Microsoft Technology Licensing, LLC

Adam Leon

David Golland

Jeffrey William Pasternack

Bing Zhao

Birjodh S Tiwana

Arpit Dhariwal

Nandeesh Channabasappa Rajashekar

Nimesh Madhavan Chakravarthi

Monique Yin

Felix Giguere Villegas

Techniques for improving an interactive user interface of a messaging application by dynamically identifying a configurable number of top-ranked targets for a new message from a plurality of possible targets for the new message are disclosed. An interaction with the messaging application is detected. The interaction is determined to be indicative of an intent to identify a target for the new message from the plurality of possible targets for the new message. The identifying of the targets is performed by applying a matching algorithm to the one or more keywords. The matching algorithm blends a ranking of individual connections associated with the member with a ranking of group conversations associated with the member. Responsive to the detection of the interaction, a listing of identifiers associated with the targets is caused to be presented in a search results portion of the user interface.

Message application improvement for recalling one to many private conversations

A method and system for providing recipient recommendations to a user wishing to share content with a contact is provided. A message having the content along with a first contact is received from the user. Profiles from an entire social graph associated with the user and the first contact are searched to determine a shared attributed among both profiles. The shared attribute is used to search for a second contact to receive the content by searching for the shared attribute in a profile associated with the second contact. When a determination is made that the profile associated with the second contact has the shared attribute, the second contact is suggested as a recipient of the content.

Online community building recommender

Disclosed are systems, methods, and non-transitory computer-readable media for generating recommended responses based on historical data. A messaging system receives a message as part of a communication session between a first client device and a second client device. The message originated from the first client device. The messaging system determines, using the message as input in a statistical model, a set of candidate responses for replying to the message. The statistical model was generated based on historical message data transmitted as part of previous communication sessions between a plurality of client devices. The set of candidate responses is a subset of a set of available candidate responses. The messaging system determines, based on a set of candidate selection rules, a subset of the candidate responses yielding a set of recommended responses to the message, and causes the set of recommended responses to be presented on the second client device.

Generating recommended responses based on historical message data

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San Francisco, CA, United States of America

Adam Leon