Information processing apparatus, information processing method, non-transitory computer readable medium, and learning model

1 . An information processing apparatus comprising:

at least one memory configured to store program code; and

at least one processor configured to operate as instructed by the program code, the program code including:

training code configured to cause at least one of the at least one processor to:

acquire map information from an external source,

acquire prestored movement trajectories from a storage of the information processing apparatus,

assign soft labels corresponding to the map information to the prestored movement trajectories,

acquire prestored movement information from the storage,

output labels based on the soft labels and the prestored movement information, and

generate a plurality of data sets, each data set of the plurality of data sets including a label among the labels associated with a corresponding prestored movement trajectory among the prestored movement trajectories and corresponding prestored movement information;

acquisition code configured to cause at least one of the at least one processor to acquire a movement trajectory of a user;

derivation code configured to cause at least one of the at least one processor to derive, from the movement trajectory of the user, movement information indicating features relating to movement; and

estimating code configured to cause at least one of the at least one processor to estimate, using a learning model, a mode of transport of the user from the movement trajectory and the movement information,

wherein the learning model is trained based on the plurality of data sets,

wherein the learning model includes a first network, which is composed of a first branch and a second branch, and a second network that follows the first network,

the first branch generates feature amounts of the movement trajectory from the movement trajectory,

the second branch generates feature amounts of the movement information from the movement information, and

the second network is configured to generate combined feature amounts by combining the feature amounts of the movement trajectory and the feature amounts of the movement information, and output data indicating the mode of transport of the user from the combined feature amounts.

2 . The information processing apparatus according to claim 1 , wherein the learning model is configured to output probabilities of a plurality of modes of transport being the mode of transport of the user, as the data indicating the mode of transport of the user, and

the estimating code is configured to cause at least one of the at least one processor to estimate a mode of transport with the highest probability as the mode of transport of the user.

3 . The information processing apparatus according to claim 1 , wherein the movement trajectory includes a latitude and a longitude of a location of the user at intervals of a predetermined time.

4 . The information processing apparatus according to claim 1 , wherein the movement information includes at least one of a speed, acceleration, jerk, bearing, and bearing difference between two points on the movement trajectory and a speed difference, acceleration difference, average speed, average speed difference, and average acceleration between a plurality of pairs of two points.

5 . The information processing apparatus according to claim 1 , wherein the mode of transport includes at least one of car, train, bus, bicycle, walking, and boat or ship.

6 . The information processing apparatus according to claim 1 , where the program code further comprises output code configured to cause at least one of the at least one processor to output information on the mode of transport estimated.

7 . The information processing apparatus according to claim 6 , wherein the output code is configured to cause at least one of the at least one processor to generate and output an advertisement relating to the mode of transport estimated.

8 . An information processing method performed by at least one processor and comprising:

acquiring map information from an external source;

acquiring prestored movement trajectories from a storage of an information processing apparatus;

assigning soft labels corresponding to the map information to the prestored movement trajectories;

acquiring prestored movement information from the storage;

outputting labels based on the soft labels and the prestored movement information;

generating a plurality of data sets, each data set of the plurality of data sets including a label among the labels associated with a corresponding prestored movement trajectory among the prestored movement trajectories and corresponding prestored movement information;

acquiring a movement trajectory of a user;

deriving, from the movement trajectory of the user, movement information indicating features relating to movement; and

estimating, using a learning model, a mode of transport of the user from the movement trajectory and the movement information,

wherein the learning model is trained based on the plurality of data sets,

wherein the learning model includes a first network, which is composed of a first branch and a second branch, and a second network that follows the first network,

the first branch generates feature amounts of the movement trajectory from the movement trajectory,

the second branch generates feature amounts of the movement information from the movement information, and

the second network is configured to generate combined feature amounts by combining the feature amounts of the movement trajectory and the feature amounts of the movement information, and output data indicating the mode of transport of the user from the combined feature amounts.

9 . A non-transitory computer readable medium storing a computer program for causing a computer to execute processing comprising:

a training process for:

acquiring map information from an external source,

acquiring prestored movement trajectories from a storage of an information processing apparatus,

assigning soft labels corresponding to the map information to the prestored movement trajectories,

acquiring prestored movement information from the storage,

outputting labels based on the soft labels and the prestored movement information, and

generating a plurality of data sets, each data set of the plurality of data sets including a label among the labels associated with a corresponding prestored movement trajectory among the prestored movement trajectories and corresponding prestored movement information;

an acquisition process for acquiring a movement trajectory of a user;

a derivation process for deriving, from the movement trajectory of the user, movement information indicating features relating to movement; and

an estimating process for estimating, using a learning model, a mode of transport of the user from the movement trajectory and the movement information,

wherein the learning model is trained based on the plurality of data sets,

wherein the learning model includes a first network, which is composed of a first branch and a second branch, and a second network that follows the first network,

the first branch generates feature amounts of the movement trajectory from the movement trajectory,

the second branch generates feature amounts of the movement information from the movement information, and

the second network is configured to generate combined feature amounts by combining the feature amounts of the movement trajectory and the feature amounts of the movement information, and output data indicating the mode of transport of the user from the combined feature amounts.

10 . An information processing apparatus comprising:

at least one memory configured to store program code; and

at least one processor configured to operate as instructed by the program code, the program code including:

training code configured to cause at least one of the at least one processor to:

acquire map information from an external source,

acquire prestored movement trajectories from a storage of the information processing apparatus,

assign soft labels corresponding to the map information to the prestored movement trajectories,

acquire prestored movement information from the storage,

output labels based on the soft labels and the prestored movement information,

generate a plurality of data sets, each data set of the plurality of data sets including a label among the labels associated with a corresponding prestored movement trajectory among the prestored movement trajectories and corresponding prestored movement information;

estimating code configured to cause at least one of the at least one processor to estimate, using a learning model, a mode of transport of a user from the movement trajectory and the movement information,

wherein the learning model is trained based on the plurality of data sets,

wherein the learning model comprises:

a first network including:

a first branch configured to:

receive a movement trajectory of the user, and

generate feature amounts of the movement trajectory, and

a second branch configured to:

receive movement information relating to movement derived from the movement trajectory, and

generate feature amounts of the movement information; and

a second network configured to generate combined feature amounts by combining the feature amounts of the movement trajectory and the feature amounts of the movement information and to output data indicating a mode of transport of the user from the combined feature amounts.