Microsoft Technology Licensing, LLC

Derek Martin Johnson

Amit Srivastava

Ji Li

Gencheng Wu

Konstantin Seleskerov

Priyanka Vikram Sinha

Lauren Michelle Janas

Muin Barkatali Momin

Huey-Ru Tsai

Charles Cummins

Lei Shi

Michael Jay Gilmore

Sheng Zhao

Alexandre Gueniot

Ramya Tridandapani

Sushant Sharma

Bohan Li

Kerry Young

Siliang Kang

Paul Scuderi

Kevin Dara Khieu

Muqi Li

Emily Lauren Tohir

Huakai Liao

SivaPriya Kalyanaraman

Sindhu Vigasini Jambunathan

Xinfeng Chen

Brittany Elizabeth Mederos

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.

Automated script generation and audio-visual presentations

Techniques performed by a data processing system include establishing an online presentation session for conducting an online presentation, receiving first media streams comprising presentation content from the first computing device, receiving second media streams from the second computing devices of a subset of the plurality of participants, the second media streams including audio content, video content, or both of the subset of the plurality of participants, analyzing the first media streams using first machine learning models to generate feedback results, analyzing the set of second media streams to identify first reactions by the participants to obtain reaction information, automatically analyzing the feedback results and the reactions to identify discrepancies between the feedback results and the reactions, and automatically updating one or more parameters of the machine learning models based on the discrepancies to improve the suggestions for improving the online presentation.

Techniques for presentation analysis based on audience feedback, reactions, and gestures

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. Further examples described herein extend to training and application of artificial intelligence (AI) processing, in real-time (or near real-time), that is configured to automatically analyze acoustic features of audio streams and automatically generate exemplary reaction indications.

Automatic reaction-triggering for live presentations

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.

Automated intelligent content generation

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.

Speaking technique improvement assistant

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.

Table of contents in a presentation program

System and method for applying presentation templates with image animation to presentation documents. A processor identifies a presentation document including an image and identifies a characteristic of the image. The processor further selects a presentation template with image animation from a plurality of available presentation templates. The selection of the processor is based on the characteristic of the image. The processor further applies the presentation template with animation of the image to the presentation document to define animation of the image. The processor then outputs the presentation document with the presentation template applied.

Image animation in a presentation document

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Sunnyvale, CA, United States of America

Derek Martin Johnson