Method of recognizing produce items using checkout frequency

• US 6,409,085 B1
• Filed: 08/16/2000
• Issued: 06/25/2002
• Est. Priority Date: 08/16/2000
• Status: Expired due to Term

• Current Assignee: NCR Voyix Corporation
• Original Assignee: NCR Corporation (NCR Voyix Corporation)
• Inventors: Gu, Yeming
• Primary Examiner(s): Pitts, Harold I.
• Application Number:
  US09/640,032
• Time in Patent Office: 678 Days
• Field of Search: 235/462.11, 235/383
• US Class Current: 235/462.11
• CPC Class Codes: G07G 1/0054 with control of supplementary check-parameters, e.g. weight or number of articles

Abstract

A method of recognizing produce items which uses checkout frequency as an a priori probability. The method includes the steps of collecting produce data from the produce item, determining DML values between the produce data and reference produce data for a plurality of types of produce items, determining conditional probability densities for all of the types of produce items using the DML values, combining the conditional probability densities together to form a combined conditional probability density, determining checkout frequencies for the produce types, determining probabilities for the types of produce items from the combined conditional probability density and the checkout frequencies, determining a number of candidate identifications from the probabilities, and identifying the produce item from the number candidate identifications.

Claims

1. A method of identifying a produce item comprising the steps of:

(a) collecting produce data from the produce item;

(b) determining DML values between the produce data and reference produce data for a plurality of types of produce items;

(c) determining conditional probability densities for all of the types of produce items using the DML values;

(d) combining the conditional probability densities together to form a combined conditional probability density;

(e) determining checkout frequencies for the produce types;

(f) determining probabilities for the types of produce items from the combined conditional probability density and the checkout frequencies;

(f) determining a number of candidate identifications from the probabilities; and

(g) identifying the produce item from the number candidate identifications.

2. The method as recited in claim 1, wherein step (g) comprises the substeps of:

(g-1) displaying the candidate identifications;

and

(g-2) recording an operator selection of one of the candidate identifications.

3. The method as recited in claim 1, wherein step (a) comprises the substep of:

collecting spectral data.

4. A method of identifying a produce item comprising the steps of:

(a) collecting produce data from the produce item;

(b) determining DML values between the produce data and reference produce data for a plurality of types of produce items;

(c) determining conditional probability densities for all of the types of produce items using the DML values;

(d) combining the conditional probability densities together to form a combined conditional probability density;

(e) determining checkout frequencies for the produce types;

(f) determining probabilities for the types of produce items from the combined conditional probability density and the checkout frequencies;

(g) determining a number of candidate identifications from the probabilities;

(h) displaying the candidate identifications; and

(i) recording an operator selection of one of the candidate identifications.

5. A produce recognition system comprising:

a number of sources of produce data for a produce item; and

a computer system which determines DML values between the produce data and reference produce data for a plurality of types of produce items, determines conditional probability densities for all of the types of produce items using the DML values, combines the conditional probability densities together to form a combined conditional probability density, determines checkout frequencies for the produce types, determines probabilities for the types of produce items from the combined conditional probability density and the checkout frequencies, determines a number of candidate identifications from the probabilities, and identifies the produce item from the number candidate identifications.

6. The system as recited in claim 5, wherein the computer system displays the candidate identifications and records an operator selection of one of the candidate identifications.

7. The system as recited in claim 6, wherein one of the sources comprises a spectrometer.