Abstract: Examples of generating contextual compliance policies are described. A meeting invitation can be created by a meeting organizer. The meeting can have compliance rules associated therewith. A compliance policy that facilitates enforcement of the compliance rules during the meeting can be transmitted to attendee client devices and enforced by a management component running on the client devices.

Abstract: Examples described herein include systems and methods for scheduling tasks based on emails intended for a user. An email application, agent, or server can identify a task by parsing an unread email intended for a user. Then a machine learning model specific to that user can be applied to the task and task list information including at least one open time slot. The machine learning model can be previously trained based on timing of prior tasks performed by the user. Based on an output from the model, the task can be scheduled at a first time within the time slot and displayed in a task list on a user device. When the user completes the task, the machine learning model can be updated based on a second time in which the task is completed relative to the first time.

Abstract: Examples of generating contextual compliance policies are described. A meeting invitation can be created by a meeting organizer. The meeting can have compliance rules associated therewith. A compliance policy that facilitates enforcement of the compliance rules during the meeting can be transmitted to attendee client devices and enforced by a management component running on the client devices.

Abstract: Systems and methods are included for detecting driving based on user-specific models for driving detection, and restricting access to an application of the user device while a user is driving. A management agent installed on the user device can collect data from sensors in a user device and provide the data to a management server, which can build a user-specific model for driving detection for that user. The management agent can then use that user-specific model for detecting when the user is driving. When the agent determines that the user is driving, it can enforce a driving policy that limits access to applications and delay or modify notifications generated by applications.

Abstract: Various examples are disclosed for predictive allocation of computing resources based on the predicted location of a user. A computing environment can generate a predictive usage model that predicts a location of a user and allocate computing resources, such as VDI sessions or VMs, to a host device that optimizes latency to the predicted location.

Abstract: Disclosed are various approaches for automating the detection and identification of anomalous devices in a management service. Device check-ins are received by a management service and housed in a data store. The quantity of device check-ins over various time periods can be analyzed using various approaches to identify anomalous devices.

Abstract: Disclosed are various approaches for authenticating a user through a voice assistant device and creating an association between the device and a user account. The request is associated with a network or federated service. A user account can be implicitly authenticated based on proximity of a client device to the voice assistant device. An association between the user account and the voice assistant device can then be created.

Abstract: Various examples are disclosed for predictive allocation of computing resources based on the predicted location of a user. A computing environment can generate a predictive usage model that predicts a location of a user and allocate computing resources, such as VDI sessions or VMs, to a host device that optimizes latency to the predicted location.

Abstract: Disclosed are various approaches for authenticating a user through a voice assistant device and creating an association between the device and a user account. The request is associated with a network or federated service. A user account can be implicitly authenticated based on proximity of a client device to the voice assistant device. An association between the user account and the voice assistant device can then be created.

Abstract: Systems and methods for 3D printer management can allow or reject printing of an object based on a model that is trained with machine learning. In one example, the model classifies the object according to object type. The object type can be compared against a list, such as a whitelist or blacklist, to determine whether to block the object from printing. The lists can be specific to users, such as based on an organizational group to which the user belongs. A print server can apply the model prior to forwarding the object to a 3D printer for printing. Both the models and the lists can evolve based on machine learning, such as based on which print decisions receive override from administrators.

Abstract: Examples described herein include systems and methods for implementing customized, on-device processing workflows. An example method can include training different natural language processing (“NLP”) models using distinct datasets relevant to different backend systems. The different NLP models can be assigned to user devices based on each device user's organizational group. The user devices can implement the customized NLP models to detect triggers within text of an application. Based on the detected trigger, the application can display a user interface element having a selectable actionable button for carrying out an action with respect to the backend system. In some examples, the detected trigger can automatically cause an action to be carried out with respect to the backend system.

Abstract: Various examples are disclosed for predictive allocation of computing resources based on the predicted location of a user. A computing environment can generate a predictive usage model that predicts a location of a user and allocate computing resources, such as VDI sessions or VMs, to a host device that optimizes latency to the predicted location.

Abstract: Disclosed are various approaches for authenticating a user through a voice assistant device and creating an association between the device and a user account. The request is associated with a network or federated service. A user account can be implicitly authenticated based on proximity of a client device to the voice assistant device. An association between the user account and the voice assistant device can then be created.

Abstract: Systems and methods are included for detecting driving based on user-specific models for driving detection, and restricting access to an application of the user device while a user is driving. A management agent installed on the user device can collect data from sensors in a user device and provide the data to a management server, which can build a user-specific model for driving detection for that user. The management agent can then use that user-specific model for detecting when the user is driving. When the agent determines that the user is driving, it can enforce a driving policy that limits access to applications and delay or modify notifications generated by applications.

Abstract: Systems and methods are included for detecting driving based on user-specific models for driving detection, and restricting access to an application of the user device while a user is driving. A management agent installed on the user device can collect data from sensors in a user device and provide the data to a management server, which can build a user-specific model for driving detection for that user. The management agent can then use that user-specific model for detecting when the user is driving. When the agent determines that the user is driving, it can enforce a driving policy that limits access to applications and delay or modify notifications generated by applications.

Abstract: Systems and methods are included for detecting driving based on user-specific models for driving detection, and restricting access to an application of the user device while a user is driving. A management agent installed on the user device can collect data from sensors in a user device and provide the data to a management server, which can build a user-specific model for driving detection for that user. The management agent can then use that user-specific model for detecting when the user is driving. When the agent determines that the user is driving, it can enforce a driving policy that limits access to applications and delay or modify notifications generated by applications.

Abstract: Systems and methods are included for detecting driving based on user-specific models for driving detection, and restricting access to an application of the user device while a user is driving. A management agent installed on the user device can collect data from sensors in a user device and provide the data to a management server, which can build a user-specific model for driving detection for that user. The management agent can then use that user-specific model for detecting when the user is driving. When the agent determines that the user is driving, it can enforce a driving policy that limits access to applications and delay or modify notifications generated by applications.

Abstract: Systems and methods are included for detecting driving based on user-specific models for driving detection, and restricting access to an application of the user device while a user is driving. A management agent installed on the user device can collect data from sensors in a user device and provide the data to a management server, which can build a user-specific model for driving detection for that user. The management agent can then use that user-specific model for detecting when the user is driving. When the agent determines that the user is driving, it can enforce a driving policy that limits access to applications and delay or modify notifications generated by applications.

Abstract: Systems and methods are included for detecting driving based on user-specific models for driving detection, and restricting access to an application of the user device while a user is driving. A management agent installed on the user device can collect data from sensors in a user device and provide the data to a management server, which can build a user-specific model for driving detection for that user. The management agent can then use that user-specific model for detecting when the user is driving. When the agent determines that the user is driving, it can enforce a driving policy that limits access to applications and delay or modify notifications generated by applications.

Abstract: Systems and methods are included for detecting driving based on user-specific models for driving detection, and restricting access to an application of the user device while a user is driving. A management agent installed on the user device can collect data from sensors in a user device and provide the data to a management server, which can build a user-specific model for driving detection for that user. The management agent can then use that user-specific model for detecting when the user is driving. When the agent determines that the user is driving, it can enforce a driving policy that limits access to applications and delay or modify notifications generated by applications.