IP Library Granted Patent US 12,437,263
Granted Patent B2
US 12,437,263 · App. 18/203,499 · Granted Oct 7, 2025

Systems and methods of monitoring location labels of product storage structures of a product storage facility

Inventors: Chongrui Zhao (San Mateo, CA); Shanthi Narayanan (Parrish, FL); Tracy E. Benson (Campbell, CA)
Assignee: Walmart Apollo, LLC
G06Q10/087G06Q10/06316G06V20/50G06V20/63G06V30/10G06K7/1413G06K15/024
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 12,437,263
App. No.
18/203,499
Granted
Oct 7, 2025
Kind
B2
Abstract

Systems and methods of monitoring location labels on product storage structures of a product storage facility include an image capture device that captures images of the product storage structures and a computing device programmed to analyze the images of the product storage structures captured by the image capture device to detect location labels located on the product storage structures. Based on detection that one or more location labels located on the product storage structures are associated with an error condition, the computing device generates a location label alert indicating at least one location label that requires a location label check by a worker at the product storage facility. A mobile application executable on a device of the worker at the product storage facility displays a user interface that lists location labels alerts and permits the worker to print replacement labels for product structures associated with the alerts.

Claims (56)

1. A system for monitoring location labels on product storage structures of a product storage facility, the system comprising:

an image capture device having a field of view that includes a product storage structure at the product storage facility configured to have products arranged thereon, wherein the image capture device is configured to capture at least one image of the product storage structure, and wherein the image capture device comprises a motorized robotic unit that autonomously moves around the product storage facility;

a computing device including a control circuit, the computing device communicatively coupled to the image capture device, the control circuit being configured to:

determine movement instructions for the motorized robotic unit;

autonomously control the motorized robotic unit by sending the movement instructions, wherein the motorized robotic unit enables the image capture device to capture the at least one image of the product storage structure in sequence;

analyze the at least one image of the product storage structure captured by the image capture device to detect at least one location label located on the product storage structure, wherein a trained machine learning model is used to detect the at least one location label; and

based on detection that the at least one location label located on the product storage structure is at least one of missing, incomplete, damaged, deformed, and at least partially obstructed, generate a location label alert indicating that the at least one location label requires a location label check by a worker at the product storage facility; and

a mobile application executable on a user device of the worker at the product storage facility, the mobile application, when executed, is configured to cause a user interface to be displayed to the worker on a display of the user device, wherein the user interface lists at least one location label alert generated by the control circuit of the computing device, wherein the user interface permits the worker to:

view a location of the product storage structure associated with the location label alert;

input an inspection result of a physical inspection of the at least one location label of the product storage structure, wherein the inspection result of the physical inspection is used to retrain the trained machine learning model;

generate a replacement label for the product storage structure associated with the location label alert and output signaling to cause the replacement label to be printed; and

scan the replacement label to verify that the worker affixed the replacement label to the product storage structure associated with the location label alert.

2. The system of claim 1 ,

wherein the user interface is configured such that each location label alert listed within the user interface is selectable by the worker; and

wherein, in response to a location label alert being selected by the worker within the user interface, the user interface is configured to display at least a portion of the at least one image of the product storage structure associated with the location label alert selected by the worker.

3. The system of claim 2 , wherein, in response to the location label alert being selected by the worker within the user interface, the user interface is configured to display a listing of the products stored on the product storage structure associated with the location label alert selected by the worker.

4. The system of claim 1 , wherein the user interface includes selectable fields configured to permit the worker to manually input the location of the product storage structure associated with the location label alert.

5. The system of claim 1 , wherein the user interface is configured to permit the worker to select a portable printer for printing the replacement label for the product storage structure associated with the location label alert.

6. The system of claim 1 , wherein the user interface is configured to:

generate a notification to the worker that the replacement label has been printed; and

generate a location label scanning feature on the display of the user device to permit the worker to scan the replacement label to verify that the worker affixed the replacement label to the product storage structure associated with the location label alert.

7. The system of claim 1 , wherein the image capture device comprises a camera to permit the motorized robotic unit to capture the at least one image of the product storage structure.

8. The system of claim 1 , wherein the control circuit is further configured to process the at least one image to:

generate virtual boundary lines that surround the at least one location label detected in the at least one image; and

extract one or more characters from the at least one location label detected in the at least one image.

9. The system of claim 1 , further comprising an electronic database configured to store the at least one image captured by the image capture device, and wherein the control circuit is programmed to transmit the location label alert to the electronic database for storage.

