Vmware, Inc.

Airwatch LLC

Ravish Chawla

Chaoting Xuan

Kar Fai Tse

Adam Samuel Chow

Rohit Pradeep Shetty

Erich Peter Stuntebeck

Jianling Wang

Kar-Fai Tse

Stephen Jonathan Parry-Barwick

Disclosed are various examples for to generating personalized user experiences in the form of short-form videos that summarize informational topics that are selected to present to a user according at least one of a user context or a user interest. As the informational topics are identified for a given user, the informational topics can be applied as inputs to a recommendation model that is trained to recommend one or more of the topics to summarize and present to a user in visual form based on at least one of the user context or the user interest. Once a topic is recommended by the recommendation model, a video or other type of visual content can be automatically generated and presented to a user to allow the user to consume the information in the summarized form.

Personalized informational user experiences using visual content

Systems and methods are described for providing recommendations for an improved user experience in online meetings. A recommendation engine can aggregate data from user devices to make recommendations before, during and after online meetings. Before a meeting, the recommendation engine can recommend which of a user's devices to use for the meeting. During the meeting, the recommendation engine can identify current or anticipated issues and recommend changes the user can make to correct or prevent the issue. After meetings, the recommendation engine can aggregate data and identify an ongoing issue for one or multiple users. The recommendation engine can identify the cause of the issue and make recommendations to the user or an administrator accordingly.

Recommendation engine for improved user experience in online meetings

Systems and methods are described for providing recommendations for a user experience in online meetings. A recommendation engine can aggregate data from user devices to make recommendations before, during and after online meetings. Before a meeting, the recommendation engine can recommend which of a user's devices to use for the meeting. During the meeting, the recommendation engine can identify current or anticipated issues and recommend changes the user can make to correct or prevent the issue. After meetings, the recommendation engine can aggregate data and identify an ongoing issue for one or multiple users. The recommendation engine can identify the cause of the issue and make recommendations to the user or an administrator accordingly.

Using hardware profiles of hardware components to determine performance issues of user devices

Disclosed are various approaches for performing automated actions in a conferencing service. Distractions can be detected and users can be muted. Breakout rooms can be suggested to attendees based upon the user's identity. Additionally, event summaries and recaps can be generated for users who are late-arriving or who depart and return to the event.

Automated actions in a conferencing service

Disclosed are various approaches for generating a text transcript of a soundtrack. The soundtrack can correspond to an event in a conferencing service. Language models can be trained on data that is specific to organizations, users within the organization, and metadata associated with an agenda for the event. The metadata can include texts, attachments, and other data associated with the event. The language models can be arranged into a convolutional neural network and output a text transcript. The text transcript can be used to retrain the language models for subsequent use.

Automated context-specific speech-to-text transcriptions

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.

Proximity based authentication of a user through a voice assistant device

Systems and methods are described for securing confidential content in a virtual meeting. In an example, a host can create a meeting agenda that identifies attendees and access restrictions for the meeting. A server can generate encryption keys corresponding to different confidentiality levels of content identified in the agenda. The encryption keys can be provided to user devices of the attendees based on the access permissions of the attendees. As the virtual meeting takes place, the server can encrypt confidential portions of the content stream for the meeting using the encryption keys. In an example, the server can also apply a machine learning algorithm to identify any confidential content in the content stream not previously identified. The server can encrypt additional confidential content where appropriate using encryption keys corresponding to the confidentiality level of the content.

Securing confidential content in a virtual meeting

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.

3D printer device management using machine learning

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.

Automated identification of anomalous devices

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.

Generating contextual compliance policies

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.

Detecting driving and modifying access to a user device

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.

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Marietta, GA, United States of America

Ravish Chawla