Control method, program, report provision method, and report provision system

Abstract

We can propose measures that reflect the comments of the target audience. [Solution] A control method for a report provision system that outputs a report including measures for the workload of a target person, using an information processing device, a target person user device used by the target person, and a device attached to the target person, wherein the control method acquires workload data indicating the workload of the target person based on activity data of the target person measured by the device, acquires the factors of the workload estimated based on comments from the target person regarding the workload, and outputs a report including a first measure based on the workload data and the factors of the workload.

Description

Technical Field

[0001] The present disclosure relates to a control method, a program, a report providing method, and a report providing system.

Background Art

[0002] In the management support device described in Patent Document 1, based on information from a mental load estimation unit and a performance calculation unit provided in a worker terminal, a mental load control measure generation unit generates measures for controlling the mental load of a worker (see Patent Document 1).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] [[ID=X]] However, in the conventional technology as described above, since the mental load estimation unit estimates the mental load using the biometric information of the worker measured by the measurement unit, and the performance calculation unit estimates the performance using the working state of the worker input to the input unit, it may be difficult to reflect the opinions of the worker in the content of the measures output by the mental load control measure generation unit.

Means for Solving the Problems

[0005] [[ID=X]] To solve the above problems, one embodiment is a control method for a report provision system that outputs a report including measures for the workload of a target person, using an information processing device, a target person user device used by the target person, and a device attached to the target person, the control method being to acquire workload data indicating the workload of the target person based on activity data of the target person measured by the device, acquire the factors of the workload estimated based on comments from the target person regarding the workload, and output a report including a first measure based on the workload data and the factors of the workload.

[0006] To solve the above problem, one embodiment provides a control method for a report provision system that outputs a report on the workload of a subject, using an information processing device, a subject user device used by the subject, and a device attached to the subject, comprising: a step of causing the device to measure the subject's activity level data; a step of causing the information processing device to acquire the activity level data; a step of causing the information processing device to calculate workload data indicating the subject's workload based on the activity level data; and a step of causing the information processing device to send a message to the subject user device to receive comments from the subject regarding the workload, according to the workload data. The control method comprises the steps of: causing the target user device to receive comments from the target user regarding the load; causing the information processing device to acquire the comments; causing the information processing device to estimate the factors of the load based on the acquired comments; causing the information processing device to determine whether the load data and the factors of the load satisfy a first condition; causing the information processing device to generate a first measure for the load if it determines that the load data and the factors of the load satisfy the first condition; and causing the information processing device to output the report including the first measure.

[0007] To solve the above problems, one embodiment is a control method for an information processing device that communicates with at least one of a user-accessible device used by a target person and a device attached to the target person, the control method comprising: causing the information processing device to acquire activity data of the target person from the device; causing the information processing device to calculate load data indicating the load of the target person based on the activity data; causing the information processing device to send a message to the user-accessible device to receive comments from the target person regarding the load, in accordance with the load data; when the user-accessible device receives the comments from the target person regarding the load, causing the user-accessible device to acquire the comments; causing the user-accessible device to estimate the factors of the load based on the acquired comments; determining whether the load data and the factors of the load satisfy a first condition; when it is determined that the load data and the factors of the load satisfy the first condition, causing the user-accessible device to generate a first measure for the load; and outputting a report including the first measure.

[0008] To solve the above problems, one embodiment is a program for a computer that enables the following steps: to cause the computer to acquire activity data of a subject from a device attached to the subject; to calculate load data indicating the subject's load based on the activity data; to cause the computer to send a message to a subject user device used by the subject to receive comments from the subject regarding the load, in accordance with the load data; to cause the computer to acquire the comments from the subject user device when the subject user device has received the comments from the subject regarding the load; to estimate the factors of the load based on the acquired comments; to determine whether the load data and the factors of the load satisfy a first condition; to generate a first measure for the load when it is determined that the load data and the factors of the load satisfy the first condition; and to output a report including the first measure.

[0009] To solve the above problems, one embodiment is a control method for a user device that communicates with a device attached to a user, comprising the steps of: causing the user device to acquire activity data of the user from the device; causing the user device to calculate load data indicating the user's load based on the activity data; causing the user device to output a message to receive comments from the user regarding the load, according to the load; when the user device receives comments from the user regarding the load, causing the user device to estimate the factors of the load based on the acquired comments; causing the user device to determine whether the load data and the factors of the load satisfy a first condition; when it is determined that the load data and the factors of the load satisfy the first condition, causing the user device to generate a first measure for the load; and outputting a report including the first measure.

[0010] To solve the above problems, one embodiment is a program for a computer that enables the following steps: to cause the computer to acquire activity data of a subject from a device attached to the subject; to calculate load data indicating the subject's load based on the activity data; to output a message to receive comments from the subject regarding the load, depending on the load; to estimate the factors of the load based on the comments received from the subject when comments regarding the load are received; to determine whether the load data and the factors of the load satisfy a first condition; to generate a first measure for the load if it is determined that the load data and the factors of the load satisfy the first condition; and to output a report including the first measure.

[0011] To solve the above problems, one embodiment is a control method for a device attached to a subject, comprising the steps of: causing the device to acquire activity data of the subject; causing the device to calculate load data indicating the subject's load based on the activity data; causing the device to output a message to an external device for receiving comments from the subject regarding the load, depending on the load; when the external device receives the comments from the subject regarding the load, causing it to estimate the factors of the load based on the acquired comments; causing the device to determine whether the load data and the factors of the load satisfy a first condition; when it is determined that the load data and the factors of the load satisfy the first condition, causing the device to generate a first measure for the load; and outputting a report including the first measure.

[0012] To solve the above problems, one embodiment is a program for implementing the following steps: causing a computer attached to a subject to acquire activity data of the subject; calculating load data indicating the subject's load based on the activity data; outputting a message to an external device to receive comments from the subject regarding the load, depending on the load; when the external device receives the comments from the subject regarding the load, estimating the factors of the load based on the acquired comments; determining whether the load data and the factors of the load satisfy a first condition; generating a first measure for the load if it is determined that the load data and the factors of the load satisfy the first condition; and outputting a report including the first measure.

[0013] To solve the above problems, one embodiment is a report provision method that provides a user with a report including measures to reduce the load using a report provision system, wherein the report is output to the display of the user's terminal by the report provision system, and the report includes load data indicating the load based on activity data of high-load individuals, and the factors of the load estimated based on comments received from the high-load individuals, wherein the high-load individuals are those who are subject to obtaining the load data based on activity data, and whose indicators for the load satisfy predetermined conditions for high-load individuals.

[0014] To solve the above problems, one embodiment is a report provision system that outputs a report on the workload of a subject, using an information processing device, a subject user device used by a subject, and a device attached to the subject, wherein the system acquires workload data indicating the workload of the subject based on activity data of the subject measured by the device, acquires the factors of the workload estimated based on comments from the subject regarding the workload, determines whether the workload data and the factors of the workload satisfy a first condition, and outputs the report including a first measure for the workload if it is determined that the workload data and the factors of the workload satisfy the first condition. [Brief explanation of the drawing]

[0015] [Figure 1] This figure shows an example configuration of the first report provision system according to the first embodiment. [Figure 2] This is a diagram showing an example configuration of the first user-accessible device according to the first embodiment. [Figure 3] This is a diagram showing an example configuration of the first server device according to the first embodiment. [Figure 4] This is a diagram showing an example configuration of the first administrator terminal device according to the first embodiment. [Figure 5]It is a diagram showing an example of a first table related to personal information according to the first embodiment. [Figure 6A] It is a diagram showing an example of physical high-load time and mental high-load time according to the first embodiment. [Figure 6B] It is a diagram showing an example of a second table related to personal physical high-load time and mental high-load time according to the first embodiment. [Figure 6C] It is a diagram showing an example of a third table related to the ranking of physical high-load time and mental high-load time of organizational units according to the first embodiment. [Figure 7] It is a diagram showing an example of a first screen related to chat according to the first embodiment. [Figure 8] It is a diagram showing an example of chat summary data according to the first embodiment. [Figure 9A] It is a diagram showing an example of measures according to the load and factors of the first group according to the first embodiment. [Figure 9B] It is a diagram showing an example of measures according to the load and factors of the second group according to the first embodiment. [Figure 9C] It is a diagram showing an example of measures according to the load and factors of the third group according to the first embodiment. [Figure 9D] It is a diagram showing an example of measures according to the load and factors of the fourth group according to the first embodiment. [Figure 9E] It is a diagram showing an example of measures according to the load and factors of the fifth group according to the first embodiment. [Figure 9F] It is a diagram showing an example of measures according to the load and factors of the sixth group according to the first embodiment. [Figure 10] It is a diagram showing an example of a second screen related to load factors according to the first embodiment. [Figure 11] It is a diagram showing an example of a third screen related to the load situation according to the first embodiment. [Figure 12A] It is a diagram showing an example of a fourth A screen related to the proposal of measures at the individual unit according to the first embodiment. [Figure 12B] It is a diagram showing an example of a fourth B screen related to the proposal of measures at the organizational unit according to the first embodiment. [Figure 13A] This figure shows an example of the first input screen, which is a pull-down format input screen for policy performance according to the first embodiment. [Figure 13B] This figure shows an example of a second input screen, which is a radio button-type policy performance input screen according to the first embodiment. [Figure 13C] This figure shows an example of a third input screen, which is a radio button format input screen with free text for policy performance according to the first embodiment. [Figure 14] This figure shows an example of the procedure for processing performed in the first report provision system according to the first embodiment. [Figure 15] This figure shows an example of the processing procedure performed in the first report provision system according to a modified version of the first embodiment. [Figure 16] This figure shows an example of a schematic functional block of the first report provisioning system according to a specific example of the first embodiment. [Figure 17] This figure shows an example configuration of the second report provision system according to the second embodiment. [Figure 18] This figure shows an example configuration of the second user device according to the second embodiment. [Figure 19] This figure shows an example of the procedure for processing performed in the second report provision system according to the second embodiment. [Figure 20] This figure shows an example configuration of the third report provision system according to the third embodiment. [Figure 21] This figure shows an example configuration of the third device according to the third embodiment. [Figure 22] This figure shows an example of the processing procedure performed in the third report provision system according to the third embodiment. [Modes for carrying out the invention]

[0016] The embodiments will be described below with reference to the drawings.

[0017] The first embodiment will be described.

[0018] Figure 1 is a diagram showing an example configuration of the first report provision system 1 according to the first embodiment. The first report provision system 1 comprises a first device 11, a first user access device 12, a first server device 13, a first administrator terminal device 14, a first database 15, and a first network 21. Figure 1 also shows the first subject 31 and the first administrator 41. The first subject 31 operates the first subject user device 12. In this embodiment, the first subject 31 owns the first subject user device 12. The first administrator 41 operates the first administrator terminal device 14.

[0019] In this embodiment, the first device 11 and the first user device 12 communicate wirelessly. This communication may be, for example, Bluetooth® Low Energy. In this embodiment, the first user device 12, the first server device 13, the first administrator terminal device 14, and the first database 15 can communicate with each other via the first network 21.

[0020] In this embodiment, the first user access device 12 establishes a communication connection with the first network 21 wirelessly. The first user access device 12 may also establish a communication connection with the first network 21 via, for example, a base station device (not shown). In this embodiment, the first server device 13, the first administrator terminal device 14, and the first database 15 each communicate with the first network 21 via wired connections.

[0021] The network configuration in this embodiment is merely an example, and two or more devices may communicate using any communication method, either wired, wireless, or both. The network may utilize, for example, one or more of the following: LTE (Long Term Evolution), Wi-Fi, Bluetooth® Low Energy, etc. Furthermore, for example, the first device 11 may also communicate with the first network 21 wirelessly, and may communicate with the first user device 12, the first server device 13, the first administrator terminal device 14, and the first database 15 via the first network 21.

[0022] The first device 11 is a wearable device and is attached to the body of the first subject 31. In this embodiment, the first device 11 is directly attached to the skin of one arm of the first subject 31. In addition, any other location on the body may be used as the location on which the first device 11 is attached.

[0023] The first device 11 acquires activity level data from the first subject 31. In this embodiment, the activity data includes heartbeat data and body movement data, but may also include other data. For example, activity data may also include at least one of the following: data on the number of steps taken and data on sleep. In this embodiment, pulsation and pulse refer to essentially the same thing.

[0024] The first device 11 comprises a first pulse measurement unit Q1 and a first body movement detection unit Q2. The first pulse measurement unit Q1 measures the pulse rate of the first subject 31 and acquires pulse data, which is the data of the measurement result. For example, a pulse sensor may be used as the first pulse measurement unit Q1. The pulse sensor may also be called, for example, a pulse meter.

[0025] The first body motion detection unit Q2 detects the body movements of the first subject 31 and acquires body motion data, which is the data of the detection result. For example, an acceleration sensor that detects acceleration may be used as the first motion detection unit Q2. As another example, the first motion detection unit Q2 may be an angular velocity sensor that detects angular velocity. Alternatively, the first motion detection unit Q2 may be two or more sensors that detect different physical quantities, for example.

[0026] In this embodiment, the first device 11 is equipped with a pulse sensor and an acceleration sensor to measure activity data, including heartbeat data. The first device 11 has functions for performing the operations described in this embodiment, and for example, it has a function for communicating wirelessly with the outside. Such a communication function may be provided in, for example, the first pulse measurement unit Q1 and the first body movement detection unit Q2, or it may be provided as a separate functional unit from the first pulse measurement unit Q1 and the first body movement detection unit Q2.

[0027] In this embodiment, the first device 11 for measuring activity data is shown as a single device; however, in other examples, the first device 11 may be composed of multiple devices. As a specific example, if the first device 11 is equipped with multiple sensors, these multiple sensors may be distributed and provided across two or more devices, either in the same number or different numbers. Furthermore, if the first device 11 consists of multiple devices, for example, each device may be attached to a different part of the body of the first subject 31.

[0028] Figure 2 shows an example of the configuration of the first user-accessible device 12 according to the first embodiment. The first user device 12 comprises a first input unit 111, a first output unit 112, a first communication unit 113, a first storage unit 114, and a first control unit 115. The first input unit 111 includes a first operation unit 131. The first output unit 112 includes a first display unit 132.

[0029] The first user device 12 is configured using a computer. The first user device 12 may be, for example, a smartphone, a tablet device, or a personal computer.

[0030] The first input unit 111 has the function of inputting information. For example, the first operation unit 131 receives and inputs the details of the operation performed by the first target person 31. Furthermore, for example, the first input unit 111 may receive information from an external device. The first output unit 112 has the function of outputting information. For example, the first display unit 132 outputs the information to be displayed to the screen. The first display unit 132 may have, for example, the screen of any display. Furthermore, for example, the first output unit 112 may output information to an external device. Here, the first operation unit 131 and the first display unit 132 may be shared, for example, by using a touch panel.

[0031] The first communication unit 113 has the function of performing communications. In this embodiment, the first communication unit 113 is shown separately from the first input unit 111 and the first output unit 112. However, for example, the receiving function of the first communication unit 113 may be included in the functions of the first input unit 111, and the transmitting function of the first communication unit 113 may be included in the functions of the first output unit 112.

[0032] The first memory unit 114 stores information. The first control unit 115 performs various processes or controls on the first user device 12. In this embodiment, the first control unit 115 is equipped with a predetermined processor, such as a CPU (Central Processing Unit), and performs various processes or controls by executing a predetermined program using this processor. The program may be stored, for example, in the first storage unit 114.

[0033] Figure 3 shows an example of the configuration of the first server device 13 according to the first embodiment. The first server device 13 includes a server input unit 211, a server output unit 212, a server communication unit 213, a server storage unit 214, and a server control unit 215. The names of the parts provided in the first server device 13 are for descriptive purposes only and may be referred to by any other name. The first server device 13 is configured using a computer.

[0034] The server input unit 211 has the function of inputting information. For example, the server input unit 211 receives information from an external device. The server output unit 212 has the function of outputting information. For example, the server output unit 212 outputs information to an external device.

