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:
Dec. 26, 2023

Filed:

May. 15, 2019
Applicant:

Grubhub Holdings Inc., Chicago, IL (US);

Inventors:

Ryan J. O'Neil, Washington, DC (US);

Sagar Sahasrabudhe, Chicago, IL (US);

Gregory Danko, Oak Park, IL (US);

Carolyn Mooney, Philadelphia, PA (US);

Assignee:

GRUBHUB HOLDINGS INC., Chicago, IL (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06Q 10/04 (2023.01); G06Q 10/08 (2023.01); G01C 21/36 (2006.01); G06N 20/00 (2019.01); G06F 17/18 (2006.01); G06N 5/04 (2023.01); G06Q 10/06 (2023.01); G06F 30/20 (2020.01); G06Q 10/063 (2023.01); G06Q 10/0835 (2023.01); G06Q 10/083 (2023.01); G06Q 10/0833 (2023.01);
U.S. Cl.
CPC ...
G06N 5/04 (2013.01); G01C 21/3691 (2013.01); G01C 21/3697 (2013.01); G06F 17/18 (2013.01); G06F 30/20 (2020.01); G06N 20/00 (2019.01); G06Q 10/04 (2013.01); G06Q 10/063 (2013.01); G06Q 10/0833 (2013.01); G06Q 10/0838 (2013.01); G06Q 10/08355 (2013.01);
Abstract

A computer-implemented method comprises receiving training data corresponding to a plurality of trips, the training data including at least a value for a set of attributes for each of the plurality of trips, the set of attributes including an indication of when in a week a trip is taken or in which of a plurality of meal-based time zones in a day the trip is taken, the training data including an actual travel time for each of the plurality of trips. The method further comprises creating and storing, in computer memory, a digital model that is configured to predict a travel time for a future trip based on the training data, the digital model including a set of parameters corresponding to the set of attributes, the digital model including a plurality of sets of values for the set of parameters. In addition, the method comprises receiving specific data for a specific trip, the specific data including an indication when in a week the specific trip will be taken or in which of a plurality of meal-based time zones of a day the specific trip will be taken; calculating a specific travel time for the specific trip using the digital model; determining a specific prediction interval for the specific travel time based on the plurality of sets of values for the set of parameters; and causing display of the specific travel time and the specific prediction interval.


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