Microsoft Technology Licensing, LLC

Yasemin Cenberoglu

Shiraz Jitendra Cupala

Amir Kantor

Kevin Michael Carter

Shai Ifrach

Shlomi Maliah

Shyam Gupta

Xiao Yan Lu

Gautam Jain

Kumud Ranjan

Ido Priness

Jaime Brooks Teevan

Adi L Miller

Lan Ye

Matthew Jonathan Gardner

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.

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Montreal, Canada

Yasemin Cenberoglu