Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A data processing system for conditioning productivity application file content for ingestion by an artificial intelligence (AI) language model includes a server system hosting a conversion service, the server system comprising processing and memory resources; an Application Programming Interface (API) for the conversion service to receive productivity application files at any scale; and a format converter of the conversion service, the format converter converting a productivity application file to a format compatible with the AI language model to enable the AI language model to operate on the file content based on a user instruction submitted with the file content as output by the format converter.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Various embodiments of the present technology include an improved system for measuring the impacts of feature rollouts on machine utilization and service performance. More specifically, embodiments of the present technology include an exposure control system based on randomized machine assignments and a corresponding score card for comparing machine utilization metrics. In an embodiment, a computing apparatus identifies a new feature in a codebase, wherein the codebase has been updated with the new feature across multiple resources, enables the new feature for a target group of the multiple resources while keeping the new feature dormant for a control group of the multiple resources, collects performance information for the target group and the control group for a time period, and generates a visualization of one or more differences between the performance information for the target group and the performance information for the control group for the time period.

Abstract: 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.