[0035] The server communication unit 213 has the function of performing communication. In this embodiment, the server communication unit 213 is shown separately from the server input unit 211 and the server output unit 212. However, for example, the receiving function of the server communication unit 213 may be included in the functions of the server input unit 211, and the transmitting function of the server communication unit 213 may be included in the functions of the server output unit 212.

[0036] The server memory unit 214 stores information. The server control unit 215 performs various processes or controls in the first server device 13. In this embodiment, the server control unit 215 is equipped with a predetermined processor, such as a CPU, and performs various processes or controls by executing a predetermined program using this processor. The program may be stored, for example, in the server storage unit 214.

[0037] Figure 4 shows an example of the configuration of the first administrator terminal device 14 according to the first embodiment. The first administrator terminal device 14 includes a management input unit 311, a management output unit 312, a management communication unit 313, a management storage unit 314, and a management control unit 315. The management input unit 311 includes a management operation unit 331. The management output unit 312 includes a management display unit 332. The names of the parts provided in the first administrator terminal device 14 are for descriptive purposes only and may be referred to by any other name.

[0038] The first administrator terminal device 14 is configured using a computer. The first administrator terminal device 14 may be, for example, a smartphone, a tablet device, or a personal computer.

[0039] The management input unit 311 has the function of inputting information. For example, the management operation unit 331 receives and inputs the details of the operation performed by the first administrator 41. Furthermore, for example, the management input unit 311 may receive information from an external device. The management output unit 312 has the function of outputting information. For example, the management display unit 332 outputs the information to be displayed to the screen. The management display unit 332 may have, for example, a screen of any display. Furthermore, for example, the management output unit 312 may output information to an external device. Here, the management operation unit 331 and the management display unit 332 may be unified using, for example, a touch panel.

[0040] The management and communication unit 313 has the function of performing communications. In this embodiment, the management communication unit 313 is shown separately from the management input unit 311 and the management output unit 312. However, for example, the receiving function of the management communication unit 313 may be included in the functions of the management input unit 311, and the transmitting function of the management communication unit 313 may be included in the functions of the management output unit 312.

[0041] The management memory unit 314 stores information. The management control unit 315 performs various processes or controls on the first administrator terminal device 14. In this embodiment, the management control unit 315 is equipped with a predetermined processor, such as a CPU, and performs various processes or controls by executing a predetermined program using this processor. The program may be stored, for example, in the management memory unit 314.

[0042] The first database 15 stores various types of information. The first database 15 may store, for example, various types of information handled in the first report provision system 1. The first database 15 is not necessarily required; for example, the necessary information may be stored in each of the first device 11, the first user device 12, the first server device 13, and the first administrator terminal device 14.

[0043] The processing performed in the first report provision system 1 according to this embodiment will be described below. In this embodiment, we will explain the case where the first report provision system 1 is applied to a nurse working in a hospital. In this embodiment, the first subject 31 is a nurse, and the first manager 41 is a supervisor or other person who manages the first subject 31.

[0044] In the first report provision system 1, the pulse information acquired as pulse data is normalized and treated as load information. This pulse information may be, for example, heart rate information, heart rate variability information, or heart rate and heart rate variability information.

[0045] There are no particular limitations on the method used to quantify the load, and various methods may be employed. In this embodiment, as a method for quantifying the load, for example, a method using maximum heart rate (HRmax) as a reference, a method using resting heart rate (HRrest) as a reference, or a method using both of these as a reference may be used. As an example, heart rate activity level may be used. Heart rate activity level is expressed as (heart rate / HRrest). Another example that may be used is the percentage of maximum heart rate (%HRmax). This percentage is expressed as (heart rate / HRmax). Another example that may be used is heart rate reserve (%HR Reserve). Heart rate reserve is expressed as {(heart rate - HRrest) / (HRmax - HRrest)}.

[0046] In the first report provision system 1, for the first subject 31, if the load data, which is load information based on heart rate, exceeds a threshold, it is determined that the load is high. This threshold may be called, for example, the load reference value. The first report provision system 1 aggregates the length of time during which the system is judged to have a high load within a predetermined period. This predetermined period is not particularly limited and may be, for example, one day, the duration of one work period, or one week.

[0047] The first report delivery system 1 calibrates the load reference value. The first report provision system 1 acquires the resting heart rate or resting RMSSD (Root Mean Square of Successive Differences), which is the basis for calculating the load standard value, for each individual. During this process, the necessary information is measured, and calculations are performed using the necessary formulas. Here, resting heart rate or resting RMSSD may be determined by referring to health checkup data such as blood pressure measurements and electrocardiograms, for example. Furthermore, resting heart rate may be determined, for example, based on a table that correlates age with resting heart rate. Furthermore, resting heart rate or resting RMSSD may be determined, for example, based on the heart rate during periods of rest and stillness. In this embodiment, the load threshold value may be adjusted based on the employee's subjective opinion.

[0048] The first report provision system 1 distinguishes whether the load is caused by physical or psychological factors based on signals from sensors used to detect body movement data. These sensors are, for example, acceleration sensors. In the first report delivery system 1, the aggregation is separated into physical factors and psychological factors. In the first report provision system 1, if the load data exceeds a threshold when there is body movement, it is determined that there is a high physical load. On the other hand, in the first report provision system 1, if the load data exceeds a threshold when there is no physical movement, it is determined that there is a high mental load. Furthermore, the presence or absence of body movement may be determined, for example, based on whether or not there is body movement that meets a predetermined standard. Furthermore, "mental" can also be called, for example, "spiritual."

[0049] In the first report provision system 1, attribute data representing attributes is attached to the load data. These attributes may include the personal attributes of the employee. This attribute may include the date and time. This attribute may include the work content. Personal attributes include, for example, the organization to which they belong, their job responsibilities, their work style, age, and gender. The work content will allow us to identify what the workload data corresponds to when an employee was performing their duties at each time period.

[0050] The first report provision system 1 aggregates and visualizes the load status based on attribute data attached to the load data. The load status may be compiled, for example, by organization or by team. The workload status can be compiled, for example, by assigned task or by role. The workload status may be compiled, for example, by work schedule. Examples of work schedules include day shifts, night shifts, and irregular shifts. The load status may be aggregated, for example, by generation. These generations could include, for instance, younger generations, mid-career generations, or veteran generations.

[0051] In the first report provision system 1, the interviewees are selected based on the load data. In this embodiment, the interview is conducted using a chat robot. For the sake of explanation, in this embodiment, the robot will be referred to as the interview robot, but it may be called by any other name. In this embodiment, the hearing robot may, for example, perform the chat autonomously without using external functions, or it may perform the chat using external functions. Here, the external function may be, for example, an AI such as Artificial Intelligence. Furthermore, a robot is not necessarily required to be used as the function for conducting interviews. Furthermore, while the chat is conducted using text information in this embodiment, it may also be conducted using voice in other examples.

[0052] For example, those ranked highly in terms of workload could be selected as the people to be interviewed. Those selected for interviews may, for example, be chosen based on the time-series changes in their workload. Specifically, individuals whose workload has increased compared to the previous week may be selected as interviewees. Those selected for interviews may be individuals experiencing real-time changes in their system load. Examples of such real-time changes in load include sudden spikes in load.

[0053] Figure 5 shows an example of a first table Ta1 relating to personal information according to the first embodiment. Table 1, Ta1, stores user ID (which identifies the employee), name, age, affiliated organization, job responsibilities, work style, resting pulse rate, resting SDNN (Standard Deviation of Normal and Normal Interval), and chat summary data (a summary of the chat content) in an associated manner. Note that in the example in Figure 5, the description of specific examples for chat summary data has been omitted.

[0054] Here, the information in the first table Ta1 may be stored, for example, in a database that manages employee data, or in another storage device. Furthermore, this database stores, for example, employee attendance data. For example, the first database 15 may be used as the database in question.

[0055] In the example in Figure 5, the information in the first table, Ta1, contains attribute information for each employee, such as their affiliated organization and personal age. In the example in Figure 5, chat summary data summarizing the key points of the interview regarding workload status is used, but data containing the results of the interview regarding workload status may be used in conjunction with or instead of this data. Note that the information in Table 1, Ta1, is illustrative, and not all of it is necessarily required to be used, or other information may be used instead.

[0056] Let me explain the load analysis cloud. In this embodiment, the load analysis cloud is a functional unit that performs load analysis in the cloud. In this embodiment, the load analysis cloud is configured, for example, by a first server device 13. As another example, the functional components of the load analysis cloud may be distributed and provided on the first server device 13 and the first administrator terminal device 14. As yet another example, the functional unit of the load analysis cloud may be comprised of a first administrator terminal device 14.

[0057] The load analysis cloud receives activity data measured by the first device 11 worn by the first subject 31, who are employees. In this embodiment, the activity data is relayed by a first user device 12, such as a smartphone provided to the employee, and acquired by a load analysis cloud. The load analysis cloud, for example, calculates load threshold values ​​by referencing activity data and employee data.

[0058] Here, we will illustrate three methods for calculating load threshold values: the first load threshold calculation method, the second load threshold calculation method, and the third load threshold calculation method.

[0059] In the first load baseline calculation method, the resting heart rate is calculated based on the heart rate before and after waking up. The resting heart rate is used as the average value over a predetermined period, such as 10 minutes. For example, the following formula may be used: {(Individual load threshold heart rate) = (Resting heart rate) × 1.5}. Specifically, using this formula, if the resting heart rate is 60, the individual load threshold heart rate would be 90. Note that the coefficient 1.5 in the calculation formula is just an example, and other values ​​may be used.

[0060] The second load threshold calculation method uses maximum heart rate. This method corresponds to %HRmax. This method calculates an approximate maximum heart rate based on age. For example, the formula {HRmax=220-(age)} may be used. Alternatively, the functions of the first device 11 may be used to calculate an estimated value of HRmax or to obtain an actual measured value of HRmax. For example, the following formula may be used: [(Individual load threshold heart rate) = {220 - (Age)} × 0.5]. Specifically, using this formula, if the age is 40, the individual load threshold heart rate would be 90. Note that the coefficients 220 and 0.5 in the calculation formula are merely examples, and other values ​​may be used.

[0061] The third load reference value calculation method uses resting heart rate and maximum heart rate. This method is equivalent to the %HRR method, heart rate reserve method, and Karvonen method. As an example, the formula [{(heart rate)-HRrest} / (HRmax-HRrest)>0.4] can be rearranged to {(individual load threshold heart rate)>(HRmax-HRrest)×0.3+HRrest}. Specifically, using this formula, if HRmax is 180 and HRrest is 60, the individual load threshold heart rate will be greater than 96. Note that the coefficient 0.3 in the calculation formula is just one example, and other values ​​may be used.

[0062] This section explains load analysis and result display. In this embodiment, the load analysis cloud considers a heart rate exceeding an individual's load threshold to be high. The load analysis cloud determines the presence or absence of body movement based on signals from acceleration sensors. Furthermore, the load analysis cloud considers high load as physical high load when there is physical movement. On the other hand, when there is no physical movement, the load analysis cloud considers high load as mental high load. Furthermore, as a method for determining whether or not there is body movement, for example, a method may be used in which body movement is determined to be present if it meets a predetermined standard, while otherwise it is determined that there is no body movement.

[0063] Here, as a condition for determining that the physical load is high, at least one of the following may be used: for example, the movement of the first subject 31 based on body movement data is at or above a predetermined first intensity, and the value based on the first subject 31's pulse data is at or above a predetermined first value; or, in the immediately preceding period, the first cumulative time during which the first subject 31's movement is at or above the said first intensity is at or above a predetermined first hour, and the second cumulative time during which the value based on the first subject 31's pulse data is at or above the said first value is at or above a predetermined second hour. Furthermore, as a condition for determining that there is a high mental load, at least one of the following may be used: for example, the movement of the first subject 31 based on body movement data is less than the first intensity, and the value based on the first subject 31's pulse data is greater than or equal to a predetermined second value; or, in the immediately preceding period, the first cumulative time during which the first subject 31's movement is greater than or equal to the first intensity is shorter than the first hour, and the third cumulative time during which the value based on the first subject 31's pulse data is greater than or equal to the second value is greater than or equal to a predetermined third hour. Note that the first and second values ​​may be the same or different. Furthermore, the second and third times may be the same value, or they may be different values.

[0064] In this example, the conditions for determining that there is physical movement and physical load on the first subject 31 are that the movement of the first subject 31, based on the body movement data, is at or above a predetermined intensity, or that the cumulative time during which the movement of the first subject 31, based on the body movement data, is at or above a predetermined intensity is at or above a predetermined time. Similarly, in this example, the conditions for determining that there is no physical movement by the first subject 31 and that the stress is mental are if the movement of the first subject 31 based on the body movement data is less than a predetermined intensity, or if the cumulative time during which the movement of the first subject 31 based on the body movement data is at or above a predetermined intensity is shorter than a predetermined time. In this example, the conditions for determining high load are that the value based on the pulse data of the first subject 31 is above a predetermined value, or that the cumulative time during which the value based on the pulse data of the first subject 31 is above a predetermined value is above a predetermined time. Similarly, in this example, the conditions for determining that the load is not high are that the value based on the pulse data of the first subject 31 is smaller than a predetermined value, or that the cumulative time during which the value based on the pulse data of the first subject 31 is equal to or greater than a predetermined value is shorter than a predetermined time. In this example, an index is used in which a larger value based on the pulse data of the first subject 31 indicates a higher load.

[0065] Here, various methods may be used to determine whether the high load is a physical high load or a mental high load. For example, a method for determining whether someone is experiencing high physical exertion could be one that uses the number of steps taken. In other words, the more steps taken, the higher the physical exertion might be considered to be. For example, as a method for determining whether a person is under high mental stress, a method based on the magnitude of variability in heart rate intervals, such as SDNN or RMSSD, may be used. In other words, if the variability in heart rate intervals is small, it may be considered that the person is under high mental stress because the sympathetic nervous system is overactive. Furthermore, mental burden may also be called, for example, psychological stress. In this embodiment, data representing physical load is also referred to as physical load data, and data representing mental load is also referred to as mental load data.

[0066] The load analysis cloud aggregates the time periods during which each individual experienced high load, in predetermined time units. For example, the period in question may be the period for each working day, in which case a high-load time for each working day is required. As another example, the period in question may be a period of time every hour, in which case the high-load time for each hour is required.

[0067] Figure 6A shows an example of the physical high-load time and mental high-load time according to the first embodiment. In the graph shown in Figure 6A, the horizontal axis represents time, and the vertical axis represents the high-load duration [minutes]. The graph shows the physical high-load time D1 and the mental high-load time D2 for each discrete time point. In the example in Figure 6A, the illustration is simplified, and only one physical high-load time D1 and one mental high-load time D2 corresponding to each time point are labeled; however, other time points are also indicated by the same bar-shaped mark.

[0068] Figure 6B shows an example of a second table Ta2 relating to an individual's physical high-load time and mental high-load time according to the first embodiment. Table 2, Ta2, contains information ranking the high-load time for all employees. Table 2, Ta2, stores the following information in association with rank, name, age, affiliated organization, assigned duties, work style, and high-load time [minutes / day]. High-load time [minutes / day] includes physical high-load time, mental high-stress time, and the sum of these.

[0069] Here, the information in the second table Ta2 may be stored, for example, in a database that manages employee data, or in another storage device. For example, the first database 15 may be used as the database in question.

[0070] Figure 6C shows an example of a third table Ta3 relating to the ranking of physical high-load time and mental high-load time for organizational units according to the first embodiment. Table 3, Ta3, contains information on the results of aggregating and ranking high-load time for each organizational unit. In this example, the information used for high-load time is the average high-load time, which is the result of averaging the high-load time of employees belonging to each organization. Table 3, Ta3, stores the ranking, affiliated organization, and average high-load time [minutes / day] in a corresponding format. Average high-load time [minutes / day] includes average physical high-load time, average mental high-stress time, and the sum of these.

[0071] Here, the information in the third table Ta3 may be stored, for example, in a database that manages employee data, or in another storage device. For example, the first database 15 may be used as the database in question.

