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:
Jun. 04, 2024

Filed:

Jun. 28, 2023
Applicant:

Descartes Systems (Usa) Llc, Atlanta, GA (US);

Inventors:

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);

Aurelio de Padua Gandra, Sao Paulo, BR;

Assignee:

Descartes Systems (USA) LLC, Atlanta, GA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
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);
U.S. Cl.
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);
Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for scoring candidate routes. One of the methods includes obtaining a predictive model trained on training examples from trip log data, wherein each training example has feature values from a particular trip and a value of a dependent variable that represents an outcome of a portion of the particular trip, wherein the features of each particular trip include values obtained from one or more external data feed sources that specify a value of a sensor measurement at a particular point in time during the trip. Sensor values from one or more external data feed sources of a sensor network are received. Feature values are generated using the sensor values received from the one or more external data feed sources. A predicted score is computed for each route using the feature values for the candidate route.


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