SPECTROSCOPIC CLASSIFICATION OF CONFORMANCE WITH DIETARY RESTRICTIONS
First Claim
1. A device, comprising:
- one or more processors to;
receive information identifying a result of a spectroscopic measurement of an unknown sample;
perform one or more classifications of the unknown sample to classify the unknown sample into a particular group based on the result of the spectroscopic measurement and one or more classification models,the one or more classification models using a support vector machine (SVM) classifier technique,the one or more classification models relating to a set of groups including the particular group,a first subset of the set of groups being a first meta-group including the particular group,a second subset of the set of groups being a second meta-group not including the particular group; and
provide information indicating a classification of the unknown sample into the first meta-group based on performing the one or more classifications of the unknown sample to classify the unknown sample into the particular group.
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Accused Products
Abstract
A device may receive a classification model generated based on a set of spectroscopic measurements performed by a first spectrometer. The device may store the classification model in a data structure. The device may receive a spectroscopic measurement of an unknown sample from a second spectrometer. The device may obtain the classification model from the data structure. The device may classify the unknown sample into a Kosher or non-Kosher group or a Halal or non-Halal group based on the spectroscopic measurement and the classification model. The device may provide information identifying the unknown sample based on the classifying of the unknown sample.
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Citations
20 Claims
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1. A device, comprising:
one or more processors to; receive information identifying a result of a spectroscopic measurement of an unknown sample; perform one or more classifications of the unknown sample to classify the unknown sample into a particular group based on the result of the spectroscopic measurement and one or more classification models, the one or more classification models using a support vector machine (SVM) classifier technique, the one or more classification models relating to a set of groups including the particular group, a first subset of the set of groups being a first meta-group including the particular group, a second subset of the set of groups being a second meta-group not including the particular group; and provide information indicating a classification of the unknown sample into the first meta-group based on performing the one or more classifications of the unknown sample to classify the unknown sample into the particular group. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A non-transitory computer-readable medium storing instructions, the instructions comprising:
one or more instructions that, when executed by one or more processors, cause the one or more processors to; receive information identifying a spectrum of an unknown sample analyzed by a spectrometer; perform a first classification of the unknown sample based on the spectrum of the unknown sample and a global classification model, the global classification model being associated with a support vector machine (SVM) classifier technique, the global classification model including a plurality of groups; generate a local classification model based on a result of the first classification of the unknown sample; perform a second classification of the unknown sample based on the spectrum of the unknown sample and the local classification model; and provide information identifying a meta-group to which the unknown sample is classified based on the local classification model, the meta-group including a subset of the plurality of groups. - View Dependent Claims (10, 11, 12, 13, 14, 15)
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16. A method, comprising:
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receiving, by a device, a classification model generated based on a set of spectroscopic measurements performed by a first spectrometer; storing, by the device, the classification model in a data structure; receiving, by the device, a spectroscopic measurement of an unknown sample from a second spectrometer; obtaining, by the device, the classification model from the data structure; classifying, by the device, the unknown sample into a Kosher or non-Kosher group or a Halal or non-Halal group based on the spectroscopic measurement and the classification model; and providing, by the device, information identifying the unknown sample based on the classifying of the unknown sample. - View Dependent Claims (17, 18, 19, 20)
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Specification