[0072] In the load analysis cloud, for example, data from the individual who reported high workload during that time period, along with data from surrounding employees, can be compiled and analyzed. The relevant individuals and their workload data can then be obtained. Here, the surrounding employees may be employees belonging to the same organization as the individual, and the relevant individuals may be some or all of the surrounding employees. This makes it possible to distinguish, for example, whether the workload is high for an individual or for the organization. In this case, the level of workload may be relative between the individual and the organization.

[0073] With a load analysis cloud, for example, you can analyze load data from the perspective of when and what kind of tasks were being performed during periods of high load. This allows, for example, assigning task details as attribute data to hourly load data, enabling analysis and identification of which tasks are burdensome. This narrows the scope of load reduction measures and makes it easier to implement countermeasures.

[0074] I will now explain how to gather information about the load situation. The load analysis cloud selects interviewees—those who will be interviewed about their load status—based on load data. For the interview, one or more individuals may be selected from, for example, those who rank highly in high-load time over a certain period, such as weekly; those whose load threshold has increased sharply compared to the average load time of the previous week; or those who have been detected to have an extremely high load.

[0075] The load analysis cloud service requests information about the load situation. In this example, we will explain the case where the first subject 31 is the person being interviewed. The first subject's device 12, such as a smartphone, used by the first subject 31, stores an application for conducting interviews in its first storage unit 114. This application may, for example, be pre-installed on the first subject's device 12.

[0076] The load analysis cloud sends a signal to the application on the first user device 12 to request a hearing regarding the load status. In response, the first user device 12 uses the application to perform a process of hearing about the load status from the first user 31.

[0077] In this embodiment, the interview is conducted using a chat function provided by a hearing robot. Another example would be when employees voluntarily report their workload status via chat and seek advice. In this embodiment, the function for conducting interviews is shown to be provided in the first user device 12, but as an alternative example, the function for conducting interviews may be provided in other devices.

[0078] The hearing robot in this embodiment will now be described. The interviewing robot processes inquiries from employees regarding their workload. The interviewing robot can handle interviews with employees about their workload, as well as assist employees in seeking advice about their workload.

[0079] The interview robot might ask the interviewee, for example, whether they feel overwhelmed. The interviewing robot may also ask questions such as, "In what ways is your workload high?" and "What would you like to do, or would you like others to do, to reduce your workload?" The interviewing robot may also convey to the person being interviewed that "we are aware of your high workload and you can consult with us at any time."

[0080] The interview robot may also ask the interviewee whether it is okay to share the interview results with their supervisor or the human resources department. If the interviewee agrees to share the interview results with their supervisor or the human resources department, the interview robot will, for example, notify them of the interview results, including the interviewee's name. On the other hand, if the interviewee responds that they will not share the interview results with their supervisor or the human resources department, the interview robot will, for example, anonymously notify them of the interview results.

[0081] Here, the data from the interviews may be stored, for example, in a database that manages employee data, or in another storage device. For example, the first database 15 may be used as the database in question. Furthermore, if the supervisor or HR representative of the interviewee has been granted access rights, the data from the interview results can be accessed by that supervisor or HR representative. Furthermore, the interviewing robot can, for example, refer to the data from the previous interview to start the next interview from where it left off.

[0082] The hearing robot may offer advice to reduce the workload. The hearing robot may seek confirmation of the system's willingness to share the situation with the supervisor. The interviewing robot may refer to personnel information and ask people in the same workplace as the person being interviewed about their workload. In this embodiment, the manager / supervisor is, for example, the first manager 41.

[0083] Figure 7 shows an example of the first screen G1 related to chat according to the first embodiment. Screen G1 is an example of an interface screen for the hearing robot, which is part of the first subject user device 12, to chat with the first subject 31, who is the subject of the hearing. In the example in Figure 7, the first screen G1 includes the first frame W1, the second frame W2, the third frame W3, and the fourth frame W4.

[0084] The text in the first frame W1 and the question in the third frame W3 are the content of a conversation conducted by a listening robot. The text in the second box, W2, is the response written by the first subject, 31, to the question in the first box, W1. Slot 4, W4, is a space for the first subject 31 to write their answer to the question in slot 3, W3.

[0085] Here, we will explain the differences in the content of the chat used for the interview, specifically regarding cases where the interviewee experiences high physical burden and cases where the interviewee experiences high mental burden. If the physical load is high, for example, a message containing advice that can be given during work may be displayed. On the other hand, if the mental burden is high, for example, a message may be output that does not include advice that can be given during work hours, but includes advice that can be given outside of work hours.

[0086] First, here is an example of how to structure a message in a chat. In this example, for the sake of explanation, we will show a case where the message consists of eight components, such as the first component M1 to the eighth component M8. Note that a message does not necessarily have to include all of its components, and may include other components as well.

[0087] The first component, M1, is a comment that conveys the results of the analysis of the activity level data of the interviewees. The second component, M2, consists of comments that express appreciation for the interviewee and inquire about their well-being. The third component, M3, consists of comments expressing concern for the person being interviewed. The fourth component, M4, consists of comments regarding advice for the interviewees. The fifth component, M5, consists of comments regarding what we want to hear from the interviewees. The sixth component M6, the seventh component M7, and the eighth component M8 are comments that encourage the interviewees to seek advice. These comments may also include, for example, the current load status, the circumstances under which the load becomes high, the factors that cause the load to increase, whether or not there are requests from the interviewees for load reduction and the content of those requests, and suggestions for administrators to reduce the load.

[0088] Next, I will explain the difference between messages related to physical stress and messages related to mental stress. In the first component M1, comments indicating a high physical load are provided for the physical load. In the first component M1, the comment regarding mental load will indicate that the mental load is high.

[0089] In the second component M2, regarding physical load, since it can be inferred that the employee is performing a large volume of work due to the high physical load during work, comments should be made acknowledging their hard work. In the second component M2, regarding mental stress, since mental stress during work may be related to workplace relationships or unexpected troubles, comments should be made asking whether any troubles occurred.

[0090] In the third component, M3, regarding physical load, comments express concern about the feeling of physical fatigue associated with physical load. In the third component, M3, regarding mental load, comments expressing concern about the mental stress associated with mental load should be included.

[0091] In the fourth component, M4, comments regarding physical stress should include advice on measures that can be taken during work. In the fourth component, M4, comments regarding mental stress should include advice on how to address it outside of work. In this context, mental stress often stems from interpersonal relationships at work or unexpected work-related problems. Even if advice is given to the interviewee regarding what they can do during work hours, it is often difficult for them to resolve the causes of their mental stress on their own. Therefore, to provide advice that can be addressed by the interviewee outside of work hours, advice is output that can alleviate their mental stress. "Outside of work hours" refers to, for example, their personal life.

[0092] In the fifth component, M5, for physical load, comments are made to inquire about the nature or volume of work that contributes to the physical load. In the fifth component, M5, regarding mental stress, comments will be made to ask about workplace interpersonal relationships or unexpected troubles that contribute to mental stress.

[0093] Next, I will show some specific examples of messages. Here are some specific examples of messages to send when someone is under a high level of physical strain. An example of a message for the first component M1 in the case of high physical load is the message, "We have found that your work has put a greater physical strain on you than before over the past week." An example of a message for the second component M2 in a situation of high physical load is the message, "You're working hard. How are you doing?" An example of a message for the third component M3 in cases of high physical stress is the message, "I'm worried if you're feeling tired or if you can't shake off your fatigue."

[0094] An example of a message for the fourth component M4 in cases of high physical load is: "If you are feeling tired, it may be helpful to change your work methods, reduce the amount of work, or take breaks between tasks." An example of a message for the fifth component, M5, in cases of high physical load is the message, "If you have any problems with the work content or workload, or if there is anything you would like to talk about," An example of a message for the sixth component, M6, in cases of high physical load is the message, "Please feel free to consult with this hearing robot or those around you." In this example, the fifth component M5 and the sixth component M6 constitute a single message.

[0095] An example of a message for the seventh component, M7, in cases of high physical stress is: "Let's think together about what we can improve. Your health and well-being are our top priority." An example of a message for the eighth component M8 in cases of high physical strain is the message, "Please don't overexert yourself, and ask for help when needed."

[0096] Here are some specific examples of messages to send when under high mental stress. An example of a message for the first component M1 in cases of high mental stress is the message, "We have observed that your work has placed a greater mental burden on you than usual over the past week." An example of a message in the second component M2 when the mental load is high is the message, "Is there some kind of trouble? How are you feeling?" An example of a message from the third component M3 in cases of high mental stress is the message, "I am concerned if you continue to experience stress or are unable to relieve it."

[0097] An example of a message for the fourth component M4 in cases of high mental stress is: "If you feel stressed, it is effective to take rest in your personal life, such as getting more sleep." An example of a message for the fifth component, M5, in cases of high mental stress is the message, "If you are having trouble with relationships or unexpected problems, or if there is anything you would like to talk about." An example of a message for the sixth component, M6, in cases of high mental load is the message, "Please feel free to consult with this listening robot or those around you." In this example, the fifth component M5 and the sixth component M6 constitute a single message.

[0098] An example of a message from the seventh component, M7, in cases of high mental stress is: "Let's think together about what we can improve. Your health and happiness are our top priority." An example of a message for the eighth component, M8, in cases of high mental stress is the message, "Please don't push yourself too hard, and ask for help when you need it."

[0099] Figure 8 shows an example of chat summary data H1 according to the first embodiment. Chat summary data H1 contains a text that summarizes the content of the chat between the interviewing robot and the interviewee, the first subject 31.

[0100] The load analysis cloud identifies the factors contributing to workload. In this embodiment, the load analysis cloud estimates and extracts the factors contributing to the workload based, for example, on chat summary data. The term "factor" may also be referred to as "cause," for example.

[0101] I will explain the policy data learning cloud. In this embodiment, the policy data learning cloud is a functional unit located in the cloud that performs learning on policy data. In this embodiment, the policy data learning cloud is configured, for example, by a first server device 13. As another example, the functional components of the policy data learning cloud may be distributed and provided on the first server device 13 and the first administrator terminal device 14. As yet another example, the functional unit of the policy data learning cloud may be comprised of a first administrator terminal device 14.

[0102] As yet another example, the functional unit of the policy data learning cloud may be provided in a device separate from the first server device 13 and the first administrator terminal device 14. Furthermore, if the functional units for the load analysis cloud and the functional units for the policy data learning cloud are provided on a device other than the first server device 13, then, for example, the first server device 13 may not be provided.

[0103] In this embodiment, the policy data learning cloud generates policies based on the load and factors, corresponding to the load and factors. Furthermore, the policy data learning cloud updates policies based on their performance, taking into account the load and contributing factors.

[0104] Here, the generation and updating of policies may involve, for example, the creation of new policies that have not existed in the past, or the setting of existing policies that were not associated with the loads and factors of interest, so that they are newly associated with the loads and factors of interest. Another example is that, in the generation and updating of policies, if there were previously multiple policies corresponding to the load and factors of interest, a process may be performed to change the priority order of these multiple policies. Thus, the generation and updating of policies may include not only processes for generating new policies, but also processes for modifying existing policies in some way, such as changing the correspondence between loads and factors and information related to the policies. Furthermore, if multiple measures are associated with a single combination of load and factor, then, for example, priorities may or may not be set for these multiple measures.

[0105] Refer to Figures 9A to 9F to explain the measures taken in response to the load and its contributing factors. In this embodiment, appropriate measures are generated for each combination of load and factor. In this embodiment, appropriate measures are generated for each combination of whether the burden is physical or mental, and the burden factors represented by categories. In this embodiment, a group having one or more measures is established for each combination. For the sake of explanation, in this embodiment, both the case with two or more measures and the case with only one measure will be referred to as a group of measures and explained.

[0106] In this embodiment, the categories of factors contributing to workload may include, for example, the content of the work, interpersonal relationships, work environment, private life, and lack of skills. The content of the work may also refer to, for example, the volume of work. Note that the categories of load factors are not limited to those in this example; any category may be used.

[0107] Figure 9A shows an example of measures corresponding to the load and factors of the first group according to the first embodiment. Group 1 consists of individuals with high mental stress, where the contributing factor is interpersonal relationships in the workplace. In the case of Group 1, considering the high level of mental stress, measures will be generated that focus on improving interpersonal relationships and changing the people involved. Measures in Group 1 include promoting workplace communication, support from workplace management, establishing an independent consultation service, mental health support, and paying attention to individuals who are contributing factors. Priorities may or may not be set for these measures. Furthermore, if there are multiple more detailed measures below each of these, priorities may or may not be set for these multiple more detailed measures.

[0108] More detailed measures to promote workplace communication include, for example, "holding regular team-building activities and workshops to build trust among employees," "creating an environment where people can talk freely," and "discussing and confirming together that despite differences in position and perspective, the overarching goals are the same, thereby improving a sense of unity." More detailed measures for workplace management support include, for example, "supervisors demonstrating a willingness to listen to employees who are having trouble with interpersonal relationships and creating an environment where it is easy for them to seek advice," "a listening attitude from managers and supervisors," and "providing constructive feedback."

[0109] More detailed measures for establishing an independent consultation service include, for example, "trying to identify the root cause of the problem or to process one's feelings by talking to someone outside of workplace relationships." More detailed measures for mental health support include, for example, "conducting seminars on mental health, how to manage one's emotions, and anger management." More detailed measures to address the contributing factors include, for example, "issueing warnings to individuals suspected of engaging in power harassment against the target individual."

[0110] Figure 9B shows an example of measures according to the second group of loads and factors in the first embodiment. The second group consists of individuals with high mental stress, where the stressor is related to the nature of their work. This work content includes, for example, the volume of work. In the case of Group 2, considering the high mental burden, measures will be generated that focus on distributing and reducing the workload. The measures in Group 2 include reviewing work processes, adding personnel resources, supporting time management, promoting workplace communication regarding workload, balancing work and rest, and establishing a system for handling problems. Priorities may or may not be set for these measures. Furthermore, if there are multiple more detailed measures under each of these, priorities may or may not be set for these multiple more detailed measures.

[0111] More detailed measures for reviewing business processes include, for example, "re-evaluating the priorities of tasks and reducing low-priority tasks," "clearly defining which tasks to do and which not to do, and concentrating resources," and "identifying tasks that take a long time to complete or have complex procedures, and simplifying those procedures." More detailed measures for adding personnel resources include, for example, "increasing personnel as needed and improving the distribution of tasks," "increasing the types of tasks each employee can perform and creating a system where they can temporarily support busy tasks," and "mutual support." More detailed measures to support time management include, for example, "providing training and tools for effective time management."

[0112] More detailed measures to promote workplace communication regarding workload include, for example, "sharing within the workplace what kinds of tasks are quantitatively and qualitatively demanding, and enabling discussions on how to reduce or level out the workload," and "establishing a workplace policy to reduce less essential tasks and to adopt an attitude of making appropriate choices regarding the content of work." More detailed measures to balance work and rest include, for example, "creating an atmosphere that makes it easy to take time off" and "ensuring that taking time off does not have a significant impact on overall workplace operations." More detailed measures for improving the trouble-handling system include, for example, "establishing a trouble-handling system" and "creating a system and atmosphere that allows for safe and calm trouble-handling."

[0113] Figure 9C shows an example of measures according to the third group of loads and factors in the first embodiment. The third group consists of individuals with high physical workloads, where the workload is primarily due to the nature of their work. This work content, for example, might be the volume of work. In the case of Group 3, considering the high physical burden, measures will be generated that focus on distributing and reducing the workload. Measures in Group 3 include encouraging breaks, automating tasks, introducing health programs, distributing workloads, changing tasks, and changing work procedures. Priorities may or may not be set for these measures. Furthermore, if there are multiple more detailed measures below each of these, priorities may or may not be set for these multiple more detailed measures.

[0114] More detailed measures to encourage breaks include, for example, "encouraging people to take regular breaks when they feel physically exhausted" and "expanding spaces and facilities for refreshing oneself." More detailed measures for automating tasks include, for example, "automating tasks as much as possible" and "introducing equipment or improving work procedures so that tasks can be performed without relying on physical strength." More detailed measures for implementing a health program include, for example, "introducing fitness programs and exercises to be performed at the start, end, and break times to improve physical fitness and reduce the likelihood of pain such as lower back pain." More detailed measures to distribute the workload include, for example, "adding more personnel" or "distributing the tasks that one person is currently doing to others." More detailed measures to change the work process include, for example, "transferring that person to another department." More detailed measures for changing work procedures include, for example, "reviewing the content and procedures of the work."

