18564986. MACHINE LEARNING GROUP SWITCHING simplified abstract (QUALCOMM Incorporated)
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 18564986 titled 'MACHINE LEARNING GROUP SWITCHING
The abstract of this patent application discusses the use of machine learning groups in wireless communication to determine whether a user equipment (UE) should switch from one group to another for improved performance.
- User equipment (UE) may receive an indication to switch from a first machine learning (ML) group to a second ML group or continue with the first ML group.
- The UE will switch to the second ML group if the indication is to switch, or continue with the first ML group if the indication is to continue.
- The UE will perform actions associated with wireless communication based on models developed with the respective ML groups.
- The first action will be based on a model developed with the first ML group if the indication is to continue.
- The second action will be based on a model developed with the second ML group if the indication is to switch.
Potential Applications: - Enhanced wireless communication performance - Adaptive machine learning algorithms for improved user experience
Problems Solved: - Optimizing machine learning groups for better wireless communication - Enhancing user equipment performance based on ML models
Benefits: - Improved efficiency in wireless communication - Enhanced user experience with adaptive machine learning algorithms
Commercial Applications: Title: "Enhanced Wireless Communication Technology for Improved User Experience" This technology can be applied in telecommunications, IoT devices, and smart city infrastructure to optimize wireless communication performance and user experience.
Questions about the technology: 1. How does machine learning improve wireless communication performance? 2. What are the potential drawbacks of switching between machine learning groups in user equipment?
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.
