Microsoft Technology Licensing, LLC

Prerana Dharmesh Gambhir

Daniel M Cheung

Kruthika Ponnusamy

Ashish Mehta

Sandhya Rajendra Rao

Sharena Pari-Monasch

Sarah Ferraro Stein

Vedant Dulori

Qiong Zhou

Oishika Chaudhury

Yiming Shi

Prayank Mathur

Aniruddha Dinesh Mallya

Thanh Trung Nguyen

Sarah Leslie Sutton

Yongchang Dong

Khoa Dang Nguyen

Sharena Meena Pari-monasch

Christine Bumpous

A shared computer device configured to receive a reservation request associated with a user at the shared computer device to reserve a predetermined period of time for usage of the shared computer device, receive an input from the user on the shared computer device during the predetermined period of time to sign in to and use the shared computer device to access and use a predetermined application, based on the signing in, downloading, from a server, predetermined personal information of the user stored in the server regarding the predetermined application and granting access to the user to use the predetermined application on the shared computer device during the predetermined period of time using the downloaded personal information of the user, and delete the personal information of the user from the shared computer device after the predetermined period of time expires.

Hot desk device and method for hybrid environment

A data processing system includes: a server having a processor and a network interface; and a memory comprising programming instructions including a prompt generation engine. When executed by the processor, alone or in combination with other processors, the instructions cause the server to implement a service to: receive multiple media files from a presentation application on a client device; restructure content of the media files into a form compatible with a generative Artificial Intelligence (AI) tool; with the prompt generation engine, structure a prompt to the generative AI tool using the content of the media files in the form compatible with the generative AI tool, the prompt comprising an instruction to generate new content by fusing content from the multiple media files; receive the new content from the generative AI tool; and transmit the new content to the presentation application on the client device to augment a presentation being generated with the presentation application.

Systems and methods of generating new content for a presentation being prepared in a presentation application

Systems and methods for identifying and evicting idle sessions include training a machine learning model as a session classifying model to learn rules for classifying active sessions between clients and the cloud-based service. The session classifying model is trained to receive a plurality of parameters pertaining to the document associated with an active session as input and to apply the rules to the plurality of parameters to determine a classification for the active session and to provide an output indicative of the classification for the active session. The session classifying model is then utilized in the cloud-based service to classify the active sessions. The active sessions classified as idle sessions may then be evicted from the cloud-based service.

Machine learning for identifying idle sessions

A recommended display layout and recommended content can be provided to a system for optimizing and personalizing the home screen of an application or special purpose computing device. The recommended display layout and recommended content can be generated by an advanced intelligence system that is trained to generate the recommendations based on input including display parameters, a user profile associated with the application or special purpose computing device, and a substrate profile. Other profiles may be used to further personalize the home screen including social media profiles, browsing profiles, productivity application profiles, and the like.

Intelligently customized and optimized home screen

A system and method and for detecting meeting participant inattentiveness during an online meeting includes retrieving meeting content data from the ongoing online meeting, meeting metadata associated with the online meeting, and user data associated with participants of the online meeting, the online meeting being conducted via an online meeting application, the user data including camera data and data about user interactions with applications other than the online meeting application. Participant inattentiveness is detected, based on at least one of the camera data or the data about user interactions with the applications other than the online meeting application. Upon detecting participant inattentiveness, notification data is provided for display to the inattentive participant, receiving an indication of the participant's return to the online meeting, and providing data for displaying a summary of meeting events that occurred during the time period to the participant.

Intelligent meeting management

Described herein are supervised machine learning techniques for generating a first machine learned model, which receives, as input, values for a set of utilization metrics for a server computer and generates, as output, a service-specific health score for the server computer, and for generating a second machine learned model, which receives, as input, values for the volume of application traffic directed to a group of server computers hosting the service for each of several prior time periods and generates, as output, an application traffic forecast for the group of server computers, for a future time period.

Techniques for generating service-specific server health scores and application traffic forecasts

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San Jose, CA, United States of America

Prerana Dharmesh Gambhir