公开(公告)号：US11854028B2

公开(公告)日：2023-12-26

申请号：US17516681

申请日：2021-11-01

申请人： Microsoft Technology Licensing, LLC

发明人： Matthew Spencer Donald Kerr ,  Shaker Asif Khaleque ,  Kelly John Forsmann ,  Tatiana Shubin ,  Dipti A. Patil

IPC分类号： G06Q30/02 ,  G06Q30/0203 ,  G06Q30/0204 ,  G06F18/214 ,  G06F18/21

CPC分类号： G06Q30/0203 ,  G06F18/214 ,  G06F18/2178 ,  G06Q30/0204

摘要： Systems and methods are directed to optimizing survey parameters using machine learning. A network system monitors user activity of a plurality of users with respect to an application and provides a notification to users of the plurality of users that satisfy a trigger condition for providing the notification. The network system obtains feedback corresponding to the notification, whereby the feedback indicates whether each of the users accepted, rejected, or ignored the notification. A machine learning model is then trained using input data obtained from the feedback to optimize on one or more parameters used by the network system in providing a future notification. Based on the machine learning model, the future notification is presented to a further set of users using the one or more optimized parameters.

公开(公告)号：US20230137708A1

公开(公告)日：2023-05-04

申请号：US17516681

申请日：2021-11-01

申请人： Microsoft Technology Licensing, LLC

发明人： Matthew Spencer Donald Kerr ,  Shaker Asif Khaleque ,  Kelly John Forsmann ,  Tatiana Shubin ,  Dipti A. Patil

IPC分类号： G06Q30/02 ,  G06K9/62

摘要： Systems and methods are directed to optimizing survey parameters using machine learning. A network system monitors user activity of a plurality of users with respect to an application and provides a notification to users of the plurality of users that satisfy a trigger condition for providing the notification. The network system obtains feedback corresponding to the notification, whereby the feedback indicates whether each of the users accepted, rejected, or ignored the notification. A machine learning model is then trained using input data obtained from the feedback to optimize on one or more parameters used by the network system in providing a future notification. Based on the machine learning model, the future notification is presented to a further set of users using the one or more optimized parameters.