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: In an information processing device, a sickbed information acquisition means acquires sickbed information concerning use states of sickbeds in each hospital. A patient information acquisition means acquires patient information concerning patients of hospitals. A facility information acquisition means acquires facility information concerning use states of facilities of each hospital. A shift information acquisition means acquires shift information concerning a work shift of medical professionals working at the hospitals. A sickbed availability prediction means predicts availability states of the sickbeds based on the sickbed information and the patient information. An unacceptable patient prediction means predicts unacceptable patients whom each hospital cannot accept, based on the patient information, the facility information, and the shift information.

Abstract: An information processing device 100 of the present disclosure includes a data acquisition unit 121 and a data selection unit 122. The data acquisition unit 121 acquires biological data measured from a person whose state related to agitation is to be determined, and a determination result of the state by a determiner with respect to the person. The data selection unit 122 selects learning data from the biological data on the basis of a skill value and the determination result. The skill value represents the ability of determining the state and is set for the determiner. Selection of the learning data is optimized by the data selection unit 122 of the present disclosure.

Abstract: The electrocardiogram evaluation device 1X mainly includes an acquisition means 30X, a selection means 31X, and an evaluation means 33X. The acquisition means 30X is configured to acquire electrocardiogram data regarding an electrocardiogram of a subject. The selection means 31X is configured to select, from the electrocardiogram data, lead data of one or more leads corresponding to a target disease of examination. The evaluation means 33X is configured to evaluate the electrocardiogram regarding the target disease based on the selected lead data.

Abstract: In a matching device, a priority setting means sets first priorities by arranging scores in a descending order which are assigned based on desired conditions of users who receive a service, and also sets second priorities by arranging scores in the descending order which are assigned based on desired conditions of providers. A priority interchange means sets at least third priorities in which the first priorities are interchanged or fourth priorities in which the second priorities are interchanged. A matching process means performs first and second matching processes based on combinations of the first to fourth priorities. A matching result selection means selects, as a final matching result, one of a first matching result acquired by the first matching process and a second matching result acquired by the second matching process.

Abstract: Provided is a learning device including a data acquisition unit that acquires a data set including a plurality of training data, a generation unit that includes a generation model that outputs pseudo data, a discrimination unit that includes a discrimination model that discriminates whether the input data is either the training data or the pseudo data according to an input of either the training data or the pseudo data, a management unit that sets a first hyperparameter to be used for updating the discrimination model based on a preset hyperparameter, and a second hyperparameter to be used for updating the generation model, and a learning processing unit that updates the discrimination model using the first hyperparameter and updates the generation model using the second hyperparameter.

Abstract: Provided is a motion data generation device including an acquisition unit that acquires a plurality of pieces of motion data to be data converted, a conversion data choosing unit that groups the plurality of pieces of motion data for each motion class that is a target motion for data augmentation, a data conversion unit that sets at least one piece of the motion data grouped for the each motion class for reference data, sets at least one piece of the motion data different from the reference data among the grouped motion data for data to be converted, and generates extension motion data in which the data to be converted is synchronized with reference to motion timing of the reference data, and an output unit that outputs the generated extension motion data.

Abstract: A schedule management apparatus includes a schedule acquisition unit, an additional plan acquisition unit, and a plan addition unit (130). The schedule acquisition unit acquires schedule information of a target person. The additional plan acquisition unit acquires additional plan information indicating an additional plan to be added to the schedule information of the target person. The plan addition unit adds the additional plan to the schedule information by using the additional plan information. The schedule information includes at least one piece of already set plan information indicating an already set plan to be performed by the target person. When the already set plan and the additional plan overlap with each other on a time axis, the plan addition unit changes a start and end time of the already set plan in such a way that the already set plan and the additional plan do not overlap with each other.

Abstract: An assessment support system includes: an assessment prediction unit that predicts, based on patient information about a target patient who is a creation target for an assessment in a nursing record, an assessment vector obtained by vectorizing the assessment of the target patient, as a prediction assessment vector; a degree-of-similarity calculation unit that calculates a degree of similarity of the assessment vector to the prediction assessment vector, based on a relationship between the predicted prediction assessment vector and the assessment vector of a patient having the assessment recorded in the nursing record; and a search unit that searches for and outputs at least one similar patient who is similar to the target patient, based on the degree of similarity.

Abstract: A prediction model generation apparatus according to an example embodiment of the present disclosure includes: at least one memory storing instructions; and at least one processor configured to execute the instructions to: divide a region in which a probability distribution of an objective variable exists into a plurality of small regions according to a property of the objective variable for learning data including the objective variable; model an existence probability that the objective variable belongs to each of the small regions; use the learning data to model, for each of the small regions, a probability distribution related to a possible value of the objective variable in the small region under a condition that the objective variable belongs to the small region; and constructs a prediction model of the objective variable by integrating the modeled probability distribution for each of the small regions using the existence probability.

