Synthetic Data Generation System with Controlled Variability
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Solution Overview
Problem
Traditional methods for generating training datasets for machine learning models are expensive, time-consuming, and often lack variability, especially in sensitive areas like healthcare due to privacy concerns, and struggle to include rare or outlier scenarios.
Innovation Solution
A system and method for generating synthetic datasets with user-controlled variability, including a graphical user interface that allows users to select object types, variability parameters, and camera characteristics, enabling the creation of customized datasets that mimic real-world scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual data collection and labeling methods are used, then data quality may be maintained, but the process becomes expensive and time-consuming
Solution Approach 1:
The patent uses synthetic data generation to create copies of real-world data scenarios through simulation. Instead of collecting and manually labeling actual images, the system generates synthetic images that replicate real-world conditions, object appearances, and scenarios, eliminating the need for time-consuming manual data collection and labeling while maintaining data quality for training machine learning models
Solution Approach 2:
The patent replaces the mechanical process of manual data collection and labeling with an automated computational system. The system uses algorithms to automatically generate synthetic training data, automatically annotate objects within the synthetic images, and simulate various real-world conditions, substituting human labor with automated computational processes that are both faster and more efficient
2Adaptability or versatility
If real-world data is collected for training, then data authenticity is maintained, but privacy concerns and regulations limit data availability
Solution Approach 1:
The patent creates synthetic copies of real-world data that preserve the statistical properties and visual characteristics of authentic data without containing any actual private or sensitive information. By generating artificial images that simulate real-world scenarios, the system maintains data authenticity for training purposes while eliminating privacy risks associated with using real patient or personal data
Solution Approach 2:
The patent introduces synthetic data as an intermediary between the need for authentic training data and privacy protection requirements. Instead of directly using real-world data that raises privacy concerns, the system uses synthesized representations that mediate between these conflicting requirements, providing the benefits of authentic data while eliminating privacy harms
3Reliability
If diverse training data is generated, then model robustness improves, but data generation complexity increases
Solution Approach 1:
The patent implements dynamic control over data generation parameters, allowing the system to adaptively adjust the complexity and diversity of synthetic data based on training needs. The system can dynamically modify object types, environmental conditions, lighting scenarios, and camera parameters to generate appropriately diverse training datasets without requiring a permanently complex generation system
Solution Approach 2:
The patent segments the data generation process into modular components that can be independently configured and controlled. By dividing the complex generation task into separate controllable parameters (object selection, environment settings, camera characteristics, annotation types), the system achieves high data diversity through simple parameter combinations rather than complex integrated systems
Data Source
AI summary
A non-transitory, computer-readable medium includes instructions that causes at least one processing device to display a graphical user interface (GUI) configured to facilitate generating a synthetic dataset including a plurality of images. The GUI includes a dataset size selector to receive user input to indicate a number of images to generate and include in the synthetic dataset; a target object type selector to receive user input indicative of at least one selected target object type to feature in the synthetic dataset; one or more image parameter variability controls to receive user input indicative of at least one variation to include in the synthetic dataset relative to target object representations generated based on the at least one selected target object type; and a dataset generation control to initiate generating the synthetic dataset. The synthetic dataset is generated according to the size input, target object type input, and variability input.