IP Library Granted Patent US 12,555,068
Granted Patent B2
US 12,555,068 · App. 18/601,324 · Granted Feb 17, 2026

System and method for improving item scan rates in distribution network

Inventor: Ryan J. Simpson (Vienna, VA)
Assignee: United States Postal Service
G06Q10/0838G06K7/1413G06K7/1456G06N5/022G06V10/40G06Q10/0833G06V30/10
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,555,068
App. No.
18/601,324
Granted
Feb 17, 2026
Kind
B2
Abstract

The present disclosure relates to a system and method for improving scan rates of an item in a distribution network. The system may include an item scan database storing item scan datasets containing barcodes, item datasets and optical character recognition (OCR) labels datasets and a processor in data communication with the item scan database. The processor may detect an item barcode with an item orientation so as to output correct orientation information of the item. The processor may also extract available barcode information from the detected item barcode based on the correct orientation information of the item. The processor may further reconstruct a correct barcode from the extracted available barcode information and one or more of the item scan datasets.

Claims (41)

1 . A system for scanning items in a distribution network, the system comprising:

one or more databases storing barcode information for a plurality of barcodes;

a scanner located at a distribution network facility, the scanner configured to scan an item and generate scan information, the scan information comprising barcode information, adjacency information identifying at least one other item processed before or after the item, and a distribution network facility identifier;

one or more processors in data communication with the one or more databases and the scanner, and configured to:

extract barcode information from the scan information, wherein the extracted barcode information comprises a computer-readable portion and a non-computer-readable portion;

identify the computer-readable portion of the extracted barcode information;

identify, in the one or more databases, a set of candidate barcodes from the plurality of barcodes based on the adjacency information and the distribution network facility identifier;

retrieve, from the one or more databases, the set of candidate barcodes of the plurality of barcodes that at least partially match the identified computer-readable portion of the extracted barcode information;

and

identify a likely barcode from the set of candidate barcodes based on the adjacency information and the distribution network facility identifier.

2 . The system of claim 1 , wherein at least one of the one or more databases further stores item information for a plurality of items and an association between each of the plurality of items with one of the plurality of barcodes.

3 . The system of claim 2 , wherein at least one of the one or more processors is further configured to identify the likely barcode by comparing item information for the items of the plurality of items associated with the one or more candidate barcodes.

4 . The system of claim 2 , wherein the scan information further comprises facility information associated with the scanner, and wherein the item information comprises an expected item location for each of the plurality of items.

5 . The system of claim 4 , wherein at least one of the one or more processors is further configured to:

identify a subset of barcodes of the plurality of barcodes which are associated with expected item locations corresponding to the facility information associated with the scanner; and

retrieve the one or more candidate barcodes from the identified subset of barcodes.

6 . The system of claim 4 , wherein at least one of the one or more processors is further to configured to identify the likely barcode by comparing the expected item information of the items of the plurality of items associated with the one or more candidate barcodes and the facility information associated with the scanner.

7 . The system of claim 1 , wherein the barcode information comprises one or more bar code elements, and wherein the identified computer-readable portion of the extracted barcode information corresponds to at least one of the one or more barcode elements.

8 . The system of claim 7 , wherein each of the one or more barcode elements comprises: a mailer ID, a sequence number, a service type identifier, recipient information, or a delivery point identifier.

9 . The system of claim 7 , wherein the one or more barcode elements comprise a mailer ID, a sequence number, a service type identifier, and a delivery point identifier, associated with the item, and wherein at least one of the one or more processors is configured to analyze two or more of the mailer ID, the sequence number, the service type identifier, or the delivery point identifier to identify the non-computer-readable portion of the extracted barcode information.

10 . The system of claim 1 , wherein at least one of the one or more processors is further configured to reconstruct the likely barcode from the extracted barcode information and the barcode information associated with the likely barcode.

