Abstract: A gaming system including one or more gaming machines provides a game to one or more players. The game includes a primary game and a bonus game with first and second features. In the first feature a plurality of objects are presented to a player. The player is allowed to select one of the objects and an associated award may be awarded. Any unselected objects are added to an escrow meter. In the second feature, the player is allowed to select one or more of the escrow objects.

Abstract: An exercise assisting apparatus 1 includes: a division unit 312 configured to divide sample motion information 3231 indicating motions of a sample person doing exercise into a plurality of pieces of motion element information 3235, according to regularity of the motions of the sample person; and a generation unit 314 configured to generate an inference model by causing a pre-trained model 322 to learn time-series antecedent dependency of the plurality of pieces of motion element information, the inference model inferring motions of a target person, based on target motion information 3211 indicating the motions of the target person doing exercise.

Abstract: An exercise assisting apparatus 1 includes: a division unit 312 configured to divide sample motion information 3231 indicating motions of a sample person doing exercise into a plurality of pieces of motion element information 3235, according to regularity of the motions of the sample person; and a generation unit 314 configured to generate an inference model by causing a pre-trained model 322 to learn time-series antecedent dependency of the plurality of pieces of motion element information, the inference model inferring motions of a target person, based on target motion information 3211 indicating the motions of the target person doing exercise.

Abstract: An exercise assisting apparatus 1 includes: a division unit 312 configured to divide sample motion information 3231 indicating motions of a sample person doing exercise into a plurality of pieces of motion element information 3235, according to regularity of the motions of the sample person; and a generation unit 314 configured to generate an inference model by causing a pre-trained model 322 to learn time-series antecedent dependency of the plurality of pieces of motion element information, the inference model inferring motions of a target person, based on target motion information 3211 indicating the motions of the target person doing exercise.

Abstract: An exercise assisting apparatus 1 includes: a division unit 312 configured to divide sample motion information 3231 indicating motions of a sample person doing exercise into a plurality of pieces of motion element information 3235, according to regularity of the motions of the sample person; and a generation unit 314 configured to generate an inference model by causing a pre-trained model 322 to learn time-series antecedent dependency of the plurality of pieces of motion element information, the inference model inferring motions of a target person, based on target motion information 3211 indicating the motions of the target person doing exercise.

Abstract: An exercise assisting apparatus 1 includes: a division unit 312 configured to divide sample motion information 3231 indicating motions of a sample person doing exercise into a plurality of pieces of motion element information 3235, according to regularity of the motions of the sample person; and a generation unit 314 configured to generate an inference model by causing a pre-trained model 322 to learn time-series antecedent dependency of the plurality of pieces of motion element information, the inference model inferring motions of a target person, based on target motion information 3211 indicating the motions of the target person doing exercise.

Abstract: An acquisition unit acquires image data indicating at least a chest and an abdomen of a test subject. A detection unit detects the displacement of each of the chest and abdomen of the test subject by using the image data. A display control unit performs control so that the movement image of each of the chest and abdomen is displayed based on the detected displacement of the chest and abdomen. When the movement of the chest and the movement of the abdomen are synchronized with each other, the display control unit performs control so that the display form of the chest movement image and the display form of the abdomen movement image are the same as each other.

Abstract: An exercise assisting apparatus 1 includes: a division unit 312 configured to divide sample motion information 3231 indicating motions of a sample person doing exercise into a plurality of pieces of motion element information 3235, according to regularity of the motions of the sample person; and a generation unit 314 configured to generate an inference model by causing a pre-trained model 322 to learn time-series antecedent dependency of the plurality of pieces of motion element information, the inference model inferring motions of a target person, based on target motion information 3211 indicating the motions of the target person doing exercise.

Abstract: A learning device of the present invention is provided with: an acquiring means for acquiring biometric information of a patient who may possibly become in agitation, and biometric information of a non-patient; and a model generating means for using the biometric information of the patient and the biometric information of the non-patient to generate an agitation determination model for determining whether, on the basis of the biometric information of a subject patient, the subject patient has become in agitation or has not become in agitation.

