Microsoft Technology Licensing, LLC

Discord Inc.

Ravi Mandliya

Tomasz Lukasz Religa

Max Wang

Huitian Jiao

Lei Cui

Furu Wei

Kaushik Ramaiah Narayanan

Si-Qing Chen

Tao Ge

Utsab Bose

Imran Akbar

Gaurav Chakravorty

Risa Pesavento

The present technology provides a smart notification service that is machine-learning driven and integrates with smart channels for determining how the user is receiving notifications and smart notification generators for determining what notifications should be sent and to whom, based on what notifications have been sent previously to the users and how they interacted with the previously sent notifications. The machine-learning model is trained by previously sent notifications to optimize the kind of notifications being sent, who receives the notifications, and in what cadence they receive such notifications.

Machine-learning notification service

A system and method for converting a document is described. The system accesses a document comprising one or more section breaks. The system detects sections of the text document demarked by the one or more section breaks and generates a section title metadata and a section summary metadata for each section of the plurality of sections. The system inserts the section title metadata and the section summary metadata at the corresponding section breaks in the text document. The system modifies the text document into slides. Each slide being formed for each section based on the corresponding section title metadata and the section summary metadata. The system generates a presentation document based on the slides.

Document conversion engine

Technologies are provided for automated generation of revision summaries for an electronic document. In some embodiments, a computing system can determine that a first section of a first version of the electronic document includes changes relative to a second section of a second version of the electronic document. The second section corresponds to the first section. The computing system also can generate respective descriptions of the first section and the second section. The computing system can then generate a summary of the changes by comparing the respective descriptions. The computing system can determine that the summary has a defined placement in a ranking of similarity scores, where a first similarity score in the ranking defines similarity between the respective descriptions. The computing system can add metadata to the first version of the electronic document. The metadata controls presentation of a visual element indicative of the summary within a user interface.

Automated generation of revision summaries

Growing community of inventors

Issaquah, WA, United States of America

Ravi Mandliya