Data
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Solution Overview
Problem
Existing methods for generating synthetic data are resource-intensive and time-consuming, requiring specific models for each problem domain, which limits their applicability and efficiency across varying domains and raises security concerns due to the need for sensitive data handling.
Innovation Solution
A modular data generation system using syntax and content generators that can be interchanged, allowing for the creation of synthetic data sets across multiple domains without the need for extensive retraining, while maintaining security through the use of modular components that do not require direct access to sensitive data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If problem domain-specific models are used for synthetic data generation, then the data generation accuracy for specific domains is improved, but the time and computing resources required increase significantly
Solution Approach 1:
The patent segments the synthetic data generation process into two independent components: a syntax generator that handles the structural framework and a content generator that handles the specific data content. This segmentation allows each component to be trained separately and reused across different domains, eliminating the need for complete retraining when changing domains while maintaining high data generation accuracy.
Solution Approach 2:
The syntax generator is designed as a universal component that can be applied across multiple problem domains. Once trained on syntactic structures, it can generate frameworks for various types of data (emails, documents, names, addresses, etc.) without requiring retraining, making the system multi-functional while reducing time and resource expenditure.
2Manufacturing precision
If problem domain-specific models are used for synthetic data generation, then the data generation accuracy for specific domains is improved, but the computing resources required increase significantly
Solution Approach 1:
By segmenting the generation process into syntax and content components, the patent reduces the computational burden. The syntax generator, being universal and pre-trained, requires minimal computing resources when generating new data, while the content generator handles domain-specific details efficiently. This segmentation avoids the need for resource-intensive full-model training for each domain.
Solution Approach 2:
The syntax generator is trained in advance on syntactic structures that can be applied across multiple domains. This preliminary action allows the system to have the structural knowledge ready before actual data generation, reducing the computing resources needed during the actual generation process while maintaining high accuracy.
3Adaptability or versatility
If new models are trained for each new problem, then the adaptability to specific problem domains is improved, but the productivity decreases due to repeated training requirements
Solution Approach 1:
The syntax generator serves as a universal component that adapts to different problem domains through the content generator rather than requiring retraining. This universality maintains high domain adaptability while significantly improving productivity by eliminating repeated training cycles.
Solution Approach 2:
The patent extracts the domain-specific elements into the content generator component, separating them from the universal syntax generator. This extraction allows the syntax generator to remain domain-agnostic and reusable, while the content generator handles domain-specific adaptations, thereby maintaining adaptability without sacrificing productivity.
4Manufacturing precision
If sensitive data is used for training, then the quality of synthetic data is improved, but security risks increase due to regulated data handling requirements
Solution Approach 1:
The patent extracts the sensitive content from the training process by using only syntactic structures for training the syntax generator. The content generator is trained on publicly available or non-sensitive data. This extraction eliminates the need to handle sensitive data during the generation process, reducing security risks while maintaining synthetic data quality through the structured approach.
Solution Approach 2:
Instead of copying or using actual sensitive data for training, the patent uses syntactic templates and structures that capture the form and organization of data without containing the sensitive content itself. This copying of structure rather than content maintains data quality for training purposes while eliminating security risks associated with handling regulated data.
Data Source
AI summary
Systems and methods for modular generators for synthetic data generation are disclosed. The disclosed systems and methods may include a system for generating synthetic data that may comprise at least one processor configured to execute instructions to perform operations. The operations may include receiving a request for synthetic data, selecting a syntax generator, generating, using the syntax generator, a token set comprising a first token and a second token, and identifying a first token type corresponding to the first token and a second token type corresponding to the second token. Operations may further include selecting first and second content generators; generating, using the first content generator, first content data, generating, using the second generator, second content data and generating a synthetic data set by replacing, in the token set, the first token with the first content data and the second token with the second content data.