Abstract: The devices, systems, and methods described herein enable automatically configuring an electronic device using artificial intelligence (AI). The devices, systems, and methods enable accessing telemetry data representing device usage data, inputting the accessed telemetry data into machine learning models that are matched to device metadata, and determining notifications to publish to components of the electronic device. The notifications represent events predicted to occur on the electronic device. The notifications are published to the components of the electronic device such that the electronic device is configured according to the published notifications. The determined notifications enable the identification of optimal settings for the electronic device based on the usage pattern of the device and enable components of the electronic device to preemptively take action on events which are predicted to occur in the future.

Abstract: This document relates to a process for autopilot re-enrollment of managed devices in a network following a reset of the managed devices. Upon determining that the managed devices lack a hardware authenticator, a one-time password can be issued to the managed devices that may be stored in a persistent memory, and upon reset of the managed devices, the one-time password can be returned for validation and the managed devices can be automatically re-enrolled in the network upon validation of the one-time password.

Abstract: The devices, systems, and methods described herein enable automatically configuring an electronic device using artificial intelligence (AI). The devices, systems, and methods enable accessing telemetry data representing device usage data, inputting the accessed telemetry data into machine learning models that are matched to device metadata, and determining notifications to publish to components of the electronic device. The notifications represent events predicted to occur on the electronic device. The notifications are published to the components of the electronic device such that the electronic device is configured according to the published notifications. The determined notifications enable the identification of optimal settings for the electronic device based on the usage pattern of the device and enable components of the electronic device to preemptively take action on events which are predicted to occur in the future.

Abstract: Techniques are described herein that are capable of registering a user device with a cloud-based management service using an intermediate cloud storage. For instance, the intermediate cloud storage may store an encrypted data blob including information that identifies the user device. The intermediate cloud storage or a registration system may decrypt the encrypted data blob so that the registration system may use the decrypted data blob to register the user device with the cloud-based management service. For instance, the registration system may retrieve the encrypted or decrypted data blob from the intermediate cloud storage by providing a requisite secret to the intermediate cloud storage. The requisite secret may be provided to the registration system by the user device (e.g., via a matrix barcode, such as a QR code).

Abstract: The devices, systems, and methods described herein enable automatically configuring an electronic device using artificial intelligence (AI). The devices, systems, and methods enable accessing telemetry data representing device usage data, inputting the accessed telemetry data into machine learning models that are matched to device metadata, and determining notifications to publish to components of the electronic device. The notifications represent events predicted to occur on the electronic device. The notifications are published to the components of the electronic device such that the electronic device is configured according to the published notifications. The determined notifications enable the identification of optimal settings for the electronic device based on the usage pattern of the device and enable components of the electronic device to preemptively take action on events which are predicted to occur in the future.

Abstract: Techniques are described herein that are capable of registering a user device with a cloud-based management service using an intermediate cloud storage. For instance, the intermediate cloud storage may store an encrypted data blob including information that identifies the user device. The intermediate cloud storage or a registration system may decrypt the encrypted data blob so that the registration system may use the decrypted data blob to register the user device with the cloud-based management service. For instance, the registration system may retrieve the encrypted or decrypted data blob from the intermediate cloud storage by providing a requisite secret to the intermediate cloud storage. The requisite secret may be provided to the registration system by the user device (e.g., via a matrix barcode, such as a QR code).

Abstract: Techniques are described herein that are capable of registering a user device with a cloud-based management service using an intermediate cloud storage. For instance, the intermediate cloud storage may store an encrypted data blob including information that identifies the user device. The intermediate cloud storage or a registration system may decrypt the encrypted data blob so that the registration system may use the decrypted data blob to register the user device with the cloud-based management service. For instance, the registration system may retrieve the encrypted or decrypted data blob from the intermediate cloud storage by providing a requisite secret to the intermediate cloud storage. The requisite secret may be provided to the registration system by the user device (e.g., via a matrix barcode, such as a QR code).

