17956996. SYSTEMS AND METHODS FOR ADJUSTING DATA PROCESSING COMPONENTS FOR NON-OPERATIONAL TARGETS simplified abstract (Capital One Services, LLC)
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 17956996 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 with inference entries to make predictions for each node on the operational status of target systems.
- Using a second machine learning model to predict non-operational target systems based on predictions and inference entries.
- 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 and prevent system failures before they occur.
Problems Solved
This technology helps in proactively identifying potential system failures, allowing for timely maintenance and reducing downtime and operational disruptions.
Benefits
The benefits of this technology include improved system reliability, increased operational efficiency, and cost savings through predictive maintenance.
Potential Commercial Applications
One potential commercial application of this technology could be in the field of predictive maintenance software for industrial equipment, where it can help companies optimize their maintenance schedules and reduce unplanned downtime.
Possible Prior Art
Prior art in the field of predictive maintenance and machine learning algorithms for system failure prediction may exist, but specific examples are not provided in this patent application.
What are the specific machine learning algorithms used in this technology?
The specific machine learning algorithms used in this technology are not mentioned in the abstract. It would be helpful to know which algorithms are employed to understand the technical aspects of the innovation better.
How accurate are the predictions made by the machine learning models in this technology?
The accuracy of the predictions made by the machine learning models is not discussed in the abstract. Knowing the accuracy rate would be crucial for assessing the reliability and effectiveness of the technology.
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.
