Huawei technologies co., ltd. (20240338573). SYSTEMS AND METHODS FOR ENABLING AUTOMATED TRANSFER LEARNING simplified abstract
Contents
- 1 SYSTEMS AND METHODS FOR ENABLING AUTOMATED TRANSFER LEARNING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SYSTEMS AND METHODS FOR ENABLING AUTOMATED TRANSFER LEARNING - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about AI Model Training on Network Platforms and Edge Computing
- 1.13 Original Abstract Submitted
SYSTEMS AND METHODS FOR ENABLING AUTOMATED TRANSFER LEARNING
Organization Name
Inventor(s)
SYSTEMS AND METHODS FOR ENABLING AUTOMATED TRANSFER LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240338573 titled 'SYSTEMS AND METHODS FOR ENABLING AUTOMATED TRANSFER LEARNING
Simplified Explanation
The patent application describes a method and system for training an artificial intelligence model using a network platform that deploys routines on a network edge.
- An AI enabler, routine manager, service manager, and data selector are key components of the network platform.
- The AI enabler acts as a classifier for the AI model, while the routine client function serves as a data source.
- The routine server function also functions as a classifier for the AI model.
Key Features and Innovation
- Training an AI model using a network platform with routines deployed on a network edge.
- Utilizing an AI enabler, routine manager, service manager, and data selector in the network platform.
- Implementing classifiers for the AI model in the AI enabler and routine server function.
Potential Applications
This technology can be applied in various industries such as healthcare, finance, marketing, and more for training AI models efficiently.
Problems Solved
- Efficient training of AI models.
- Optimizing hyper-parameters for AI models.
- Filtering data effectively for training AI models.
Benefits
- Improved accuracy and performance of AI models.
- Faster training process.
- Enhanced data selection and filtering capabilities.
Commercial Applications
This technology can be used in industries such as healthcare for medical diagnosis, finance for fraud detection, and marketing for customer segmentation.
Prior Art
Researchers can explore prior art related to AI model training on network platforms and edge computing technologies.
Frequently Updated Research
Stay updated on the latest advancements in AI model training on network platforms and edge computing technologies for improved efficiency and performance.
Questions about AI Model Training on Network Platforms and Edge Computing
How does this technology improve the efficiency of training AI models?
This technology improves efficiency by deploying routines on a network edge, utilizing AI enablers, and optimizing hyper-parameters.
What are the potential applications of this technology beyond the ones mentioned in the abstract?
This technology can also be applied in autonomous vehicles, cybersecurity, and industrial automation for enhanced decision-making and predictive analytics.
Original Abstract Submitted
a method and system for training an artificial intelligence (ai) model with a network platform deploying routines on a network edge. the network platform includes at least one ai enabler, associated to a routine manager, and topologically located between a routine client function and a routine server function implementing a routine; a service manager including an ai hyper-parameter optimizer; and a data selector filtering data from a routine client function to an ai enabler. a routine client function can represent a data source, an ai enabler can implement a classifier of an ai model, and a routine server function can implement a classifier of the ai model.