[0115] Figure 9D shows an example of measures corresponding to the load and factors of the fourth group according to the first embodiment. Group 4 consists of individuals with high physical stress, where the stress is caused by the work environment. In the case of Group 4, considering the high physical burden, measures will be generated that focus on improving facilities such as equipment. The measures in Group 4 include improving ergonomics, developing the environment, and strengthening safety measures. Priorities may or may not be set for these measures. Furthermore, if there are multiple more detailed measures under each of these, priorities may or may not be set for these multiple more detailed measures.

[0116] More detailed measures to improve ergonomics include, for example, "improving the work environment based on ergonomics," "eliminating the need for work in postures that lead to physical strain or tasks that rely solely on manual labor," and "providing chairs, desks, and work environments that are effective for this purpose." More detailed measures for improving the work environment include, for example, "optimizing the workplace environment such as lighting, temperature, and air quality." More detailed measures to strengthen safety measures include, for example, "reviewing workplace safety measures and making necessary improvements to reduce physical strain."

[0117] Figure 9E shows an example of measures corresponding to the load and factors of the fifth group according to the first embodiment. Group 5 consists of individuals who experience high levels of either physical or mental stress, and whose stressors are primarily private. For Group 5, the goal is to generate measures that encourage breaks. Measures in Group 5 include, for example, displaying a predetermined message on the administrator's terminal and displaying a predetermined message on the target user's terminal. Priorities may or may not be set for these measures. In this embodiment, the first administrator terminal device 14 is an example of an administrator terminal, and the first user device 12 is an example of a user terminal.

[0118] Messages displayed on administrator terminals could include suggestions such as encouraging employees to take breaks, or proposing measures like holding seminars on how to take rest effectively. Messages displayed on the target user's device could include, for example, messages such as "Make sure to get proper rest outside of work hours," or advice on how to get enough sleep.

[0119] Figure 9F shows an example of measures corresponding to the load and factors of the sixth group according to the first embodiment. Group 6 consists of individuals who experience high levels of either physical or mental stress, or both, and whose contributing factor is a lack of skills. In the case of Group 6, measures will be generated that focus on skill development, etc. Measures in Group 6 include, for example, displaying a predetermined message on the administrator's terminal and displaying a predetermined message on the target user's terminal. Priorities may or may not be set for these measures. In this embodiment, the first administrator terminal device 14 is an example of an administrator terminal, and the first user device 12 is an example of a user terminal.

[0120] Messages displayed on administrator terminals may include, for example, messages encouraging employees to undergo training, messages suggesting they work with someone performing the same tasks, and messages objectively confirming that an employee has sufficient skills, especially if the employee feels they lack them. Messages displayed on the target user's device may include messages such as "Let's take a look at how others doing the same work are doing," aimed at showing users who are not feeling overwhelmed; messages that objectively indicate the user has sufficient skills, as some users may feel they are the only ones lacking them; and messages that explain general work procedures and key points.

[0121] This section describes the administrator UI, which is a user interface (UI) designed for administrators. In this embodiment, the first administrator 41 is an example of an administrator. In this embodiment, the functions of the administrator UI are configured by the first administrator terminal device 14.

[0122] Figure 10 shows an example of the second screen G2 relating to load factors according to the first embodiment. The administrator UI displays the extracted load factors to the first administrator 41 via the second screen G2. Screen 2, G2, displays examples of factors that contribute to workload. In the example in Figure 10, the relevant items include "the length of time spent in the operating room," "excessive consideration for the doctor," "surgical preparation and post-operative care," and "the transfer of some members."

[0123] Figure 11 is a diagram showing an example of the third screen G3 relating to the load status according to the first embodiment. The administrator UI displays the load status to the first administrator (41) via the third screen, G3. In the example shown in Figure 11, the third screen G3 displays the names of individuals with high workloads, the organizations to which these individuals belong, the physical workload values ​​of these individuals, the mental workload values ​​of these individuals, and the chat summary data, all linked together.

[0124] In the example shown in Figure 11, the name of the subject is the registered name, which is the name of the registrant registered in the first report provision system 1. In the example in Figure 11, a value representing the cumulative physical load is used as the physical load value, but other values ​​may also be used. In the example in Figure 11, a value representing cumulative mental load is used as the mental load value, but other values ​​may also be used. In the example in Figure 11, the chat summary data lists estimated workload factors, such as workload factor α1 and workload factor α2.

[0125] In the example shown in Figure 11, the workload status of multiple individuals is displayed in a list format. In the example shown in Figure 11, the workload of multiple subjects is displayed in ascending order of the overall workload value, which takes into account both physical and mental workload. However, the order in which the workload of these multiple subjects is displayed may be any other order.

[0126] I will explain the proposed policies. The administrator UI is used to propose measures to the first administrator 41 in the first report provision system 1. Here, the administrator UI may, for example, display a screen suggesting measures for each specified target user, starting from a load status display screen as shown in Figure 11. Additionally, the administrator UI may display a screen that proposes measures to the organization or to specific target individuals all at once.

[0127] Figure 12A shows an example of screen G4A of the 4A screen related to the proposal of individual-level measures according to the first embodiment. In screen 4A G4A, the first area K1 displays the names of individuals subject to high workload, their affiliated organizations, physical workload values, and mental workload values, as shown in Figure 11. The workload status of the subject is displayed in the second domain, K2. In the third domain, K3, the factors causing stress for the subjects are displayed. In the fourth area, K4, the proposed measures are displayed. In area 5, K5, a button for entering achievements is displayed.

[0128] In this example, in the second domain K2, graphs are displayed showing the temporal progression of the subject's physical load and the load threshold, as well as graphs showing the temporal progression of the subject's mental load and the load threshold. Furthermore, in this example, if there are multiple proposed measures in the fourth domain K4, the contents of the multiple measures are displayed in ascending order according to their effectiveness. Note that other order may be used for the arrangement of the multiple proposed measures.

[0129] Thus, on the individual-level policy proposal screen, the trends in the individual's physical burden, the trends in the individual's mental burden, and the factors contributing to that burden are all displayed on the same screen. In this example, the proposed measures are displayed according to their effectiveness level. Furthermore, if there are multiple proposed measures, some of these measures may be selected and displayed. In this case, for example, the number of proposed measures may be limited to make it easier for the first administrator 41 to decide on a measure. As another example, if there are multiple proposed measures, even if there are many of these measures, a configuration may be used in which all measures are displayed. In this embodiment, the measures proposed to the first administrator 41 by the administrator UI may or may not be adopted by the first administrator 41. For this reason, the measures proposed by the administrator UI may be called, for example, candidate measures.

[0130] Figure 12B shows an example of screen G4B of the 4B screen related to the proposal of measures for organizational units according to the first embodiment. In screen 4B, G4B, the 11th area K11 displays the affiliated organization, physical load value, and mental load value, as shown in Figure 11. The 12th area, K12, displays the overall workload of the organization. For example, the overall workload could be the average or total workload of all individuals within the organization. In area 13, K13, the burden factors for the target organization are displayed. In area 14, K14, the proposed measures are displayed. In area 15, K15, a button for entering achievements is displayed.

[0131] In this example, the display content of organizational units in screen 4B G4B is the same as the display content of individual units in screen 4A G4A shown in Figure 12A, except that the individual units have been replaced by organizational units.

[0132] This section explains how to input the results of the implemented measures. The administrator UI accepts input from the first administrator 41 regarding the details of the measures implemented by the first administrator 41. Here, the input may be entered by the first administrator 41 specifying one of the options pre-configured on the system side, or it may be entered as content freely written by the first administrator 41. The input of policy results may be done, for example, for policies at the individual level, or for policies at the organizational level.

[0133] In this embodiment, the first report provision system 1 is considered to have implemented a measure when the first administrator 41 selects and inputs the measure on the screen for inputting the results of the measure, but the system is not necessarily limited to this configuration.

[0134] In this embodiment, when the "Performance Input" button in the fifth area K5 of the fourth screen G4A shown in Figure 12A is clicked, or when the "Performance Input" button in the fifteenth area K15 of the fourth screen G4B shown in Figure 12B is clicked, one of the input screens shown in Figure 13A, Figure 13B, or Figure 13C is displayed via the administrator UI.

[0135] Figure 13A shows an example of the first input screen J1, which is a pull-down format policy performance input screen according to the first embodiment. On the first input screen J1, the message "Enter the measures you have implemented" is displayed, and below this message, several measures are displayed as options. The first administrator 41 can select a desired measure by clicking or other means, and then input that measure into the first administrator terminal device 14 via the administrator UI as a measure that has been implemented.

[0136] Figure 13B shows an example of a second input screen J2, which is a radio button-type policy performance input screen according to the first embodiment. On the second input screen, J2, multiple options are displayed using radio buttons. The first administrator 41 can select a desired measure by clicking a desired radio button or the like, and then input that measure into the first administrator terminal device 14 via the administrator UI as a measure that has been implemented.

[0137] Figure 13C shows an example of the third input screen J3, which is a radio button format input screen with free text for policy performance according to the first embodiment. On the third input screen, J3, multiple measures are displayed as options using radio buttons, and an "Other" column is also displayed. The first administrator 41 can select a desired measure by clicking a desired radio button or the like, and then input that measure into the first administrator terminal device 14 via the administrator UI as a measure that has been implemented. Furthermore, the first administrator 41 can select the "Other" field by clicking the radio button corresponding to "Other," and then enter the details of the measure in free text in that field. The described measure can then be entered into the first administrator terminal device 14 via the administrator UI as an implemented measure.

[0138] I will explain how to measure the effectiveness of policies. The policy data learning cloud measures the effectiveness of implemented policies based on policy performance data entered by the first administrator 41 and the trends in physical and mental load, and stores the degree of effectiveness based on the measurement results as information on the effectiveness of the policy. Here, the greater the reduction in physical and mental load, the higher the effectiveness of the measure. This effectiveness may be set in stages for each of the multiple ranges used to define the reduction in physical and mental load. Furthermore, if the policy data learning cloud obtains information from chat data indicating, for example, that the workload has improved, then this information may also be used as input when calculating the effectiveness level. The measurement of the effect may also be called, for example, the determination of the effect or the evaluation of the effect. Furthermore, the effectiveness of a policy may be referred to as, for example, a policy evaluation value.

[0139] Furthermore, any method may be used to measure the effectiveness of the measures. For example, the extent to which the burden on the first subject 31 has decreased may be measured based on an overall evaluation value of physical and mental burden, or it may be measured based on an evaluation value of either physical or mental burden.

[0140] I will explain how to learn from policy data. The policy data learning cloud learns the effectiveness of policies based on the difference data between the load and the load threshold, load factor data, and policy performance data. For example, the policy data learning cloud learns whether each policy is effective based on the correlation between the combination of differential data, load factor data, and policy performance data, and its effectiveness.

[0141] Here, various methods may be used to learn the effectiveness of the measures. For example, various types of data may be used as input data when learning the effectiveness of a policy.

[0142] I will explain the process of generating policies. The policy data learning cloud generates policies that have been effective in the past based on newly identified high-load individuals, increases in physical and mental load from similar load thresholds, load factor data representing the causes of the load, and attribute information such as ward information. The generated policies are then displayed to the first administrator 41 via a policy proposal screen. For example, the difference data between the load and the load reference value may be used as the increase value. In this example, we showed a case where, for newly identified high-load individuals, the increase in physical and mental load from similar load thresholds was used. However, for example, for newly identified high-load individuals, the load threshold for that individual may be used, or the increase in physical and mental load from that individual's load threshold may be used.

[0143] Here, various methods may be used to generate the proposed measures. For example, various types of data may be used as input data when generating proposed measures. In this embodiment, the input data includes attribute information representing the organization to which the high-loader subject belongs, but it is not necessary to include attribute information. For example, the input data may include attendance data for the first subject 31.

[0144] As another example, the configuration may allow the first administrator 41 to add new policy candidates by entering the content of the policy in free text via the administrator UI, even for policies that have not been previously proposed or implemented. In this way, for example, if a newly added policy is evaluated as effective, that policy will be displayed as a policy candidate on the policy proposal screen when making subsequent proposals. In this embodiment, for example, information about measures entered as free text is also included in the measure performance data and treated in the same way as measures implemented in the past.

[0145] In this embodiment, the functions of the policy data learning cloud are configured using AI such as machine learning, but AI is not necessarily required. As another example, the functions may be implemented by processing according to a predetermined algorithm.

[0146] Referring to Figures 14 and 15, an example of the processing procedure performed in the first report provision system 1 according to the first embodiment is shown. Figure 14 is a diagram showing an example of the processing procedure performed in the first report provision system 1 according to the first embodiment.

[0147] Figure 14 shows the first user device 12, the first device 11, the first server device 13, and the first administrator terminal device 14. In this example, the first server device 13 is equipped with the functions of a load analysis cloud and a policy data learning cloud. In this example, the first subject, 31, is the person being interviewed. In this example, the first manager 41 is the person who manages the first subject 31, and is, for example, the first subject 31's superior or a human resources person. In this example, the first device 11 is attached to the body of the first subject 31.

[0148] In process T1, the first server device 13 manages employee data. In this example, the employee data may be stored in the first database 15. Furthermore, employee data management may, for example, be performed on an ongoing basis.

[0149] During processing T2, the first device 11 acquires activity data of the first subject 31. This activity data includes pulsation data and body movement data. In processing T3, the first device 11 transmits the acquired activity data to the first user device 12. In process T4, the first user device 12 transmits the activity data received from the first device 11 to the first server device 13 at a predetermined timing. This transmission may be called, for example, uploading.

[0150] During processing T5, the first server device 13 analyzes the activity level data received from the first user device 12. In process T6, the first server device 13 stores the analysis results linked to employee data. In this example, the first database 15 may also store the analysis results linked to employee data. This linkage connects each employee with the analysis results of the workload associated with that employee. This linking process may also be called, for example, correspondence.

[0151] In processing T7, the first server device 13 identifies high-load users according to the analysis results. In processing T8, the first server device 13 selects individuals to be interviewed from among the identified high-load users. In process T9, the first server device 13 transmits predetermined message information to the selected interviewee. In this example, the first server device 13 transmits the message information to the first interviewee's user device 12 of the selected interviewee.

[0152] In process T10, the first user device 12 initiates a chat for interviewing purposes using the functions of the interviewing robot, in response to message information from the first server device 13. In process T11, the first user device 12 sends the chat content to the first server device 13.

[0153] In process T12, the first server device 13 analyzes the chat content received from the first user device 12 and generates a summary of the chat content.

[0154] In process T13, the first server device 13 estimates the load factors from a summary of the chat content.

[0155] In processing T14, the first server device 13 generates measures based on the analysis results of the activity data and the estimated load factors.

[0156] In process T15, the first server device 13 generates a report that includes the generated measures.

[0157] In process T16, the first server device 13 sends the generated report to the first administrator terminal device 14.

[0158] In process T17, the first administrator terminal device 14 displays the report received from the first server device 13.

[0159] In process T21, the first administrator terminal device 14 can access and display the analysis results linked to employee data. Furthermore, process T21 can be executed at any time after process T6, for example, after process T117.

[0160] Process T31 is a variation of this flow and may not be performed if the above processes are already performed. As a variation of this flow, process T31 may be executed instead of processes T3 and T4. In other words, in process T31, the first device 11 directly transmits the acquired activity data to the first server device 13. In other words, in this flow, activity data is transmitted from the first device 11 to the first server device 13 via the first user device 12 during processes T3 and T4. However, in a modified version of this flow, the activity data is transmitted directly from the first device 11 to the first server device 13.

[0161] In the example shown in Figure 14, the processing sections T12 to T16 are shown as the first processing section P1. In the example shown in Figure 14, the first processing unit P1 is performed by the first server device 13.

