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.

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: 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: 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 information processing device, the information acquisition means acquires patient information, medical professional information, and environment information. The treatment order determination means determines an order of treatment of the patients for each medical professional based on the patient information, the medical professional information, and the environment information.

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 rehabilitation approach proposal apparatus according an example embodiment includes: a bodily state information input unit configured to input bodily state information of a person to be undergoing rehabilitation as bodily state information, the bodily state information being expressed by current states and tilts of external body parts of the person to be undergoing rehabilitation; a desired outcome information input unit configured to select and input a desired outcome of rehabilitation from a plurality of desired outcome candidates as desired outcome information of the person to be undergoing rehabilitation; and an approach determining unit configured to determine a rehabilitation approach to be taken by the person to be undergoing rehabilitation based on the bodily state information and the desired outcome information.

Abstract: An information processing apparatus inputs expiratory phase data being data of expiratory phase and inspiratory phase data being data of inspiratory phase, for each of abdominal waveform data indicating a breathing waveform of an abdomen of a subject performing breathing training and chest waveform data indicating a breathing waveform of a chest of the subject. The information processing apparatus calculates a phase difference between the abdominal waveform data and the chest waveform data in each of the expiratory phase and the inspiratory phase.

Abstract: A classification apparatus includes: an encoding module that includes an element classification part that extracts a feature of input data and outputs classification information based on an element classification model stored in a first storage unit; an integration module that includes an element estimation part that receives the classification information and converts the classification information to a collation vector based on an element estimation model stored in a second storage unit; and a determination module that includes a determination part that determines a group to which the collation vector belongs by collating the collation vector with a representative vector of an individual group stored as a semantic model in a third storage unit and outputs a group ID of the group as a classification result.

Abstract: A classification apparatus includes: an encoding module that includes an element classification part that extracts a feature of input data and outputs classification information based on an element classification model stored in a first storage unit; an integration module that includes an element estimation part that receives the classification information and converts the classification information to a collation vector based on an element estimation model stored in a second storage unit; and a determination module that includes a determination part that determines a group to which the collation vector belongs by collating the collation vector with a representative vector of an individual group stored as a semantic model in a third storage unit and outputs a group ID of the group as a classification result.

Abstract: A service control device includes a controller that receives one of an external request and an execution result of a functional element and directs a functional element to be carried out next by referring to a control list holding a plan of combination of functional elements; an execution unit configured to carry out the functional element directed by the controller; a monitoring and recording unit configured to monitor an execution state of a functional element and record; and an activation level calculating unit configured to calculate an activation level of a functional element based on the execution state of a functional element having been recorded and update the control list based on the activation level.

Abstract: An information processing apparatus according to the present invention includes: a detection unit that detects detection information that is information indicating an external state of the apparatus; a communication unit that receives reception information that is a determination result given by another apparatus; and a control unit that calculates a first determination result that is a result acquired by determining a state of a surrounding of the apparatus based on the detection information and the reception information, transmits the first determination result to the another apparatus via the communication unit, and activates a necessary function for the detection unit or the communication unit and stops an unnecessary function thereof.

Abstract: A parameter determination unit 110 substitutes, for each of a plurality of applications, a recommended amount of resources and a quality of experience corresponding to the recommended amount of resources, and a minimum amount of resources and a quality of experience corresponding to the minimum amount of resources into a quality function f in expression (1) indicating a relation between an amount of resources R and a quality of experience Q, to determine parameters a and b. A resource amount determination unit 120 determines an amount of resources to be allocated to the plurality of applications using the quality function f for each application in which the parameters a and b are determined. The quality function f(x) is a monotonically increasing function having an inverse function f?1, connects (??,0) and (+?,1), and is symmetrical with respect to x=0.

Abstract: To reduce performance degradation due to a high-performance core's waiting for a processing result of a low-performance core in a multi-core processor including a plurality of cores with different running performance, included are a task pool that stores executable tasks for each computational core, a task scheduler, and a reference count analysis module that acquires hint information and a reference count estimation method from a running task and estimates a reference count of a task to be newly inserted into the task pool or a task existing in the task pool based on the specified method. The scheduler performs insertion and acquisition of a task by mainly using performance of the cores and the reference count.

Abstract: In a multi-core processor system, cache memories are provided respectively for a plurality of processors. An assignment management unit manages assignment of tasks to the processors. A cache status calculation unit calculates a cache usage status such as a memory access count and a cache hit ratio, with respect to each task. A first processor handles a plurality of first tasks that belong to a first process. If computation amount of the first process exceeds a predetermined threshold value, the assignment management unit refers to the cache usage status to preferentially select, as a migration target task, one of the plurality of first tasks whose memory access count is smaller or whose cache hit ratio is higher. Then, the assignment management unit newly assigns the migration target task to a second processor handling another process different from the first processor.

Abstract: Provided are a synchronization control section that executes a current thread and a reference thread in parallel, a waiting time calculation section that calculates the time needed for the reference thread to reach a second synchronization point as a waiting time of the current thread when the reference thread does not reach the second synchronization point at a time when the current thread reaches a first synchronization point, a quality difference calculation section that estimates a quality difference between data that the current thread generates by referring to processing data at the second synchronization point of the reference thread and data that the current thread generates without referring to the processing data, and a synchronization determination section that determines whether to make the current thread wait until the reference thread reaches the second synchronization point depending on the waiting time and the magnitude of the quality difference.

Abstract: Provided is a computing resource allocation device capable of allocating computing resources to accommodate changing activity patterns. The device is equipped with an external environment recognition means that analyzes input values from sensors to specify the current environment, a memory means that stores a table in which the sensors required to specify the environment are correlated, a transition frequency computation means that computes the transition frequency at which a transition is made from an environment to another environment, and a computing resource allocation means that computes the amount of allocation of the computing resources to be used for the analysis based on the current environment by referencing the table and the transition frequency, and that allocates the computing resources for the analysis.