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:
Oct. 25, 2022

Filed:

Jan. 12, 2018
Applicant:

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

Inventors:

Kenneth Liam Kiemele, Redmond, WA (US);

John Benjamin Hesketh, Kirkland, WA (US);

Evan Lewis Jones, Kirkland, WA (US);

James Lewis Nance, Kirkland, WA (US);

LaSean Tee Smith, Bellevue, WA (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/62 (2022.01); G06N 20/20 (2019.01); G06V 10/25 (2022.01); G06V 20/52 (2022.01); G06V 40/10 (2022.01); G06V 40/20 (2022.01); G06T 7/73 (2017.01); G06N 20/00 (2019.01); H04W 4/70 (2018.01);
U.S. Cl.
CPC ...
G06K 9/6227 (2013.01); G06K 9/6262 (2013.01); G06K 9/6288 (2013.01); G06N 20/00 (2019.01); G06N 20/20 (2019.01); G06T 7/75 (2017.01); G06V 10/25 (2022.01); G06V 20/52 (2022.01); G06V 40/10 (2022.01); G06V 40/20 (2022.01); H04W 4/70 (2018.02);
Abstract

Techniques for generating a machine learning model to detect event instances from physical sensor data, including applying a first machine learning model to first sensor data from a first physical sensor at a location to detect an event instance, determining that a performance metric for use of the first machine learning model is not within an expected parameter, obtaining second sensor data from a second physical sensor during a period of time at the same location as the first physical sensor, obtaining third sensor data from the first physical sensor during the period of time, generating location-specific training data by selecting portions of the third sensor data based on training event instances detected using the second sensor data, training a second ML model using the location-specific training data, and applying the second ML model instead of the first ML model for detecting event instances.


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