Data classification using machine learning techniques
First Claim
Patent Images
1. A system for classifying documents, comprising:
- a memory; and
a processor in communication with the memory, the processor being configured to process at least some instructions stored in the memory,wherein the memory stores computer executable program code comprising instructions for;
receiving at least one labeled seed document having a known confidence level of label assignment;
receiving unlabeled documents;
receiving at least one predetermined cost factor;
training a transductive classifier through iterative calculation using the at least one predetermined cost factor, the at least one seed document, and the unlabeled documents, wherein for each iteration of the calculations the cost factor is adjusted as a function of an expected label value;
after at least some of the iterations, storing confidence scores for the unlabeled documents; and
outputting identifiers of the unlabeled documents having the highest confidence scores to at least one of a user, another system, and another process.
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Abstract
Systems, methods and computer program products for classifying documents are presented. Systems, methods and computer program products for analyzing documents, e.g., associated with legal discovery are also presented. Systems, methods and computer program products for cleaning up data are also presented. Systems, methods and computer program products for verifying an association of an invoice with an entity are also presented. Systems, methods and computer program products for managing medical records are presented. Systems, methods and computer program products for face recognition are presented.
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Citations
34 Claims
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1. A system for classifying documents, comprising:
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a memory; and a processor in communication with the memory, the processor being configured to process at least some instructions stored in the memory, wherein the memory stores computer executable program code comprising instructions for; receiving at least one labeled seed document having a known confidence level of label assignment; receiving unlabeled documents; receiving at least one predetermined cost factor; training a transductive classifier through iterative calculation using the at least one predetermined cost factor, the at least one seed document, and the unlabeled documents, wherein for each iteration of the calculations the cost factor is adjusted as a function of an expected label value; after at least some of the iterations, storing confidence scores for the unlabeled documents; and outputting identifiers of the unlabeled documents having the highest confidence scores to at least one of a user, another system, and another process. - View Dependent Claims (2, 3, 4)
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5. A system for analyzing documents, comprising:
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a memory; and a processor in communication with the memory, the processor being configured to process at least some instructions stored in the memory, wherein the memory stores computer executable program code comprising instructions for; training a transductive classifier; receiving documents; performing a document classification technique on the documents using the transductive classifier trained through iterative calculation using at least one predetermined cost factor and at least one seed document, wherein for each iteration of calculations during the training, the cost factor is adjusted as a function of an expected label value; and outputting identifiers of at least some of the documents based on the classification thereof. - View Dependent Claims (6, 7, 8, 9, 10, 11)
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12. A system for cleaning up data, comprising:
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a memory; and a processor in communication with the memory, the processor being configured to process at least some instructions stored in the memory, wherein the memory stores computer executable program code comprising instructions for; receiving a plurality of labeled data items; selecting subsets of the data items for each of a plurality of categories; setting an uncertainty for the data items in each subset to about zero; setting an uncertainty for the data items not in the subsets to a predefined value that is not about zero; training a transductive classifier through iterative calculation using the uncertainties, the data items in the subsets, and the data items not in the subsets as training examples; applying the trained classifier to each of the labeled data items to classify each of the data items; and outputting a classification of the input data items, or derivative thereof, to at least one of a user, another system, and another process. - View Dependent Claims (13, 14, 15, 16)
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17. A system for verifying an association of an invoice with an entity, comprising:
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a memory; and a processor in communication with the memory, the processor being configured to process at least some instructions stored in the memory, wherein the memory stores computer executable program code comprising instructions for; training a classifier based on an invoice format associated with a first entity; accessing a plurality of invoices labeled as being associated with at least one of the first entity and other entities; performing a document classification technique on the invoices using the classifier; and outputting an identifier of at least one of the invoices having a high probability of not being associated with the first entity, wherein the classifier is a transductive classifier, and further comprising training the transductive classifier through iterative calculation using at least one predetermined cost factor, at least one seed document, and the invoices, wherein for each iteration of the calculations the cost factor is adjusted as a function of an expected label value. - View Dependent Claims (18, 19, 20, 21)
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22. A system for managing medical records, comprising:
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a memory; and a processor in communication with the memory, the processor being configured to process at least some instructions stored in the memory, wherein the memory stores computer executable program code comprising instructions for; accessing a plurality of medical records; training a transductive classifier based on a medical diagnosis through iterative calculation using; at least one predetermined cost factor, at least one seed document, and the medical records, performing a document classification technique on the medical records using the classifier; and outputting an identifier of at least one of the medical records having a low probability of being associated with the medical diagnosis, wherein the document classification technique includes a transductive process, and wherein for each iteration of the calculations the cost factor is adjusted as a function of an expected label value. - View Dependent Claims (23, 24)
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25. A system for managing medical records, comprising:
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a memory; and a processor in communication with the memory, the processor being configured to process at least some instructions stored in the memory, wherein the memory stores computer executable program code comprising instructions for; accessing a plurality of medical records; training a transductive classifier based on a medical diagnosis through iterative calculation using; at least one predetermined cost factor, at least one seed document, and the medical records, performing a document classification technique on the medical records using the classifier, and outputting an identifier of at least one of the medical records having a low probability of being associated with the medical diagnosis, wherein the document classification technique includes a maximum entropy discrimination process.
