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:
Sep. 08, 2020

Filed:

Jan. 07, 2019
Applicant:

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

Inventors:

Ye Tu, San Carlos, CA (US);

Yiping Yuan, Sunnyvale, CA (US);

Chun Lo, Mountain View, CA (US);

Shaunak Chatterjee, Sunnyvale, CA (US);

Yijie Wang, Sunnyvale, CA (US);

Assignee:
Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06F 15/16 (2006.01); G06F 16/954 (2019.01); G06F 16/953 (2019.01); G06Q 50/00 (2012.01); G06N 20/00 (2019.01); H04L 29/08 (2006.01);
U.S. Cl.
CPC ...
G06F 16/954 (2019.01); G06F 16/953 (2019.01); G06N 20/00 (2019.01); G06Q 50/01 (2013.01); H04L 67/22 (2013.01);
Abstract

A machine for content-feedback-based machine learning to incent online content creation. The machine accesses a relevance value that identifies a level of relevance of a content item to a user. The content item is created by a content creator. The machine generates, using a machine learning model, a feedback sensitivity score associated with the content creator. The machine generates, based on the relevance value and a product between the feedback sensitivity score and a likelihood of the user providing a feedback signal in relation to the content item, a ranking score for the content item. The machine causes display of the content item, based on the ranking score, in a user interface of a client device associated with the user. An input pertaining to the content item received via the user interface causes improvement of the machine learning model based on updating the one or more feedback features.


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