18121387. MAINTAINING CONFIGURABLE SYSTEMS BASED ON CONNECTIVITY DATA simplified abstract (Dell Products L.P.)
Contents
- 1 MAINTAINING CONFIGURABLE SYSTEMS BASED ON CONNECTIVITY DATA
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MAINTAINING CONFIGURABLE SYSTEMS BASED ON CONNECTIVITY DATA - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about the Technology
- 1.13 Original Abstract Submitted
MAINTAINING CONFIGURABLE SYSTEMS BASED ON CONNECTIVITY DATA
Organization Name
Inventor(s)
MAINTAINING CONFIGURABLE SYSTEMS BASED ON CONNECTIVITY DATA - A simplified explanation of the abstract
This abstract first appeared for US patent application 18121387 titled 'MAINTAINING CONFIGURABLE SYSTEMS BASED ON CONNECTIVITY DATA
Simplified Explanation
The patent application describes methods, apparatus, and storage media for maintaining configurable systems based on connectivity data. It involves using machine learning to predict changes in system configurations and initiating automated actions based on the predictions.
- Obtaining connectivity data from system components
- Using machine learning to predict changes in system configurations
- Adjusting forecasted values based on the predictions
- Initiating automated actions based on the adjusted values
Key Features and Innovation
- Utilizing connectivity data to predict changes in system configurations
- Incorporating machine learning regression models for forecasting
- Automating actions based on predicted changes
- Enhancing system maintenance and adaptability
Potential Applications
This technology can be applied in various industries such as telecommunications, manufacturing, and smart home systems. It can optimize system configurations, improve efficiency, and reduce downtime.
Problems Solved
This technology addresses the challenge of maintaining and adapting configurable systems based on usage behavior. It streamlines the process of predicting and implementing changes in system configurations.
Benefits
- Improved system efficiency and adaptability
- Enhanced predictive maintenance capabilities
- Reduced downtime and operational costs
- Streamlined system configuration management
Commercial Applications
- Predictive maintenance solutions for industrial equipment
- Smart home automation systems
- Telecommunications network optimization tools
- Manufacturing process automation software
Prior Art
Readers can explore prior research on machine learning in system maintenance and predictive analytics in configurable systems to understand the background of this technology.
Frequently Updated Research
Stay updated on advancements in machine learning algorithms for predictive modeling and system configuration optimization to enhance the application of this technology.
Questions about the Technology
How does this technology improve system maintenance processes?
This technology enhances system maintenance by using connectivity data and machine learning to predict and adapt to changes in system configurations.
What industries can benefit from this technology?
Industries such as telecommunications, manufacturing, and smart home systems can benefit from this technology by optimizing system configurations and improving efficiency.
Original Abstract Submitted
Methods, apparatus, and processor-readable storage media for maintaining configurable systems based on connectivity data are provided herein. An example computer-implemented method includes: obtaining connectivity data, from a plurality of components of a system, indicating usage behavior of the plurality of components with respect to a first configuration of the system; providing, to a machine learning regression model, at least a portion of the connectivity data corresponding to a particular period of time, wherein the machine learning regression model generates a regression score indicating a probability of a change from the first configuration to one or more second configurations; causing an adjustment to a forecasted value associated with the one or more second configurations of the system based at least in part on the generated regression score; and initiating one or more automated actions based at least in part on one or more results of the adjusting.
