Abstract: Methods and systems are disclosed that include the identification of one or more actions in an action flow that is intended to resolve a problem, and to guide a user through the one or more actions of such an action flow, dynamically adjusting the action flow during such guidance and/or subsequent thereto, using machine learning techniques. In some embodiments, such a method can include. for example, receiving outcome information at a machine learning system (where the outcome information is associated with an action of an action flow and the action flow comprises a plurality of actions), generating update information (where the update information is generated by the machine learning system based, at least in part, on the outcome information), and updating action information of the action (where the action information is updated based, at least in part, on the update information).

Abstract: In some examples, a server may receive a user communication describing an issue with a computing device and assign a case to the computing device. The server may determine previously provided telemetry data (e.g., logs and usage data sent by the computing device) as well as previous cases associated with the computing device. Machine learning may be used to predict, based on the user communication, the telemetry data, and the previous cases, a predicted cause of the issue, a predicted time to close the case, and a predicted set of steps to resolve the issue. The machine learning may predict a bottleneck in at least one step of the set of steps that causes the predicted time to close to exceed a threshold and predict one or more actions to address the bottleneck. The server may automatically perform at least one action of the one or more actions.