View source for 20240013633. IDENTIFYING BARCODE-TO-PRODUCT MISMATCHES USING POINT OF SALE DEVICES simplified abstract (Target Brands, Inc.)

=IDENTIFYING BARCODE-TO-PRODUCT MISMATCHES USING POINT OF SALE DEVICES=


==Organization Name==

[[:Category:Target Brands, Inc.|Target Brands, Inc.]]


[[Category:Target Brands, Inc.]]

==Inventor(s)==

[[:Category:Christopher Brakob of Minneapolis MN (US)|Christopher Brakob of Minneapolis MN (US)]][[Category:Christopher Brakob of Minneapolis MN (US)]]

[[:Category:Ethan Sommer of Minneapolis MN (US)|Ethan Sommer of Minneapolis MN (US)]][[Category:Ethan Sommer of Minneapolis MN (US)]]

[[:Category:Arun Patil of Minneapolis MN (US)|Arun Patil of Minneapolis MN (US)]][[Category:Arun Patil of Minneapolis MN (US)]]

[[:Category:Dharmavaram Arbaaz of Minneapolis MN (US)|Dharmavaram Arbaaz of Minneapolis MN (US)]][[Category:Dharmavaram Arbaaz of Minneapolis MN (US)]]

[[:Category:Arun Vaishnav of Minneapolis MN (US)|Arun Vaishnav of Minneapolis MN (US)]][[Category:Arun Vaishnav of Minneapolis MN (US)]]

[[:Category:Prakash Mall of Minneapolis MN (US)|Prakash Mall of Minneapolis MN (US)]][[Category:Prakash Mall of Minneapolis MN (US)]]

[[:Category:Neha Dixit of Minneapolis MN (US)|Neha Dixit of Minneapolis MN (US)]][[Category:Neha Dixit of Minneapolis MN (US)]]

==IDENTIFYING BARCODE-TO-PRODUCT MISMATCHES USING POINT OF SALE DEVICES - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240013633 titled 'IDENTIFYING BARCODE-TO-PRODUCT MISMATCHES USING POINT OF SALE DEVICES

==Simplified Explanation==

The patent application describes systems and methods for determining whether an unknown product matches a scanned barcode during a checkout process. The process involves using an overhead camera at a checkout lane to capture image data of the unknown product placed on a flatbed scanning area. This image data is then analyzed using a classification model and/or product identification models to identify candidate product identifications for the unknown product. Based on these candidate product identifications, it is determined whether the unknown product matches a product associated with a barcode that is scanned at a POS terminal in the checkout lane.

* The system uses an overhead camera to capture image data of an unknown product placed on a flatbed scanning area.
* A classification model and/or product identification models are applied to the image data to identify candidate product identifications for the unknown product.
* Based on the candidate product identifications, it is determined whether the unknown product matches a product associated with a scanned barcode at a POS terminal.
* The classification model determines n-dimensional space feature values for the unknown product to determine which product it likely matches.
* The product identification models determine whether the unknown product is one of the products that are modeled.

Potential applications of this technology:

* Retail industry: This technology can be used in retail stores to improve the accuracy and efficiency of the checkout process by automatically verifying whether the scanned barcode matches the actual product placed on the scanning area.
* Inventory management: The system can help in maintaining accurate inventory records by ensuring that the correct products are being scanned and accounted for during the checkout process.
* Fraud prevention: By accurately matching the scanned barcode with the actual product, this technology can help prevent fraudulent activities such as swapping higher-priced items with lower-priced ones during the checkout process.

Problems solved by this technology:

* Eliminates human error: By automating the process of matching the scanned barcode with the actual product, this technology reduces the chances of human error in identifying and verifying products during the checkout process.
* Improves efficiency: The system allows for faster and more accurate identification of products, leading to a more efficient checkout process and reducing customer wait times.
* Enhances customer experience: By ensuring that the correct products are being scanned and accounted for, this technology improves customer satisfaction and reduces the likelihood of disputes or errors in the checkout process.

Benefits of this technology:

* Increased accuracy: The system improves the accuracy of product identification during the checkout process, reducing the chances of errors or discrepancies.
* Time-saving: By automating the product identification process, this technology saves time for both customers and store employees, leading to a more efficient checkout process.
* Fraud prevention: The system helps in preventing fraudulent activities by ensuring that the scanned barcode matches the actual product being purchased.


==Original Abstract Submitted==

disclosed herein are systems and methods for determining whether an unknown product matches a scanned barcode during a checkout process. an edge computing device or other computer system can receive, from an overhead camera at a checkout lane, image data of an unknown product that is placed on a flatbed scanning area, identify candidate product identifications for the unknown product based on applying a classification model and/or product identification models to the image data, and determine based on the candidate product identifications, whether the unknown product matches a product associated with a barcode that is scanned at a pos terminal in the checkout lane. the classification model can be used to determine n-dimensional space feature values for the unknown product and determine which product the unknown product likely matches. the product identification models can be used to determine whether the unknown product is one of the products that are modeled.



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