M2M Data Labeling Resource Generation for AI Training
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing machine-to-machine (M2M) systems lack effective methods for labeling and managing data, particularly for training artificial intelligence (AI) models, which hinders efficient data sharing and utilization across different AI models.
Innovation Solution
A method and apparatus for effectively labeling data in M2M systems by determining labels for data and generating resources that include information related to these labels, such as training data indicators, annotation types, labeling formats, and ontology references.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data labeling is performed manually in M2M systems, then labeling accuracy can be maintained, but the time and resource consumption increases significantly
Solution Approach 1:
The system enables automated data labeling through AI models that perform labeling tasks independently without human intervention. The labeling service receives data, automatically generates labels using trained AI models, and returns labeled data, allowing the system to serve itself in the labeling process while maintaining consistent accuracy standards.
Solution Approach 2:
The system performs preliminary data processing and pre-labeling using AI models before final labeling is needed. By pre-training models and preparing labeling frameworks in advance, the system reduces the time required for actual labeling operations while maintaining quality standards through established validation protocols.
2Productivity
If comprehensive label information is stored for all data, then data utilization efficiency improves, but the complexity of data management increases
Solution Approach 1:
The label information is segmented into structured components including label identifiers, data identifiers, ontology references, and format specifications. This segmentation allows the system to manage complex label data through modular, organized units that can be independently processed and retrieved, reducing overall management complexity while maintaining comprehensive information.
Solution Approach 2:
The system introduces an intermediary labeling service that acts as a mediator between raw data and AI models. This service manages the complex label information storage and retrieval processes, providing simplified interfaces for data utilization while handling the underlying complexity of comprehensive label management through standardized protocols and data structures.
3Adaptability or versatility
If multiple AI models are trained with the same data, then model diversity improves, but data sharing and interoperability become difficult
Solution Approach 1:
The system creates universal label formats that can be used across multiple AI models and applications. By establishing standardized label structures with common ontologies and formats, the same labeled data can serve multiple different AI models simultaneously, enabling both model diversity and effective data sharing through a universal interface that maintains information integrity across different uses.
Data Source
AI summary
An apparatus for labeling data in a machine-to-machine (M2M) system, and a method for operating the apparatus may include determining a label for data and generating a resource including information related to the label. The resource may include at least one of information indicating being training data, information indicating a type of an annotation, information indicating a format of labeling, information describing an annotation, information on an applied ontology, and information referencing the data.