10. The system of claim 9 , wherein the user interface is further configured to cause the user device of the worker to transmit a notification to at least one of the computing device and the electronic database, the notification indicating that the worker scanned the replacement label to verify that the worker affixed the replacement label to the product storage structure associated with the location label alert.

11. A method of monitoring location labels on product storage structures of a product storage facility, the method comprising:

capturing at least one image of a product storage structure at the product storage facility configured to have products arranged thereon by an image capture device having a field of view that includes the product storage structure at the product storage facility, wherein the image capture device comprises a motorized robotic unit that autonomously moves around the product storage facility;

by a computing device including a control circuit and communicatively coupled to the image capture device:

determining movement instructions for the motorized robotic unit;

autonomously controlling the motorized robotic unit by sending the movement instructions, wherein the motorized robotic unit enables the image capture device to capture the at least one image of the product storage structure in sequence;

analyzing the at least one image of the product storage structure captured by the image capture device to detect at least one location label located on the product storage structure, wherein a trained machine learning model is used to detect the at least one location label; and

based on detection that the at least one location label located on the product storage structure is at least one of missing, incomplete, damaged, deformed, and at least partially obstructed, generating a location label alert indicating that the at least one location label requires a location label check by a worker at the product storage facility; and

by a mobile application executable on a user device of the worker at the product storage facility, causing a user interface to be displayed to the worker on a display of the user device, wherein the user interface lists at least one location label alert generated by the control circuit of the computing device, wherein the user interface permits the worker to:

view a location of the product storage structure associated with the location label alert;

input an inspection result of a physical inspection of the at least one location label of the product storage structure, wherein the inspection result of the physical inspection is used to retrain the trained machine learning model;

generate a replacement label for the product storage structure associated with the location label alert and output signaling to cause the replacement label to be printed; and

scan the replacement label to verify that the worker affixed the replacement label to the product storage structure associated with the location label alert.

12. The method of claim 11 ,

wherein the user interface is configured such that each location label alert listed within the user interface is selectable by the worker; and

further comprising, in response to a location label alert being selected by the worker within the user interface, displaying within the user interface at least a portion of the at least one image of the product storage structure associated with the location label alert selected by the worker.

13. The method of claim 12 , further comprising, in response to the location label alert being selected by the worker within the user interface, displaying within the user interface a listing of the products stored on the product storage structure associated with the location label alert selected by the worker.

14. The method of claim 11 , further comprising permitting the worker, via user-selectable fields of the user interface, to manually input the location of the product storage structure associated with the location label alert.

15. The method of claim 11 , further comprising permitting the worker to select, via the user interface, a portable printer for printing the replacement label for the product storage structure associated with the location label alert.

16. The method of claim 11 , further comprising, via the user interface:

generating a notification to the worker that the replacement label has been printed; and

generating a location label scanning feature on the display of the user device to permit the worker to scan the replacement label to verify that the worker affixed the replacement label to the product storage structure associated with the location label alert.

17. The method of claim 11 , wherein the image capture device comprises a camera to permit the motorized robotic unit to capture the at least one image of the product storage structure.

18. The method of claim 11 , further comprising, by the control circuit, processing the at least one image to:

generate virtual boundary lines that surround the at least one location label detected in the at least one image; and

extract one or more characters from the at least one location label detected in the at least one image.

19. The method of claim 11 , further comprising:

storing the at least one image captured by the image capture device in an electronic database; and

transmitting, the location label alert from the computing device to the electronic database for storage.

