Abstract: An information processing device includes one or more memories; and one or more processors coupled to the one or more memories and the one or more processors configured to acquire allocation data that indicates an allocation result of a plurality of resources for a plurality of allocation destinations, extract data that violates a first condition from the acquired allocation data, classify the extracted data into a plurality of groups, acquire an index value regarding an appearance frequency of an allocation pattern of a resource of the plurality of resources based on data of each of the plurality of classified groups, and generate a second condition for the allocation pattern based on the index value.

Abstract: A medical information providing method including: acquiring first injury-or-disease information that indicates a type of an injury-or-disease; accessing a storage device that stores, for each provider of a plurality of providers each of which provides medical service, identification information of the provider, and injury-or-disease treatment information, the injury-or-disease treatment information including information on a type of an injury-or-disease for which the provider has an experience of treatment or that the provider is able to treat, and information on a type of treatment; extracting, for each provider of the plurality of providers, from the storage device, the injury-or-disease treatment information that includes information on a type of injury-or-disease corresponding to the first injury-or-disease information; and in a case where the information on a type of treatment included in the injury-or-disease treatment information extracted for each provider of the plurality of providers satisfies a cri

Abstract: A computer-implemented method for generating a predicting model includes: accumulating a plurality of combinations of a parameter representing a branch condition and a branch probability in relation to one or more condition branch components in a workflow; and generating, using the plurality of combinations accumulated, a model that predicts a branch probability corresponding to a parameter used when a particular condition branch component among the one or more condition branch components is used.

Abstract: A display method includes accepting designation of a first workflow, first counting, by applying a plurality of objects to the first workflow, a number of object distributed by an element of a conditional branch of the first workflow for each of element at an end out of elements included in the first workflow, second counting, by applying the plurality of objects to a second workflow that is different from the first workflow, a number of objects distributed by an element of a conditional branch of the second workflow for each of element at an end out of elements included in the second workflow, and displaying the number of objects distributed to an element at the end, associating with the element at the end, for each of the first workflow and the second workflow, by a processor.

Abstract: A method for generating a workflow includes: generating multiple candidate workflows by making a modification to part of an existing workflow that defines multiple condition branches and following contents at respective destinations of the condition branches; obtaining KPI fluctuation vectors one for each candidate workflow from the existing workflow; generating multiple synthesized policies by synthesizing two or more of the KPI fluctuation vectors; calculating, when one of selected routes of multiple candidate workflows contains the modifications, a KPI predicted value of each synthesized policy by reflecting a weight parameter on the KPI fluctuation vector, the weight parameter being set such that a weight for a modification to an upper point of the selected route is higher than that to a lower point of the selected route among the modifications; and outputting a workflow of one of the synthesized policies the KPI predicted value of which satisfies a target value.

Abstract: A computer-implemented method of performing planning processing, the method including: acquiring information of a menu of a predetermined service and information of a stakeholder related to the service; calculating a change frequency of execution of the menu for a combination of the stakeholder and the menu; specifying a menu to be recommended on the basis of the calculated change frequency; and outputting the specified menu to the stakeholder of the calculated combination.

Abstract: An information processing device includes one or more memories; and one or more processors coupled to the one or more memories and the one or more processors configured to acquire allocation data that indicates an allocation result of a plurality of resources for a plurality of allocation destinations, extract data that violates a first condition from the acquired allocation data, classify the extracted data into a plurality of groups, acquire an index value regarding an appearance frequency of an allocation pattern of a resource of the plurality of resources based on data of each of the plurality of classified groups, and generate a second condition for the allocation pattern based on the index value.

Abstract: A meal detection method executed by a processor of a computer, the meal detection method includes acquiring first sensing data from a first sensor configured to detect a motion of an arm of a target person; acquiring second sensing data from a second sensor configured to detect a heart rate of the target person; calculating a first evaluation value indicating likelihood that a meal is being performed based on a first feature amount extracted from the first sensing data; calculating a second evaluation value indicating likelihood that the meal is performed based on a second feature amount extracted from the second sensing data; and determining whether the target person has eaten the meal or not, based on the first evaluation value and the second evaluation value.

Abstract: A meal detection method executed by a processor of a computer, the meal detection method includes acquiring first sensing data from a first sensor configured to detect a motion of an arm of a target person; acquiring second sensing data from a second sensor configured to detect a heart rate of the target person; setting a motion section in which a specific motion by the arm of the target person is executed at least twice or more based on the first sensing data; and determining whether a meal is performed in a section including at least a portion of the motion section, based on the second sensing data corresponding to the motion section and learning information corresponding to a unit motion of the specific motion at a time of the meal.

Abstract: A pulse wave analysis apparatus including a memory, and a processor coupled to the memory and the processor configured to execute a process, the process including extracting, from each of a plurality of captured images of a subject, a plurality of image areas corresponding to each of a plurality of parts of the subject respectively, generating pieces of waveform data corresponding to the plurality of parts based on an image analysis for the plurality of image areas, each of the pieces of waveform data indicating a pulse wave of the subject, calculating a first matching degree between the pieces of waveform data, and determining whether a noise is included in the pieces of waveform data based on the first matching degree.

