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: 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 a user query for creating content in a content generation application and determining an action from an intent of the user query. A prompt is generated based on the action and provided to a natural language generation model. In response to the prompt, output is received from the natural language generation model. Response content is generated based on the output in a format compatible with the content generation application. At least some of the response content is displayed to the user. The user can choose to keep, edit, or discard the response content. The user can iterate with additional queries until the content document reflects the user's desired content.

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 a user query for creating content in a content generation application and determining an action from an intent of the user query. A prompt is generated based on the action and provided to a natural language generation model. In response to the prompt, output is received from the natural language generation model. Response content is generated based on the output in a format compatible with the content generation application. At least some of the response content is displayed to the user. The user can choose to keep, edit, or discard the response content. The user can iterate with additional queries until the content document reflects the user's desired content.

Abstract: An intelligent speech assistant receives information collected while a user is speaking. The information can comprise speech data, vision data, or both, where the speech data is from the user speaking and the vision data is of the user while speaking. The assistant evaluates the speech data against a script which can contain information that the user should speak, information that the user should not speak, or both. The assistant collects instances where the user utters phrases that match the script or instances where the user utters phrases that do not match the script, depending on whether phases should or should not be spoken. The assistant evaluates vision data to identify gestures, facial expressions, and/or emotions of the user. Instances where the gestures, facial expressions, and/or emotions are not appropriate to the context are flagged. Real-time prompts and/or a summary is presented to the user as feedback.

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: An intelligent speech assistant receives information collected while a user is speaking. The information can comprise speech data, vision data, or both, where the speech data is from the user speaking and the vision data is of the user while speaking. The assistant evaluates the speech data against a script which can contain information that the user should speak, information that the user should not speak, or both. The assistant collects instances where the user utters phrases that match the script or instances where the user utters phrases that do not match the script, depending on whether phases should or should not be spoken. The assistant evaluates vision data to identify gestures, facial expressions, and/or emotions of the user. Instances where the gestures, facial expressions, and/or emotions are not appropriate to the context are flagged. Real-time prompts and/or a summary is presented to the user as feedback.

Abstract: A system and method for personalizing a display of a recommendation in a user interface element of an application is described. The system accesses application activities of a user of the application. A user preference is formed based on the application activities. The system identifies a context of a current activity of the application and generates a content recommendation in the application based on the context of the current activity of the application and the user preference.

Abstract: In a non-limiting example of the present disclosure, an exemplary table of contents slide may be displayed for a slide deck of a presentation program. The table of contents slide may comprise one or more sections of grouped slides for the slide deck. A selection of a section link may be received. The section link links the table of contents slide to a section of grouped slides. An exemplary presentation program may navigate the slide deck to a first slide of the section based on the received selection. When the navigation of the section is completed, the presentation program returns the slide deck to one of: the table of contents slide and the first slide of the section. Other examples described relate to creation and rendering of an exemplary table of contents slide and/or section links within an exemplary table of contents slide of a presentation program.

Abstract: In a non-limiting example of the present disclosure, an exemplary table of contents slide may be displayed for a slide deck of a presentation program. The table of contents slide may comprise one or more sections of grouped slides for the slide deck. A selection of a section link may be received. The section link links the table of contents slide to a section of grouped slides. An exemplary presentation program may navigate the slide deck to a first slide of the section based on the received selection. When the navigation of the section is completed, the presentation program returns the slide deck to one of: the table of contents slide and the first slide of the section. Other examples described relate to creation and rendering of an exemplary table of contents slide and/or section links within an exemplary table of contents slide of a presentation program.

Abstract: Technology is disclosed herein that enhances the user experience with presentation programs and the operational aspects of such programs. In an implementation, a presentation program includes a hierarchy of parent slides and child slides in a collection of slides. Navigating from a parent slide to a child slide triggers a contextual zoom-in transition into the child slide. Navigating back to the parent slide from the child slide triggers a contextual zoom-out transition to the parent slide. Other non-limiting examples describe smart slide functionality of an exemplary presentation program. A smart slide is a slide of a slide deck that comprises one or more slide links, which provide an active link to another slide of the slide deck.