Common Data Infrastructure for Multi-Source Labeling
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Solution Overview
Problem
The challenge in industries dealing with confidential information is the inefficiency and lack of security in data labeling processes for machine learning workflows, which hinders the effective use of machine learning classifiers for predictions and insights.
Innovation Solution
The implementation of a system that provides multiple data labeling interfaces with a common data infrastructure, allowing data from various sources in different formats to be converted into a common schema, and using a machine learning classifier to generate label suggestions, which are then processed by different data labeling tools.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data from multiple sources in different formats is processed by multiple specialized labeling tools, then labeling capability and versatility are improved, but system complexity and integration difficulty increase
Solution Approach 1:
The patent introduces a data infrastructure layer as an intermediary between diverse data sources and multiple labeling tools. This layer provides standardized data access, format conversion, and management capabilities, allowing labeling tools to work with unified data interfaces while the infrastructure handles the complexity of multiple formats and sources.
Solution Approach 2:
The data infrastructure is designed as a universal platform that serves multiple labeling tools simultaneously. It provides common data access, storage, and management functions that can be shared across different labeling tools, eliminating the need for each tool to independently handle data acquisition and preprocessing.
2Ease of manufacture
If data is accessed and processed by multiple specialized labeling tools independently, then each tool can be optimized for its specific task, but data access efficiency and consistency deteriorate
Solution Approach 1:
The patent merges data access, storage, and management functions into a single centralized data infrastructure. This consolidation allows all labeling tools to access the same data through unified interfaces, improving access efficiency and ensuring consistency across different labeling operations while still allowing individual tools to be optimized for their specific labeling tasks.
3Reliability
If confidential information is processed through multiple independent labeling tools, then specialized processing capability is improved, but security and privacy protection deteriorate
Solution Approach 1:
The data infrastructure acts as a secure intermediary between data sources and labeling tools. It implements centralized security controls, access management, and data protection mechanisms that ensure confidential information is handled securely while still allowing specialized labeling tools to process the data according to their specific capabilities.
4Adaptability or versatility
If each data labeling tool has its own data access and storage methods, then tool independence and flexibility are improved, but overall system scalability and maintainability worsen
Solution Approach 1:
The patent segments the system into two independent layers: a data infrastructure layer that handles data access, storage, and management, and a labeling tools layer that provides specialized labeling capabilities. This segmentation allows labeling tools to remain independent and flexible in their processing logic while the infrastructure layer handles the complexity of data management, improving overall system scalability and maintainability.
Data Source
AI summary
Systems as described herein may provide multiple data labeling interfaces with a common data infrastructure. An annotation system may retrieve data from a plurality of data sources and convert the data to a common schema. The annotation system may train a machine learning classifier to output a plurality of label suggestions, which may be sent to a plurality of data labeling tools. A plurality of labels may be received from the data labeling tools. The annotation system may accordingly export the plurality of labels and the converted data in the common schema to a label database.