Abstract: A meal intake detection device includes: a memory; and a processor coupled to the memory and configured to: detect a rise of heart rate; extract as a meal intake candidate a time range from a detected rise to time when the heart rate decreases to a predetermined value in temporal changes in heart rate, and when a second meal intake candidate is extracted in a time range of an extracted first meal intake candidate, separate the first meal intake candidate and the second meal intake candidate along a predetermined line; and detect whether each of the first meal intake candidate and the second meal intake candidate is a meal intake based on feature quantities relating to the first meal intake candidate and the second meal intake candidate that are separated.

Abstract: A meal time estimation method includes: acquiring time series data of heart rate, by a processor; calculating a feature amount obtained by indexing a degree of similarity with a feature of heart rate change that appears at end of a meal from the time series data of the heart rate, by the processor; and estimating a meal time from the feature amount, by the processor.

Abstract: A blood flow index calculating method includes: obtaining, by a camera, an image capturing a first site of a living body and a second site of the living body; extracting an area of the first site of the living body and an area of the second site of the living body, by a processor; detecting a pulse wave pattern of the first site of the living body from the area of the first site of the living body and detecting a pulse wave pattern of the second site of the living body from the area of the second site of the living body, by the processor; first calculating a delay amount from the pulse wave pattern of the first site and the pulse wave pattern of the second site, by the processor; and second calculating an index related to blood flow by using the delay amount, by the processor.

Abstract: A meal detection method executed by a processor of a computer, the meal detection method includes acquiring first sensing data from a first sensor configured to detect a motion of an arm of a target person; acquiring second sensing data from a second sensor configured to detect a heart rate of the target person; setting a motion section in which a specific motion by the arm of the target person is executed at least twice or more based on the first sensing data; and determining whether a meal is performed in a section including at least a portion of the motion section, based on the second sensing data corresponding to the motion section and learning information corresponding to a unit motion of the specific motion at a time of the meal.

Abstract: A meal detection method executed by a processor of a computer, the meal detection method includes acquiring first sensing data from a first sensor configured to detect a motion of an arm of a target person; acquiring second sensing data from a second sensor configured to detect a heart rate of the target person; calculating a first evaluation value indicating likelihood that a meal is being performed based on a first feature amount extracted from the first sensing data; calculating a second evaluation value indicating likelihood that the meal is performed based on a second feature amount extracted from the second sensing data; and determining whether the target person has eaten the meal or not, based on the first evaluation value and the second evaluation value.

Abstract: A pulse wave analysis apparatus including a memory, and a processor coupled to the memory and the processor configured to execute a process, the process including extracting, from each of a plurality of captured images of a subject, a plurality of image areas corresponding to each of a plurality of parts of the subject respectively, generating pieces of waveform data corresponding to the plurality of parts based on an image analysis for the plurality of image areas, each of the pieces of waveform data indicating a pulse wave of the subject, calculating a first matching degree between the pieces of waveform data, and determining whether a noise is included in the pieces of waveform data based on the first matching degree.

Abstract: An information processing apparatus includes a memory, and a processor coupled to the memory and configured to obtain time series data indicating a time-dependent change of a biological signal value after a meal, determine, based on the obtained time series data, a first feature amount of the time-dependent change of the biological signal value after the meal, determine, based on the determined first feature amount, an index value related to the meal, and output the determined index value.

Abstract: A behavior sensing device includes: a processor configured to: acquire sensor data from one or more sensors; calculate a first characteristic amount serving as an index of characteristic of biological reaction to a behavior to be sensed from the sensor data; calculate a probability of occurrence of the behavior as a first behavior occurrence probability from the first characteristic amount; calculate a second characteristic amount serving as another index of characteristic of a habit relating to the behavior to be sensed from the sensor data; calculate another probability of occurrence of the behavior as a second behavior occurrence probability from the second characteristic amount; and determine whether specific behavior is performed based on the first behavior occurrence probability and the second behavior occurrence probability.

Abstract: A meal intake detection device includes: a memory; and a processor coupled to the memory and configured to: detect a rise of heart rate; extract as a meal intake candidate a time range from a detected rise to time when the heart rate decreases to a predetermined value in temporal changes in heart rate, and when a second meal intake candidate is extracted in a time range of an extracted first meal intake candidate, separate the first meal intake candidate and the second meal intake candidate along a predetermined line; and detect whether each of the first meal intake candidate and the second meal intake candidate is a meal intake based on feature quantities relating to the first meal intake candidate and the second meal intake candidate that are separated.

Abstract: An information processing apparatus includes a memory, and a processor coupled to the memory and configured to acquire time series data of heart rate over a plurality of days, specify a first time zone of a day in which an increasing change of the heart rate satisfies a first condition in common with the time series data on the plurality of days, specify the increasing change in a second time zone of the day for each of the time series data on the plurality of days, the second time zone overlapped with at least a part of the first time zone, specify a meal time based on the increasing of the second time zone for each of the time series data, and output the specified meal time.