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:
Jan. 14, 2025

Filed:

Aug. 25, 2023
Applicant:

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

Inventors:

Ajaykartheek Vasagiri, Milpitas, CA (US);

Faraz Hussain, Mississauga, CA;

Kamal Manglani, Fremont, CA (US);

Ashwin Sureshkumar, Woodbridge, CA;

Sriram Krishnaswamy, Fremont, CA (US);

Chittaranjan Tripathy, Sunnyvale, CA (US);

Assignee:

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

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06Q 10/1093 (2023.01); G06N 20/00 (2019.01); G06Q 10/0835 (2023.01); G06Q 50/40 (2024.01);
U.S. Cl.
CPC ...
G06Q 10/1093 (2013.01); G06N 20/00 (2019.01); G06Q 10/0835 (2013.01); G06Q 50/40 (2024.01);
Abstract

This application relates to automatically scheduling timeslots, such as timeslots for scheduling item pickups and deliveries. The embodiments may employ machine learning process to determine, for each of a plurality of timeslots, a timeslot capacity. The embodiments may further determine a time as to when each of the plurality of timeslots become available for selection, such as on a webpage. In some examples, the time determined to make available for selection each of the plurality of timeslots is based on a predetermined amount of time before the timeslot. Further, the embodiments may provide for display each of the plurality of timeslots at the determined time. The embodiments may further include receiving a selection of one of the plurality of timeslots, and scheduling a pickup or delivery based on the selected timeslot. The machine learning processes may be trained with features generated from historical timeslot data and workforce availability.


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