CHECKOUT COUNTER

• US 20150062560A1
• Filed: 06/23/2011
• Published: 03/05/2015
• Est. Priority Date: 07/08/2010
• Status: Active Grant

• Current Assignee: ITAB Shop Products AB
• Original Assignee: ITAB SCANFLOW AB
• Inventors: Tornwall, Magnus, Von Sydow, Carl, Moller, Johan, Kooi, Erik, Boiten, Hugo
• Granted Patent: US 9,301,626 B2
• Time in Patent Office: Days
• US Class Current: 356/51
• CPC Class Codes: A47F 9/047 for recording self-service articles without cashier or assistant
• CPC Class Codes: G01J 3/28 Investigating the spectrum using colour filters G01J3/51
• CPC Class Codes: G01N 21/255 Details, e.g. use of specially adapted sources, lighting or optical systems
• CPC Class Codes: G01N 21/84 Systems specially adapted for particular applications
• CPC Class Codes: G06Q 20/208 Input by product or record sensing, e.g. weighing or scanner processing
• CPC Class Codes: G07G 1/0054 with control of supplementary check-parameters, e.g. weight or number of articles

Abstract

A classification device (2) for identification of articles (3) in an automated checkout counter is presented. The device comprises a memory unit (5) capable of storing digital reference signatures, each of which digital reference signatures corresponds to an article identity, a processor (6) connected to the memory unit (5), and at least one sensor (4, 7, 14, 15, 16, 17, 18, 24) configured to determine a measured signature of an article (3) wherein said processor (6) is configured to compare said measured signature with the digital reference signatures, and to calculate a matching probability of a predetermined number of article identities.

Claims

1-16. -16. (canceled)

17. A classification device for identification of articles in an automated checkout counter, comprising:

a memory unit capable of storing digital reference signatures, each of which digital reference signatures corresponds to an article identity,

a processor connected to the memory unit, and

at least one sensor configured to determine a measured signature of an article,

wherein said processor is configured to compare said measured signature with the digital reference signatures, and to calculate a matching probability of a predetermined number of article identities.

18. The classification device according to claim 17, wherein the at least one sensor is a spectroscopy sensor.

19. The classification device according to claim 17, wherein said spectroscopy sensor is a single array spectrometer.

20. The classification device according to claim 17, wherein said spectroscopy sensor is configured to measure light having a wavelength between 200 nm and 2500 nm.

21. The classification device according to claim 20, wherein said spectroscopy sensor is an infrared spectroscopy sensor configured to measure light having a wavelength between 850 nm and 2500 nm.

22. The classification device according to claim 20, wherein said spectroscopy sensor is a visual spectroscopy sensor configured to measure light having a wavelength between 200 nm and 400 nm, and between 660 nm and 1100 nm.

23. The classification device according to claim 22, wherein said visual spectroscopy sensor is configured to measure light having a wavelength in the complete interval between 200 nm and 1100 nm.

24. The classification device according to claim 18, further comprising a further sensor being selected from the group consisting of:

a spectroscopy sensor, a contour sensor, a barcode reader, a symbol reading sensor, a color texture sensor, a color histogram sensor, or a scale.

25. The classification device according to claim 17, comprising at least two different sensors, wherein said processor is configured to determine specific article identities by comparing the matching probability from the different sensors, and selecting the article identities having the highest matching probability.

26. An automated checkout counter, comprising a classification device according to claim 17.

27. A method for classifying articles in an automated checkout counter, comprising the steps of:

providing a classification device comprising a memory unit capable of storing digital reference signatures, each of which digital reference signatures corresponds to an article identity, a processor connected to the memory unit, and at least one sensor configured to determine a measured signature of an article,

comparing said measured signature with the digital reference signatures, and

calculating a matching probability of a predetermined number of article identities.

28. The method according to claim 27, further comprising:

comparing the highest matching probability with an alarm threshold and, in case the highest matching probability is below the alarm threshold, awaiting manual input before proceeding.

29. The method according to claim 28, further comprising:

comparing the highest matching probability with a two alarm threshold,

in case the highest matching probability is below the lowest alarm threshold, awaiting manual input from an attendant before proceeding, and

in case the highest matching probability is above the lowest alarm threshold but below the upper alarm threshold, awaiting manual input from a user before proceeding.

30. The method according to claim 28, wherein the step of comparing the highest matching probability with an alarm threshold comprises the step of comparing the weight of the article with a weight interval associated with the article identity corresponding to the reference signature having the highest matching probability.

31. The method according to claim 28, wherein the step of comparing the highest matching probability with an alarm threshold comprises the step of comparing the shape of the article with a shape interval associated with the article identity corresponding to the reference signature having the highest matching probability.

32. The method according to claim 28, wherein the step of comparing the highest matching probability with an alarm threshold comprises the step of scanning a barcode of the article and comparing the information of the scanned barcode with barcode information associated with the article identity corresponding to the reference signature having the highest matching probability.