Synthetic Data Frameset Assembly Engine for ML Training
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Solution Overview
Problem
Conventional methods for developing machine-learning training datasets are inefficient and inaccessible, leading to limited availability and high costs, and lack comprehensive functionality for democratizing their use across different domains.
Innovation Solution
A distributed computing system providing synthetic data as a service (SDaaS) using a service-oriented architecture, which includes engines like asset assembly, scene assembly, frameset package generator, and crowdsourcing engines to automate the generation, management, and processing of synthetic data training datasets, reducing the complexity of manual dataset development.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used to develop machine-learning training datasets, then manual effort and time are required for data labeling and development, but the process becomes tedious and leads to inaccuracies in the labeling process
Solution Approach 1:
The patent uses synthetic data generation to create artificial training datasets that copy the essential characteristics and patterns of real-world data without requiring manual collection or labeling. The synthetic data engine generates realistic training examples by simulating target domain data distributions, thereby eliminating tedious manual labeling while maintaining high accuracy for machine learning model training
Solution Approach 2:
The patent replaces the mechanical manual labeling process with an automated synthetic data generation system. Instead of human operators manually labeling data, the system uses computational algorithms to automatically generate synthetic training datasets that preserve the statistical properties and patterns needed for effective machine learning training
2Adaptability or versatility
If comprehensive functionality for developing machine-learning training datasets is provided, then democratizing access to training datasets is enabled, but the infrastructure becomes inaccessible or far too expensive to undertake
Solution Approach 1:
The patent creates a universal synthetic data generation platform that can serve multiple domains and applications through a single infrastructure. The system is designed to generate synthetic training data for various machine learning tasks by configuring different target domain parameters and data characteristics, eliminating the need for separate infrastructure deployments for each domain while maintaining broad adaptability
Solution Approach 2:
The patent enables domain adaptation and versatility by allowing users to modify parameters that define target domain characteristics, data distributions, and synthesis conditions. By changing these parameters, the same synthetic data engine can generate appropriate training datasets for different applications and domains without requiring fundamental infrastructure changes, thereby reducing complexity while maintaining adaptability
3Reliability
If high-quality training datasets are created to improve machine learning algorithms, then better model performance is achieved, but the effort required for data collection and labeling increases significantly
Solution Approach 1:
The patent generates high-quality synthetic training data that copies the essential statistical properties, patterns, and distributions of real-world target domain data. This synthetic data maintains the fidelity and realism needed for effective machine learning model training while being generated automatically through computational processes, thereby improving model training quality without proportionally increasing manual effort
Solution Approach 2:
The patent substitutes manual data collection and labeling operations with automated synthetic data generation. The system uses computational algorithms to produce high-quality training datasets that would otherwise require significant manual effort to collect, annotate, and validate, thereby maintaining high model training quality while dramatically improving dataset generation efficiency
Data Source
AI summary
Various embodiments, methods and systems for implementing a distributed computing frameset assembly engine are provided. Initially, a synthetic data scene is accessed. A first set of values for scene-variation parameters is determined. The first set of values is automatically determined for generating a synthetic data scene frameset. The synthetic data scene frameset is generated based on the first set of values. The synthetic data scene frameset comprises at least a first frame in the frameset comprising the synthetic data scene updated based on a value for a scene-variation parameter. The synthetic data scene frameset is stored.