Zebra Technologies Corporation

Symbol Technologies, LLC

David R Turturro

Christopher J Fjellstad

Abhilash Gururaja

John Britts

JaeHo Choi

Joseph D Giordano

Eric M Johnson

Robert James Pang

Robert A Doran

Sajan Wilfred

Thomas Lackemann

Yuly Mitelman

Patrick Mauro

Paul Severino

Pierre Craen

Fatima Haq

Techniques for volumetric assessment of a product based on images captured by a bi-optic scanner are disclosed herein. An example system includes a first imaging assembly, having a field of view of a product scanning region, configured to capture a first image of an item passing through the product scanning region; a second imaging assembly, having an orthogonal field of view, configured to capture a second image of the item passing through the product scanning region at the same time; processors; and a memory storing instructions that, when executed, cause the processors to: determine a first distance of the item from an edge of the first field of view based on the first image; determine a second distance of the item from the a corresponding edge of the second field of view based on the second image; and generate an estimated volume of the item based on the first image, the first distance, the second image, and the second distance.

Product volumetric assessment using bi-optic scanner

A method of auxiliary data capture control includes: storing, at a data capture device, a trigger condition and a baseline value captured by a sensor; controlling the sensor to obtain captured data; determining, based on the captured data and the baseline value, whether the trigger condition is satisfied, wherein the trigger condition includes whether the captured data exceeds the baseline value; and when the trigger condition is satisfied, controlling an auxiliary data capture sensor to obtain auxiliary captured data.

System and method of selective auxiliary data capture

A method of auxiliary data capture control includes: storing, at a data capture device, a trigger condition and a baseline value captured by a sensor; controlling the sensor to obtain captured data; determining, based on the captured data and the baseline value, whether the trigger condition is satisfied, wherein the trigger condition includes whether the captured data exceeds the baseline value and whether a variance in the captured data over a configurable time period is below a variance threshold; and when the trigger condition is satisfied, controlling an auxiliary data capture sensor to obtain auxiliary captured data.

Methods for optimizing improper product barcode detection are disclosed herein. An example method, includes analyzing an object-based identification data against an indicia-based identification data to determine if there is a match. Responsive to there not being a match, entering a release condition state, where, instead of automatically sending a failed match signal to a symbology reader, a remote server awaits satisfaction of the release condition, before sending to the symbology reader. In some examples, a release condition is automatically satisfied by identifying a match in a subsequent comparison. In some examples, a user of the remote server is to satisfy the release condition.

Method for optimizing improper product barcode detection

A method of auxiliary data capture control includes: storing, at a data capture device, a trigger condition and a baseline infrared value captured by an infrared sensor; controlling the infrared sensor to obtain captured infrared data; determining, based on the captured infrared data and the baseline infrared value, whether the trigger condition is satisfied, wherein the trigger condition includes whether the captured infrared data exceeds the baseline infrared value and whether a variance in the captured infrared data over a configurable time period is below a variance threshold; and when the trigger condition is satisfied, controlling an auxiliary data capture sensor to obtain auxiliary captured data.

Successive products associated with targets to be electro-optically read are swiped, in their respective turns, past a window in a point-of-transaction workstation. A motion detector detects movement of each product. A reader electro-optically reads a target associated with each moving product. If the target associated with each moving product has not been read, then this event is deemed suspicious, and the event is flagged for a security investigation designed to deter product theft by sweethearting.

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Farmingville, NY, United States of America

David R Turturro