Qualcomm incorporated (20240163694). CONFIGURING A MULTI-MODEL MACHINE LEARNING APPLICATION simplified abstract
Contents
- 1 CONFIGURING A MULTI-MODEL MACHINE LEARNING APPLICATION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 CONFIGURING A MULTI-MODEL MACHINE LEARNING APPLICATION - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
CONFIGURING A MULTI-MODEL MACHINE LEARNING APPLICATION
Organization Name
Inventor(s)
CONFIGURING A MULTI-MODEL MACHINE LEARNING APPLICATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240163694 titled 'CONFIGURING A MULTI-MODEL MACHINE LEARNING APPLICATION
Simplified Explanation
The abstract describes methods, systems, and devices for wireless communications, specifically focusing on a multi-model machine learning application that combines a backbone model with task-specific models to process signals and generate outputs for communication with wireless devices.
- User equipment (UE) receives a control message identifying a backbone model and at least one task-specific model.
- The UE then receives the backbone model and a first task-specific model.
- The UE uses a multi-model machine learning application that combines the backbone model and the first task-specific model to process signals and generate outputs.
- The UE communicates with a wireless device based on the generated outputs.
Potential Applications
This technology can be applied in various wireless communication systems, such as mobile networks, IoT devices, and smart home systems.
Problems Solved
This technology solves the problem of efficiently processing signals in wireless communications by utilizing a multi-model machine learning application.
Benefits
The benefits of this technology include improved signal processing efficiency, enhanced communication performance, and the ability to adapt to different task-specific requirements.
Potential Commercial Applications
Potential commercial applications of this technology include telecommunications companies, IoT device manufacturers, and smart home automation companies.
Possible Prior Art
One possible prior art could be the use of machine learning in wireless communications, but the specific combination of backbone models and task-specific models for signal processing may be a novel approach.
Unanswered Questions
How does this technology impact battery life in wireless devices?
This article does not address the potential impact of using multi-model machine learning applications on the battery life of wireless devices. It would be interesting to know if there are any optimizations in place to minimize power consumption.
What are the security implications of using machine learning in wireless communications?
The article does not delve into the security aspects of implementing machine learning in wireless communications. It would be important to understand how vulnerabilities are mitigated and data privacy is maintained in such systems.
Original Abstract Submitted
methods, systems, and devices for wireless communications are described. a user equipment (ue) may receive a control message identifying a backbone model that is combinable with at least one task-specific model to generate a multi-model machine learning application. the ue may receive the backbone model and a first task-specific model identified by the control message. the ue use a multi-model machine learning application that is a combination of the backbone model and the first task-specific model to process one or more received signals to generate one or more outputs. the ue may communicate with a wireless device based on the one or more outputs.
