Learning apparatus and method, prediction apparatus and method, and program

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:

• acquiring time series data on locations of the user;
• acquiring time series data on times;
• 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;
• acquiring time series data on additional information of one or more types other than the locations of the user and the times;
• generating time series data on state nodes from the time series data on the locations and the times by inputting the acquired time series data on the locations and the times in the learned activity model of the user and outputting the time series data on the state nodes of the activity model of the user;
• correspondingly storing the acquired time series data on the additional information and the generated time series data on the state nodes, and selecting only the time series data on the state nodes corresponding to the acquired additional information at a current time point from among the stored time series data in a case where the activity model of the user at the current time point is predicted using the learned activity model of the user;
• and
• re-calculating a parameter for the activity model of the user by a statistical process, using the selected time series data on the state nodes.

A program which functions as the following means in a computer, the means comprising:

• location acquiring means for acquiring time series data on locations of a user;
• time acquiring means for acquiring time series data on times;
• 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;
• additional information acquiring means for acquiring time series data on additional information of one or more types other than the locations of the user and the times;
• state series generating means for generating time series data on state nodes from the time series data on the locations and the times by inputting the acquired time series data on the locations and the times in the activity model of the user learned by the learning means and outputting the time series data on the state nodes of the activity model of the user;
• selecting means for correspondingly storing the time series data on the additional information acquired by the additional information acquiring means and the time series data on the state nodes generated by the state series generating means, and for selecting only the time series data on the state nodes corresponding to the additional information at a current time point acquired by the additional information acquiring means from among the stored time series data in a case where the activity model of the user at the current time point is predicted using the activity model of the user learned by the learning means;
• and
• re-calculating means for re-calculating a parameter for the activity model of the user by a statistical process, using the time series data on the state nodes selected by the selecting means.

A prediction 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;
• 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.

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:

• 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;
• predicting an arrival time and an arrival probability for arrival at a destination from the predicted path and selection probability;
• and
• displaying, on a display screen, information on the predicted arrival time and information on the predicted arrival probability.

A non-transitory computer-readable medium having embodied thereon a program, which when executed by a computer, causes the computer to perform a method, the method comprising:

• acquiring time series data on locations of a user;
• acquiring time series data on times;
• 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;
• 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.

A learning apparatus comprising:

• 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 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;
• an additional information acquiring section which acquires time series data on additional information of one or more types other than the locations of the user and the times;
• a state series generating section which generates time series data on state nodes from the time series data on the locations and the times by inputting the acquired time series data on the locations and the times in the activity model of the user learned by the learning means and outputs the time series data on the state nodes of the activity model of the user;
• a selecting section which correspondingly stores the time series data on the additional information acquired by the additional information acquiring section and the time series data on the state nodes generated by the state series generating section, and selects only the time series data on the state nodes corresponding to the additional information at a current time point acquired by the additional information acquiring section from among the stored time series data in a case where the activity model of the user at the current time point is predicted using the activity model of the user learned by the learning section;
• and
• a re-calculating section which re-calculates a parameter for the activity model of the user by a statistical process, using the time series data on the state nodes selected by the selecting section.