IP Library Granted Patent US 12,639,667
Granted Patent B2
US 12,639,667 · App. 16/737,717 · Granted May 26, 2026

Systems, apparatuses, and methods for triggering object recognition and planogram generation via shelf sensors

Inventors: David Bellows (Old Westbury, NY); Thomas E. Wulff (Brookhaven, NY)
Assignee: Zebra Technologies Corporation
G06Q10/0875G06V20/20
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Quick Facts
Patent No.
US 12,639,667
App. No.
16/737,717
Granted
May 26, 2026
Kind
B2
Abstract

Systems and methods for triggering object recognition and planogram generation via shelf sensors are disclosed herein. An example apparatus may comprise a shelf, at least one sensor affixed to the shelf, at least one camera affixed to the shelf, and at least one server communicatively coupled to the at least one camera and the at least one sensor. The apparatus may be configured such that the at least one sensor is configured to detect that at least one threshold has been met, and the at least one sensor is further configured to trigger an image to be taken by the least one camera; wherein the at least one camera is configured to capture at least one image based on the met threshold, and the at least one server is configured to identify at least one product identifier based on the at least one captured image.

Claims (44)

1 . A shelf, comprising:

a surface configured to support objects;

at least one sensor coupled to the shelf, the at least one sensor configured to detect the objects supported on the surface, and that at least one threshold has been met; and

at least one camera coupled to the shelf, the at least one camera configured to:

in response to a trigger by the at least one sensor, capture at least one image of the objects supported on the surface based on the met at least one threshold;

identify at least one product identifier of at least one object based on the at least one captured image; and

determine, based on the captured at least one image and a machine learning model, at least one condition for capturing images of at least one of the shelf and the objects supported on the surface of the shelf, wherein

to trigger an image to be taken, the at least one sensor transmits a notification to the at least one camera, the notification comprising information about the at least one threshold that has been met, and

the at least one threshold is when objects on the shelf are moved from a rear portion of the shelf to a front edge of the shelf for image capture by the at least one camera.

2 . The apparatus of claim 1 , wherein the at least one sensor is a resistive sensor, capacitive sensor, light sensor, or a combination thereof.

3 . The apparatus of claim 1 , wherein the at least one threshold further comprises when the number of objects on the shelf met a threshold, when a change in the number of objects on the shelf met a threshold, when a certain percentage of an area of the shelf is occupied by objects, or a combination thereof.

4 . The apparatus of claim 1 , wherein to identify the at least one product identifier, at least one server is configured to identify in the at least one captured image a product SKU, product UPC, or a combination thereof.

5 . The apparatus of claim 1 , wherein the camera is further configured to generate or update a planogram for the shelf based on the at least one identified product identifier.

6 . The apparatus of claim 1 , wherein the camera is further configured to communicate with at least one server to generate or update an electronic shelf label for the shelf based on the at least one identified product identifier.

7 . A method, comprising:

detecting, by at least one sensor coupled to the shelf, that at least one threshold has been met;

triggering, by the at least one sensor, at least one image to be captured by at least one camera coupled to the shelf;

capturing, at the least one camera coupled to the shelf, at least one image based on the met at least one threshold;

identifying, by the at least one camera, at least one product identifier based on the at least one captured image; and

determining, based on the captured at least one image and a machine learning model, at least one condition for capturing images of at least one of the shelf and objects supported on a surface of the shelf, wherein

triggering further comprises transmitting, at the at least one sensor, a notification to the at least one camera, the notification comprising information about the at least one threshold that has been met, and

the at least one threshold is when objects on the shelf are moved from a rear portion of the shelf to a front edge of the shelf where the objects for image capture by the at least one camera.

8 . The method of claim 7 wherein the at least one sensor is a resistive sensor, capacitive sensor, light sensor, or a combination thereof.

9 . The method of claim 7 , wherein the at least one threshold further comprises when the number of objects on the shelf met a threshold, when a change in the number of objects on the shelf met a threshold, when a certain percentage of an area of the shelf is occupied by objects, or a combination thereof.

10 . The method of claim 7 , wherein identifying further comprises:

identifying, by at least one server, in the at least one captured image a product SKU, product UPC, or a combination thereof.

11 . The method of claim 7 further comprising:

generating or updating, by at least one server, a planogram for the shelf based on the at least one identified product identifier.

12 . The method of claim 7 , further comprising:

generating or updating, by at least one server, an electronic shelf label for the shelf based on the at least one identified product identifier.