11 . A method for scanning items in a distribution network, the method comprising:

storing, in one more databases, barcode information for a plurality of barcodes;

receive, from a scanner located at a distribution network facility, the scanner, scan information from a scan of an item, the scan information comprising barcode information, adjacency information identifying at least one other item processed before or after the item, and a distribution network facility identifier;

extracting, by one or more processors, barcode information from the scan information, wherein the extracted barcode information comprises a computer-readable portion and a non-computer-readable portion;

identifying, by the one or more processors, the computer-readable portion of the extracted barcode information;

identifying, in the one or more databases, a set of candidate barcodes from the plurality of barcodes based on the adjacency information and the distribution network facility identifier;

retrieving, by the one or more processors, the set of candidate barcodes of the plurality of barcodes that at least partially match the identified computer-readable portion of the extracted barcode information;

identifying a likely barcode from the set of candidate barcodes based on the adjacency information and the distribution network facility identifier.

12 . The method of claim 11 , further comprising storing, in the one or more databases, item information for a plurality of items and an association between each of the plurality of items with one of the plurality of barcodes.

13 . The method of claim 12 , wherein identifying the likely barcode further comprises comparing item information for the items of the plurality of items associated with the one or more candidate barcodes.

14 . The method of claim 12 , wherein the scan information further comprises facility information associated with the scanner, and wherein the item information comprises an expected item location for each of the plurality of items.

15 . The method of claim 14 , further comprising:

identifying a subset of barcodes of the plurality of barcodes which are associated with expected item locations corresponding to the facility information associated with the scanner; and

retrieving the one or more candidate barcodes from the identified subset of barcodes.

16 . The method of claim 14 , wherein identifying the likely barcode comprises comparing the expected item information of the items of the plurality of items associated with the one or more candidate barcodes and the facility information associated with the scanner.

17 . The method of claim 11 , wherein the barcode information comprises one or more barcode elements, and wherein the identified computer-readable portion of the extracted barcode information corresponds to at least one of the one or more barcode elements.

18 . The method of claim 17 , wherein each of the one or more barcode elements comprises: a mailer ID, a sequence number, a service type identifier, recipient information, or a delivery point identifier.

19 . The method of claim 11 , further comprising: reconstructing the likely barcode from the extracted barcode information and the barcode information associated with the likely barcode.

20 . The method of claim 19 , further comprising attaching the reconstructed likely barcode to the item.

21 . A non-transitory computer readable medium storing instructions, which, when executed by one or more processors, causes the one or more processors to perform the method of claim 10 .

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 20, 2026
From: SIMPSON, RYAN J.
To: UNITED STATES POSTAL SERVICE
Reel/Frame 073517/0963 →
Continuity (3)
Continuation 17449156 · Sep 28, 2021
Provisional Application 63085858 · Sep 30, 2020
Related Publication 20240211869A1 · Jun 27, 2024
References Cited (13)
US 5880451A · Smith et al. · 1999 [cited by applicant]
US 6363484B1 · Cordery et al. · 2002 [cited by applicant]
US 7387251B2 · Baker et al. · 2008 [cited by applicant]
US 9836635B2 · Negro et al. · 2017 [cited by applicant]
US 11961040B2 · Simpson · 2024 [cited by examiner]
US 20120106787A1 · Nechiporenko et al. · 2012 [cited by applicant]
US 20130193211A1 · Baqai et al. · 2013 [cited by applicant]
US 20160110703A1 · Herring et al. · 2016 [cited by applicant]
US 20180300519A1 · Trajkovic · 2018 [cited by examiner]
US 20230196527A1 · Zhang · 2023 [cited by examiner]
International Search Report and Written Opinion dated Mar. 2, 2022, in International Application No. PCT/US2021/052384. [cited by applicant]
International Preliminary Report on Patentability dated Mar. 28, 2023, in International Application No. PCT/US2021/052384. [cited by applicant]
Suh et al. “Robust Shipping Label Recognition and Validation for Logistics by Using Deep Neural Networks”, 2019 IEEE International Conference on Image Processing (ICIP), IEEE, Sep. 22, 2019 (Sep. 22, 2019), pp. 4509-451… [cited by applicant]