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26. A system for face recognition, comprising:
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a memory; and a processor in communication with the memory, the processor being configured to process at least some instructions stored in the memory, wherein the memory stores computer executable program code comprising instructions for; receiving at least one labeled seed image of a face, the seed image having a known confidence level; receiving unlabeled images; receiving at least one predetermined cost factor; training a transductive classifier through iterative calculation using the at least one predetermined cost factor, the at least one seed image, and the unlabeled images, wherein for each iteration of the calculations the cost factor is adjusted as a function of an expected label value; after at least some of the iterations, storing confidence scores for the unlabeled seed images; and outputting identifiers of the unlabeled images having the highest confidence scores to at least one of a user, another system, and another process. - View Dependent Claims (27, 28, 29, 30)
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31. A product for classifying documents, comprising:
a non-transitory storage medium readable by a computer, where the medium tangibly embodies one or more programs of instructions executable by the computer to perform a method, comprising; receiving at least one labeled seed document having a known confidence level of label assignment; receiving unlabeled documents; receiving at least one predetermined cost factor; training a transductive classifier through iterative calculation using the at least one predetermined cost factor, the at least one seed document, and the unlabeled documents, wherein for each iteration of the calculations the cost factor is adjusted as a function of an expected label value; after at least some of the iterations, storing confidence scores for the unlabeled documents; and outputting identifiers of the unlabeled documents having the highest confidence scores to at least one of a user, another system, and another process.
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32. A product for analyzing documents, comprising:
a non-transitory storage medium readable by a computer, where the medium tangibly embodies one or more programs of instructions executable by the computer to perform a method, comprising; training a transductive classifier; receiving documents; performing a document classification technique on the documents using the transductive classifier trained through iterative calculation using at least one predetermined cost factor and at least one seed document, wherein for each iteration of the calculations during the training the cost factor is adjusted as a function of an expected label value; and outputting identifiers of at least some of the documents based on the classification thereof.
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33. A product for cleaning up data, comprising:
a non-transitory storage medium readable by a computer, where the medium tangibly embodies one or more programs of instructions executable by the computer to perform a method, comprising; receiving a plurality of labeled data items; selecting subsets of the data items for each of a plurality of categories; setting an uncertainty for the data items in each subset to about zero; setting an uncertainty for the data items not in the subsets to a predefined value that is not about zero; training a transductive classifier through iterative calculation using the uncertainties, the data items in the subsets, and the data items not in the subsets as training examples; applying the trained classifier to each of the labeled data items to classify each of the data items; and outputting a classification of the input data items, or derivative thereof, to at least one of a user, another system, and another process.
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34. A product for face recognition, comprising:
a non-transitory storage medium readable by a computer, where the medium tangibly embodies one or more programs of instructions executable by the computer to perform a method, comprising; receiving at least one labeled seed image of a face, the seed image having a known confidence level; receiving unlabeled images; receiving at least one predetermined cost factor; training a transductive classifier through iterative calculation using the at least one predetermined cost factor, the at least one seed image, and the unlabeled images, wherein for each iteration of the calculations the cost factor is adjusted as a function of an expected label value; after at least some of the iterations, storing confidence scores for the unlabeled seed images; and outputting identifiers of the unlabeled images having the highest confidence scores to at least one of a user, another system, and another process.
Specification