[0162] Here, let's explain a more detailed example of uploading during processing T4. First, the first user device 12 stores the activity level data received from the first device 11 in the first storage unit 114. Next, the first user device 12 determines whether or not it has stored a certain amount of activity data. Here, the threshold for this certain amount of data may be, for example, a predetermined number of data points, or a predetermined amount of data. If, as a result of this determination, the first user device 12 determines that it does not have certain activity level data stored, it will continue to wait for activity level data. In this example, the first user device 12 is configured not to upload activity data if sufficient data has not been stored.

[0163] On the other hand, if the first user device 12 determines, based on this determination, that it has stored a certain amount of activity data, it then determines whether or not it is the predetermined time to upload the data. As a result of this determination, if the first subject use device 12 determines that it is not the predetermined timing for uploading, it waits until the predetermined timing. On the other hand, as a result of this determination, if the first subject use device 12 determines that it is the predetermined timing for uploading, it uploads the stored activity amount data to the first server device 13 at the predetermined timing.

[0164] Here, the predetermined timing may be set in advance, for example. As an example, the predetermined timing may be the timing when a predetermined application is opened by the operation of the first subject 31 or automatically. The predetermined application may be, for example, an application that uses the activity amount data of the first subject 31. As another example, the predetermined timing may be a regular timing. As the regular timing, for example, the timing every hour or the timing every day may be used.

[0165] Note that such a specific example of the process T4 is an example for explanation. For example, in the first subject use device 12, the process of waiting to upload the already stored activity amount data until the predetermined timing and the process of storing the newly received activity amount data to be uploaded next during the waiting may overlap in the same time zone.

[0166] Here, in this example, as the transmission condition for defining whether the first subject use device 12 uploads the activity amount data, the transmission conditions related to both the data amount of the stored activity amount data and the predetermined timing are used. However, as another example, the transmission condition related to any one of these may be used.

[0167] FIG. 15 is a diagram showing an example of the procedure of the process performed in the first report providing system 1 according to a modification of the first embodiment. Figure 15 shows the first user device 12, the first device 11, and the first administrator terminal device 14. In this example, the load analysis cloud function and the policy data learning cloud function are provided on the first administrator terminal device 14.

[0168] In general terms, this flow differs from the flow shown in Figure 14 in that the processing performed by the first server device 13 in the example in Figure 14 is performed by the first administrator terminal device 14. When this flow is processed, for example, the first server device 13 does not need to be provided.

[0169] In process T101, the first administrator terminal device 14 manages employee data. In this example, employee data may be stored in the first database 15. Furthermore, employee data management may, for example, be performed on an ongoing basis.

[0170] Processes T102 and T103 are similar to processes T2 and T3 performed by the first device 11 in the example in Figure 14. In process T104, the first user device 12 transmits the activity data received from the first device 11 to the first administrator terminal device 14 at a predetermined timing. This transmission may be called, for example, an upload.

[0171] In process T105, the first administrator terminal device 14 analyzes the activity level data received from the first target user device 12. Processes T106, T107, and T108 are the same as processes T6, T7, and T8 performed by the first server device 13 in the example of Figure 14, except that they are performed by the first administrator terminal device 14, respectively. In process T109, the first administrator terminal device 14 transmits predetermined message information to the selected interviewee. In this example, the first administrator terminal device 14 transmits the message information to the first interviewee's user device 12 of the selected interviewee.

[0172] In process T110, the first user device 12 initiates a chat for interviewing purposes using the functions of the interviewing robot, in response to message information from the first administrator terminal device 14. In process T111, the first user device 12 sends the chat content to the first administrator terminal device 14.

[0173] In process T112, the first administrator terminal device 14 analyzes the chat content received from the first target user device 12 and generates a summary of the chat content. Processes T113, T114, and T115 are the same as processes T13, T14, and T15 performed by the first server device 13 in the example of Figure 14, except that they are performed by the first administrator terminal device 14. In process T116, the first administrator terminal device 14 displays the generated report.

[0174] Process T131 is a variation of this flow and may not be performed if the above processes are already performed. As a variation of this flow, process T131 may be executed instead of processes T103 and T104. In other words, in process T131, the first device 11 directly transmits the acquired activity data to the first administrator terminal device 14. In other words, in this flow, activity data is transmitted from the first device 11 to the first administrator terminal device 14 via the first user device 12 during processes T103 and T104. However, in a modified version of this flow, activity data is transmitted directly from the first device 11 to the first administrator terminal device 14.

[0175] In the example shown in Figure 15, the processing section from T112 to T115 is shown as the second processing section P2. In the example shown in Figure 15, the second processing part P2 is performed by the first administrator terminal device 14. Incidentally, the second processing part P2 is a part where the same processing is performed although it is different from the first processing part P1 in the example of FIG. 14 in terms of the main body and the like.

[0176] FIG. 16 is a diagram showing an example of a schematic functional block of the first report providing system 1 according to a specific example of the first embodiment. The first report providing system 1 includes a wearable device B1, a hearing robot B2, a load analysis cloud B3, an administrator-oriented user interface B4, an employee data management database B5, and a policy data learning cloud B6.

[0177] Here, in the present embodiment, the wearable device B1 is constituted by a first device 11. In the present embodiment, the hearing robot B2 is constituted by the function of an application included in the first subject use device 12. In the present embodiment, the load analysis cloud B3 and the policy data learning cloud B6 are constituted by the first server device 13, or the first administrator terminal device 14, or both of these devices. In the present embodiment, the administrator-oriented user interface B4 is constituted by the first administrator terminal device 14. In the present embodiment, the employee data management database B5 is constituted by a first database 15. Incidentally, in the example of FIG. 16, the administrator-oriented user interface B4 has the function of the administrator-oriented UI in the present embodiment.

[0178] The wearable device B1 includes a pulse measurement unit C1 and a body movement detection unit C2. The pulse measurement unit C1 measures the pulse of the first subject 31 and acquires the pulsation data which is the measurement result. The body movement detection unit C2 detects the body movement of the first subject 31 and acquires the body movement data which is the detection result.

[0179] The hearing robot B2 includes a chat interface C11 and a chat data output unit C12. The chat interface C11 initiates a chat with the first target person 31 for the purpose of conducting an interview. The chat data output unit C12 outputs chat data, which is data such as the content of the chat. In the example in Figure 16, the chat data output unit C12 outputs the chat data to the employee data management database B5 for storage.

[0180] The employee data management database B5 stores employee attribute data C41, attendance data C42, and chat data C43. Here, employee attribute data C41 may, for example, be stored in advance and updated as needed. Furthermore, attendance data C42 may be updated, for example, as needed. Furthermore, chat data C43 may be updated as needed.

[0181] The load analysis cloud B3 comprises a load reference value setting unit C21, a load calculation unit C22, a load analysis unit C23, a hearing target selection instruction unit C24, and a load factor identification unit C25. The load reference value setting unit C21 sets reference values, such as thresholds, used in load-related judgments. This setting may be done in advance or as needed. The load calculation unit C22 calculates load-related values ​​based on the pulsation data and body movement data acquired by the wearable device B1. In this case, the load calculation unit C22 may refer to the reference value set by the load reference value setting unit C21.

[0182] The load analysis unit C23 performs load analysis based on the calculation results from the load calculation unit C22. In this process, the load analysis unit C23 may refer to data stored in the employee data management database B5. This data may be, for example, either or both of the employee attribute data C41 and attendance data C42. Alternatively, the term "analysis" may be used instead of "analysis."

[0183] The interview participant selection instruction unit C24 selects interview participants based on the results of the analysis performed by the load analysis unit C23. Furthermore, the interviewee selection instruction unit C24 instructs the interview robot B2 of the selected interviewee, the first interviewee 31, to conduct a chat to inquire about the load status.

[0184] The load factor identification unit C25 identifies load factors based on chat data C43, which reflects the results of a chat for gathering information on the load situation. In this process, the load factor identification unit C25 may refer to one or more of the employee attribute data C41 and attendance data C42.

[0185] Here, the load factor identification unit C25 may, for example, identify load factors for a subject who has been determined to be a high-load individual. For example, conditions related to physical and mental workload may be set using thresholds, and individuals whose workload is high may be determined based on the calculated numerical value representing the workload and these conditions. For example, if the conditions related to physical workload are met, the individual is determined to be physically overloaded, and if the conditions related to mental workload are met, the individual is determined to be mentally overloaded. Each condition may include one or more arbitrary requirements related to thresholds, such as a predetermined numerical value representing the workload exceeding a predetermined threshold, or a predetermined numerical value representing the workload being below a predetermined threshold.

[0186] In this embodiment, the load factor identification unit C25 identifies the factors contributing to the workload of high-load individuals. In this embodiment, the load factors include factors related to workload obtained through chat. Furthermore, the load factors in this embodiment may also include other information obtained through chat, such as problems or requests for organizational improvement. In this embodiment, the factors related to workload, problems, and requests for organizational improvement obtained through chat are collectively referred to as load factors. Furthermore, information such as problems and requests for organizational improvement may also be considered as information that indirectly represents the factors contributing to the workload, and in this case, information such as problems and requests for organizational improvement may also be included as information that represents the factors contributing to the workload.

[0187] Load factors can be identified through conversational methods, for example, by using the chat function of the AI-powered hearing robot B2. For example, if one or a certain number of predetermined subjects meet the conditions for physical or mental stress, the hearing robot B2 may automatically initiate a conversation with the subject who is experiencing high stress. The load factor identification unit C25 may generate chat summary data by summarizing the chat conversation content into sentences that focus on the load factors of the target employee, taking into account personal information, abusive language and other content that attacks individuals, or redundant expressions. Furthermore, certain expressions such as abusive language may be filtered out of the output results by a function such as an AI that generates the summary, so that they are not reflected in the summary result. Furthermore, the summary results may be presented as data that can be displayed in a list format for each subject, for example, and output via the administrator user interface B4 or similar.

[0188] The administrator user interface B4 includes a load status display unit C31, a policy performance input unit C32, and a proposed policy display unit C33. The load status display unit C31 displays information regarding the load status based on the results of the analysis performed by the load analysis unit C23.

[0189] The policy performance input unit C32 inputs the results of the policies. In this embodiment, the policy performance input unit C32 inputs information about the implemented policies as policy results via the administrator UI in response to operations performed by the first administrator 41. The policy performance input unit C32 transmits the entered policy information to the policy data learning cloud B6. Here, policy information includes, for example, information that identifies the policy in question. Furthermore, the policy performance input unit C32 may also input information about policies that have not been implemented, in accordance with operations performed by the first administrator 41. In this example, information on the measures is entered in response to operations performed by the first administrator 41. However, as another example, the configuration may be such that information on the implemented measures is automatically sent to the measure data learning cloud B6. The proposed policy display unit C33 displays the policy information received from the policy data learning cloud B6 as information on proposed policies to the first administrator 41.

[0190] The policy data learning cloud B6 receives policy performance data C51, workload value data C52, and workload factor data C53. Furthermore, the policy data learning cloud B6 includes a policy effectiveness measurement unit C54 and a policy generation unit C55.

[0191] Policy performance data C51 is data that includes the results of the policies. In this embodiment, policy performance data C51 is data based on policy information received from the administrator user interface B4. The workload value data C52 is data that includes values ​​related to workload. In this embodiment, the workload value data C52 is data based on the analysis results from the workload analysis unit C23. Load factor data C53 is data containing information about load factors. In this embodiment, load factor data C53 is data based on information about load factors identified by load factor identification unit C25.

[0192] The policy effectiveness measurement unit C54 measures the effectiveness of improvement measures for high-stress individuals, taking into account the identified stress factors. This effectiveness may also be referred to as the "effectiveness level." In this embodiment, an automated suggestion function that automatically learns proposed measures is used, and the results of measuring the effects are used as learning information for the automated suggestion function.

[0193] In order to determine the effectiveness of the measures, for example, the workload figures, which are numerical values ​​representing the workload, data showing the trend of the workload figures, summary data of the load factors, and performance data of the implemented measures may be referenced, and other data may also be referenced. The workload figures are quantitative data that quantifies the workload calculated from biological information. The workload trend data is data that shows the trend of workload by adding a time axis to the workload figures. The summary data for load factors represents the load factors as a result of the summary. The performance data for implemented measures includes the name of the measure implemented for high-burdened individuals, the timing of its implementation, and its effectiveness. The effectiveness may be expressed using, for example, a continuous evaluation value or a stepped evaluation value.

[0194] In the initial stages of providing services through the policy data learning cloud B6, for example, it may involve presenting pre-registered policy proposals or proposing policies based on data learned from general information such as the web. Subsequently, the system may measure the temporal changes in the workload of the target individuals from the time the measure was implemented, and if a decrease in workload is determined, it may add the effectiveness score of the measure, which is internally maintained by the system. This effectiveness may be calculated, for example, by adding up the degree to which the workload decreases, or by setting it based on the correlation between the timing of the implementation of the measures and the degree to which the workload decreases. In this example, the effectiveness score used is a numerical value where a higher value indicates greater effectiveness.

[0195] The policy generation unit C55 generates proposed policies in order to suggest improvement measures to individuals who have been detected as experiencing high load. The proposed measures could be structured in a way that, for example, incorporates measures actually implemented during service operation as proposed measures, and then uses this information to train the AI, thereby improving the accuracy of the proposals.

[0196] In order to generate the proposed measures, for example, similar to how the effectiveness of the measures is assessed, data representing the trend of workload figures, summary data of the load factors, and performance data of the implemented measures may be referenced, and other data may also be referenced. The policy generation unit C55 may automatically propose the policy that is estimated to be the most effective based on the correlation between the workload figures and the summary data of the workload factors, to the first administrator 41. Furthermore, after proposing a policy, the policy generation unit C55 acquires performance data and uses that data to further improve the accuracy of the proposal. The automatically proposed measures may be, for example, a single measure with particularly high effectiveness, or multiple effective measures whose effectiveness exceeds a certain threshold. In other words, the first administrator 41 may be proposed with the single measure that has the highest effectiveness, or multiple highly effective measures may be proposed for selection.

[0197] Here, we will explain the function for verifying the implementation of proposed measures. In this embodiment, the policy implementation input unit C32 has a function to confirm that the improvement measures proposed by the system have actually been implemented. As a specific example, the policy performance input unit C32 may present the policies proposed by the service as input candidates when inputting performance data after the implementation of a policy. The policy generation unit C55 may prioritize proposing policies with high effectiveness based on the correlation between the content of implemented policies and the effectiveness of performance data.

[0198] Furthermore, for measures that were proposed but not implemented, the reason why the first administrator 41 was unable to implement them may be entered in the measure performance input unit C32. This makes it possible to strengthen the system's learning process. Furthermore, the policy generation unit C55 may, for example, prioritize and propose policies that have not been implemented in the past, considering their potential effectiveness.

[0199] As described above, the first report provision system 1, report provision method, and control method according to this embodiment can propose measures that reflect the content of the comments of the first target person 31. For example, if you send a message to accept comments only from users who have been determined to have a high load, you can reduce the amount of processing required to send the message.

[0200] In the first report provision system 1, report provision method, and control method according to this embodiment, load data based on activity data detected by a first device 11 attached to a first subject 31 and load factors estimated based on the content of interviews received from the first subject 31 are used to generate measures to propose ways to improve the load of the first subject 31, and a report containing the generated measures is generated. As a result, the first report provision system 1, report provision method, and control method according to this embodiment can propose measures using, for example, quantitative information on the load and information on load factors based on opinions from the first subject 31.

[0201] For example, traditionally, measures to improve workload were often considered, planned, and implemented by the person in charge of work improvement, based on what was appropriate for each organization. However, such improvement measures depended on the abilities and experience of the person in charge, and the consideration and implementation of effective measures varied from organization to organization. As a result, many organizations were unable to implement effective work improvement measures, and there was a possibility that mental health problems caused by high workloads would occur in all industries and organizations. In contrast, in this embodiment, for example, it is possible to automatically propose business improvement measures to the person in charge that are expected to have a certain effect in all organizations, thereby eliminating differences in proposed measures and achieving improvements in business conditions.

