17956200. MONITORING AND UPDATING MACHINE LEARNING MODELS simplified abstract (QUALCOMM Incorporated)
Contents
- 1 MONITORING AND UPDATING MACHINE LEARNING MODELS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MONITORING AND UPDATING MACHINE LEARNING MODELS - 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 Unanswered Questions
- 1.11 Original Abstract Submitted
MONITORING AND UPDATING MACHINE LEARNING MODELS
Organization Name
Inventor(s)
Rajeev Kumar of San Diego CA (US)
Gavin Bernard Horn of La Jolla CA (US)
Aziz Gholmieh of Del Mar CA (US)
MONITORING AND UPDATING MACHINE LEARNING MODELS - A simplified explanation of the abstract
This abstract first appeared for US patent application 17956200 titled 'MONITORING AND UPDATING MACHINE LEARNING MODELS
Simplified Explanation
The abstract of the patent application describes methods, systems, and devices for wireless communications involving machine learning models. The user equipment (UE) receives a control signal indicating an event trigger for reporting a performance parameter associated with the machine learning model. The UE also receives signals indicating input data for monitoring the performance of the machine learning model. When the event trigger is detected, the UE transmits a report with the performance parameter.
- User equipment (UE) receives a control signal indicating an event trigger for reporting a performance parameter associated with a machine learning model.
- UE receives signals indicating input data for monitoring the performance of the machine learning model.
- Upon detecting the event trigger, the UE transmits a report comprising the performance parameter.
Potential Applications
This technology can be applied in:
- Wireless communication systems
- Machine learning model monitoring and reporting
Problems Solved
This technology helps in:
- Efficient monitoring of machine learning model performance
- Timely reporting of performance parameters
Benefits
The benefits of this technology include:
- Improved performance monitoring in wireless communications
- Enhanced machine learning model optimization
Potential Commercial Applications
Commercial applications of this technology can be seen in:
- Telecommunications industry
- IoT devices
Possible Prior Art
One possible prior art could be:
- Existing systems for monitoring machine learning model performance in wired communications.
Unanswered Questions
How does this technology impact the overall efficiency of wireless communication systems?
This technology can potentially improve the efficiency of wireless communication systems by enabling real-time monitoring and reporting of machine learning model performance, leading to better optimization and decision-making processes.
What are the security implications of transmitting performance parameters over wireless networks?
Transmitting performance parameters over wireless networks may raise concerns about data security and privacy. It is essential to ensure secure transmission protocols and encryption methods to protect sensitive information.
Original Abstract Submitted
Methods, systems, and devices for wireless communications are described. The method may include a user equipment (UE) may receive a control signal indicating an event trigger for reporting a performance parameter associated with a machine learning model. Further, the UE may receive one or more signals indicating input data for monitoring a performance of the machine learning model by the UE. Upon detecting the event trigger, the UE may transmit a report comprising the performance parameter.
