Patent Document ID: 8560467
Application ID: 12839321

Base Claim:
1. A data processing apparatus comprising: obtaining means configured to obtain time-series data from a wearable sensor; activity model learning means configured to learn an activity model representing a user activity state as a stochastic state transition model from the obtained time-series data; recognition means configured to recognize a current user activity state by using the activity model of the user obtained by the activity model learning means; and prediction means configured to predict a user activity state after a predetermined time elapses from a current time from the current user activity state recognized by the recognition means, wherein the prediction means predicts the user activity state after the predetermined time elapses as an occurrence probability, and calculates the occurrence probabilities of the respective states after the predetermined time elapses on the basis of the state transition probability of the stochastic state transition model to predict the user activity state after the predetermined time elapses, while it is presumed that observation probabilities of the respective states at the respective times of the stochastic state transition model are an equal probability.

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Claim 2:
2. The data processing apparatus according to claim 1 , wherein the prediction means calculates the occurrence probabilities of the respective states until the predetermined time elapses of the stochastic state transition model decided in an experimental manner with use of random numbers on the basis of the state transition probability of the stochastic state transition model to predict the user activity state.