US 12,002,001 B2
Integrated multi-location scheduling, routing, and task management
Patrick Miller Coughran, Tucson, AZ (US); Douglas David Coughran, IV, Tucson, AZ (US); Evan Fields, Cambridge, MA (US); Rany Polany, Foster City, CA (US); Raimundo Onetto, Walnut Creek, CA (US); Nathalie Saade, Berkeley, CA (US); Nasser Mohamed, Oakland, CA (US); and Aurelio de Padua Gandra, Sao Paulo (BR)
Assigned to Descartes Systems (USA) LLC, Atlanta, GA (US)
Filed by Descartes Systems (USA) LLC, Atlanta, GA (US)
Filed on Jun. 28, 2023, as Appl. No. 18/215,466.
Application 18/215,466 is a continuation of application No. 17/208,708, filed on Mar. 22, 2021, granted, now 11,727,345.
Application 17/208,708 is a continuation of application No. 15/238,708, filed on Aug. 16, 2016, granted, now 10,956,855, issued on Mar. 23, 2021.
Claims priority of provisional application 62/259,295, filed on Nov. 24, 2015.
Claims priority of provisional application 62/219,608, filed on Sep. 16, 2015.
Claims priority of provisional application 62/205,727, filed on Aug. 16, 2015.
Prior Publication US 2023/0342708 A1, Oct. 26, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06Q 10/0835 (2023.01); G06N 5/04 (2023.01); G06N 7/01 (2023.01); G06N 20/00 (2019.01); G06N 20/10 (2019.01); G06N 20/20 (2019.01); G06Q 10/04 (2023.01); G06Q 10/047 (2023.01)
CPC G06Q 10/08355 (2013.01) [G06N 5/04 (2013.01); G06N 7/01 (2023.01); G06N 20/00 (2019.01); G06N 20/10 (2019.01); G06N 20/20 (2019.01); G06Q 10/04 (2013.01); G06Q 10/047 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
determining a plurality of candidate routes for a vehicle, wherein each respective candidate route (i) includes each waypoint of a same predetermined plurality of waypoints and (ii) specifies a corresponding ordering of the same predetermined plurality of waypoints;
generating, for each respective candidate route of the plurality of candidate routes, independent feature values for the respective candidate route based on the corresponding ordering of the same predetermined plurality of waypoints;
computing, for each respective candidate route and using a machine learning model, a corresponding score for the respective candidate route based on the independent feature values for the respective candidate route, wherein the machine learning model has been trained using a plurality of training samples each comprising (i) training independent feature values from a particular trip and (ii) a corresponding dependent score that represents an outcome of at least a portion of the particular trip;
ranking the plurality of candidate routes based on the respective score computed for each respective candidate route; and
providing a representation of at least one candidate route based on the ranking of the plurality of candidate routes.