20. The method of claim 19 , further comprising transmitting, from the user device of the worker and via the user interface, a notification to at least one of the computing device and the electronic database, the notification indicating that the worker scanned the replacement label to verify that the worker affixed the replacement label to the product storage structure associated with the location label alert.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 30, 2023
From: ZHAO, CHONGRUI; NARAYANAN, SHANTHI; BENSON, TRACY E.
To: WALMART APOLLO, LLC
Reel/Frame 063800/0285 →
Continuity (1)
Related Publication 20240403819A1 · Dec 5, 2024
References Cited (149)
US 5074594A · Laganowski · 1991 [cited by applicant]
US 6570492B1 · Peratoner · 2003 [cited by applicant]
US 8700494B2 · Carlson · 2014 [cited by applicant]
US 8923650B2 · Wexler · 2014 [cited by applicant]
US 8965104B1 · Hickman · 2015 [cited by applicant]
US 9275308B2 · Szegedy · 2016 [cited by applicant]
US 9477955B2 · Goncalves · 2016 [cited by applicant]
US 9526127B1 · Taubman · 2016 [cited by applicant]
US 9576310B2 · Cancro · 2017 [cited by applicant]
US 9659204B2 · Wu · 2017 [cited by applicant]
US 9811754B2 · Schwartz · 2017 [cited by applicant]
US 10002344B2 · Wu · 2018 [cited by applicant]
US 10019803B2 · Venable · 2018 [cited by applicant]
US 10032072B1 · Tran · 2018 [cited by applicant]
US 10129524B2 · Ng · 2018 [cited by applicant]
US 10210432B2 · Pisoni · 2019 [cited by applicant]
US 10373116B2 · Medina · 2019 [cited by applicant]
US 10572757B2 · Graham · 2020 [cited by applicant]
US 10592854B2 · Schwartz · 2020 [cited by applicant]
US 10796352B2 · Chechuy · 2020 [cited by applicant]
US 10839452B1 · Guo · 2020 [cited by applicant]
US 10922574B1 · Tariq · 2021 [cited by applicant]
US 10943278B2 · Benkreira · 2021 [cited by applicant]
US 10956711B2 · Adato · 2021 [cited by applicant]
US 10990950B2 · Garner · 2021 [cited by applicant]
US 10991036B1 · Bergstrom · 2021 [cited by applicant]
US 11036949B2 · Powell · 2021 [cited by applicant]
US 11055905B2 · Tagra · 2021 [cited by applicant]
US 11087272B2 · Skaff · 2021 [cited by applicant]
US 11151426B2 · Dutta · 2021 [cited by applicant]
US 11163805B2 · Arocho · 2021 [cited by applicant]
US 11276034B2 · Shah · 2022 [cited by applicant]
US 11282287B2 · Gausebeck · 2022 [cited by applicant]
US 11295163B1 · Schoner · 2022 [cited by applicant]
US 11308775B1 · Sinha · 2022 [cited by applicant]
US 11409977B1 · Glaser · 2022 [cited by applicant]
US 20050238465A1 · Razumov · 2005 [cited by applicant]
US 20090063307A1 · Groenovelt et al. · 2009 [cited by applicant]
US 20100188580A1 · Paschalaks · 2010 [cited by applicant]
US 20110040427A1 · Ben-Tzvi · 2011 [cited by applicant]
US 20120303412A1 · Etzioni · 2012 [cited by applicant]
US 20130232039A1 · Jackson et al. · 2013 [cited by applicant]
US 20140002239A1 · Rayner · 2014 [cited by applicant]
US 20140247116A1 · Davidson · 2014 [cited by applicant]
US 20140307938A1 · Doi · 2014 [cited by applicant]
US 20150363660A1 · Vidal · 2015 [cited by applicant]
US 20160203525A1 · Hara · 2016 [cited by applicant]
US 20170106738A1 · Gillett · 2017 [cited by applicant]
US 20170286773A1 · Skaff · 2017 [cited by applicant]
US 20170357937A1 · Edens · 2017 [cited by applicant]
US 20180005176A1 · Williams · 2018 [cited by applicant]
US 20180018788A1 · Olmstead · 2018 [cited by applicant]
US 20180089613A1 · Chen et al. · 2018 [cited by applicant]
US 20180108134A1 · Venable · 2018 [cited by applicant]
US 20180197223A1 · Grossman · 2018 [cited by applicant]
US 20180260772A1 · Chaubard · 2018 [cited by applicant]
US 20180276596A1 · Murthy et al. · 2018 [cited by applicant]
US 20190025849A1 · Dean · 2019 [cited by applicant]
US 20190043003A1 · Fisher · 2019 [cited by applicant]
US 20190050932A1 · Dey · 2019 [cited by applicant]
US 20190080277A1 · Trivelpiece · 2019 [cited by examiner]
US 20190087772A1 · Medina · 2019 [cited by applicant]
US 20190163698A1 · Kwon · 2019 [cited by applicant]
