US6606579B1
Expired / Lapsed
US
⏱️ 0.0 years remaining
Method of combining spectral data with non-spectral data in a produce recognition system
Assignee: NCR Voyix Corp
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Filing: 2000-08-16
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Granted: 2003-08-12
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Expires: 2020-08-16
Abstract
Abstract A method of combining spectral data with non-spectral data which uses a defined distance measure of likeness (DML) value and conditional probabilities. The method includes the steps of collecting the spectral and non-spectral data for the produce item, determining DML values between the spectral and the non-spectral 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 to form a combined conditional probability density, and determining probabilities for the types of produce items from the combined conditional probability density.
Inventors
- 👤 Yeming Gu
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-11-10 00:48
CPC Classifications
G; G01; G01G; G01G19/00; G01G19/40; G01G19/413; G01G19/414; G01G19/4144; G06; G06F; G06F18/00; G06F18/20; G06F18/25; G06F18/254; G06F18/256; G07; G07G; G07G1/00; G07G1/0036; G07G1/0045; G07G1/0054; G01N; G01N21/00; G01N21/84; G01N21/85; G06V; G06V20/00; G06V20/60; G06V20/68
Claims Analysis
Total Claims: 17
Claim 1
Independent Claim
111 words
A method of identifying a produce item comprising the steps of: (a) collecting produce data from the produce item; (b) determining Distance Measure of Likeness (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 probabilities for the types of produce items from the combined conditional probability density; (f) determining a number of candidate identifications from the probabilities; and (g) identifying the produce item from the number of candidate identifications.
Claim 2
Independent Claim
111 words
A method of identifying a produce item comprising the steps of: (a) collecting produce data from the produce item; (b) determining Distance Measure of Likeness (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 probabilities for the types of produce items from the combined conditional probability density; (f) determining a number of candidate identifications from the probabilities; and (g) identifying the produce item from the number of candidate identifications.
Claim 3
Dependent Claim
49 words
The method as recited in claim 1 , wherein step (e) comprises the substeps of: (e-1) determining an a priori probability for the types of produce items; and (e-2) determining the probabilities for the types of produce items from the combined conditional probability density and the a priori probability.
Claim 4
Dependent Claim
36 words
The method as recited in claim 1 , wherein step (g) comprises the substeps of: (g-1) displaying the number of candidate identifications; and (g-2) recording an operator selection of one of the number of candidate identifications.
Claim 5
Dependent Claim
18 words
The method as recited in claim 1 , wherein step (a) comprises the substep of: collecting spectral data.
Claim 6
Dependent Claim
18 words
The method as recited in claim 1 , wherein step (a) comprises the substep of: collecting non-spectral data.
Claim 7
Dependent Claim
25 words
The method as recited in claim 1 , wherein step (a) comprises the substeps of: collecting spectral data from a spectrometer; and collecting non-spectral data.
Claim 8
Independent Claim
137 words
A method of identifying a produce item comprising the steps of: (a) collecting produce data from the produce item; (b) determining Distance Measure of Likeness (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 an a priori probability for the types of produce items; (f) determining probabilities for the types of produce items from the combined conditional probability density and the a priori probability; (g) determining a number of candidate identifications from the probabilities; (h) displaying the number of candidate identifications; and (i) recording an operator selection of one of the number of candidate identifications.
Claim 9
Independent Claim
137 words
A method of identifying a produce item comprising the steps of: (a) collecting produce data from the produce item; (b) determining Distance Measure of Likeness (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 an a priori probability for the types of produce items; (f) determining probabilities for the types of produce items from the combined conditional probability density and the a priori probability; (g) determining a number of candidate identifications from the probabilities; (h) displaying the number of candidate identifications; and (i) recording an operator selection of one of the number of candidate identifications.
Claim 10
Independent Claim
102 words
A method of combining spectral data with non-spectral data for recognizing a produce item comprising the steps of: (a) collecting the spectral and non-spectral data for the produce item; (b) determining Distance Measure of Likeness (DML) values between the spectral and the non-spectral 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 to form a combined conditional probability density; and (e) determining probabilities for the types of produce items from the combined conditional probability density.
Claim 11
Independent Claim
102 words
A method of combining spectral data with non-spectral data for recognizing a produce item comprising the steps of: (a) collecting the spectral and non-spectral data for the produce item; (b) determining Distance Measure of Likeness (DML) values between the spectral and the non-spectral 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 to form a combined conditional probability density; and (e) determining probabilities for the types of produce items from the combined conditional probability density.
Claim 12
Dependent Claim
49 words
The method as recited in claim 8 , wherein step (e) comprises the substeps of: (e-1) determining an a priori probability for the types of produce items; and (e-2) determining the probabilities for the types of produce items from the combined conditional probability density and the a priori probability.
Claim 13
Dependent Claim
107 words
A produce recognition system comprising: a number of sources of produce data for a produce item; and a computer system which determines Distance Measure of Likeness (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 probabilities for the types of produce items from the combined conditional probability density, determines a number of candidate identifications from the probabilities, and identifies the produce item from the number of candidate identifications.
Claim 14
Dependent Claim
107 words
A produce recognition system comprising: a number of sources of produce data for a produce item; and a computer system which determines Distance Measure of Likeness (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 probabilities for the types of produce items from the combined conditional probability density, determines a number of candidate identifications from the probabilities, and identifies the produce item from the number of candidate identifications.
Claim 15
Dependent Claim
131 words
A produce recognition system comprising: a number of sources of produce data for a produce item; and a computer system which determines Distance Measure of Likeness (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 an a priori probability for the types of produce items, determines probabilities for the types of produce items from the combined conditional probability density and the a priori probability, determines a number of candidate identifications from the probabilities, displays the number of candidate identifications, and records an operator selection of one of the number of candidate identifications.
Claim 16
Dependent Claim
131 words
A produce recognition system comprising: a number of sources of produce data for a produce item; and a computer system which determines Distance Measure of Likeness (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 an a priori probability for the types of produce items, determines probabilities for the types of produce items from the combined conditional probability density and the a priori probability, determines a number of candidate identifications from the probabilities, displays the number of candidate identifications, and records an operator selection of one of the number of candidate identifications.
Claim 17
Dependent Claim
16 words
The system as recited in claim 11 , wherein one of the sources comprises a spectrometer.
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USPTO Annuity & Maintenance Windows
| Annuity Stage | Window Opens | Payment Deadline | Surcharge Period Until | Estimated Fee | Status |
|---|---|---|---|---|---|
| 3.5 Years (Year 4) | 2006-08-11 | 2007-02-10 | 2007-08-12 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2010-08-11 | 2011-02-10 | 2011-08-12 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2014-08-11 | 2015-02-10 | 2015-08-12 | $7700.00 | Window Closed / Lapsed |