System and method for constructing and personalizing a universal information classifier
First Claim
1. A system for personalizing an information classifier, comprising:
- a first classifier, pre-trained with training data, that produces a first measure associated with a message classification;
a second classifier, trained with adapting data, that produces a second measure associated with the message classification;
a combining component that combines the first measure and the second measure to produce a third measure associated with message classification; and
a user interface that accepts information concerning personalizing the second classifier, the information comprises information related to at least one of an amount of the adapting data required before a confidence level is associated with the personalized classifier and the coverage of adapting data required before a confidence level is associated with the personalized classifier.
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Accused Products
Abstract
A system and method for personalizing an information classifier is provided. The system includes a default or universal classifier that is pre-trained with features that have relatively universal discriminatory relationships for multiple people and that is operable to produce a measure that a message is classified as having one of several characteristics. The system further includes a second classifier that is constructed and personalized through via a more general search through the space of potentially discriminatory features. The second classifier, after personalization, is intended to classify information at a level exceeding that of the first classifier based on the specific preferences, habits, and desires of the user who personalizes the second classifier. The system further includes a weighting component that facilitates a combining component producing an integrated measure based on input from both the first classifier and the second classifier.
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Citations
45 Claims
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1. A system for personalizing an information classifier, comprising:
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a first classifier, pre-trained with training data, that produces a first measure associated with a message classification;
a second classifier, trained with adapting data, that produces a second measure associated with the message classification;
a combining component that combines the first measure and the second measure to produce a third measure associated with message classification; and
a user interface that accepts information concerning personalizing the second classifier, the information comprises information related to at least one of an amount of the adapting data required before a confidence level is associated with the personalized classifier and the coverage of adapting data required before a confidence level is associated with the personalized classifier. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33)
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34. A data packet adapted to be transmitted between two or more computer processes comprising:
information related to personalizing an information classifier, the information comprising at least one of weighting data, aging and adapting data, the information corresponds to at least one of an amount of adapting data that yields a confidence level associated with a classifier that is personalized and a coverage of adapting data that results in a confidence level associated with a classifier that is personalized.
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35. A computer readable medium containing computer executable components of a system for personalizing an information classifier, comprising:
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a first classifying component, pre-trained with training data, operable to produce a first measure associated with a message classification;
a second classifying component, trained with adapting data, operable to produce a second measure associated with the message classification;
a weighting component adapted to assign a first weight to the first measure and a second weight to the second measure;
a combining component adapted to combine the first measure and the second measure to produce a third measure associated with the message classification, the combining component basing the combination, at least in part, on the first measure, the second measure, the first weight and the second weight;
an aging component adapted to modify the relevance of one or more messages based and/or one or more pieces of message data, at least in part, on time-based information associated with a message;
an adapting component operable to modify the second classifier; and
a user interface that accepts information concerning personalizing the second classifier, the information comprises information related to at least one of an amount of the adapting data required before a confidence level is associated with the personalized classifier and the coverage of adapting data required before a confidence level is associated with the personalized classifier.
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36. A method for personalizing an information classifying process, comprising:
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receiving a message to be classified;
producing a first measure that the message is classified as having one of N characteristics, N being an integer;
producing a second measure that the message is classified as having one of N characteristics, N being an integer;
combining the first measure with the second measure to produce a third measure that the message is classified as having one of N characteristics, N being an integer, where the combining depends, at least in part, on a first weight associated with the first measure and a second weight associated with the second measure;
updating at least one of a data store, a data structure, an algorithm, a process, a thread and a rule employed in generating the second measure, based, at least in part, on a relationship between the first measure and the second measure; and
accepting information associated with personalizing the information classifying process, the information comprises information related to at least one of an amount of adapting data required before a confidence level is associated with the personalization and a coverage of adapting data required before a confidence level is associated with the personalization. - View Dependent Claims (37, 38, 39, 40, 41, 42, 43)
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44. A method for personalizing information classifying process, comprising:
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producing a measure associated with a message classification; and
updating at least one of a data store, a data structure, an algorithm, a process, a thread and a rule employed in generating the measure, based, at least in part, on a relationship between the measure and a user input provided during supervised learning, updating utilizes information concerning personalizing a classifier related to at least one of an amount of the adapting data required before a confidence level is associated with the personalized classifier and the coverage of adapting data required before a confidence level is associated with the personalized classifier.
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45. A system for personalizing an information classifier, comprising:
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means for producing a first measure associated with a message classification, the first measure being associated with at least one of a probability that the message has a known classification type, a priority of the message and an urgency score of the message;
means for producing a second measure associated with the message classification, the second measure being associated with at least one of the probability that the message has a known classification type, the priority of the message and the urgency score of the message;
means for combining the first measure and the second measure to produce a third measure associated with the message classification, the third measure being produced using the formula F=m1(1−
w)+m2(w), m1 is the first measure, m2 is the second measure, w is the weight assigned to the second measure and (1−
w) is the weight assigned to the first measure; and
means for accepting information concerning personalization, the information comprises information related to at least one of an amount of the adapting data required before a confidence level is associated with the personalization and the coverage of adapting data required before a confidence level is associated with the personalization.
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Specification