[0202] In the first report provision system 1, report provision method, and control method according to this embodiment, for example, measures for load improvement can be proposed using quantified load, load factors based on chat data, and actual data of load balancing measures. This allows, for example, for managers and supervisors of an organization to automatically plan and propose measures to improve the workload of the organization's employees. In this embodiment, not only is it possible to quantitatively grasp the physical and mental workload of employees, but the factors contributing to the workload can also be identified, and measures based on those factors can be proposed.

[0203] In the first report provision system 1, report provision method, and control method according to this embodiment, for example, by automatically proposing measures that take into account the workload figures and the factors causing the workload, it is possible to implement effective measures even for employees who have a potential workload. In the first report provision system 1, report provision method, and control method according to this embodiment, for example, by learning the relationship between load factors and the effectiveness of business improvement measures and outputting proposed measures based on the learning results, it is possible to optimize the system so that more effective business improvement measures can be automatically output according to the load factors.

[0204] In the first report provision system 1, report provision method, and control method according to this embodiment, for example, by taking into account the workload figures in addition to the load factors, it is also possible to propose organizational improvement measures to optimize the entire organization. In the first report provision system 1, report provision method, and control method according to this embodiment, for example, by proposing multiple measures that are estimated to be highly effective, managers and supervisors can select the measure they judge to be the most effective from among the multiple measure options. Furthermore, in the first report provision system 1, report provision method, and control method according to this embodiment, for example, by learning the selection results by managers and supervisors, it becomes possible to propose measures that are more effective.

[0205] In the first report provision system 1, report provision method, and control method according to this embodiment, for example, by acquiring the results of the implemented measures, the effectiveness of the measures implemented by managers and supervisors can be calculated from the correlation with the workload figures. In the first report provision system 1, report provision method, and control method according to this embodiment, by maintaining the effectiveness of the measures in this way, it is possible to grasp the trends of what kind of workload reduction measures are highly effective for the relevant workplace, and thereby increase the accuracy of workload reduction proposals. In the first report provision system 1, report provision method, and control method according to this embodiment, for example, measures not held as options by the system can be incorporated by a manager or supervisor inputting them, or such measures can be incorporated based on general information in the world. As a result, the first report provision system 1, report provision method, and control method according to this embodiment can propose measures for consideration even in the early stages of a service when there is little actual performance data, and can also verify the effectiveness of measures based on general information.

[0206] In this embodiment, we have shown a case where the workload situation within a single hospital is digitized and measures are proposed. However, as another example, it is also possible to digitize the workload situation across multiple hospitals and propose measures. This makes it possible to propose common measures to multiple hospitals within the same group. Another example is the ability to identify the workload levels among multiple hospitals located in a neighboring area. This allows for the proposal of effective measures based on which departments or wards within each hospital are experiencing high workloads. Furthermore, there are no particular limitations on the length of time for analyzing load conditions; it is possible to verify load conditions over a long period.

[0207] In this embodiment, the first report provision system 1 and control method were shown as being applied to the workload of hospital employees. However, the first report provision system 1 and control method are not limited to this and may be applied to various other types of human workloads, such as the workload of employees of a company. Furthermore, the load is not limited to, for example, the workload of work in a hospital or company, but may be applied to various other loads such as the workload of studying or the workload of work at home. Furthermore, a variety of topics may be used in the chat for gathering information regarding the load status. Furthermore, various types of information may be used in reports regarding load conditions.

[0208] Now, let me explain Patent Document 1. Patent Document 1 describes a management support device that generates measures for performance improvement that take into account the mental state of employees. This management support device stores characteristic information representing the relationship between user performance information and mental workload information, using each as a reference value, and generates information on measures that managers should take for users by referring to this characteristic information. Such technologies, for example, provide a numerical value for mental stress by quantifying the degree of mental stress from a person's biometric information. This biometric information includes, for example, changes in blood flow obtained from facial images, and fluctuations in blood pressure and respiration that affect heart rate intervals. Furthermore, these technologies generate measures to control mental stress based on a statistical relationship between mental stress and performance information that takes into account individual work performance. This work performance is based, for example, on job title and years of service. However, with such technologies, it was sometimes difficult to generate measures to improve workload that addressed mental stress caused by factors other than the actual work itself, such as interpersonal relationships or harassment within the organization. The factors contributing to employees' mental stress are not limited to excessive workload; there are also many cases where mental stress is increased due to interpersonal relationships, and the technology described in Patent Document 1 was insufficient from the perspective of reducing employee workload. In contrast, in this embodiment, it is possible to propose measures to reduce the workload by taking into account the factors that contribute to the workload of employees.

[0209] A second embodiment will be described. In this embodiment, we will primarily describe in detail the parts that differ from the first embodiment.

[0210] Figure 17 shows an example of the configuration of the second report provision system 1a according to the second embodiment. The second report provision system 1a comprises a second device 11a, a second user access device 12a, a second administrator terminal device 14a, a second database 15a, and a second network 21a. Figure 17 also shows the second subject 31a and the second administrator 41a. The second device 11a comprises a second pulse measurement unit Q1a and a second body movement detection unit Q2a.

[0211] Here, the configuration and operation of the second report provision system 1a differ in general terms from the configuration and operation of the first report provision system 1 shown in Figure 1 according to the first embodiment in that the functions of the load analysis cloud and the policy data learning cloud in the first embodiment are provided in the second target user device 12a. In this embodiment, a server device corresponding to the first server device 13 in the first embodiment is not provided.

[0212] In this embodiment, the second device 11a having a second pulse measurement unit Q1a and a second body movement detection unit Q2a, the second user device 12a, the second administrator terminal device 14a, the second database 15a, and the second network 21a each have functions similar to, for example, the first device 11 having a first pulse measurement unit Q1 and a first body movement detection unit Q2, the first user device 12, the first administrator terminal device 14, the first database 15, and the first network 21 in the first embodiment, except for the differences described in this embodiment. In this embodiment, the second subject 31a and the second administrator 41a are the same as the first subject 31 and the first administrator 41 in the first embodiment, respectively.

[0213] Figure 18 shows an example of the configuration of the second user-accessible device 12a according to the second embodiment. The second user device 12a comprises a second input unit 111a, a second output unit 112a, a second communication unit 113a, a second storage unit 114a, and a second control unit 115a. The second input unit 111a includes a second operation unit 131a. The second output unit 112a includes a second display unit 132a.

[0214] In this embodiment, the second input unit 111a, the second output unit 112a, the second communication unit 113a, the second storage unit 114a, the second control unit 115a, the second operation unit 131a, and the second display unit 132a each have functions similar to, for example, the first input unit 111, the first output unit 112, the first communication unit 113, the first storage unit 114, the first control unit 115, the first operation unit 131, and the first display unit 132 in the first embodiment, except for the differences described in this embodiment.

[0215] Figure 19 is a diagram showing an example of the processing procedure performed in the second report provision system 1a according to the second embodiment.

[0216] Figure 19 shows the second user device 12a, the second device 11a, and the second administrator terminal device 14a. In this example, the second user device 12a is equipped with functions for performing load analysis and learning from policy data. In this example, the second subject, 31a, is the person being interviewed. In this example, the second manager 41a is the person who manages the second subject 31a, and is, for example, the second subject 31a's superior or a human resources person. In this example, the second device 11a is attached to the body of the second subject 31a.

[0217] In this embodiment, the second target user device 12a manages data relating to at least the second target user 31a from the employee data. In this example, for instance, employee data may be stored in the second database 15a. Furthermore, employee data management may, for example, be performed on an ongoing basis.

[0218] In process T301, the second device 11a acquires activity data of the second subject 31a. This activity data includes pulsation data and body movement data. In process T302, the second device 11a transmits the acquired activity data to the second user device 12a. In process T303, the second user device 12a analyzes the activity level data received from the second device 11a. In process T304, the second subject user device 12a stores the analysis results. In this example, the analysis results are those relating to the second subject 31a.

[0219] In process T305, the second user device 12a determines whether the user is a high-load user based on the analysis results. In this example, the second target user device 12a indicates the case where the second target user 31a is determined to be a high-load user and a target for interview. Furthermore, the second user device 12a may terminate the processing of this flow if it determines that the second user 31a is not a high-load user. In this case, for example, the processing of this flow may be terminated after processing T306 has been performed.

[0220] In process T306, the second subject user device 12a displays a predetermined message to the second subject 31a, who is the subject of the hearing. This message may also be displayed, for example, in the chat during process T307. Alternatively, for example, the second database 15a may store information on the analysis results linked to employee data. In this linking, each employee is associated with the analysis results of the workload related to that employee.

[0221] In process T307, the second user device 12a initiates a chat for the purpose of conducting an interview, using the functions of the interview robot. In process T308, the second user device 12a analyzes the chat content and generates a summary of the chat content.

[0222] In process T309, the second user device 12a estimates the load factors from a summary of the chat content.

[0223] In processing T310, the second user device 12a generates measures based on the analysis results of the activity level data and the estimated factors of the load.

[0224] In process T311, the second user device 12a generates a report including the generated measures.

[0225] In process T312, the second user device 12a displays the generated report.

[0226] In the example shown in Figure 19, the processing section from T308 to T311 is shown as the third processing section P3. In the example shown in Figure 19, the third processing section P3 is performed by the second user-accessible device 12a.

[0227] As described above, the second report provision system 1a, report provision method, and control method according to this embodiment can propose measures that reflect the content of comments from the second target person 31a. In this embodiment, except for the difference that load analysis, etc., is performed in the second user device 12a, it is possible to obtain the same effects as in the first embodiment, for example.

[0228] In the example shown in Figure 19, the report is displayed by the second user device 12a. However, as another example, the report may be displayed by the second administrator terminal device 14a, either together with or instead of the second user device 12a. In this case, in the flow shown in Figure 19, after processing T311, the second user device 12a sends the generated report to the second administrator terminal device 14a, and the second administrator terminal device 14a outputs the report received from the second user device 12a.

[0229] A third embodiment will be described. In this embodiment, we will primarily describe in detail the parts that differ from the first embodiment.

[0230] Figure 20 shows an example of the configuration of the third report provision system 1b according to the third embodiment. The third report provision system 1b comprises a third device 11b, a third user access device 12b, a third administrator terminal device 14b, a third database 15b, and a third network 21b. Figure 20 also shows the third target person 31b and the third administrator 41b. The third device 11b comprises a third pulse measurement unit Q1b and a third body motion detection unit Q2b.

[0231] Here, the configuration and operation of the third report provision system 1b differ in general from the configuration and operation of the first report provision system 1 shown in Figure 1 according to the first embodiment in that the load analysis cloud function and the policy data learning cloud function in the first embodiment are provided on the third device 11b. In this embodiment, a server device corresponding to the first server device 13 in the first embodiment is not provided.

[0232] In this embodiment, the third device 11b having a third pulse measurement unit Q1b and a third body movement detection unit Q2b, the third user device 12b, the third administrator terminal device 14b, the third database 15b, and the third network 21b each have functions similar to, for example, the first device 11 having a first pulse measurement unit Q1 and a first body movement detection unit Q2, the first user device 12, the first administrator terminal device 14, the first database 15, and the first network 21 in the first embodiment, except for the differences described in this embodiment. In this embodiment, the third subject 31b and the third administrator 41b are the same as the first subject 31 and the first administrator 41 in the first embodiment, respectively.

[0233] Figure 21 shows an example configuration of the third device 11b according to the third embodiment. The third device 11b includes a device input unit 411, a device output unit 412, a device communication unit 413, a device storage unit 414, a device control unit 415, and a device sensor unit 416. The device input unit 411 includes a device operation unit 431. The device output unit 412 includes a device display unit 432. The device sensor unit 416 includes a third pulse measurement unit Q1b and a third body motion detection unit Q2b. The names of the parts provided in the third device 11b are for descriptive purposes only and may be referred to by any other name.

[0234] Here, the example configuration of the third device 11b shown in Figure 21 is just one example, and other configurations may be used. For example, if operation and display are not required for the third device 11b, the device operation unit 431 and the device display unit 432 do not need to be provided. Furthermore, for example, if the third device 11b does not require information input or output other than communication, the device input unit 411 and the device output unit 412 do not need to be provided.

[0235] The third device 11b is configured using a computer. The device input unit 411 has the function of inputting information. For example, the device operation unit 431 receives and inputs the details of the operation performed by the third subject 31b. Furthermore, for example, the device input unit 411 may receive information from an external device. The device output unit 412 has the function of outputting information. For example, the device display unit 432 outputs the information to be displayed to the screen. The device display unit 432 may have, for example, the screen of any display. Furthermore, for example, the device output unit 412 may output information to an external device. Here, the device operation unit 431 and the device display unit 432 may be shared, for example, by using a touch panel.

[0236] The device communication unit 413 has the function of performing communication. In this embodiment, the device communication unit 413 is shown separately from the device input unit 411 and the device output unit 412. However, for example, the receiving function of the device communication unit 413 may be included in the functions of the device input unit 411, and the transmitting function of the device communication unit 413 may be included in the functions of the device output unit 412.

[0237] The device storage unit 414 stores information. The device control unit 415 performs various processes or controls on the third device 11b. In this embodiment, the device control unit 415 includes a predetermined processor, such as a CPU, and performs various processes or controls by executing a predetermined program using this processor. The program may be stored, for example, in the device storage unit 414.

[0238] Figure 22 is a diagram showing an example of the processing procedure performed in the third report provision system 1b according to the third embodiment. Figure 22 shows the third user device 12b, the third device 11b, and the third administrator terminal device 14b. In this example, the third device 11b is equipped with functions for performing load analysis and learning from policy data. In this example, the third subject, 31b, is the person being interviewed. In this example, the third manager 41b is the person who manages the third subject 31b, and is, for example, the superior or HR manager of the third subject 31b. In this example, the third device 11b is attached to the body of the third subject 31b.

[0239] In this embodiment, the third device 11b manages employee data, specifically data relating to at least the third subject 31b. In this example, for instance, employee data may be stored in the third database 15b. Furthermore, employee data management may, for example, be performed on an ongoing basis.

[0240] In process T401, the third device 11b acquires activity data of the third subject 31b. This activity data includes pulsation data and body movement data. In process T402, the third device 11b analyzes the acquired activity data. In process T403, the third device 11b stores the analysis results. In this example, the analysis results pertain to the third subject 31b.

[0241] In process T404, the third device 11b determines whether the user is a high-load user based on the analysis results. In this example, the third device 11b indicates the case where the third subject 31b is determined to be a high-load individual and therefore a subject for interview. Furthermore, the third device 11b may terminate the processing of this flow if it determines that the third target person 31b is not a high-load user. In this case, for example, the processing of this flow may be terminated after process T405 has been performed.

[0242] In process T405, the third device 11b transmits a predetermined message to the third subject user device 12b of the third subject 31b, who is the subject of the hearing. For example, the third database 15b may store the analysis results linked to employee data. In this linking, each employee is associated with the analysis results of the workload related to that employee.

[0243] In process T406, the third user device 12b initiates a chat for the purpose of conducting an interview, using the functions of the interview robot. In process T407, the third user device 12b sends the chat content to the third device 11b.

[0244] In process T408, the third device 11b analyzes the chat content received from the third user device 12b and generates a summary of the chat content.

[0245] In process T409, the third device 11b estimates the load factors from a summary of the chat content.

[0246] In process T410, the third device 11b generates measures based on the analysis results of the activity data and the estimated load factors. In process T411, the third device 11b generates a report that includes the generated measures.

[0247] In process T412, the third device 11b transmits the generated report to a designated device. In this example, the specified devices are the third user device 12b and the third administrator terminal device 14b.

[0248] In process T413, the third user device 12b displays the report received from the third device 11b. If a report is not sent from the third device 11b to the third user device 12b, process T413 will not be performed.

[0249] In process T414, the third administrator terminal device 14b displays the report received from the third device 11b. If a report is not sent from the third device 11b to the third administrator terminal device 14b, process T414 will not be performed.

[0250] In the example shown in Figure 22, the processing section from T408 to T412 is shown as the fourth processing section P4. In the example shown in Figure 22, the fourth processing section P4 is performed by the third device 11b.

