DATA PROCESSING DEVICE, DATA PROCESSING METHOD, AND PROGRAM
First Claim
1. A data processing device comprising:
- learning means configured to obtain a parameter of a probability model when a user'"'"'s movement history data to be obtained as data for learning is represented as a probability model that represents the user'"'"'s activity;
destination and route point estimating means configured to estimate, of state nodes of the probability model using the parameter obtained by the learning means, a destination node and a route point node equivalent to a movement destination and a route point;
prediction data generating means configured to obtain the user'"'"'s movement history data within a predetermined period of time from the present which differs from the data for learning, as data for prediction, and in the event that there is a data missing portion included in the obtained data for prediction, to generate the data missing portion thereof by interpolation processing, and to calculate virtual error with actual data corresponding to interpolated data generated by the interpolation processing;
current point estimating means configured to input the data for prediction of which the data missing portion has been interpolated to the probability model using the parameter obtained by learning, and with estimation of state node series corresponding to the data for prediction of which the data missing portion has been interpolated, to estimate a current point node equivalent to the user'"'"'s current location by using the virtual error as an observation probability of the state nodes regarding the interpolated data, and using an observation probability with less contribution of data than actual data;
search means configured to search a route from the user'"'"'s current location to a destination using information regarding the estimated destination node and route point node and the current point node, and the probability model obtained by learning; and
calculating means configured to calculate an arrival probability and time required for the searched destination.
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Abstract
The present invention relates to a data processing device, a data processing method, and a program which enable prediction to be performed even when there is a gap in the current location data to be obtained in real time. A learning main processor 23 represents movement history data serving as data for learning, as a probability model which represents a user'"'"'s activity, and obtains a parameter thereof. A prediction main processor 33 uses the probability model obtained by learning to estimate a user'"'"'s current location from movement history data to be obtained in real time. In the event that there is a data missing portion included in movement history data to be obtained in real time, the prediction main processor 33 generates the data missing portion thereof by interpolation processing, and estimates state nose series corresponding to the interpolated data for prediction. With estimation of state node series, an observation probability less contribution of data than actual data is employed regarding interpolated data. The present invention may be applied to a data processing device configured to predict a destination from movement history data, for example.
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Citations
8 Claims
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1. A data processing device comprising:
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learning means configured to obtain a parameter of a probability model when a user'"'"'s movement history data to be obtained as data for learning is represented as a probability model that represents the user'"'"'s activity; destination and route point estimating means configured to estimate, of state nodes of the probability model using the parameter obtained by the learning means, a destination node and a route point node equivalent to a movement destination and a route point; prediction data generating means configured to obtain the user'"'"'s movement history data within a predetermined period of time from the present which differs from the data for learning, as data for prediction, and in the event that there is a data missing portion included in the obtained data for prediction, to generate the data missing portion thereof by interpolation processing, and to calculate virtual error with actual data corresponding to interpolated data generated by the interpolation processing; current point estimating means configured to input the data for prediction of which the data missing portion has been interpolated to the probability model using the parameter obtained by learning, and with estimation of state node series corresponding to the data for prediction of which the data missing portion has been interpolated, to estimate a current point node equivalent to the user'"'"'s current location by using the virtual error as an observation probability of the state nodes regarding the interpolated data, and using an observation probability with less contribution of data than actual data; search means configured to search a route from the user'"'"'s current location to a destination using information regarding the estimated destination node and route point node and the current point node, and the probability model obtained by learning; and calculating means configured to calculate an arrival probability and time required for the searched destination. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A data processing method comprising the steps of:
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obtaining, with learning means of a data processing device configured to processing a user'"'"'s movement history data, a parameter of a probability model when a user'"'"'s movement history data to be obtained as data for learning is represented as a probability model that represents the user'"'"'s activity; estimating, with destination and route point estimating means of the data processing device, of state nodes of the probability model using the parameter obtained by the learning means, a destination node and a route point node equivalent to a movement destination and a route point; obtaining, with prediction data generating means of the data processing device, the user'"'"'s movement history data within a predetermined period of time from the present which differs from the data for learning, as data for prediction, and in the event that there is a data missing portion included in the obtained data for prediction, generating the data missing portion thereof by interpolation processing, and calculating virtual error with actual data corresponding to interpolated data generated by the interpolation processing; inputting, with current point estimating means of the data processing device, the data for prediction of which the data missing portion has been interpolated to the probability model using the parameter obtained by learning, and with estimation of state node series corresponding to the data for prediction of which the data missing portion has been interpolated, estimating a current point node equivalent to the user'"'"'s current location by using the virtual error as an observation probability of the state nodes regarding the interpolated data, and using an observation probability with less contribution of data than actual data; searching, with search means of the data processing device, a route from the user'"'"'s current location to a destination using information regarding the estimated destination node and route point node and the current point node, and the probability model obtained by learning; and calculating, with calculating means of the data processing device, an arrival probability and time required for the searched destination.
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8. A program causing a computer to serve as:
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learning means configured to obtain a parameter of a probability model when a user'"'"'s movement history data to be obtained as data for learning is represented as a probability model that represents the user'"'"'s activity; destination and route point estimating means configured to estimate, of state nodes of the probability model using the parameter obtained by the learning means, a destination node and a route point node equivalent to a movement destination and a route point; prediction data generating means configured to obtain the user'"'"'s movement history data within a predetermined period of time from the present which differs from the data for learning, as data for prediction, and in the event that there is a data missing portion included in the obtained data for prediction, to generate the data missing portion thereof by interpolation processing, and to calculate virtual error with actual data corresponding to interpolated data generated by the interpolation processing; current point estimating means configured to input the data for prediction of which the data missing portion has been interpolated to the probability model using the parameter obtained by learning, and with estimation of state node series corresponding to the data for prediction of which the data missing portion has been interpolated, to estimate a current point node equivalent to the user'"'"'s current location by using the virtual error as an observation probability of the state nodes regarding the interpolated data, and using an observation probability with less contribution of data than actual data; search means configured to search a route from the user'"'"'s current location to a destination using information regarding the estimated destination node and route point node and the current point node, and the probability model obtained by learning; and calculating means configured to calculate an arrival probability and time required for the searched destination.
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Specification