Intel corporation (20240298194). ENHANCED ON-THE-GO ARTIFICIAL INTELLIGENCE FOR WIRELESS DEVICES simplified abstract
Contents
ENHANCED ON-THE-GO ARTIFICIAL INTELLIGENCE FOR WIRELESS DEVICES
Organization Name
Inventor(s)
Markus Dominik Mueck of Unterhaching (DE)
Miltiadis Filippou of Muenchen (DE)
Thomas Luetzenkirchen of Taufkirchen (DE)
Leonardo Gomes Baltar of Muenchen (DE)
ENHANCED ON-THE-GO ARTIFICIAL INTELLIGENCE FOR WIRELESS DEVICES - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240298194 titled 'ENHANCED ON-THE-GO ARTIFICIAL INTELLIGENCE FOR WIRELESS DEVICES
The abstract describes a system that facilitates machine learning-based operations at a user equipment (UE) connected to a radio access network (RAN). A network AI/ML service may identify a request for a machine learning model configuration from a UE device, determine the location of the UE device, select an available machine learning agent based on the request and location, format a request to the agent for the configuration, receive and identify the configuration from the agent, and respond to the initial request with the configuration for the UE device.
- System for facilitating machine learning-based operations at a UE connected to a RAN
- Network AI/ML service identifies and processes requests for machine learning model configurations
- Location of the UE device is determined to select an available machine learning agent
- Agent is chosen based on the request and location, and a configuration request is formatted
- Configuration received from the agent is identified and sent as a response to the initial request
Potential Applications: - Enhancing machine learning capabilities for UE devices in a network environment - Optimizing machine learning model configurations based on device location - Improving efficiency and performance of machine learning operations in a RAN setting
Problems Solved: - Streamlining the process of configuring machine learning models for UE devices - Enhancing the accuracy and relevance of machine learning operations in a network - Facilitating seamless communication between network AI/ML services and UE devices
Benefits: - Increased efficiency in deploying machine learning models to UE devices - Enhanced performance and customization of machine learning operations - Improved user experience and network optimization through tailored configurations
Commercial Applications: Title: "Enhanced Machine Learning Configuration for User Equipment in Radio Access Networks" This technology can be utilized by telecommunications companies to optimize machine learning operations for UE devices, leading to improved network performance, user experience, and overall efficiency. It can also be integrated into IoT devices to enhance data processing and analytics capabilities.
Questions about Machine Learning-Based Operations in RANs: 1. How does this system improve the efficiency of machine learning operations for UE devices? 2. What are the key factors considered when selecting an available machine learning agent based on the request and location?
Original Abstract Submitted
this disclosure describes systems, methods, and devices related to facilitating machine learning-based operations at a user equipment (ue) connected to a radio access network (ran). a network ai/ml (artificial intelligence/machine learning) service or function may identify a first request, received from a user equipment (ue) device, for a machine learning model configuration; determine a location of the ue device; select, based on the first request and the location, an available machine learning agent; format a second request to the available machine learning agent for the machine learning configuration; identify the machine learning configuration received from the available machine learning agent based on the second request; and format a response to the first request, the response comprising the machine learning configuration for the ue device.
