CONTINUOUS INTERACTION LEARNING AND DETECTION IN REAL-TIME
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Accused Products
Abstract
Systems and methods may provide for partitioning a plurality of training samples into a first sequential list of centroids, removing one or more repeating centroids in the first sequential list of centroids to obtain a first reduced list of centroids and generating a set of Hidden Markov Model (HMM) parameters based on the first reduced list of centroids. Additionally, a plurality of detection samples may be partitioned into a second sequential list of centroids, wherein one or more repeating centroids in the second sequential list of centroids may be removed to obtain a second reduced list of centroids. The second reduced list of centroids may be used to determine a match probability for the plurality of detection samples against the set of HMM parameters. In one example, the reduced lists of centroids lack temporal variability.
18 Citations
49 Claims
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1-25. -25. (canceled)
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26. An apparatus to process training samples, comprising:
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a partition module to partition a plurality of training samples into a sequential list of centroids; a filter to remove one or more repeating centroids in the sequential list of centroids to obtain a reduced list of centroids; and a parameter module to generate a set of Hidden Markov Model (HMM) parameters based on the reduced list of centroids. - View Dependent Claims (27, 28, 29, 30, 31)
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32. A method to process training samples, comprising:
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partitioning a plurality of training samples into a sequential list of centroids; removing one or more repeating centroids in the sequential list of centroids to obtain a reduced list of centroids; and generating a set of Hidden Markov Model (HMM) parameters based on the reduced list of centroids. - View Dependent Claims (33, 34, 35, 36, 37)
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38. A method to process detection samples, comprising:
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partitioning a plurality of detection samples into a sequential list of centroids; removing one or more repeating centroids in the sequential list of centroids to obtain a reduced list of centroids; and using the reduced list of centroids to determine a match probability for the plurality of detection samples against a set of Hidden Markov Model (HMM) parameters associated with a training session. - View Dependent Claims (39, 40, 41, 42, 43)
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44. At least one computer readable storage medium comprising a set of instructions which, if executed by a computing device, cause the computing device to:
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partition a plurality of detection samples into a sequential list of centroids; remove one or more repeating centroids in the sequential list of centroids to obtain a reduced list of centroids; and use the reduced list of centroids to determine a match probability for the plurality of detection samples against a set of Hidden Markov Model (HMM) parameters associated with a training session. - View Dependent Claims (45, 46, 47, 48, 49)
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Specification