公开(公告)号：US20240053429A1

公开(公告)日：2024-02-15

申请号：US18382996

申请日：2023-10-23

Applicant: United Parcel Service of America, Inc.

Inventor： Scott Alan LOVERICH ,  Ted ABEBE ,  Lawrence A. LAGROSA ,  Richard Allen KERSLAKE ,  Jonathan C. GRAY ,  Carl M. SKONBERG

IPC: G01S5/02 ,  G06Q10/08 ,  G06K7/10 ,  G06Q10/0833

CPC classification number: G01S5/0284 ,  G06Q10/08 ,  G06K7/10297 ,  G01S5/02 ,  G06Q10/0833 ,  G01S5/0018

Abstract: 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.

公开(公告)号：US20230259875A1

公开(公告)日：2023-08-17

申请号：US18140317

申请日：2023-04-27

Applicant: United Parcel Service of America, Inc.

Inventor： Ted ABEBE ,  Ed HOJECKI ,  Ilya LAVRIK ,  Vinay RAO ,  Donald HICKEY

IPC: G06Q10/083 ,  G06N20/00 ,  G06Q10/0833 ,  G06Q10/04 ,  G06F18/21 ,  G06F18/243

CPC classification number: G06Q10/0838 ,  G06N20/00 ,  G06Q10/0833 ,  G06Q10/083 ,  G06Q10/04 ,  G06F18/21 ,  G06F18/24323

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.