Qualcomm incorporated (20240267710). MACHINE LEARNING GROUP SWITCHING simplified abstract
Contents
MACHINE LEARNING GROUP SWITCHING
Organization Name
Inventor(s)
June Namgoong of San Diego CA (US)
MACHINE LEARNING GROUP SWITCHING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240267710 titled 'MACHINE LEARNING GROUP SWITCHING
The abstract of this patent application discusses aspects related to wireless communication, specifically focusing on machine learning groups for user equipment (UE). The UE may receive an indication to switch from one ML group to another or continue with the current group, based on which it will perform actions associated with wireless communication.
- User equipment (UE) may switch between machine learning (ML) groups based on received indications.
- Actions associated with wireless communication are performed by UE using models developed by ML groups.
- The UE may switch to a second ML group or continue with the first ML group based on the indication received.
Potential Applications: - This technology can be applied in wireless communication systems to enhance performance and efficiency. - It can be used in IoT devices to optimize communication processes based on machine learning models.
Problems Solved: - Efficient utilization of machine learning in wireless communication systems. - Seamless switching between ML groups for improved performance.
Benefits: - Enhanced performance and efficiency in wireless communication. - Adaptive communication processes based on machine learning models.
Commercial Applications: Title: "Enhancing Wireless Communication Efficiency with Machine Learning Groups" This technology can be commercially utilized in telecommunications companies to improve network performance and optimize communication processes. It can also be integrated into IoT devices to enhance connectivity and data transmission.
Questions about Machine Learning Groups in Wireless Communication: 1. How does the indication mechanism work for switching between ML groups in user equipment? 2. What are the key factors considered when developing machine learning models for wireless communication systems?
Original Abstract Submitted
various aspects of the present disclosure generally relate to wireless communication. in some aspects, a first user equipment (ue) may receive an indication on whether to switch from a first machine learning (ml) group to a second ml group or to continue with the first ml group. the ue may switch to the second ml group if the indication is to switch or continuing with the first ml group if the indication is to continue. the ue may perform a first action associated with wireless communication based at least in part on a first model developed with the first ml group if the indication is to continue. the ue may perform a second action associated with wireless communication based at least in part on a second model developed with the second ml group if the indication is to switch. numerous other aspects are described.
