Capital one services, llc (20240112017). SYSTEMS AND METHODS FOR ADJUSTING DATA PROCESSING COMPONENTS FOR NON-OPERATIONAL TARGETS simplified abstract
Contents
- 1 SYSTEMS AND METHODS FOR ADJUSTING DATA PROCESSING COMPONENTS FOR NON-OPERATIONAL TARGETS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SYSTEMS AND METHODS FOR ADJUSTING DATA PROCESSING COMPONENTS FOR NON-OPERATIONAL TARGETS - 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
SYSTEMS AND METHODS FOR ADJUSTING DATA PROCESSING COMPONENTS FOR NON-OPERATIONAL TARGETS
Organization Name
Inventor(s)
Aamer Charania of Flower Mound TX (US)
Abhisek Jana of Herndon VA (US)
Jiankun Liu of Flower Mound TX (US)
Behrouz Saghafi Khadem of Frisco TX (US)
SYSTEMS AND METHODS FOR ADJUSTING DATA PROCESSING COMPONENTS FOR NON-OPERATIONAL TARGETS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240112017 titled 'SYSTEMS AND METHODS FOR ADJUSTING DATA PROCESSING COMPONENTS FOR NON-OPERATIONAL TARGETS
Simplified Explanation
The patent application describes systems and methods for adjusting data processing components using machine learning models to predict the operational status of target systems within a future period of time.
- Training a machine learning model using a similarity graph generated from training entries to predict if a target system will be non-operational in the future.
- Updating the similarity graph based on training and inference entries to predict the operational status of target systems.
- Using a second machine learning model to make predictions on the operational status of target systems.
- Adjusting data processing components related to the target system based on the predictions.
Potential Applications
This technology could be applied in various industries such as manufacturing, healthcare, and telecommunications to predict system failures and proactively address maintenance issues.
Problems Solved
This technology helps in predicting system failures before they occur, allowing for preventive maintenance and reducing downtime and associated costs.
Benefits
The benefits of this technology include improved system reliability, reduced maintenance costs, increased operational efficiency, and enhanced overall performance.
Potential Commercial Applications
Potential commercial applications of this technology include predictive maintenance solutions, asset management systems, and real-time monitoring platforms for critical infrastructure.
Possible Prior Art
One possible prior art for this technology could be predictive maintenance systems using historical data and machine learning algorithms to forecast equipment failures.
What are the specific machine learning models used in this technology?
The specific machine learning models used in this technology are not explicitly mentioned in the abstract. Further details on the types of models and algorithms employed would provide a clearer understanding of the innovation.
How is the training data collected and processed to generate the similarity graph?
The abstract does not elaborate on the specific methods used to collect and process training data for generating the similarity graph. Understanding the data collection process and preprocessing steps would shed light on the effectiveness and accuracy of the predictions made by the system.
Original Abstract Submitted
systems and methods for adjusting data processing components. in some aspects, the systems and methods include training a first machine learning model using a similarity graph generated based on training entries to predict whether a target system related to a node in the similarity graph will be non-operational within a future period of time, processing using the trained first machine learning model an updated similarity graph generated based on training and inference entries to predict for each node for the inference entries whether a target system related to the node will be non-operational within the future period of time, processing using a second machine learning model predictions and associated inference entries to predict that a target system related to a node for an entry will be non-operational within the future period of time, and adjusting data processing components related to the target system.
