INCREMENTALLY BUILDING ASPECT MODELS
First Claim
1. A system that builds an aspect model from observed data, comprising:
- an interface component that receives and incrementally extracts one or more aspects from the observed data; and
an analysis component that merges the one or more extracted aspects with an existing aspect model.
2 Assignments
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Accused Products
Abstract
The claimed subject matter relates to an unsupervised incremental learning framework, and in particular, to the creation and utilization of an unsupervised incremental learning framework that facilitates object discovery, clustering, characterization and/or grouping. Such an unsupervised incremental learning framework, once created, can thereafter be employed to incrementally estimate a latent variable model through the utilization of spectral and/or probabilistic models in order to incrementally cluster, discover, group and/or characterize tightly knit themes/topics within document sets and/or streams, thus leading to the generation of a set of themes/topics that better correlate with human perceptual labeling schemes.
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Citations
20 Claims
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1. A system that builds an aspect model from observed data, comprising:
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an interface component that receives and incrementally extracts one or more aspects from the observed data; and an analysis component that merges the one or more extracted aspects with an existing aspect model. - View Dependent Claims (2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13)
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5. The system of claim 5, the analysis component assigns a low weight to observed data adequately described by the existing aspect model, and assigns a high weight to observed data inadequately described by the existing aspect model.
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14. A method for building an aspect model from observed data, comprising:
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employing a component to receive a stream of observed data; incrementally extracting one or more aspects from the stream of observed data; and adding the one or more extracted aspects to an existing aspect model. - View Dependent Claims (15, 16, 17, 18, 19)
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20. A system that effectuates aspect model construction and notification, comprising:
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means for continuously applying a dynamically expandable clustering series to a stream of data that comprises extractable aspects until an ascertainable stop condition is satisfied and extracting an extractable aspect; means for receiving one or more user preferences; means for ascertaining a match between the extractable aspect and the one or more user preferences; and means for notifying one or more users of the match.
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Specification