United Parcel Service of America, Inc.

Ted Abebe

Carl M Skonberg

Jonathan C Gray

Scott Alan Loverich

Lawrence A Lagrosa

Richard Allen Kerslake

Ilya A Lavrik

Donald Hickey

Ed Hojecki

Vinay Rao

I Lavrik

Systems and methods for facilitating the sorting of assets to sort locations. In various embodiments, a sort employee scans an asset indicia using a user device, which stores asset data corresponding to the stored asset. As the sort employee nears a sort location (e.g., a delivery vehicle) with the asset and the user device, the user device automatically communicates wirelessly with a sort location receiver to associate the asset data with data indicative of the sort location where the user deposits the asset. In various embodiments, a device may determine whether the user device is proximate the appropriate sort location for the item, and may generate an alert upon a determination that the user device is proximate an incorrect sort location.

Identifying an asset sort location

Systems and methods for facilitating the sorting of assets to sort locations. In various embodiments, a sort employee scans an indicia using a user device, which stores asset data corresponding to the stored asset. As the sort employee nears a sort location (e.g., a delivery vehicle) with the asset and the user device, the user device automatically communicates wirelessly with a sort location receiver to associate the asset with data indicative of the sort location where the user deposits the asset. In various embodiments, a device may determine whether the user device is proximate the appropriate sort location for the item, and may generate an alert upon a determination that the user device is proximate an incorrect sort location.

Concepts for identifying an asset sort location

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.

Automatically predicting shipper behavior using machine learning models

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Jersey City, NJ, United States of America

Ted Abebe