Real-Time Synthetic Data Generation Using Statistical Binning
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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 real-world data, especially when dealing with sensitive data, and they do not efficiently manage memory constraints in real-time data processing.
Innovation Solution
A system that generates synthetic data in real-time by processing incoming data streams using multiple thresholds, populating bins with synthetic data points to match the statistical properties of the original data, and dynamically managing memory to ensure efficient processing and storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-world sensitive data is used for training AI systems, then training data quality and statistical accuracy are improved, but compliance burden and security risks increase
Solution Approach 1:
The patent creates synthetic copies of real-world data that replicate statistical characteristics and distributions without containing actual sensitive information. The synthetic data generation system produces artificial datasets that mimic the properties of source data, enabling AI training while eliminating compliance risks associated with using real sensitive data.
2Object-affected harmful factors
If existing synthetic data generation methods are used, then compliance burden is reduced, but data processing speed and accuracy deteriorate
Solution Approach 1:
The patent replaces manual and rule-based synthetic data generation methods with an automated machine learning system. The system uses trained models to generate synthetic data, substituting human expertise and simple algorithms with sophisticated computational approaches that achieve both speed and accuracy in generating statistically faithful synthetic datasets.
Solution Approach 2:
The patent performs preliminary training of synthetic data generation models using real data to learn statistical distributions and characteristics. This pre-training phase enables the system to rapidly generate accurate synthetic data without requiring complex real-time analysis, thereby improving processing speed while maintaining data quality.
3Object-affected harmful factors
If existing synthetic data generation methods are used, then compliance burden is reduced, but statistical fidelity to original data deteriorates
Solution Approach 1:
The patent employs advanced machine learning models including generative adversarial networks and variational autoencoders to capture complex statistical relationships and distributions in the source data. These sophisticated algorithms preserve statistical fidelity much better than traditional methods like data masking or simple sampling, while still producing synthetic data that contains no real sensitive information.
4Quantity of substance
If all incoming real-time data is stored in memory, then data availability for processing is improved, but memory constraints cause system bottlenecks
Solution Approach 1:
The patent extracts only the essential statistical properties and features from incoming real-time data streams rather than storing all raw data. The system identifies and captures key statistical characteristics, distributions, and relationships, then uses these extracted features to generate synthetic data, thereby reducing memory requirements while maintaining data utility.
Solution Approach 2:
The patent transforms raw data into compressed statistical representations by changing the parameter space. Instead of storing complete datasets, the system converts data into statistical parameters such as mean, variance, distribution shapes, and correlation coefficients, which occupy minimal memory space yet retain all necessary information for synthetic data generation.
Data Source
AI summary
Systems and methods for synthetic data generation. A system includes at least one processor and a storage medium storing instructions that, when executed by the one or more processors, cause the at least one processor to perform operations including receiving a continuous data stream from an outside source, processing the continuous data stream in real-time, and using machine learning techniques to generating synthetic data to populate the dataset. The operations also include creating a plurality of bins, wherein the plurality of bins occupy a data range between the determined minimum and maximum values without overlapping; and determining a number of samples within each of the created bin, based on a bin edges, wherein the bin edges are bounds within the data range.