Synthetic Data Generation for AI Training Reliability
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Solution Overview
Problem
Existing methods for creating synthetic data for training AI systems are slow, error-prone, and fail to replicate the statistical characteristics of sensitive data, posing challenges for developers working with regulated data like customer financial records or patient healthcare data, and require large datasets for specific tasks like log file parsing.
Innovation Solution
A platform that uses neural networks to automatically generate synthetic data and tokenize sensitive data, enabling the automatic selection and training of specific parsers for log files, even with insufficient initial training sets, by generating templates and applying them to create synthetic documents with similar statistical characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual desensitization methods (regular expressions or manual processes) are used, then some level of data protection is achieved, but the process is slow, error-prone, and cannot create synthetic data with statistical characteristics similar to original data
Solution Approach 1:
The patent replaces manual mechanical processes (regular expressions, manual desensitization) with an automated neural network system. The neural network automatically identifies sensitive data patterns, performs desensitization, and generates synthetic data with preserved statistical characteristics, eliminating human error and dramatically increasing productivity while maintaining reliability.
Solution Approach 2:
The patent creates synthetic data copies that replicate the statistical characteristics, distributions, and relationships of the original sensitive data without containing the actual sensitive information. This copying approach enables training AI systems with data that mimics real data properties while ensuring data protection.
2Measurement precision
If specialized neural networks are trained for specific log file types, then parsing accuracy is improved, but large training datasets are required for each specific task which is not always practical
Solution Approach 1:
The patent trains a universal neural network parser that can handle multiple log file types and formats. Instead of creating separate specialized parsers for each log type, the universal parser learns common patterns and structures across different log formats, achieving high accuracy across diverse log files without requiring large training datasets for each specific type.
Solution Approach 2:
The patent employs parameter-based adaptation where the neural network adjusts its processing parameters and attention mechanisms based on the characteristics of the input log file. This allows the same network architecture to adapt to different log types by changing its internal parameters rather than requiring separate trained networks for each type.
3Measurement precision
If sensitive data is widely distributed for training models, then model accuracy is improved, but data security and compliance with regulations are compromised
Solution Approach 1:
The patent extracts and removes sensitive information from the training data before using it to train AI models. The neural network identifies sensitive data elements (personal identifiers, financial information, health records) and either removes them or replaces them with synthetic equivalents, allowing models to be trained on data that retains statistical properties without containing actual sensitive information.
Solution Approach 2:
The patent uses synthetic data as a disposable substitute for real sensitive data in training processes. The synthetic data serves the purpose of training models with realistic statistical characteristics but has no intrinsic value or sensitivity, making it safe to distribute and use widely for training purposes without security concerns.
Data Source
AI summary
The present disclosure relates to systems and methods for generating synthetic documents. In one implementation, a system for generating synthetic data from a plurality of documents may include at least one processor and at least one non-transitory memory storing instructions that, when executed by the at least one processor cause the system to: receive a plurality of documents, individual documents of the plurality of documents having a same document type; generate a distribution of values for a corresponding pixel in the individual documents of plurality of documents; determine, based on the distributions, one or more common features of the plurality of documents; determine, based on the comparison, one or more input fields; generate a template including the one or more common features and the one or more input fields; and input synthetic data into the one or more input fields of the template thereby generating a plurality of synthetic documents.