Abstract: Described herein is a system and method for transforming contextually relevant items. A search query is received, and, for each of a plurality of electronically stored items (e.g., stored in a source platform), a probability that the stored item is relevant to the search query is calculated using a classification model. Stored items having a calculated probability greater than or equal to a threshold probability are identified. Contextually relevant portions of the identified stored items is determined using a contextual model. The determined contextually relevant portions of the identified stored items are extracted. The extracted contextually relevant portions of the identified stored items are transformed into a target platform. An output of the extracted contextually relevant portions of the identified stored items in the target platform is provided.

Abstract: This document relates to a process for autopilot re-enrollment of managed devices in a network following a reset of the managed devices. Upon determining that the managed devices lack a hardware authenticator, a one-time password can be issued to the managed devices that may be stored in a persistent memory, and upon reset of the managed devices, the one-time password can be returned for validation and the managed devices can be automatically re-enrolled in the network upon validation of the one-time password.

Abstract: Described herein is a system and method for utilizing a virtual secure mode instance to protect an artificial intelligence model from unauthorized access (e.g., inspection, copying) during execution of an application utilizing the AI model (e.g., training and/or inference) on a client device. An encrypted artificial intelligence model is received in a virtual secure mode instance of the client device. The encrypted artificial intelligence model is decrypted in the virtual secure mode instance using a decryption secret. The decrypted artificial intelligence model is stored in the virtual secure mode instance. An application that utilizes the decrypted artificial intelligence model is executed (e.g., training and/or inference) in the virtual secure mode instance.

Abstract: Described herein is a system and method for utilizing a virtual secure mode instance to protect an artificial intelligence model from unauthorized access (e.g., inspection, copying) during execution of an application utilizing the AI model (e.g., training and/or inference) on a client device. An encrypted artificial intelligence model is received in a virtual secure mode instance of the client device. The encrypted artificial intelligence model is decrypted in the virtual secure mode instance using a decryption secret. The decrypted artificial intelligence model is stored in the virtual secure mode instance. An application that utilizes the decrypted artificial intelligence model is executed (e.g., training and/or inference) in the virtual secure mode instance.

Abstract: The devices, systems, and methods described herein enable automatically configuring an electronic device using artificial intelligence (AI). The devices, systems, and methods enable accessing telemetry data representing device usage data, inputting the accessed telemetry data into machine learning models that are matched to device metadata, and determining notifications to publish to components of the electronic device. The notifications represent events predicted to occur on the electronic device. The notifications are published to the components of the electronic device such that the electronic device is configured according to the published notifications. The determined notifications enable the identification of optimal settings for the electronic device based on the usage pattern of the device and enable components of the electronic device to preemptively take action on events which are predicted to occur in the future.

Abstract: Described herein is a system and method for transforming contextually relevant items. A search query is received, and, for each of a plurality of electronically stored items (e.g., stored in a source platform), a probability that the stored item is relevant to the search query is calculated using a classification model. Stored items having a calculated probability greater than or equal to a threshold probability are identified. Contextually relevant portions of the identified stored items is determined using a contextual model. The determined contextually relevant portions of the identified stored items are extracted. The extracted contextually relevant portions of the identified stored items are transformed into a target platform. An output of the extracted contextually relevant portions of the identified stored items in the target platform is provided.

Abstract: Various technologies described herein pertain to evaluating configuration compliance of a computing device. The computing device operates in a configuration compliance evaluation mode to test a set of configuration requests for a configuration source. Configuration changes to the computing device can be applied in a virtual machine run on the computing device when operating in the configuration compliance evaluation mode. Responsive to each configuration request being received and when the computing device is operating in the configuration compliance evaluation mode, the computing device can store the configuration request in a data store, apply the configuration request in the virtual machine to cause a configuration change in the virtual machine, and store data for verifying enforcement of the configuration change in the data store.

Abstract: Various technologies described herein pertain to evaluating configuration compliance of a computing device. The computing device operates in a configuration compliance evaluation mode to test a set of configuration requests for a configuration source. Configuration changes to the computing device can be applied in a virtual machine run on the computing device when operating in the configuration compliance evaluation mode. Responsive to each configuration request being received and when the computing device is operating in the configuration compliance evaluation mode, the computing device can store the configuration request in a data store, apply the configuration request in the virtual machine to cause a configuration change in the virtual machine, and store data for verifying enforcement of the configuration change in the data store.