Microsoft Technology Licensing, LLC

Arpit Dhariwal

Jeffrey William Pasternack

Bing Zhao

Nandeesh Channabasappa Rajashekar

Nimesh Madhavan Chakravarthi

Birjodh S Tiwana

Christopher Szeto

Jesse Jyh-Cherng Hsia

Adam Leon

Haiyang Liu

Kurt Mcculloch

Chung Yu Wang

Qianru Zhang

Techniques for generating smart replies involving image files are disclosed herein. In some example embodiments, a computer system detects that a first message comprising a first image file has been transmitted from a first computing device of a first user to a second computing device of a second user, and generates a first plurality of smart replies based on a first embedding vector of the first image file, where the first embedding vector of the first image file is based on at least one of first textual metadata of the first image file, first image data of the first image file, and a first set of query text used by a first set of users in a first set of searches that resulted in the first image file being included in a first set of transmitted messages.

Generating smart replies involving image files

Techniques for generating diverse smart replies using a synonym hierarchy are disclosed herein. A computer system may detect that a first set of one or more messages having first content has been transmitted from a first computing device of a first user to a second computing device of a second user, determine a plurality of candidate replies based on the first content of the first set of one or more messages, and then select a plurality of smart replies from the plurality of candidate replies using a hierarchical graph data structure and at least one diversity rule. The selecting of the plurality of smart replies comprises omitting at least one of the plurality of candidate replies from selection based on the at least one diversity rule, which limits a number of the plurality of smart replies that have a common parent node.

Generating diverse smart replies using synonym hierarchy

Described herein is a technique to facilitate the sharing of a content item presented in a content feed of a social networking service. Upon detecting that a member has selected an option to share a content item, a content sharing interface is presented. The content sharing interface includes options share the content item publically via a content feed, and privately via a messaging service. The content sharing interface provides a ranked list of recommended recipients, where the recommended recipients in the list are selected and ordered based on several factors, including factors relating to the relationship between the sharing member and the recommended recipients, as well as factors relating to the subject matter of the content item and the likelihood that a recommended recipient would be interested in the content item.

Sharing content to multiple public and private targets in a social network

Disclosed are systems, methods, and non-transitory computer-readable media for a sequence to sequence to classification model for generating recommended messages. A messaging system generates, using a sequence to sequence encoder, an embedding vector from a message being transmitted as part of a communication session, the sequence to sequence encoder having been trained based on historical message data that includes messages transmitted between users of the messaging system. The messaging system determines, based on the embedding vector, a set of candidate responses for replying to the first message, the set of candidate responses being a subset of a set of available responses. The messaging system selects, from the set of candidate responses, a set of recommended responses to the first message, and causes the set of recommended responses to be presented by a client device of a recipient user of the first message.

Sequence to sequence to classification model for generating recommended messages

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.

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San Francisco, CA, United States of America

Arpit Dhariwal