The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jun. 16, 2020

Filed:

Sep. 09, 2016
Applicant:

Microsoft Technology Licensing, Llc, Redmond, WA (US);

Inventors:

Roberto Bojorquez Alfaro, Bothell, WA (US);

Matus Lipka, Kirkland, WA (US);

Lee Dicks Clark, Seattle, WA (US);

Boaz Sapir, Givatayim, IL;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
H04L 12/58 (2006.01); H04L 29/08 (2006.01); H04W 4/14 (2009.01);
U.S. Cl.
CPC ...
H04L 51/12 (2013.01); H04L 51/16 (2013.01); H04L 51/24 (2013.01); H04L 67/22 (2013.01); H04L 67/26 (2013.01); H04L 67/306 (2013.01); H04L 51/32 (2013.01); H04W 4/14 (2013.01);
Abstract

Techniques for learned user preference- and behavior-based notification filtering are described herein. In one or more implementations, notifications obtained from computer applications are filtered for presentation to a user. Example notifications include notifications about emails, text messages, phone calls, web-page specific messages, antivirus application messages, and so forth. As part of filtering the notifications, interactions of a user with the notifications and with events for which the notifications can be generated are monitored. The monitored interactions are used to learn user preferences and behaviors for notifications in different contexts of user interaction with computing devices. Data is collected that describes characteristics of a current context. Based on the current context, importance scores are computed for new notifications using the learned user preferences and behaviors. The importance scores can then be used to determine which of the new notifications to present to the user.


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