Data processing device that calculates an arrival probability for a destination using a user's movement history including a missing portion

摘要

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.

主权项

1. A data processing device comprising:
circuitry 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 the probability model that represents the user's activity; estimate, of state nodes of the probability model using the parameter obtained by the circuitry, a destination node and a route point node equivalent to a movement destination and a route point; 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, generate the data missing portion thereof by interpolation processing, and calculate virtual error with actual data corresponding to interpolated data generated by the interpolation processing: 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, 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 a full set of actual data; determine whether the current point node obtained by the circuitry is a known-state node or an unknown-state node; search, in the event that the current point node has been determined to be a known-state node, a route from the user's current location to a destination using information regarding the destination node and the route point node and the current point node, and the probability model obtained by learning; perform known/unknown determination based on an observation likelihood expected value of each of the state nodes to be calculated from the observation probability of the state nodes, and use the observation likelihood expected value to which a correction term that corrects the data missing portion so as to obtain a greater value than actual data is added; calculate an arrival probability and time required for the searched destination; and control a display or a speaker to output the arrival probability and the time required for the searched destination.