Qualcomm incorporated (20240114364). MONITORING AND UPDATING MACHINE LEARNING MODELS simplified abstract
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 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 20240114364 titled 'MONITORING AND UPDATING MACHINE LEARNING MODELS
Simplified Explanation
The abstract 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 control signal for reporting performance parameter of machine learning model.
- UE receives input data for monitoring machine learning model performance.
- UE transmits report with performance parameter upon detecting event trigger.
Potential Applications
This technology can be applied in various industries such as telecommunications, IoT, and data analytics for optimizing machine learning model performance monitoring.
Problems Solved
1. Efficient monitoring of machine learning model performance in wireless communications. 2. Timely reporting of performance parameters based on event triggers.
Benefits
1. Improved performance optimization of machine learning models. 2. Real-time monitoring and reporting capabilities for enhanced decision-making. 3. Enhanced user experience in wireless communication networks.
Potential Commercial Applications
Optimizing network performance in telecommunications. Enhancing IoT device monitoring and management. Improving data analytics processes for machine learning models.
Possible Prior Art
One possible prior art could be the use of machine learning models for performance optimization in wireless communications, but the specific method of triggering event-based reporting of performance parameters may be a novel aspect of this technology.
Unanswered Questions
How does this technology impact network efficiency in wireless communications?
This technology can potentially improve network efficiency by enabling real-time monitoring and reporting of machine learning model performance, leading to quicker adjustments and optimizations.
What are the implications of using machine learning models in wireless communications for data security?
The use of machine learning models in wireless communications may raise concerns about data security and privacy, especially when transmitting performance parameters. Implementing robust encryption and security measures would be crucial to address these implications.
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.