[0251] As described above, the third report provision system 1b, report provision method, and control method according to this embodiment can propose measures that reflect the content of the comments of the third target person 31b. In this embodiment, except for the difference that load analysis, etc., is performed in the third device 11b, it is possible to obtain the same effects as in the first embodiment, for example.

[0252] As another possible configuration, the third device 11b may be equipped with a function for conducting a chat for the purpose of hearing. In this case, in the flow shown in Figure 22, process T406 is performed by the third device 11b instead of processes T405 to T407. Furthermore, in this case, the third-party user device 12b does not necessarily have to be provided.

[0253] An example configuration according to the above embodiment is shown. As an example configuration, the control method for the report provision system uses an information processing device, a user-accessible device used by the target person, and a device attached to the target person to output a report that includes measures to address the target person's workload. In this control method, load data indicating the subject's load is obtained based on the subject's activity level data measured by the device. This control method obtains the factors contributing to the load, which are estimated based on comments from the subject regarding the load. This control method outputs a report that includes a first measure based on the load data and the factors causing the load.

[0254] Therefore, it is possible to propose measures that reflect the content of the comments from the target audience. Here, in the example shown in Figure 1 according to the first embodiment, the first report provision system 1, the first target person 31, the first target person user device 12, and the first device 11 are examples of a report provision system, an example of a target person, an example of a target person user device, and an example of a device, respectively. Also, in the example shown in Figure 1 according to the first embodiment, the first server device 13, or the first administrator terminal device 14, or both, are examples of information processing devices. Furthermore, in the example shown in Figure 17 according to the second embodiment, the second report provision system 1a, the second target person 31a, the second target person user device 12a, and the second device 11a are examples of a report provision system, an example of a target person, an example of a target person user device, and an example of a device, respectively. Also, in the example shown in Figure 17 according to the second embodiment, the second administrator terminal device 14a is an example of an information processing device. Furthermore, in the example shown in Figure 20 according to the third embodiment, the third report provision system 1b, the third target person 31b, the third target person user device 12b, and the third device 11b are examples of a report provision system, an example of a target person, an example of a target person user device, and an example of a device, respectively. Also, in the example shown in Figure 20 according to the third embodiment, the third administrator terminal device 14b is an example of an information processing device.

[0255] In this embodiment, as shown in Figures 9A to 9F, the policy information is arranged hierarchically. That is, one or more more detailed policies are contained below a higher-level concept of a group of policies. Furthermore, although this embodiment shows a two-layer structure for policy information, it may also have a three-layer or higher structure. In this way, when policy information is arranged hierarchically, for example, the information for the first policy may include information from any level of policy, and may also include information from two or more different levels.

[0256] As an example configuration, in the control method of the report provision system, the activity level data includes pulse data related to the subject's heartbeat. Therefore, the load can be determined based on the pulse data. In this way, a determination can be made according to the subject's pulse rate.

[0257] As an example configuration, in the control method of the report provision system, the activity level data includes body movement data related to the subject's body movements. Therefore, based on body movement data, it is possible to determine whether or not the subject is moving. In this way, it is possible to make judgments in accordance with the subject's pulse rate and body movement.

[0258] As an example configuration, in the control method of the report provision system, the load data includes physical load data that indicates the physical load on the subject, which is classified based on pulse data and body movement data. Therefore, it is possible to generate policies using physical exertion.

[0259] As an example configuration, the control method of the report provision system determines whether the first condition is met based on load data and load factors. If the control method determines that the load data and load factors meet the first condition, it outputs a report including the first measure. Therefore, if certain conditions are met, measures corresponding to those conditions can be proposed.

[0260] As an example configuration, in the control method of the report provision system, the first condition is that the physical load is above a predetermined value, and the estimated cause of the load is the nature of the work. The first measure includes measures related to the distribution of workload. Therefore, the first measure can be output in accordance with the physical load and the factors causing the load. This example corresponds to, for example, the example in Figure 9C, which is an example of the first condition and the first measure.

[0261] As an example configuration, the control method of the report provision system determines whether the load data and load factors satisfy a second condition that is different from the first condition. If the control method determines that the load data and load factors satisfy the second condition, it outputs a report that includes a second measure that is different from the first measure. The second condition is that the physical load is above a predetermined value, and the estimated cause of the load is the work environment. This second measure includes measures related to facility improvements. Therefore, measures can be formulated to match the physical burden and the factors contributing to that burden. This example corresponds to, for example, the example in Figure 9D, which is an example of the first condition and the first measure.

[0262] As an example configuration, in the control method of the report provision system, the load data includes mental load data that indicates the mental load of the subject, which is classified based on heartbeat data and body movement data. This control method determines whether the load data and load factors satisfy a third condition that is different from the first and second conditions. If this control method determines that the load data and load factors satisfy the third condition, it outputs a report that includes a third measure that is different from the first and second measures. The third condition is that the mental burden is above a predetermined value, and the estimated cause of the burden is the nature of the work. This third measure includes measures related to the distribution of workload. Therefore, it is possible to output policies tailored to the mental burden and the factors contributing to that burden. This example corresponds to, for example, the example in Figure 9B, which is an example of the first condition and the first measure.

[0263] As one example of a configuration, the control method for the report provision system involves assigning the attributes of the target person to the load data. Therefore, it is possible to propose measures tailored to the attributes of the target group. Specifically, this could involve proposing measures that were effective for the same attributes as the target group, or measures that were effective for attributes similar to the target group.

[0264] As an example configuration, the control method for the report provision system acquires load data of the target individuals after the first measure has been implemented. Based on how the load data has changed before and after the first measure has been implemented, the control method changes from the first measure to the fourth measure. Therefore, the measures generated under the first condition are changed from the first measure to the fourth measure. This allows the measures output when the first condition is met to be updated to more accurate measures based on the load data before and after the implementation of the first measure.

[0265] Furthermore, the first and fourth measures are distinct measures that may be generated depending on whether the first condition is met. Here, there are no particular limitations on the method used to update the measures generated when certain conditions are met, based on past performance, to more suitable measures. For example, methods using AI such as machine learning may be used, or methods using processing according to a predetermined algorithm may be used.

[0266] As an example configuration, in the control method of the report provision system, the first measure includes multiple measures. This control method acquires load data of the target individuals after the first measure has been implemented. This control method changes the priority of multiple measures based on how the load data changed before and after the implementation of the first measure. Therefore, based on load data before and after the implementation of the first measure, the priority order of the multiple measures included in the first measure can be updated to a more accurate ranking.

[0267] Here, the priority order set for multiple pieces of information may be used in any manner. For example, it may be used to select some pieces of information from among multiple pieces of information in order of priority, or it may be used to display multiple pieces of information in order of priority. Furthermore, there are no particular limitations on the method used to update the priority of multiple measures to a more appropriate order when certain conditions are met, based on past performance. For example, methods using AI such as machine learning may be used, or methods using processing according to a predetermined algorithm may be used.

[0268] As an example configuration, the control method for the report provision system uses an information processing device, a user-accessible device used by the target person, and a device attached to the target person to output a report on the target person's workload. The control method comprises the steps of: causing the device to measure the activity level data of the subject; causing the information processing device to acquire the activity level data; causing the information processing device to calculate load data indicating the subject's load based on the activity level data; causing the information processing device to send a message to the subject's user device to receive comments from the subject regarding the load, in accordance with the load data; causing the subject's user device to receive comments from the subject regarding the load; causing the information processing device to acquire the comments; causing the information processing device to estimate the load factors based on the acquired comments; causing the information processing device to determine whether the load data and load factors satisfy a first condition; causing the information processing device to generate a first measure for the load if it determines that the load data and load factors satisfy the first condition; and causing the information processing device to output a report including the first measure. Therefore, it is possible to propose measures that reflect the content of the comments from the target audience. Here, this configuration example is a configuration example according to the first embodiment.

[0269] As an example configuration, the control method is a control method in an information processing device that communicates with at least one of the user-accessible device used by the user and a device attached to the user. The control method includes the steps of: causing the information processing device to acquire activity data of the subject from the device; causing the device to calculate load data indicating the subject's load based on the activity data; causing the device to send a message to the subject user device to receive comments from the subject regarding the load, in accordance with the load data; causing the device to acquire comments from the subject user device when the subject user device has received comments from the subject regarding the load; causing the device to estimate the factors of the load based on the acquired comments; determining whether the load data and the factors of the load satisfy a first condition; generating a first measure for the load if it is determined that the load data and the factors of the load satisfy the first condition; and outputting a report including the first measure. Therefore, it is possible to propose measures that reflect the content of the comments from the target audience. Here, this configuration example is a configuration example according to the first embodiment. Furthermore, communication between the information processing device and the device may be performed, for example, via a device used by the target user.

[0270] As an example configuration, the program is a program that enables the following steps to be implemented: causing a computer to acquire activity data of a subject from a device attached to the subject; calculating load data indicating the subject's load based on the activity data; sending a message to a subject-user device used by the subject to receive comments from the subject regarding the load, in accordance with the load data; acquiring the comments from the subject-user device when the subject-user device receives comments from the subject regarding the load; estimating the factors of the load based on the acquired comments; determining whether the load data and the factors of the load satisfy a first condition; generating a first measure for the load if it is determined that the load data and the factors of the load satisfy the first condition; and outputting a report including the first measure. Therefore, it is possible to propose measures that reflect the content of the comments from the target audience. Herein, this configuration example is applicable to a program for controlling a computer that constitutes the first server device 13 or the first administrator terminal device 14 according to the first embodiment.

[0271] As an example configuration, the control method is a control method for a user-accessible device that communicates with a device attached to the user. The control method includes the steps of: causing the user device to acquire activity data of the user from the device; causing the device to calculate load data indicating the user's load based on the activity data; causing the device to output a message to receive comments from the user regarding the load, depending on the load; causing the device to estimate the load factors based on the comments received from the user when comments regarding the load are received; causing the device to determine whether the load data and load factors satisfy a first condition; causing the device to generate a first measure for the load if it is determined that the load data and load factors satisfy the first condition; and outputting a report including the first measure. Therefore, it is possible to propose measures that reflect the content of the comments from the target audience. Here, this configuration example is a configuration example according to the second embodiment.

[0272] As an example configuration, this program enables the following steps: causing a computer to acquire activity data of a subject from a device attached to the subject; calculating load data indicating the subject's workload based on the activity data; outputting a message to receive comments from the subject regarding the workload, depending on the workload; estimating the factors contributing to the workload based on the comments received from the subject; determining whether the workload data and the factors contributing to the workload satisfy a first condition; generating a first countermeasure for the workload if the workload data and the factors contributing to the workload satisfy the first condition; and outputting a report including the first countermeasure. Therefore, it is possible to propose measures that reflect the content of the comments from the target audience. Herein, this configuration example is applicable to a program for controlling the computer that constitutes the second user-accessible device 12a according to the second embodiment.

[0273] As an example configuration, the control method is a control method for a device attached to a person. The control method includes the steps of: causing the device to acquire activity data of the subject; causing the device to calculate load data indicating the subject's load based on the activity data; causing the device to output a message to an external device to receive comments from the subject regarding the load, depending on the load; causing the external device to estimate the load factors based on the comments received when it receives comments from the subject regarding the load; causing the device to determine whether the load data and load factors satisfy a first condition; causing the device to generate a first measure for the load if it determines that the load data and load factors satisfy the first condition; and outputting a report including the first measure. Therefore, it is possible to propose measures that reflect the content of the comments from the target audience. Here, this configuration example is a configuration example according to the third embodiment. Furthermore, there are no particular limitations on the external devices; for example, they may be devices used by the target users, administrator terminal devices, or both.

[0274] As an example configuration, the program is designed to implement the following steps: causing a computer attached to a subject to acquire activity data of the subject; calculating load data indicating the subject's workload based on the activity data; outputting a message to an external device to receive comments from the subject regarding the workload, depending on the workload; estimating the factors contributing to the workload based on the comments received by the external device when the external device receives comments from the subject; determining whether the workload data and the factors contributing to the workload satisfy a first condition; generating a first countermeasure for the workload if the load data and the factors contributing to the workload satisfy the first condition; and outputting a report including the first countermeasure. Therefore, it is possible to propose measures that reflect the content of the comments from the target audience. Here, this configuration example is applicable to a program for controlling the computer that constitutes the third device 11b according to the third embodiment.

[0275] As one example of a configuration, the report delivery method involves providing users with reports that include measures to reduce the workload, using a report delivery system. The report is output to the user's device display by the report delivery system. The report includes load data indicating the load based on activity data of high-load individuals, and measures proposed based on the factors contributing to the load estimated from comments received from those high-load individuals. A high-load individual is a person whose load data is collected based on activity data, and whose load indicators meet the prescribed conditions for such high-load individuals. Therefore, it is possible to propose measures that reflect the content of the comments from the target audience.

[0276] Furthermore, the user may be, for example, an administrator who operates an administrator terminal device. These predetermined conditions are, for example, conditions for determining that a high load is present. Various indicators may be used as the indicator in question. Here, this example configuration is an example configuration according to the first to third embodiments.

[0277] As an example configuration, the report provision system uses an information processing device, a user-accessible device used by the target person, and a device attached to the target person to output a report on the target person's workload. The reporting system acquires load data indicating the subject's workload based on the subject's activity data measured by the device. The reporting system obtains estimated factors contributing to the workload based on comments from the subject regarding the workload. The report provision system determines whether the load data and the load factors satisfy the first condition. The report provision system outputs a report including the first measure to address the load when it determines that the load data and the load factors satisfy the first condition. Therefore, it is possible to propose measures that reflect the content of the comments from the target audience. Here, this example configuration is an example configuration according to the first to third embodiments.

[0278] A program for realizing the function of any component in any of the devices described above may be recorded on a computer-readable recording medium, and the program may be loaded into a computer system and executed. Here, "computer system" includes the operating system and hardware such as peripheral devices. "Computer-readable recording medium" refers to portable media such as flexible disks, magneto-optical disks, ROM (Read Only Memory), CD (Compact Disc)-ROMs, and storage devices such as hard disks built into the computer system. "Computer-readable recording medium" also includes volatile memory within a computer system that acts as a server or client when a program is transmitted via a network such as the Internet or a communication line such as a telephone line, which retains the program for a certain period of time. Such volatile memory may be RAM. The recording medium may also be a non-temporary recording medium.

[0279] The above program may be transmitted from a computer system that stores this program in a memory device or the like to another computer system via a transmission medium, or by transmission waves within the transmission medium. The "transmission medium" used to transmit the program refers to a medium that has the function of transmitting information, such as a network like the Internet or a communication line like a telephone line. The above program may be intended to implement some of the functions described above. The above program may also be a so-called differential file, capable of implementing the aforementioned functions in combination with programs already recorded in the computer system. A differential file may also be called a differential program.

[0280] The functions of any component in any device described above may be implemented by a processor. Each process in the embodiment may be implemented by a processor that operates based on information such as a program, and a computer-readable recording medium that stores information such as a program. The functions of each part of the processor may be implemented by separate hardware, or the functions of each part may be implemented by integrated hardware. The processor includes hardware, and the hardware may include at least one of a circuit that processes digital signals and a circuit that processes analog signals. The processor may be configured using one or more circuit devices or one or both of one or more circuit elements mounted on a circuit board. ICs (Integrated Circuits) may be used as circuit devices, and resistors or capacitors may be used as circuit elements.

[0281] The processor may be a CPU. However, the processor is not limited to a CPU; various types of processors such as a GPU (Graphics Processing Unit) or a DSP (Digital Signal Processor) may be used. The processor may be a hardware circuit using an ASIC (Application Specific Integrated Circuit). The processor may consist of multiple CPUs, or it may consist of hardware circuits using multiple ASICs. The processor may consist of a combination of multiple CPUs and hardware circuits using multiple ASICs. The processor may include one or more amplifier circuits or filter circuits that process analog signals.

[0282] Although embodiments have been described in detail above with reference to the drawings, the specific configuration is not limited to these embodiments and includes designs and the like that do not depart from the gist of this disclosure.

