Digital point-of-sale analyzer

• US 9,135,491 B2
• Filed: 07/19/2010
• Issued: 09/15/2015
• Est. Priority Date: 08/31/2007
• Status: Active Grant

• Current Assignee: Accenture Global Services Limited (Accenture PLC)
• Original Assignee: Accenture Global Services Limited (Accenture PLC)
• Inventors: Fano, Andrew E., Morandi, Simone, Dini, Marco, Sassano, Michele, Campari, Pier Paolo
• Primary Examiner(s): Zeender, Ryan
• Assistant Examiner(s): Amsdell, Dana
• Application Number:
  US12/839,135
• Publication Number:
  US 20110011936A1
• Time in Patent Office: 1,884 Days
• US Class Current: 1/1
• CPC Class Codes: G06F 18/00 Pattern recognition
• CPC Class Codes: G06Q 10/087 Inventory or stock management, e.g. order filling, procurement or balancing against orders
• CPC Class Codes: G06Q 20/203 Inventory monitoring
• CPC Class Codes: G06Q 30/06 Buying, selling or leasing transactions

Abstract

A digital point-of-sale system for determining key performance indicators (KPIs) at a point-of-sale includes a product identification unit and a realogram creation unit. The product identification unit is configured to receive a captured image of a product display and to identify products in the captured image by comparing features determined from the captured image to features determined from products templates. The realogram creation unit is configured to create a realogram from the identified products and product templates. A product price KPI unit is configured to identify a product label proximally located to each identified product, and to recognize the product price on each product label. Each product price is compared to a predetermined range of prices to determine whether the product label proximally located to the identified product is a correct product label for the identified product.

Claims

1. A digital point-of-sale system comprising:

a product identification unit to;

receive at least one captured image of a product display, the at least one captured image including a plurality of products including physical product features, the at least one captured image further including at least one product label; and

analyze the at least one captured image to identify each of the plurality of products in the at least one captured image by matching the physical product features determined from the at least one captured image with physical product features determined from a plurality of product templates stored in a data store;

a key performance indicator (KPI) unit to;

identify, for each identified product in the at least one captured image, a proximally located product label of the at least one product label;

recognize a product price on the proximally located product label; and

determine whether the proximally located product label is a correct label for the identified product in response to a determination of whether the product price on the proximally located product label is within a predetermined range of prices stored in the data store for the identified product; and

a realogram creation unit to generate a realogram from the identified plurality of products and product templates, wherein the realogram includes an electronically-generated diagram of the product display.

2. The digital point-of-sale system of claim 1, wherein one or more values in the predetermined range of prices are based on historical product pricing data for the identified product.

3. The digital point-of-sale system of claim 1, wherein the product price on each product label is recognized using optical character recognition.

4. The digital point-of-sale system of claim 1,

wherein the KPI unit is to further compare the realogram to a matching planogram or to matching guidelines, and to further determine key performance indicators (KPIs) for the product display based on the comparison of the realogram to the matching planogram or to the matching guidelines.

5. The digital point-of-sale system of claim 1, wherein the KPI unit is to further measure key performance indicators (KPIs) to determine whether the identified products are in a desired orientation, in a desired sequence, or located on a predetermined shelf.

6. The digital point-of-sale system of claim 1, wherein each of the plurality of product templates is a previously collected forward-facing template for each product containing identifying data.

7. The digital point-of-sale system of claim 1, wherein the at least one image comprises a plurality of captured images, and the realogram creation unit is to generate a realogram from the plurality of captured images aligned and merged into a single image.

8. A method for determining key performance indicators (KPIs) for a point-of-sale, the method comprising:

receiving, by a processor, at least one captured image of a product display including a plurality of products including physical product features, the at least one captured image further including at least one product label;

comparing the physical product features determined from the at least one captured image with physical product features determined from a plurality of product templates in a data store;

matching product templates with the physical product features determined from the at least one captured image based on the comparison;

identifying each of the plurality of products in the at least one captured image based on the matching;

identifying, for each product in the at least one captured image, a proximally located product label of the at least one product labels;

recognizing a product price on each identified proximally located product label;

determining whether each identified proximally located product label is a correct product label for each identified product by determining whether the product price on each identified proximally located product label is within a predetermined range of prices for each identified product; and

generating, by a computer system, a realogram based on the identified plurality of products.

9. The method of claim 8, wherein recognizing the product price on each identified proximally located product label

is based on optical character recognition of the product price.

10. The method of claim 8, wherein one or more values in the predetermined range are based on historical product pricing data for the identified product.

11. The method of claim 8, further comprising:

comparing the generated realogram to a matching planogram or to matching guidelines to determine whether the realogram complies with the matching planogram or the matching guidelines, wherein the planogram or matching guidelines indicate a desired orientation of the plurality of products in the product display.

12. The method of claim 8, wherein each of the plurality of product templates is a previously collected forward facing template for each product comprising identifying data.

13. The method of claim 8, further comprising:

identifying at least one product out-of-stock from the realogram; and

identifying product shelves from the realogram.

14. The method of claim 8, wherein the at least one captured image comprises a plurality of captured images, and the method comprises:

aligning and merging the plurality of captured images into a single image.

15. The method of claim 8, further comprising determining whether the identified products are in a desired orientation, in a desired sequence, and located on a predetermined shelf.

16. A method for determining a product price for a plurality of products at a point-of-sale, the method comprising:

receiving at least one captured image of a product display including the plurality of products including physical product features;

comparing, by a computer system, the physical product features determined from the at least one captured image with physical product features determined from a plurality of product templates in a data store;

matching product templates with the physical product features determined from the at least one captured image based on the comparison of the physical product features determined from the at least one captured image to the physical product features determined from the plurality of product templates;

identifying each of the plurality of products in the at least one captured image based on the matching;

identifying a product label proximally located to each identified product;

recognizing a product price on each identified product label; and

determining whether each identified proximally located product label is a correct product label for each identified product by determining whether the product price on each identified proximally located product label is within a predetermined range of prices for each identified product.

17. The method of claim 16, wherein one or more values in the predetermined range are based on at least one of historical product pricing data for the identified product and at least one price provided by an entity.

18. The method of claim 16, wherein the predetermined range of prices for each identified product includes one or more values for determining the correct price for each identified product.