US Patent Application 17739707. LEARNING-AUGMENTED APPLICATION DEPLOYMENT PIPELINE simplified abstract
Contents
LEARNING-AUGMENTED APPLICATION DEPLOYMENT PIPELINE
Organization Name
Inventor(s)
Roli Agrawal of Centreville VA (US)
Varun Nalamati of Cumming GA (US)
Laxmi Kadariya of Chantilly VA (US)
Patrick Tirtapraja of Arlington VA (US)
Nicholas Sorkin of Vienna VA (US)
Taylor Gaskins of San Francisco CA (US)
Hasan Alatrakchi of Vienna VA (US)
LEARNING-AUGMENTED APPLICATION DEPLOYMENT PIPELINE - A simplified explanation of the abstract
This abstract first appeared for US patent application 17739707 titled 'LEARNING-AUGMENTED APPLICATION DEPLOYMENT PIPELINE
Simplified Explanation
- The patent application describes a method that uses a neural network to determine rule-related weights and scale rule results for an application. - The method involves providing the neural network with metrics obtained from executing the application in a test environment. - The neural network uses these metrics to determine the rule-related weights, which are then used to scale the rule results. - The scaled rule results are compared against a threshold to determine if they pass or fail. - If the scaled rule results fail the threshold but the application is still selected for deployment in a production environment, the neural network is re-trained. - The re-training involves using the rule results of the application, an indication of its selection for deployment, and the rule results of other applications in the test environment. - The re-trained neural network generates updated rule-related weights, which are used to scale the rule results and determine updated scaled rule results.
Original Abstract Submitted
A method includes providing a neural network with metrics obtained from an execution of an application in a test environment to determine rule-related weights, scaling rule results with the rule-related weights to determine scaled rule results. The method also includes re-training the neural network with the rule results of the application, an indication that the executed application is selected for deployment in the production environment, and rule results of other applications in the test environment in response to a determination that the scaled rule results fail a threshold but that the application is selected for deployment in a production environment. The method also includes providing the re-trained neural network with the rule results to generate updated rule-related weights and scaling the rule results by the updated rule-related weights to determine updated scaled rule results.
