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:
Feb. 08, 2022

Filed:

Oct. 28, 2019
Applicant:

Walmart Apollo, Llc, Bentonville, AR (US);

Inventors:

Shengyang Zhang, Santa Clara, CA (US);

Mingang Fu, Palo Alto, CA (US);

Arun Prasad Nagarathinam, Milpitas, CA (US);

Apeksha Mehta, Mountain View, CA (US);

Pawan Kumar, Sunnyvale, CA (US);

Madhavan Kandhadai Vasantham, Dublin, CA (US);

Ankit Jasuja, San Jose, CA (US);

Surnaik Prakash Srivastava, Santa Clara, CA (US);

Jennifer Chen, Union City, CA (US);

Vidyanand Krishnan, Sunnyvale, CA (US);

Assignee:

Walmart Apollo, LLC, Bentonville, AR (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G07C 11/00 (2006.01); G06Q 10/06 (2012.01); G06Q 10/04 (2012.01); H04M 3/523 (2006.01); G06Q 30/00 (2012.01); G06N 20/00 (2019.01); G06Q 30/02 (2012.01);
U.S. Cl.
CPC ...
G07C 11/00 (2013.01); G06N 20/00 (2019.01); G06Q 10/04 (2013.01); G06Q 10/06 (2013.01); G06Q 30/016 (2013.01); G06Q 30/02 (2013.01); H04M 3/5238 (2013.01); G07C 2011/04 (2013.01);
Abstract

A system is provided and generally includes a server, an associate computing device, and a customer computing device. The server may receive data from the customer computing device indicating that a customer is picking up items from a predetermined location. The server may compute an estimated wait time for the customer based on one or more machine learning processes. In some examples, a number of unexpected customers that may arrive is determined. The machine learning process may compute the estimated wait time based on the number of unexpected customers. The machine learning process may be trained with historical data. The estimated wait time is transmitted to the customer computing device, and is displayed to the customer. In some examples, the server sends a list of customers waiting to be serviced to the associate computing device. The list may be prioritized based on estimated wait times for those customers.


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