Microsoft Technology Licensing, LLC

Pujita Mathur

Jeffrey William Pasternack

Zheng Li

Jia Liu

Chao Zhang

Ying Xuan

Parinkumar D Shah

Haowen Ning

Shubham Gupta

Divye Kapoor

Heidi Jaywing Wang

Howard Mario Narvaez

Marco Varela Alvarado

Neil Cruz Pomerleau

Brett Konold

Yizhou Pan

Kelly K Hosokawa

Ngoc M Vu

Ryan A Espiritu

Akshaya Subbaraya Bhat

Christian Wilhelm Kurt Byza

Arya Ghatak Choudhury

Methods are presented for classifying responses to communications. One method includes presenting, in a first user interface (UI), a message from a sender to a recipient requesting a response. The first UI includes response options of a first classification, a second classification, and an entry field for entering a free text message. Another operation is for receiving a recipient response with a selection of: (a) the first or the second button and (b) the free text message entered in the text entry field. The method further includes generating a featurized recipient response based on the free text message and the selection. An acceptance machine-learning (ML) model calculates a classification value of the recipient response from a set of possible classification values based on the featurized recipient response. The method further includes presenting, in a second UI for the sender, an indicator for the message and the calculated classification value.

Communication response analyzer

Techniques for enhancing usability and electronic resource efficiency using comment relevance are disclosed herein. In some embodiments, a comment relevance system receives member feature data, including professional identity data such as professional experience or professional skill, and at least one of comment feature data and commenter feature data for comments for post on a social networking service, generates a score for each comment based on the member feature data and the comment or commenter feature data, ranks the comments based on the scores, and causes one or more of the comments to be displayed in a particular position based on the ranking. In some embodiments, the member feature data comprises professional identity data of a member of the social networking service, the comment feature data indicates at least one attribute of the comment, and the commenter feature data indicates at least one attribute of the commenter of the comment.

Usability and resource efficiency using comment relevance

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San Francisco, CA, United States of America

Pujita Mathur