Automatically predicting shipper behavior using machine learning models

US11651326B2 · kind B2 · utility

Assignee

• UNITED PARCEL SERVICE OF AMERICA, INC. · US

Inventors

• Ted Abebe · Jersey City, US
• Ed Hojecki · Atlanta, US
• Ilya A. Lavrik · Atlanta, US
• Vinay Rao · Wayne, US
• Donald D. Hickey · Sayre, US

Key dates

• Filing date: Nov 20, 2018
• Grant date: May 16, 2023
• Expiry date: Sep 7, 2041

Classification

• Technology area (CPC G): Physics
• CPC primary: G06Q10/0833
• WIPO field: IT methods for management
• WIPO sector: Electrical engineering

Abstract

Embodiments are disclosed for autonomously predicting shipper behavior. An example method includes the following operations. One or more learning models are generated. Shipper behavior data for at least one shipper is extracted. The shipper behavior data includes a plurality of features associated with the at least one shipper scheduled to ship one or more parcels. It is predicted whether one or more shipments will be sent or arrive at a particular time based at least in part on running the plurality of features of the at least one shipper through the one or more learning models.