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.