Abstract: An agitation determination apparatus includes an acquisition unit 521 configured to acquire body motion data according to a motion of a body of a target and vital data according to a state of the body of the target, a calculation unit 522 configured to calculate a body motion score serving as an index for determining an agitation state based on the body motion data acquired by the acquisition unit 521 and calculate a vital score serving as an index for determining the agitation state based on the vital data acquired by the acquisition unit 521, and a determination unit 523 configured to determine the agitation state of the target based on the body motion score and the vital score calculated by the calculation unit 522.

Abstract: A text data generation device 100 includes an acquisition unit 121 that acquires object person information representing time-series physical condition of an object person, an estimation unit 122 that estimates time-series states of the object person on the basis of the object person information, and a generation unit 123 that generates text data representing the state of the object person in a given time period, on the basis of the estimated time-series states of the object person.

Abstract: This learning device comprises: an acquisition unit for acquiring biometric information of a patient and medical chart information of the patient; a selection unit for selecting, on the basis of the medical chart information, the biometric information of the patient that allows the determination of whether the patient is in agitation; and a model generation unit for generating an agitation determination model for determining whether a subject patient is in agitation, by using the selected biometric information and on the basis of the biometric information of the subject patient.

Abstract: An exercise assisting apparatus 1 includes: a division unit 312 configured to divide sample motion information 3231 indicating motions of a sample person doing exercise into a plurality of pieces of motion element information 3235, according to regularity of the motions of the sample person; and a generation unit 314 configured to generate an inference model by causing a pre-trained model 322 to learn time-series antecedent dependency of the plurality of pieces of motion element information, the inference model inferring motions of a target person, based on target motion information 3211 indicating the motions of the target person doing exercise.

Abstract: An exercise assisting apparatus 1 includes: a division unit 312 configured to divide sample motion information 3231 indicating motions of a sample person doing exercise into a plurality of pieces of motion element information 3235, according to regularity of the motions of the sample person; and a generation unit 314 configured to generate an inference model by causing a pre-trained model 322 to learn time-series antecedent dependency of the plurality of pieces of motion element information, the inference model inferring motions of a target person, based on target motion information 3211 indicating the motions of the target person doing exercise.

Abstract: An exercise assisting apparatus 1 includes: a division unit 312 configured to divide sample motion information 3231 indicating motions of a sample person doing exercise into a plurality of pieces of motion element information 3235, according to regularity of the motions of the sample person; and a generation unit 314 configured to generate an inference model by causing a pre-trained model 322 to learn time-series antecedent dependency of the plurality of pieces of motion element information, the inference model inferring motions of a target person, based on target motion information 3211 indicating the motions of the target person doing exercise.

Abstract: A text data generation device 100 includes an acquisition unit 121 that acquires object person information representing time-series physical condition of an object person, an estimation unit 122 that estimates time-series states of the object person on the basis of the object person information, and a generation unit 123 that generates text data representing the state of the object person in a given time period, on the basis of the estimated time-series states of the object person.

Abstract: A text data generation device 100 includes an acquisition unit 121 that acquires object person information representing time-series physical condition of an object person, an estimation unit 122 that estimates time-series states of the object person on the basis of the object person information, and a generation unit 123 that generates text data representing the state of the object person in a given time period, on the basis of the estimated time-series states of the object person.

Abstract: A text data generation device 100 includes an acquisition unit 121 that acquires object person information representing time-series physical condition of an object person, an estimation unit 122 that estimates time-series states of the object person on the basis of the object person information, and a generation unit 123 that generates text data representing the state of the object person in a given time period, on the basis of the estimated time-series states of the object person.

Abstract: A determination apparatus 600 includes an agitation state determination unit 621 configured to determine an agitation state of a target based on acquired information, a condition determination unit 622 configured to determine a condition of the target based on the acquired information, and a specifying unit 623 configured to specify which to perform, the determination processing performed by the agitation state determination unit 621 or the determination processing performed by the condition determination unit 622, according to an acquisition condition of the information.

Abstract: A motion estimation system 80 includes a pose acquisition unit 81 and an action estimation unit 82. The pose acquisition unit 81 acquires, in time series, pose information representing a posture of one person and a posture of another person identified simultaneously in a situation in which a motion of the one person affects a motion of the other person. The action estimation unit 82 divides the acquired time series pose information on each person by unsupervised learning to estimate an action series that is a series of motions including two or more pieces of pose information.