Huawei technologies co., ltd. (20240211770). COMMUNICATION METHOD AND APPARATUS simplified abstract
Contents
COMMUNICATION METHOD AND APPARATUS
Organization Name
Inventor(s)
Xiaomeng Chai of Shanghai (CN)
COMMUNICATION METHOD AND APPARATUS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240211770 titled 'COMMUNICATION METHOD AND APPARATUS
Simplified Explanation: The patent application describes a method and apparatus for communication where a terminal receives information from a network device, uses this information to determine training data, and autonomously performs model training to obtain an AI model.
- The terminal receives information from a network device.
- The terminal determines pieces of training data based on the received information.
- The terminal performs model training to obtain an AI model.
- The network device configures the information used to determine the training data.
- The terminal autonomously performs model training, reducing the need for separate AI model configuration and minimizing air interface overheads.
Potential Applications: 1. Telecommunications industry for efficient model training. 2. IoT devices for autonomous data processing. 3. Healthcare sector for AI model development.
Problems Solved: 1. Reducing the need for manual AI model configuration. 2. Minimizing air interface overheads. 3. Enhancing efficiency in model training processes.
Benefits: 1. Streamlined model training process. 2. Reduced overhead costs. 3. Improved accuracy in AI model development.
Commercial Applications: The technology can be utilized in telecommunications, IoT, and healthcare industries for efficient data processing and AI model development, leading to cost savings and improved performance.
Questions about the Technology: 1. How does this technology improve the efficiency of model training processes? 2. What are the potential cost-saving benefits for businesses implementing this communication method?
Original Abstract Submitted
a communication method and apparatus. a terminal receives first information from a network device. the terminal determines n pieces of training data based on the first information, and n is an integer. the terminal performs model training based on the n pieces of training data, to obtain a first ai model. the network device configures the first information used to determine the n pieces of training data for the terminal. the terminal performs model training based on the n pieces of training data autonomously. separately configuring an ai model for the terminal is not necessary and air interface overheads are reduced.
