AI Dataset Creation Platform for Automated Labeling and Verification
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Solution Overview
Problem
Generative artificial intelligence models require high-quality, domain-specific datasets that are often unavailable and manually compiled, labeled, and annotated, which is time-consuming and costly.
Innovation Solution
An integrated platform allows non-technical users to create custom datasets using automated dataset creation and evaluation tools, including data cleaning, labeling, synthetic data generation, and verification, enabling the training and fine-tuning of AI models for specific business applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If datasets are manually compiled, labeled, and annotated, then data quality can be ensured, but the process becomes time-consuming and costly
Solution Approach 1:
The system enables automated dataset creation where AI models perform data compilation, labeling, and annotation tasks that were traditionally done manually. The automated system serves itself by generating synthetic data and automatically evaluating dataset quality metrics, eliminating the need for manual human intervention while maintaining high data quality standards.
Solution Approach 2:
The patent replaces manual mechanical processes of data labeling and annotation with automated computational processes. AI models and algorithms automatically compile, label, and annotate datasets, substituting human manual work with automated systems that process data much faster while maintaining or improving quality through consistent application of labeling criteria.
2Adaptability or versatility
If datasets are manually compiled and annotated by third parties, then domain specific data can be obtained, but costs increase significantly
Solution Approach 1:
The system automatically generates domain-specific datasets by configuring data generation parameters that reflect specific business domains. Users can specify domain requirements through configuration files or interfaces, and the automated system generates appropriate synthetic data without requiring expensive third-party annotation services, thereby reducing costs while maintaining domain relevance.
Solution Approach 2:
The system creates synthetic copies of real-world domain-specific data through automated generation processes. Instead of manually collecting and annotating actual domain data, the system generates synthetic data that replicates the characteristics, patterns, and distributions of real domain data, providing cost-effective alternatives to expensive manual data collection and annotation processes.
3Productivity
If automated tools are used for dataset creation, then productivity increases, but ensuring data quality and security becomes more challenging
Solution Approach 1:
The system implements automated feedback loops where AI models generate datasets and automatically evaluate them against quality metrics and security criteria. The evaluation results feed back into the data generation process, allowing iterative refinement of dataset quality. This closed-loop feedback mechanism ensures that productivity gains from automation do not compromise data quality or security standards.
Solution Approach 2:
The system performs preliminary quality assessment and security verification during the data generation process itself, rather than as separate post-processing steps. Configuration files and automated evaluators are set up in advance with quality thresholds and security requirements, ensuring that data quality and security are built into the automated generation process from the beginning, maintaining reliability while achieving high productivity.
Data Source
AI summary
Disclosed herein are systems and methods for generating custom datasets. For example, a method may include using one or more computer systems to gather a first dataset comprising example data relevant to a use case. The method may also include using a first artificial intelligence (AI) model implemented by the one or more computer systems to generate a second dataset. Input to the first AI model includes at least a portion of the first dataset. The method may also include configuring a second AI model using the second dataset. The gathering, generating, and configuring may occur within an integrated platform.