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:
Apr. 05, 2022

Filed:

Apr. 11, 2017
Applicant:

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

Inventors:

Arunabh P. Verma, Seattle, WA (US);

Sophie A. Beland, Seattle, WA (US);

Oluwadara Oke, Seattle, WA (US);

William M. Geraci, II, Sammamish, WA (US);

Kevin J. Jeyakumar, Bellevue, WA (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 17/00 (2019.01); G06K 9/00 (2022.01); H04W 4/02 (2018.01); G06N 20/00 (2019.01); G06F 3/04883 (2022.01); G06K 9/46 (2006.01); G06F 40/171 (2020.01); G06N 5/00 (2006.01); G06N 20/10 (2019.01); G06N 20/20 (2019.01); G06N 3/04 (2006.01);
U.S. Cl.
CPC ...
G06K 9/00436 (2013.01); G06F 3/04883 (2013.01); G06F 40/171 (2020.01); G06K 9/00429 (2013.01); G06K 9/4604 (2013.01); G06N 20/00 (2019.01); H04W 4/02 (2013.01); G06N 3/0454 (2013.01); G06N 5/003 (2013.01); G06N 20/10 (2019.01); G06N 20/20 (2019.01);
Abstract

The electronic devices described herein are configured to enhance user experience associated with drawing or otherwise inputting shape data into the electronic devices. Shape input data is identified and matched against known shape patterns and, when a match is found, an entity associated with the shape is determined. The entity is converted into an annotation for rendering and/or displaying to the user. The shape identification, entity determination, and annotation conversion may all be based on one or more context elements to increase the accuracy of the shape interpretation. In particular, elements of conversations held via the electronic devices may be used as context for the shape interpretation. Further, machine learning techniques may be applied based on a variety of feedback data to improve the accuracy, speed, and/or performance of the shape interpretation process.


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