Microsoft Technology Licensing, LLC

Kevin Michael Carter

Shiraz Jitendra Cupala

Amir Kantor

Shai Ifrach

Shlomi Maliah

Yasemin Cenberoglu

Kumud Ranjan

Shyam Gupta

Gautam Jain

Xiao Yan Lu

Ryen William White

Ido Priness

Jaime Brooks Teevan

Adi L Miller

Ke Wang

Yong Xie

Lan Ye

Tiancong Zhou

Omar Shaya

Honghao Qiu

Chao Chen

Matthew Jonathan Gardner

Yongli Ren

Jonathan Liono

Flora Dilys Salim

Eleonora Shtotland

Gilad Gildin

Ofek David

A system for providing a personalized assistant within a network-based communication service includes one or more processors and a memory storage device storing instructions thereon. During a network-based communication session, the system receives a query from a computing device of a first communication session participant and processes the query by determining that a second communication session participant has shared content via a content sharing feature of the network-based communication service. In response, the system provides the query and at least a portion of the shared content as input to a model, which dynamically constructs a prompt for use as input with a generative language model. The system then presents the prompt as input to the generative language model, receives a response as output from the generative language model, and causes presentation of the response to be presented to the communication session participant.

Network-based communication session copilot

A system for providing a personalized assistant within a network-based communication session includes a processor and a memory storage device storing instructions. The system determines when a first communication session participant joins the network-based communication session after a threshold duration of time subsequent to the start time of the session. Upon determining the first participant has joined, the system obtains content associated with the session and creates request data for a pre-trained generative language model. The request data includes an instruction requesting a predetermined number of suggested utterances not present in the content, each utterance relating to one or more topics corresponding to the content. The system transforms the request data to a command based on a command template and provides the command to the generative language model. The system receives a response from the model, including the predetermined number of suggested utterances, and presents them to the communication session participant in a graphical user interface while the session is in session.

Methods and systems are disclosed for scheduling a task of a user based on a cyber-physical-social (CPS) context of activities. The present disclosure is directed to increasing the efficiency of performing tasks by grouping tasks with the same or similar task CPS contexts so that they are performed in conjunction with one another. A stream of user activities is received that encompasses the CPS context, tasks are identified and classified based on a CPS context model. The CPS model is trained using CPS context and annotations for identified tasks as input to create classes of tasks. The classes of tasks from the model are used to group similar tasks. The present disclosure enables users to receive recommendations on clustering tasks with the same or similar contexts based on an online tool, a location, and collaborators to be performed together to improve productivity.

Scheduling tasks based on cyber-physical-social contexts

A messaging system configured for recommending messages is provided. The messaging system machine trains a prediction model associated with a user based on past review and response times for past messages. During runtime, the messaging system generates a predicted user action time for each new message based on the prediction model and tags each new message with a corresponding predicted user action time. Based on an amount of available time, the predicted user action time for each new message, and importance for each new message, the messaging system determines a recommended set of new messages that the user should read and respond to during the available time, whereby a sum of the predicted user action time of the recommended set of new messages is equal to or less than the available time. The recommended set of new messages are visually distinguished for the user.

Context and time prediction based message recommendation system

Growing community of inventors

Atlanta, GA, United States of America

Kevin Michael Carter