US 20190197561A1 · Adato · 2019 [cited by applicant]
US 20190220482A1 · Crosby · 2019 [cited by applicant]
US 20190236531A1 · Adato · 2019 [cited by applicant]
US 20200118063A1 · Fu · 2020 [cited by applicant]
US 20200118064A1 · Perrella · 2020 [cited by examiner]
US 20200246977A1 · Swietojanski · 2020 [cited by applicant]
US 20200265494A1 · Glaser · 2020 [cited by applicant]
US 20200324976A1 · Diehr · 2020 [cited by applicant]
US 20200356813A1 · Sharma · 2020 [cited by applicant]
US 20200380226A1 · Rodriguez · 2020 [cited by applicant]
US 20200387858A1 · Hasan · 2020 [cited by applicant]
US 20200402429A1 · Cho · 2020 [cited by applicant]
US 20210049541A1 · Gong · 2021 [cited by applicant]
US 20210049542A1 · Dalal · 2021 [cited by applicant]
US 20210142105A1 · Siskind · 2021 [cited by applicant]
US 20210150231A1 · Kehl · 2021 [cited by applicant]
US 20210192780A1 · Kulkarni · 2021 [cited by applicant]
US 20210216954A1 · Chaubard · 2021 [cited by applicant]
US 20210272269A1 · Suzuki · 2021 [cited by applicant]
US 20210319684A1 · Ma · 2021 [cited by applicant]
US 20210342914A1 · Dalal · 2021 [cited by applicant]
US 20210374662A1 · Bogolea · 2021 [cited by applicant]
US 20210400195A1 · Adato · 2021 [cited by examiner]
US 20210406812A1 · Deshmukh · 2021 [cited by applicant]
US 20220043547A1 · Jahjah · 2022 [cited by applicant]
US 20220051179A1 · Savvides · 2022 [cited by applicant]
US 20220058425A1 · Savvides · 2022 [cited by applicant]
US 20220067085A1 · Nihas · 2022 [cited by applicant]
US 20220114403A1 · Shaw · 2022 [cited by applicant]
US 20220114821A1 · Arroyo · 2022 [cited by applicant]
US 20220138914A1 · Wang · 2022 [cited by applicant]
US 20220165074A1 · Srivastava · 2022 [cited by applicant]
US 20220222924A1 · Pan · 2022 [cited by applicant]
US 20220262008A1 · Kidd · 2022 [cited by applicant]
US 20220303445A1 · Skaff · 2022 [cited by examiner]
CN 106347550B · 2019 [cited by applicant]
CN 110348439B · 2019 [cited by applicant]
CN 110443298B · 2022 [cited by applicant]
CN 114898358A · 2022 [cited by applicant]
CN 115205584A · 2022 [cited by applicant]
EP 2821185A1 · 2015 [cited by examiner]
EP 3217324A1 · 2017 [cited by applicant]
EP 3437031 · 2019 [cited by applicant]
EP 3479298 · 2019 [cited by applicant]
WO 2006113281A2 · 2006 [cited by applicant]
WO 2017201490A1 · 2017 [cited by applicant]
WO 2018093796 · 2018 [cited by applicant]
WO 2020051213A1 · 2020 [cited by applicant]
WO 2021186176A1 · 2021 [cited by applicant]
WO 2021247420A2 · 2021 [cited by applicant]
U.S. Appl. No. 17/963,751, filed Oct. 11, 2022, Yilun Chen. [cited by applicant]
U.S. Appl. No. 17/963,787, filed Oct. 11, 2022, Lingfeng Zhang. [cited by applicant]
U.S. Appl. No. 17/963,802, filed Oct. 11, 2022, Lingfeng Zhang. [cited by applicant]
U.S. Appl. No. 17/963,903, filed Oct. 11, 2022, Raghava Balusu. [cited by applicant]
U.S. Appl. No. 17/966,580, filed Oct. 14, 2022, Paarvendhan Puviyarasu. [cited by applicant]
U.S. Appl. No. 17/971,350, filed Oct. 21, 2022, Jing Wang. [cited by applicant]
U.S. Appl. No. 17/983,773, filed Nov. 9, 2022, Lingfeng Zhang. [cited by applicant]
U.S. Appl. No. 18/102,999, filed Jan. 30, 2023, Han Zhang. [cited by applicant]
U.S. Appl. No. 18/103,338, filed Jan. 30, 2023, Wei Wang. [cited by applicant]
U.S. Appl. No. 18/106,269, filed Feb. 6, 2023, Zhaoliang Duan. [cited by applicant]
U.S. Appl. No. 18/158,925, filed Jan. 24, 2023, Raghava Balusu. [cited by applicant]
U.S. Appl. No. 18/158,950, filed Jan. 24, 2023, Ishan Arora. [cited by applicant]
U.S. Appl. No. 18/158,969, filed Jan. 24, 2023, Zhaoliang Duan. [cited by applicant]
U.S. Appl. No. 18/158,983, filed Jan. 24, 2023, Ashlin Ghosh. [cited by applicant]
U.S. Appl. No. 18/161,788, filed Jan. 30, 2023, Raghava Balusu. [cited by applicant]
U.S. Appl. No. 18/165,152, filed Feb. 6, 2023, Han Zhang. [cited by applicant]
U.S. Appl. No. 18/168,174, filed Feb. 13, 2023, Abhinav Pachauri. [cited by applicant]
