Abstract: The present disclosure relates to processing operations configured to provide processing that automatically analyzes acoustic signals from attendees of a live presentation and automatically triggers corresponding reaction indications from results of analysis thereof. Exemplary reaction indications provide feedback for live presentations that can be presented in real-time (or near real-time) without requiring a user to manually take action to provide any feedback. As a non-limiting example, reaction indications may be presented in a form that is easy to visualize and understand such as emojis or icons. Another example of a reaction indication is a graphical user interface (GUI) notification that provides a predictive indication of user intent derived from analysis of acoustic signals.

Abstract: The present disclosure relates to processing operations configured to provide processing that automatically analyzes acoustic signals from attendees of a live presentation and automatically triggers corresponding reaction indications from results of analysis thereof. Exemplary reaction indications provide feedback for live presentations that can be presented in real-time (or near real-time) without requiring a user to manually take action to provide any feedback. As a non-limiting example, reaction indications may be presented in a form that is easy to visualize and understand such as emojis or icons. Another example of a reaction indication is a graphical user interface (GUI) notification that provides a predictive indication of user intent derived from analysis of acoustic signals.

Abstract: The present disclosure relates to processing operations configured to provide processing that automatically analyzes acoustic signals from attendees of a live presentation and automatically triggers corresponding reaction indications from results of analysis thereof. Exemplary reaction indications provide feedback for live presentations that can be presented in real-time (or near real-time) without requiring a user to manually take action to provide any feedback. As a non-limiting example, reaction indications may be presented in a form that is easy to visualize and understand such as emojis or icons. Another example of a reaction indication is a graphical user interface (GUI) notification that provides a predictive indication of user intent derived from analysis of acoustic signals.

Abstract: Automatic generation of intelligent content is created using a system of computers including a user device and a cloud-based component that processes the user information. The system performs a process that includes receiving an input document and parsing the input document to generate inputs for a natural language generation model using a text analysis model. The natural language generation model generates one or more candidate presentation scripts based on the inputs. A presentation script is selected from the candidate presentation scripts and displayed. A text-to-speech model may be used to generate a synthesized audio presentation of the presentation script. A final presentation may be generated that includes a visual display of the input document and the corresponding audio presentation in sync with the visual display.

Abstract: Techniques performed by a data processing system for facilitating an online presentation session include establishing an online presentation session for conducting an online presentation for a first computing device of a presenter and a plurality of second computing devices of a plurality of participants, receiving a set of first media streams comprising presentation content from the first computing device, receiving a set of second media streams from the second computing devices of a first subset of the plurality of participants, the set of second media streams including audio content, video content, or both of first subset of the plurality of participants, analyzing the set of first media streams using one or more first machine learning models n to generate a set of first feedback results, analyzing the set of second media streams using one or more second machine learning models to identify a set of first reactions by the participants to obtain first reaction information, automatically analyzing the set of

Abstract: The present disclosure relates to processing operations configured to provide processing that automatically analyzes acoustic signals from attendees of a live presentation and automatically triggers corresponding reaction indications from results of analysis thereof. Exemplary reaction indications provide feedback for live presentations that can be presented in real-time (or near real-time) without requiring a user to manually take action to provide any feedback. As a non-limiting example, reaction indications may be presented in a form that is easy to visualize and understand such as emojis or icons. Another example of a reaction indication is a graphical user interface (GUI) notification that provides a predictive indication of user intent derived from analysis of acoustic signals.

Abstract: Technologies are described to provide a personalized search environment to users without requiring enterprise environment access. Upon access of a personal service account such as one in a productivity service, a user's personal environment may be created by an aggregation service using graph based data infrastructure. Sources of information may include personal email accounts, calendars, social/professional networks, task list applications, online data storage services, health applications, gaming applications, and communication applications associated with the user. A personalized search application may then use the data from the aggregation service and/or (if available) user's enterprise account information to perform personalized searches with relevant results for the user.

Abstract: Technologies are described to provide a personalized search environment to users without requiring enterprise environment access. Upon access of a personal service account such as one in a productivity service, a user's personal environment may be created by an aggregation service using graph based data infrastructure. Sources of information may include personal email accounts, calendars, social/professional networks, task list applications, online data storage services, health applications, gaming applications, and communication applications associated with the user. A personalized search application may then use the data from the aggregation service and/or (if available) user's enterprise account information to perform personalized searches with relevant results for the user.

Abstract: Exposing content to individuals in an enterprise is provided. An external content sharing system includes a graph server comprising an API engine, a graph index, and an activity processing and analytics engine. When a user selects to share external content via a user agent, the API engine receives an API call including a URL of the external content from the user agent. The API engine accesses the content, extracts metadata, and stores the metadata as a node in the graph index, where connections are made between the node and individuals who are socially close to the user. The API engine receives a query request for content associated with an individual socially close to the user, queries the graph index, and provides a result including the metadata of the external content and the URL for generating and exposing a visual information element representative of the external content to the individual.