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. 11, 2022

Filed:

Oct. 26, 2018
Applicant:

Herman Miller, Inc., Zeeland, MI (US);

Inventors:

Patrick Harrington, Boulder, CO (US);

Alex K. Cammenga, Hudsonville, MI (US);

Assignee:

MillerKnoll, Inc., Zeeland, MI (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 20/20 (2019.01); G06N 7/00 (2006.01); G06K 9/62 (2022.01); G06N 3/04 (2006.01); G06N 3/08 (2006.01); G06F 16/909 (2019.01); H04W 4/021 (2018.01); H04W 4/33 (2018.01); H04W 4/02 (2018.01);
U.S. Cl.
CPC ...
G06N 3/0427 (2013.01); G06F 16/909 (2019.01); G06K 9/6256 (2013.01); G06N 3/08 (2013.01); H04W 4/021 (2013.01); H04W 4/025 (2013.01); H04W 4/33 (2018.02);
Abstract

Methods and systems for training a multivariate model predicting utilization of a space. One method includes receiving, over a period of time, signals from each of a plurality of mobile devices located in the space and generating, based on the signals received from each of the plurality of mobile devices, a plurality of location data points for each of the plurality of mobile devices, each of the plurality of location data points for a mobile device including a timestamp and a position within the space of the mobile device. The method also includes accessing metadata of the space, and using, with an electronic processor, the plurality location data points for each of the plurality of mobile devices and the metadata of the space to train machine learning engine. In addition, the method includes predicting a utilization of the space using the machine learning engine.


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