LEARNING APPARATUS AND METHOD, PREDICTION APPARATUS AND METHOD, AND PROGRAM
First Claim
Patent Images
1. A learning apparatus comprising:
- location acquiring means for acquiring time series data on locations of a user;
time acquiring means for acquiring time series data on times; and
learning means for learning an activity model indicating an activity state of the user as a probabilistic state transition model, using the respective acquired time series data on the locations and the times as an input.
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Abstract
A learning apparatus includes: a location acquiring section for acquiring time series data on locations of a user; a time acquiring section for acquiring time series data on times; and learning section for learning an activity model indicating an activity state of the user as a probabilistic state transition model, using the respective acquired time series data on the locations and the times as an input.
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Citations
14 Claims
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1. A learning apparatus comprising:
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location acquiring means for acquiring time series data on locations of a user; time acquiring means for acquiring time series data on times; and learning means for learning an activity model indicating an activity state of the user as a probabilistic state transition model, using the respective acquired time series data on the locations and the times as an input. - View Dependent Claims (2, 3, 4)
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5. A learning method performed by a learning apparatus which recognizes an activity model indicating an activity state of a user and learns the activity model of the user for use in a prediction apparatus which predicts a behavior of the user as a probabilistic state transition model, the method comprising the steps of:
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acquiring time series data on locations of the user; acquiring time series data on times; and learning the activity model of the user as the probabilistic state transition model, using the respective acquired time series data on the locations and the times as an input.
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6. A program which functions as the following means in a computer, the means comprising:
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location acquiring means for acquiring time series data on locations of a user; time acquiring means for acquiring time series data on times; and learning means for learning an activity model indicating an activity state of the user as a probabilistic state transition model, using the respective acquired time series data on the locations and the times as an input.
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7. A prediction apparatus comprising:
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location acquiring means for acquiring time series data on locations of a user; time acquiring means for acquiring time series data on times; behavior recognizing means for recognizing a current location of the user using an activity model of the user, wherein the activity model indicates an activity state of the user and is obtained by being learned as a probabilistic state transition model, using the respective acquired time series data on the locations and the times as an input, by a learning apparatus; behavior predicting means for predicting a possible path and a selection probability of the path from the current location of the user recognized by the behavior recognizing means; and arrival time predicting means for predicting an arrival time and an arrival probability for arrival at a destination from the predicted path and selection probability. - View Dependent Claims (8, 9, 10)
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11. A prediction method performed by a prediction apparatus which performs prediction using an activity model of a user in which a learning apparatus learns the activity model indicating an activity state of the user as a probabilistic state transition model, the method comprising the steps of:
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acquiring time series data on locations of the user; acquiring time series data on times; and recognizing a current location of the user using the activity model of the user learned by the learning apparatus, using the respective acquired time series data on the locations and the times as an input; predicting a possible path and a selection probability of the path from the recognized current location of the user; and predicting an arrival time and an arrival probability for arrival at a destination from the predicted path and selection probability.
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12. A program which functions as the following means in a computer, the means comprising:
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location acquiring means for acquiring time series data on locations of a user; time acquiring means for acquiring time series data on times; behavior recognizing means for recognizing a current location of the user using an activity model of the user, wherein the activity model indicates an activity state of the user and is obtained by being learned as a probabilistic state transition model, using the respective acquired time series data on the locations and the times as an input, by a learning apparatus; behavior predicting means for predicting a possible path and a selection probability of the path from the current location of the user recognized by the behavior recognizing means; and arrival time predicting means for predicting an arrival time and an arrival probability for arrival at a destination from the predicted path and selection probability.
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13. A learning apparatus comprising:
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a location acquiring section which acquires time series data on locations of a user; a time acquiring section which acquires time series data on times; and a learning section which learns an activity model indicating an activity state of the user as a probabilistic state transition model, using the respective acquired time series data on the locations and the times as an input.
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14. A prediction apparatus comprising:
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a location acquiring section which acquires time series data on locations of a user; a time acquiring section which acquires time series data on times; a behavior recognizing section which recognizes a current location of the user using an activity model of the user, wherein the activity model indicates an activity state of the user and is obtained by being learned as a probabilistic state transition model, using the respective acquired time series data on the locations and the times as an input, by a learning apparatus; a behavior predicting section which predicts a possible path and a selection probability of the path from the current location of the user recognized by the behavior recognizing section; and an arrival time predicting section which predicts an arrival time for arrival at a destination and an arrival probability from the predicted path and selection probability.
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Specification