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:
Jul. 20, 2021

Filed:

Jun. 21, 2018
Applicant:

Kronos Technology Systems Limited Partnership, Lowell, MA (US);

Inventors:

Michael A. Scarpati, Chelmsford, MA (US);

Alexander Krowitz, Chelmsford, MA (US);

Martin Tapp, Montreal, CA;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06Q 30/02 (2012.01); G06N 7/00 (2006.01); G06Q 10/06 (2012.01); G06N 20/00 (2019.01); G06N 5/00 (2006.01); G06N 20/20 (2019.01); G06N 3/02 (2006.01); G06N 5/02 (2006.01);
U.S. Cl.
CPC ...
G06Q 30/0202 (2013.01); G06N 5/003 (2013.01); G06N 7/00 (2013.01); G06N 20/00 (2019.01); G06N 20/20 (2019.01); G06Q 10/063116 (2013.01); G06N 3/02 (2013.01); G06N 5/02 (2013.01);
Abstract

Methods for estimating multiple types of retail business volume based on multiple types of data are described. Historical volume data, prior recorded business volume, characteristics of the store including departments, and geographical location are used. Historical data is transformed into multiple features that capture seasonality, trends, the effects of special events and other business characteristics. This data can be pooled based on business characteristics, and then machine learning regression models, e.g., multiple regression trees, are fitted to each pool of data. To estimate future volume, the same features are computed, and the regression model is applied. The estimates are presented back to the user, or transmitted electronically to other systems, including systems for creating worker schedules based on predicted volumes. Systems, apparatus and computer readable media are also described.


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