13 . A tangible machine-readable medium comprising instructions that, when executed, cause a machine to at least:

detect that at least one threshold has been met at a shelf of a shelving unit by at least one sensor coupled to the shelf;

trigger at least one image to be captured by at least one camera coupled to the at least one sensor and the at least one shelf;

capture at least one image of the shelf based on the met at least one threshold by at least one camera;

identify at least one product identifier based on the at least one captured image by the at least one camera; and

determine, based on the captured at least one image and a machine learning model, at least one condition for capturing images of at least one of the shelf and objects supported on a surface of the shelf, wherein

causing the machine to trigger the at least one image to be captured comprises transmitting a notification from the at least one sensor to the at least one camera comprising information about the at least one threshold that has been met, and

the at least one threshold is when objects on the shelf are moved from a rear portion of the shelf to a front edge of the shelf for image capture by the at least one camera.

14 . The tangible machine-readable medium of claim 13 wherein the at least one sensor is a resistive sensor, capacitive sensor, light sensor, or a combination thereof.

15 . The tangible machine-readable medium of claim 13 , wherein the at least one threshold further comprises when the number of objects on the shelf met a threshold, when a change in the number of objects on the shelf met a threshold, when a certain percentage of an area of the shelf is occupied by objects, or a combination thereof.

16 . The tangible machine-readable medium of claim 13 , wherein the instructions further cause the machine to at least:

identify in the at least one captured image a product SKU, product UPC, or a combination thereof at the camera.

17 . The tangible machine-readable medium of claim 13 , wherein the instructions further cause the machine to at least:

update a planogram, an electronic shelf label, or a combination thereof for the shelf based on the at least one identified product identifier.

Assignments (4)
SECURITY INTEREST Recorded Apr 12, 2021
From: ZEBRA TECHNOLOGIES CORPORATION
To: JPMORGAN CHASE BANK, N.A.
Reel/Frame 056471/0906 →
RELEASE OF SECURITY INTEREST - 364 - DAY Recorded Mar 5, 2021
From: JPMORGAN CHASE BANK, N.A.
To: ZEBRA TECHNOLOGIES CORPORATION; LASER BAND, LLC; TEMPTIME CORPORATION
Reel/Frame 056036/0590 →
SECURITY INTEREST Recorded Sep 1, 2020
From: ZEBRA TECHNOLOGIES CORPORATION; LASER BAND, LLC; TEMPTIME CORPORATION
To: JPMORGAN CHASE BANK, N.A.
Reel/Frame 053841/0212 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 21, 2020
From: BELLOWS, DAVID; WULFF, THOMAS E.
To: ZEBRA TECHNOLOGIES CORPORATION
Reel/Frame 052724/0087 →
Continuity (1)
Related Publication 20210209550A1 · Jul 8, 2021
References Cited (31)
US 20040034581A1 · Hill et al. · 2004 [cited by applicant]
US 20040099741A1 · Dorai · 2004 [cited by examiner]
US 20090059270A1 · Opalach et al. · 2009 [cited by applicant]
US 20090295592A1 · Mizukawa · 2009 [cited by examiner]
US 20100287057A1 · Aihara · 2010 [cited by examiner]
US 20110241843A1 · Marsanne · 2011 [cited by examiner]
US 20120133623A1 · Byun · 2012 [cited by examiner]
US 20120169585A1 · Kim · 2012 [cited by examiner]
US 20130313317A1 · Waters · 2013 [cited by applicant]
US 20140210692A1 · Waters · 2014 [cited by examiner]
US 20140249916A1 · Verhaeghe · 2014 [cited by examiner]
US 20140358656A1 · Poole · 2014 [cited by examiner]
US 20150199942A1 · Mochizuki · 2015 [cited by examiner]
US 20150348450A1 · Park · 2015 [cited by examiner]
US 20160048907A1 · Park · 2016 [cited by examiner]
US 20160217417A1 · Ma et al. · 2016 [cited by applicant]
US 20170293959A1 · Itou · 2017 [cited by examiner]
US 20180005035A1 · Bogolea · 2018 [cited by examiner]
US 20190034864A1 · Skaff · 2019 [cited by examiner]
US 20190073775A1 · Lam et al. · 2019 [cited by applicant]
US 20190149725A1 · Adato · 2019 [cited by examiner]
US 20190213212A1 · Adato · 2019 [cited by examiner]
US 20190215424A1 · Adato · 2019 [cited by examiner]
US 20200111053A1 · Bogolea · 2020 [cited by examiner]
US 20200151692A1 · Gao · 2020 [cited by examiner]
US 20200232797A1 · Al Amour · 2020 [cited by examiner]
US 20200335015A1 · Okuma · 2020 [cited by examiner]
US 20210065281A1 · Haulk · 2021 [cited by examiner]
US 20210067744A1 · Buibas · 2021 [cited by examiner]
US 20220116737A1 · White · 2022 [cited by examiner]
International Search Report and Written Opinion for International Application No. PCT/US2021/12274 mailed on Mar. 26, 2021. [cited by applicant]