Vmware, Inc.

Omnissa, LLC

Adam Samuel Chow

Rohit Pradeep Shetty

Ravish Chawla

Mayank Kulkarni

Max Blinder

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

Disclosed are various embodiments for generating a list of predicted contacts that can be provided to a client device. The predicted contacts can be generated based upon an analysis of user interaction data. The predicted contacts can be made available to a phone application or messaging application on a client device so that contact information can be displayed in response to an incoming call or a message.

Generating predicted contacts for a phone application

Disclosed herein are examples of systems and methods for formatting electronic messages using machine learning. An electronic message can be obtained, and a processed message can be generated based at least in part on the electronic message. At least one attribute for the processed message can be determined. A formatting specification can be generated based at least in part on the at least one attribute. A reformatted message can be generated based at least in part on the formatting specification.

Formatting electronic messages using machine learning

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

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 herein assist users by identifying actionable tasks in an email and providing reminders and other tracking mechanisms for those tasks. For example, a method can include extracting a portion of the email and assigning a number to each word of the extracted portion of the email according to a library. The method can further include determining, based on the assigned numbers, whether the extracted portion of the email includes at least one actionable portion. In response to determining that the extracted portion of the email includes at least one actionable portion, at least one actionable portion can be presented to the user on a GUI associated with the user's device.

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Atlanta, GA, United States of America

Adam Samuel Chow