Abstract: Based on receipt data indicating a detailed statement of medical fees, receipt data conversion information indicating a presence or absence of a medical care information item that corresponds to a plurality of examination failure conditions is generated. A failure risk level based on the medical care information item included in the receipt data is calculated by using the receipt data conversion information and an examination failure risk predictor. A risk factor indicating a medical care information item with a degree of contribution to the failure risk level calculation that is greater than or equal to a prescribed threshold value for degree of contribution is specified. A revision screen for the medical care information item that is a risk factor when the failure risk level is greater than or equal to a prescribed threshold value for risk level is output.

Abstract: A learning device 1X mainly includes a first acquisition means 31X, a second acquisition means 32X, and a learning means 34X. The first acquisition means 31X acquires a partial waveform of electrocardiogram data regarding an electrocardiogram of a subject. The second acquisition means 32X acquires an attention interval, which is used as a basis for a diagnosis of a target disease, in a sequential waveform of the electrocardiogram data. The learning means 34X trains a model configured to diagnose the target disease, based on the partial waveform and the attention interval.

Abstract: A training device including a motion data acquisition unit that acquires first motion data related to a target motion, a first generation unit that generates pseudo first motion data by using a first generation model, a determination unit that calculates a determination loss indicating a degree of deviation between the first motion data and the pseudo first motion data using a determination model, a relevance calculation unit that reconfigures the target motion by a combination of basis motions and calculate a degree of relevance between the target motion and the basis motions, a regularization loss calculation unit that calculates a regularization loss indicating a degree of deviation between motion data related to the basis motions and the pseudo first motion data, and an adversarial training processing unit that adversarially trains the first generation model and the determination model using the determination loss and the regularization loss.

Abstract: A data conversion device including a feature amount calculation unit that normalizes posture data estimated in each frame constituting moving image data including a synchronization target motion into an angular representation, and calculates a feature amount in an embedded space by inputting the posture data normalized into the angular representation to an encoder, a distance calculation unit that calculates a distance between a feature amount calculated in each frame constituting reference moving image data and a feature amount calculated in each frame constituting synchronization target moving image data, a synchronization processing unit that calculates an optimal path for each frame based on the calculated distance and synchronizes the synchronization target moving image data with the reference moving image data by aligning timings of frames connected by the optimal path, and an output unit that outputs the synchronization target moving image data synchronized with the reference moving image data.

Abstract: An information processing device includes a feature extraction means for extracting, from input data that is motion data representing a motion of a person, basic feature data representing a feature of the motion data corresponding to a basic motion set with respect to the motion, motion feature data representing a feature of the motion data corresponding to a motion style set with respect to the motion, and person feature data representing a feature of the motion data corresponding to the person; a motion data generation means for generating first motion data based on the basic feature data and the motion feature data, and generating second motion data based on the basic feature data and the person feature data; and a learning means for learning the feature extraction means and the motion data generation means based on the first motion data and the second motion data.

Abstract: A visit schedule creation device 100 of the present invention includes an assignment unit 121 that, on the basis of location information of a visitor and a visited person, creates a visit schedule in which the visited person, to be visited by the visitor, is assigned to the visitor; a calculation unit 122 that calculates priority representing a level of priority of the visited person assigned to the visitor, on the basis of the location information; and a reassignment unit 123 that creates the visit schedule in which assignment of the visited person to the visitor is changed, on the basis of the priority.

Abstract: In order to attain an object of generating a prediction model which not only is capable of reducing a calculation load in a prediction phase but also has a good interpretability, a prediction model generation apparatus includes: a contribution degree calculation section that calculates, with use of a test data set different from a training data set used in training of a prediction model to be tested, a degree of contribution of each of a plurality of features to a prediction result, a value of the each of the plurality of features being inputted to the prediction model to be tested; a feature selection section that selects, on the basis of the degree of contribution of the each of the plurality of features, at least one feature from among the plurality of features; and a prediction model generation section that generates a new prediction model which, upon receiving input of a value of the at least one feature selected, outputs a prediction result.

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: Based on receipt data indicating a detailed statement of medical fees, receipt data conversion information indicating a presence or absence of a medical care information item that corresponds to a plurality of examination failure conditions is generated. A failure risk level based on the medical care information item included in the receipt data is calculated by using the receipt data conversion information and an examination failure risk predictor. A risk factor indicating a medical care information item with a degree of contribution to the failure risk level calculation that is greater than or equal to a prescribed threshold value for degree of contribution is specified. A revision screen for the medical care information item that is a risk factor when the failure risk level is greater than or equal to a prescribed threshold value for risk level is output.