Dell products l.p. (20240303174). DEVICE PRIORITY PREDICTION USING MACHINE LEARNING simplified abstract
Contents
DEVICE PRIORITY PREDICTION USING MACHINE LEARNING
Organization Name
Inventor(s)
Parminder Singh Sethi of Ludhiana (IN)
Nithish Kote of Bangalore (IN)
Sajit Siddalingappa Manvi of Bangalore (IN)
DEVICE PRIORITY PREDICTION USING MACHINE LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240303174 titled 'DEVICE PRIORITY PREDICTION USING MACHINE LEARNING
Simplified Explanation:
The method described in the abstract involves using machine learning techniques to analyze application data and performance data of multiple devices. It determines the performance states of each device and the priorities of applications on those devices. Based on this analysis, it predicts the priority of the devices and generates a report on their priorities.
- Key Features and Innovation:
- Utilizes machine learning techniques to analyze application and performance data. - Determines performance states and application priorities for multiple devices. - Predicts device priorities based on performance states and application priorities.
Potential Applications: This technology could be applied in various industries such as telecommunications, network management, and IoT devices to optimize device performance and prioritize applications effectively.
Problems Solved: - Helps in efficiently managing device performance and application priorities. - Provides insights into device priorities for better decision-making.
Benefits: - Optimizes device performance based on application priorities. - Enhances overall efficiency and productivity of devices. - Enables proactive management of device priorities.
Commercial Applications: Title: "Optimizing Device Performance with Machine Learning" This technology can be used in network management systems, IoT devices, and mobile applications to improve performance and prioritize tasks effectively. It has implications for optimizing resource allocation and enhancing user experience.
Prior Art: Readers can explore prior research on machine learning techniques in device management and performance optimization to understand the evolution of this technology.
Frequently Updated Research: Stay updated on advancements in machine learning algorithms for device management and performance optimization to leverage the latest innovations in this field.
Questions about Device Performance Optimization: 1. How does machine learning contribute to optimizing device performance?
- Machine learning algorithms analyze data to determine performance states and application priorities, enabling effective device management.
2. What are the potential implications of prioritizing applications on device performance?
- Prioritizing applications based on performance data can lead to improved efficiency and user experience on devices.
Original Abstract Submitted
a method comprises analyzing application data and performance data of a plurality of devices using one or more machine learning techniques. in the method, performance states of respective ones of the plurality of devices are determined, and priorities of applications of the respective ones of the plurality of devices is determined based at least in part on the analyzing. the method further comprises predicting a priority of the plurality of devices based at least in part on the performance states and the priorities of the applications. a report of the priority of the plurality of devices is generated.
