Abstract: Machine learning-based part selection in relation to one or more end use environmental conditions is provided. The process includes training a machine learning model to facilitate evaluation of a part for use in a product based on an environmental condition. Further, the process includes receiving measurement data for the part, and establishing a score for the part by comparing the measurement data for the part to a specification for the part. In addition, the method includes using the machine learning model and the established score for the part in determining whether to use the part in the product based on the environmental condition.

Abstract: Described are techniques for artificial intelligence (AI) assisted recommendations for component parts for end-products that reduce the occurrence of a product failures. The techniques include obtaining measurement data for groups of component parts configured to be assembled as part of an end-product. The techniques further include obtaining specification scores for the component parts included in the groups of component parts based on the measurement data. The techniques further include selecting a component part combination from the groups of component parts using artificial intelligence analysis of the specification scores to determine that the component part combination decreases a probability of a product failure as compared to historical occurrences of the product failure. The techniques further include outputting information for at least one component part included in the component part combination.

Abstract: A computer-implemented method for updating a device is disclosed. The computer-implemented method includes identifying that an update associated with the device is available. The computer-implemented method further includes determining whether the available update associated with the device is permitted. The computer-implemented method further includes determining, in response to the available update associated with the device being permitted, an optimal scheduled time for performing the update on the device. The computer-implemented method further performing the update on the device at the scheduled time.

Abstract: A computer-implemented method for updating a device is disclosed. The computer-implemented method includes identifying that an update associated with the device is available. The computer-implemented method further includes determining whether the available update associated with the device is permitted. The computer-implemented method further includes determining, in response to the available update associated with the device being permitted, an optimal scheduled time for performing the update on the device. The computer-implemented method further performing the update on the device at the scheduled time.

Abstract: A tool for performing remote part reseat actions. Responsive to receiving a request for a scheduled operation, the tool generates an operation table in a push file. Responsive to a determination that there is at least one redundant component for the scheduled operation, the tool identifies the at least one redundant component. The tool determines one or more tolerable errors for the at least one redundant component. The tool appends the at least one redundant component and the one or more tolerable errors to the operation table in the push file. The tool schedules the push file to prescribe one or more recovery operations for the scheduled operation.