U.S. Appl. No. 18/168,198, filed Feb. 13, 2023, Ashlin Ghosh. [cited by applicant]
Chaudhuri, Abon et al.; “A Smart System for Selection of Optimal Product Images in E-Commerce”; 2018 IEEE Conference on Big Data (Big Data); Dec. 10-13, 2018; IEEE; <https://ieeexplore.ieee.org/document/8622259>; pp. 17… [cited by applicant]
Chenze, Brandon et al.; “Iterative Approach for Novel Entity Recognition of Foods in Social Media Messages”; 2022 IEEE 23rd International Conference on Information Reuse and Integration for Data Science (IRI); Aug. 9-11… [cited by applicant]
Kaur, Ramanpreet et al.; “A Brief Review on Image Stitching and Panorama Creation Methods”; International Journal of Control Theory and Applications; 2017; vol. 10, No. 28; International Science Press; Gurgaon, India; <… [cited by applicant]
Naver Engineering Team; “Auto-classification of Naver Shopping Product Categories using TensorFlow”; <https://blog.tensorflow.org/2019/05/auto-classification-of-naver-shopping.html>; May 20, 2019; pp. 1-15. [cited by applicant]
Paolanti, Marine et al.; “Mobile robot for retail surveying and inventory using visual and textual analysis of monocular pictures based on deep learning”; European Conference on Mobile Robots; Sep. 2017, 6 pages. [cited by applicant]
Refills; “Final 3D object perception and localization”; European Commision, Dec. 31, 2016, 16 pages. [cited by applicant]
Retech Labs; “Storx | RetechLabs”; <https://retechlabs.com/storx/>; available at least as early as Jun. 22, 2019; retrieved from Internet Archive Wayback Machine <https://web.archive.org/web/20190622012152/https://retec… [cited by applicant]
Schroff, Florian et al.; “Facenet: a unified embedding for face recognition and clustering”; 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); Jun. 7-12, 2015; IEEE; <https://ieeexplore.ieee.org/do… [cited by applicant]
Singh, Ankit; “Automated Retail Shelf Monitoring Using AI”; <https://blog.paralleldots.com/shelf-monitoring/automated-retail-shelf-monitoring-using-ai/>; Sep. 20, 2019; pp. 1-10. [cited by applicant]
Singh, Ankit; “Image Recognition and Object Detection in Retail”; <https://blog.paralleldots.com/featured/image-recognition-and-object-detection-in-retail/>; Sep. 26, 2019; pp. 1-11. [cited by applicant]
Tan, Mingxing et al.; “EfficientDet: Scalable and Efficient Object Detection”; 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); Jun. 13-19, 2020; IEEE; <https://ieeexplore.ieee.org/document/91… [cited by applicant]
Tan, Mingxing et al.; “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks”; Proceedings of the 36th International Conference on Machine Learning; 2019; vol. 97; PLMR; <http://proceedings.mlr.press/… [cited by applicant]
Technology Robotix Society; “Colour Detection”; <https://medium.com/image-processing-in-robotics/colour-detection-e15bc03b3f61>; Jul. 2, 2019; pp. 1-6. [cited by applicant]
Tonioni, Alessio et al.; “A deep learning pipeline for product recognition on store shelves”; 2018 IEEE International Conference on Image Processing, Applications and Systems (IPAS); Dec. 12-14, 2018; IEEE; <https://iee… [cited by applicant]
Trax Retail; “Image Recognition Technology for Retail | Trax”; <https://traxretail.com/retail/>; available at least as early as Apr. 20, 2021; retrieved from Internet Wayback Machine <https://web.archive.org/web/2021042… [cited by applicant]
Verma, Nishchal, et al.; “Object identification for inventory management using convolutional neural network”; IEEE Applied Imagery Pattern Recognition Workshop (AIPR); Oct. 2016, 6 pages. [cited by applicant]
Zhang, Jicun, et al.; “An Improved Louvain Algorithm for Community Detection”; Advanced Pattern and Structure Discovery from Complex Multimedia Data Environments 2021; Nov. 23, 2021; Mathematical Problems in Engineering… [cited by applicant]
Rodriquez, K., “International Search Report & Written Opinion”, International Patent Application No. PCT/US24/27420, mailed Jul. 29, 2024, 9 pages. [cited by applicant]