Abstract: A method for automated product update analysis and management based on post-distribution feedback. The method includes distributing a product update to a first subset of endpoints and generating a data gathering mechanism to assess the functionality of the affected endpoints. After implementing the update, feedback data is collected from the endpoints and input into a product update validation model to identify any update rollout failures. If a failure is detected, the cause is mitigated before distributing the update to a second subset of endpoints. This approach ensures that product updates are thoroughly validated, and any issues are addressed before wider distribution.

Abstract: A method of managed network service assessment across a plurality of diverse management services implemented in the managed network. The method includes receiving information associated with the managed network, including identification of endpoints, product status, use behaviour, and incident resolution data. The method further includes receiving implementation data representing the execution of management services in the managed network, categorizing the received information, and generating metrics related to the management services based on the received data and information. The metrics are standardized relative to the managed network to generate standard metrics indicating the maturity and adoption of the management services in the managed network. The standard metrics are grouped and combined according to service class. Combined class metrics are generated and displayed in a user interface.

Abstract: Security compliance may be facilitated for multiple endpoints associated with a network. Multiple batch endpoint assessments may be performed. Each batch endpoint assessment of the multiple batch endpoint assessments may include receiving multiple status indicators associated with at least a subset of endpoints of the multiple endpoints. For each batch endpoint assessment of the multiple batch endpoint assessments, a status may be assigned to each endpoint of the multiple endpoints based on the plurality of status indicators. A state may be generated for each endpoint of the multiple endpoints based on the statuses assigned to the multiple endpoints.

Abstract: A method for diagnosing and remediating system anomalies in a managed network using artificial intelligence (AI). The method includes receiving data from managed devices, discovering anomalies indicative of events in the network, and using an AI engine to analyze the anomalies and related data to determine additional relevant information. A data gathering mechanism is generated based on the AI engine to collect this additional information, which is then distributed to managed devices. The collected data is used to determine alterations to the network to resolve the anomalies, and these alterations are implemented in the managed network.

Abstract: An embodiment includes a method of data collection optimization in a managed network having a digital experience platform that includes collecting data from managed endpoints using first collection criteria. The first collection criteria include a first frequency and a first verbosity. The method includes identifying, in the collected data, device context data that indicates a defined event exists relative to an endpoint. The collected data or the device context data are used to compute a digital experience index. Responsive to the identified device context data, the method includes modifying the first frequency or the first verbosity to implement a second collection criteria relative to a subset of managed endpoints; collecting additional data from the subset of managed endpoints using the second collection criteria; receiving additional context data that indicates the defined event no longer exists; and in response, collecting data from the managed endpoints using the first collection criteria.

Abstract: A method of identification and remediation of a mass event in a managed network. The method includes receiving a first report of an event occurring at a first endpoint of the managed network. In response, a first action is performed. The first action being initiated based on a repeating event trigger not being triggered. The method includes receiving a second report of the event occurring at a second endpoint. In response, the method includes determining whether a threshold that triggers the repeating event trigger is exceeded. The threshold being indicative of a mass event in the managed network. Responsive to the threshold being exceeded, the method includes triggering the repeating event trigger and performing a second automated resolution action in the managed network. The second action being different from the first action. Responsive to the threshold not being exceeded, the method includes performing the first action at the second endpoint.

Abstract: A method of identification and remediation of a mass event in a managed network. The method includes receiving a first report of an event occurring at a first endpoint of the managed network. In response, a first action is performed. The first action being initiated based on a repeating event trigger not being triggered. The method includes receiving a second report of the event occurring at a second endpoint. In response, the method includes determining whether a threshold that triggers the repeating event trigger is exceeded. The threshold being indicative of a mass event in the managed network. Responsive to the threshold being exceeded, the method includes triggering the repeating event trigger and performing a second automated resolution action in the managed network. The second action being different from the first action. Responsive to the threshold not being exceeded, the method includes performing the first action at the second endpoint.