公开(公告)号：US10263941B2

公开(公告)日：2019-04-16

申请号：US15010765

申请日：2016-01-29

Applicant: Microsoft Technology Licensing, LLC

Inventor： Rishi Jobanputra ,  Romer E. Rosales ,  Joshua Daniel Hartman ,  Shubhanshu Nagar ,  Ryan Oblak ,  Cameron Alexander Lee ,  Hsiao-Ping Tseng ,  Shaunak Chatterjee ,  Rupesh Gupta

IPC: G06F15/16 ,  H04L12/58 ,  H04L29/08

Abstract: This disclosure relates to systems and methods for managing multiple messages. In one example, a method includes determining a message transmission frequency threshold for a member of an online social networking service using responses from the member; receiving a message that is to be transmitted to the member; storing the message, without transmitting the message to the member, in a digest of messages for the member; and transmitting the digest to the member in response to a send score for the digest exceeding a send score threshold, the send score calculated using the number of messages in the digest.

公开(公告)号：US12223554B2

公开(公告)日：2025-02-11

申请号：US16912245

申请日：2020-06-25

Applicant: Microsoft Technology Licensing, LLC

Inventor： Rohan Ramanath ,  Konstantin Salomatin ,  Jeffrey Douglas Gee ,  Onkar Anant Dalal ,  Gungor Polatkan ,  Sara Smoot Gerrard ,  Deepak Kumar ,  Rupesh Gupta ,  Jiaqi Ge ,  Lingjie Weng ,  Shipeng Yu

IPC: G06Q50/00 ,  G06F16/9535 ,  G06F16/958 ,  G06F18/214 ,  G06N5/04 ,  G06Q10/1053

Abstract: In some embodiments, a computer system generates a recommendation for a user of an online service based on user actions that have been performed by the user within a threshold amount of time before the generation of the recommendation. For each user action, the computer system determines an intent classification that identifies an activity of the user and that corresponds to different types of user actions, as well as a preference classification that identifies a target of the activity, and then stores these intent and preference classifications as part of indications of the user actions for use in generating different types of recommendations using different types of recommendation models. Additionally, the computer system may use mini-batches of data from an incoming stream of logged data to train an incremental update to one or more recommendation models.

公开(公告)号：US10650325B2

公开(公告)日：2020-05-12

申请号：US14814555

申请日：2015-07-31

Applicant: Microsoft Technology Licensing, LLC

Inventor： Rupesh Gupta ,  Hsiao-Ping Tseng ,  Ravi Kiran Holur Vijay ,  Romer E. Rosales

IPC: G06N20/00 ,  H04L12/58 ,  G06N5/04 ,  H04L29/08

Abstract: This disclosure relates to systems and methods that include configuring a machine learning system to train on a plurality of messages, the machine learning system to output an expected number of positive responses and an expected number of negative responses based on an input message, determining a threshold differential and a weight value using responses to the plurality of messages, and sending the input message in response to a differential between the expected number of positive responses and the weight multiplied by the expected number of negative responses being above the threshold differential.