[0283] [Note] The following are configuration examples 1 through 20. Furthermore, the lower-level configuration examples may or may not be applied to the higher-level configuration examples. Furthermore, a lower-level configuration example applicable to any of the two or more higher-level configuration examples may be applied to any of those two or more higher-level configuration examples. Moreover, if two or more application examples arise in this manner, a configuration example even lower than the lower-level example may be applied to any of those two or more application examples.

[0284] <Configuration Example 1> A control method for a report provision system that outputs a report including measures to address the workload of a target person, using an information processing device, a target person user device used by the target person, and a device attached to the target person, Based on the activity data of the subject measured by the device, load data indicating the subject's load is acquired. Based on comments from the subject regarding the load, the factors of the load are estimated and obtained. Output a report including the first measure based on the load data and the factors causing the load. Control method.

[0285] <Configuration Example 2> The activity data includes pulse data relating to the subject's heartbeat. The control method described in <Configuration Example 1>.

[0286] <Configuration Example 3> The activity data includes body movement data relating to the subject's body movements. The control method described in <Configuration Example 2>. Note that <Configuration Example 3> can also be subordinate to <Configuration Example 1>.

[0287] <Configuration Example 4> The load data includes physical load data indicating the physical load on the subject, which is classified based on the pulsation data and the body movement data. The control method described in <Configuration Example 3>.

[0288] <Configuration Example 5> Based on the load data and the load factors, it is determined whether the first condition is met. When it is determined that the load data and the load factors satisfy the first condition, the report including the first measure is output. The control method described in <Configuration Example 4>. Note that <Configuration Example 5> can also be subordinate to any one of <Configuration Example 1>, <Configuration Example 2>, or <Configuration Example 3>.

[0289] <Configuration Example 6> The first condition is that the physical load is greater than or equal to a predetermined value, and the estimated cause of the load is the nature of the work. The first measure mentioned above includes measures related to the distribution of workload, The control method described in <Configuration Example 5>. Note that <Configuration Example 6> can also be subordinate to <Configuration Example 4>.

[0290] <Configuration Example 7> The system determines whether the load data and the load factors satisfy a second condition different from the first condition, and if it determines that the load data and the load factors satisfy the second condition, it outputs the report including a second measure different from the first measure. The second condition is that the physical load is greater than or equal to a predetermined value, and the estimated cause of the load is the work environment. The second measure mentioned above includes measures related to facility improvement, The control method described in <Configuration Example 6>.

[0291] <Configuration Example 8> The load data includes mental load data indicating the mental load of the subject, which is classified based on the pulse data and the body movement data. The system determines whether the load data and the load factors satisfy a third condition different from the first and second conditions, and if it determines that the load data and the load factors satisfy the third condition, it outputs the report including a third measure different from the first and second measures. The third condition is that the mental burden is greater than or equal to a predetermined value, and the estimated cause of the burden is the nature of the work. The third measure mentioned above includes measures related to the distribution of workload, The control method described in <Configuration Example 7>.

[0292] <Configuration Example 9> The attributes of the subject are assigned to the load data. The control method described in <Configuration Example 8>. Note that <Configuration Example 9> can also be subordinate to any one of <Configuration Example 1> to <Configuration Example 7>.

[0293] <Configuration Example 10> After the first measure is implemented, the load data of the subject is obtained, Based on how the load data changed before and after the implementation of the first measure, the measure is changed from the first measure to the fourth measure. A control method described in any one of the following items from <Configuration Example 1> to <Configuration Example 9>.

[0294] <Configuration Example 11> The aforementioned first measure includes multiple measures, After the first measure is implemented, the load data of the subject is obtained, Based on how the load data changes before and after the implementation of the first measure, the priority of the multiple measures is changed. A control method described in any one of the following items from <Configuration Example 1> to <Configuration Example 9>.

[0295] <Configuration Example 12> A control method for a report provision system that outputs a report on the workload of a target person, using an information processing device, a target person user device used by the target person, and a device attached to the target person, The process of having the device measure the activity level data of the subject, The process of causing the information processing device to acquire the activity data, The process of causing the information processing device to calculate load data indicating the load of the subject based on the activity data, The process of causing the information processing device to send a message to the target user device in accordance with the load data, for receiving comments from the target user regarding the load, The process of causing the user-accessible device to receive comments from the user regarding the load, The process of causing the information processing device to acquire the comment, The process of causing the information processing device to estimate the factors of the load based on the acquired comments, The process of causing the information processing device to determine whether the load data and the load factors satisfy the first condition, The information processing device is configured to generate a first measure for the load when it determines that the load data and the load factors satisfy the first condition. The process of causing the information processing device to output the report including the first measure, A control method comprising:

[0296] <Configuration Example 13> A control method for an information processing device that communicates with at least one of a device used by a subject and a device attached to the subject, The aforementioned information processing device, A step of obtaining activity data of the subject from the device, A step of calculating load data indicating the load on the subject based on the activity data, The process of sending a message to the user-accessible device in accordance with the load data, to receive comments from the user regarding the load, When the user-accessible device receives the comment regarding the load from the user, the user-accessible device is configured to retrieve the comment. A step of estimating the factors of the load based on the comments obtained, A step of determining whether the load data and the load factors satisfy the first condition, A step of generating a first measure for the load when it is determined that the load data and the load factors satisfy the first condition, The process of generating a report that includes the aforementioned first measure, A control method comprising:

[0297] <Configuration Example 14> On the computer, A step of acquiring activity data of the subject from a device attached to the subject, A step of calculating load data indicating the load on the subject based on the activity data, The process of sending a message to the user-accessible device used by the user, in accordance with the load data, to receive comments from the user regarding the load, When the user-accessible device receives the comment regarding the load from the user, the user-accessible device is configured to retrieve the comment. A step of estimating the factors of the load based on the comments obtained, A step of determining whether the load data and the load factors satisfy the first condition, A step of generating a first measure for the load when it is determined that the load data and the load factors satisfy the first condition, The process of generating a report that includes the aforementioned first measure, A program to achieve this.

[0298] <Configuration Example 15> A control method for a user-accessible device that communicates with a device attached to the user, The aforementioned device used by the target person, A step of obtaining activity data of the subject from the device, A step of calculating load data indicating the load on the subject based on the activity data, The process of outputting a message to receive comments from the target person regarding the load, depending on the load, When the aforementioned comments regarding the load are received from the aforementioned subject, the process involves estimating the factors of the load based on the acquired comments, A step of determining whether the load data and the load factors satisfy the first condition, A step of generating a first measure for the load when it is determined that the load data and the load factors satisfy the first condition, The process of generating a report that includes the aforementioned first measure, A control method comprising:

[0299] <Configuration Example 16> On the computer, A step of acquiring activity data of the subject from a device attached to the subject, A step of calculating load data indicating the load on the subject based on the activity data, The process of outputting a message to receive comments from the target person regarding the load, depending on the load, When the aforementioned comments regarding the load are received from the aforementioned subject, the process involves estimating the factors of the load based on the acquired comments, A step of determining whether the load data and the load factors satisfy the first condition, A step of generating a first measure for the load when it is determined that the load data and the load factors satisfy the first condition, The process of generating a report that includes the aforementioned first measure, A program to achieve this.

[0300] <Configuration Example 17> A control method for a device attached to a subject, The aforementioned device, A step of obtaining activity data of the aforementioned subject, A step of calculating load data indicating the load on the subject based on the activity data, The process of having an external device output a message to receive comments from the subject regarding the load, depending on the load, When the external device receives the comments regarding the load from the subject, the process involves estimating the factors of the load based on the acquired comments. A step of determining whether the load data and the load factors satisfy the first condition, A step of generating a first measure for the load when it is determined that the load data and the load factors satisfy the first condition, The process of generating a report that includes the aforementioned first measure, A control method comprising:

[0301] <Configuration Example 18> On the computer attached to the subject, A step of obtaining activity data of the aforementioned subject, A step of calculating load data indicating the load on the subject based on the activity data, The process of having an external device output a message to receive comments from the subject regarding the load, depending on the load, When the external device receives the comments regarding the load from the subject, the process involves estimating the factors of the load based on the acquired comments. A step of determining whether the load data and the load factors satisfy the first condition, A step of generating a first measure for the load when it is determined that the load data and the load factors satisfy the first condition, The process of generating a report that includes the aforementioned first measure, A program to achieve this.

[0302] <Configuration Example 19> A method for providing reports to users that include measures to reduce the load, using a report delivery system, The report is output to the display of the user's terminal by the report provision system, The report includes load data indicating the load based on activity data of high-load individuals, and the factors of the load estimated based on comments received from the high-load individuals, and the measures proposed based on these, The aforementioned high-load individuals are those who, among the subjects from whom load data based on the activity level data is acquired, whose load index satisfies predetermined conditions for the aforementioned high-load individuals. How to submit the report.

[0303] <Configuration Example 20> A report provision system that outputs a report on the workload of a target person using an information processing device, a target person user device used by the target person, and a device attached to the target person, Based on the activity data of the subject measured by the device, load data indicating the subject's load is acquired. Based on comments from the subject regarding the load, the factors of the load are estimated and obtained. Determine whether the load data and the load factors satisfy the first condition. When it is determined that the load data and the load factors satisfy the first condition, the report including the first measure for the load is output. Report delivery system. [Explanation of Symbols]

[0304] 1…First report delivery system, 1a…Second report delivery system, 1b…Third report delivery system, 11…First device, 11a…Second device, 11b…Third device, 12…First user device, 12a…Second user device, 12b…Third user device, 13…First server device, 14…First administrator terminal device, 14a…Second administrator terminal device, 14b…Third administrator terminal device, 15…First database, 15a…Second database, 15b…Third database, 21…First network, 21a…Second network, 21b…Third network 31...First target person, 31a...Second target person, 31b...Third target person, 41...First administrator, 41a...Second administrator, 41b...Third administrator, 111...First input unit, 111a...Second input unit, 112...First output unit, 112a...Second output unit, 113...First communication unit, 113a...Second communication unit, 114...First storage unit, 114a...Second storage unit, 115...First control unit, 115a...Second control unit, 131...First operation unit, 131a...Second operation unit, 132...First display unit, 132a...Second display unit, 211...Server input unit, 212...Server output unit, 213...Server communication unit, 214...Server - Memory unit, 215... Server control unit, 311... Management input unit, 312... Management output unit, 313... Management communication unit, 314... Management memory unit, 315... Management control unit, 331... Management operation unit, 332... Management display unit, 411... Device input unit, 412... Device output unit, 413... Device communication unit, 414... Device memory unit, 415... Device control unit, 431... Device operation unit, 432... Device display unit, 416... Device sensor unit, B1... Wearable device, B2... Hearing robot, B3... Load analysis cloud, B4... User interface for administrators, B5... Employee B6...Employee data management database, C1...Policy data learning cloud, C2...Pulse measurement unit, C11...Body movement detection unit, C12...Chat data output unit, C21...Load standard value setting unit, C22...Load calculation unit, C23...Load analysis unit, C24...Interview target selection instruction unit, C25...Load factor identification unit, C31...Load status display unit, C32...Policy performance input unit, C33...Proposed policy display unit, C41...Employee attribute data, C42...Attendance data, C43...Chat data, C51...Policy performance data, C52...Work load value data, C53...Load factor data,C54...Policy effect measurement section, C55...Policy generation section, D1...Physical high load time, D2...Mental high load time, G1...1st screen, G2...2nd screen, G3...3rd screen, G4A...4th A screen, G4B...4th B screen, H1...Chat summary data, J1...3rd screen 1 input screen, J2...2nd input screen, J3...3rd input screen, K1...1st area, K2...2nd area, K3...3rd area, K4...4th area, K5...5th area, K11...11th area, K12...12th area, K13...13th area, K14...14th area, K1 5…15th area, Q1…1st pulse measurement unit, Q1a…2nd pulse measurement unit, Q1b…3rd pulse measurement unit, Q2…1st body movement detection unit, Q2a…2nd body movement detection unit, Q2b…3rd body movement detection unit, Ta1…1st table, Ta2…2nd table, Ta3…3rd table, Ta4A…4th A table, Ta4B…4th B table, Ta4C…4th C table, Ta4D…4th D table, Ta4E…4th E table, Ta4F…4th F table, W1…1st frame, W2…2nd frame, W3…3rd frame, W4…4th frame,

Claims

1. A control method for a report provision system that outputs a report including measures to address the workload of a target person, using an information processing device, a target person user device used by the target person, and a device attached to the target person, Based on the activity data of the subject measured by the device, load data indicating the subject's load is acquired. Based on comments from the subject regarding the load, the factors of the load are estimated and obtained. Output a report including a first measure based on the load data and the factors causing the load. Control method.

2. The activity data includes pulse data relating to the subject's heartbeat. The control method according to claim 1.

3. The activity data includes body movement data relating to the subject's body movements. The control method according to claim 2.

4. The load data includes physical load data indicating the physical load on the subject, which is classified based on the pulsation data and the body movement data. The control method according to claim 3.

5. Based on the load data and the load factors, it is determined whether the first condition is met. When it is determined that the load data and the load factors satisfy the first condition, the report including the first measure is output. The control method according to claim 4.

6. The first condition is that the physical load is greater than or equal to a predetermined value, and the estimated cause of the load is the nature of the work. The first measure mentioned above includes measures related to the distribution of workload, The control method according to claim 5.

7. The system determines whether the load data and the load factors satisfy a second condition different from the first condition, and if it determines that the load data and the load factors satisfy the second condition, it outputs the report including a second measure different from the first measure. The second condition is that the physical load is greater than or equal to a predetermined value, and the estimated cause of the load is the work environment. The second measure mentioned above includes measures related to facility improvement, The control method according to claim 6.

8. The load data includes mental load data indicating the mental load of the subject, which is classified based on the pulse data and the body movement data. The system determines whether the load data and the load factors satisfy a third condition different from the first and second conditions, and if it determines that the load data and the load factors satisfy the third condition, it outputs the report including a third measure different from the first and second measures. The third condition is that the mental burden is greater than or equal to a predetermined value, and the estimated cause of the burden is the nature of the work. The third measure mentioned above includes measures related to the distribution of workload, The control method according to claim 7.

9. The attributes of the subject are assigned to the load data. The control method according to claim 8.

10. After the first measure is implemented, the load data of the subject is obtained, Based on how the load data changed before and after the implementation of the first measure, the measure is changed from the first measure to the fourth measure. The control method according to any one of claims 1 to 9.

11. The aforementioned first measure includes multiple measures, After the first measure is implemented, the load data of the subject is obtained, Based on how the load data changes before and after the implementation of the first measure, the priority of the multiple measures is changed. The control method according to any one of claims 1 to 9.

12. A control method for a report provision system that outputs a report on the workload of a target person, using an information processing device, a target person user device used by the target person, and a device attached to the target person, The process of having the device measure the activity level data of the subject, The process of causing the information processing device to acquire the activity data, The process of causing the information processing device to calculate load data indicating the load of the subject based on the activity data, The process of causing the information processing device to send a message to the user-accessible device in accordance with the load data, for receiving comments from the user regarding the load, The process of causing the user-accessible device to receive comments from the user regarding the load, The process of causing the information processing device to acquire the comment, The process of causing the information processing device to estimate the factors of the load based on the acquired comments, The process of causing the information processing device to determine whether the load data and the load factors satisfy the first condition, The information processing device is configured to generate a first measure for the load when it determines that the load data and the load factors satisfy the first condition. The process of causing the information processing device to output the report including the first measure, A control method comprising:

13. A control method for an information processing device that communicates with at least one of a device used by a subject and a device attached to the subject, The aforementioned information processing device, A step of obtaining activity data of the subject from the device, A step of calculating load data indicating the load on the subject based on the activity data, The process of sending a message to the user-accessible device in accordance with the load data, to receive comments from the user regarding the load, When the user-accessible device receives the comment regarding the load from the user, the user-accessible device is configured to retrieve the comment. A step of estimating the factors of the load based on the comments obtained, A step of determining whether the load data and the load factors satisfy the first condition, A step of generating a first measure for the load when it is determined that the load data and the load factors satisfy the first condition, The process of generating a report that includes the first measure mentioned above, A control method comprising:

14. On the computer, A step of acquiring activity data of the subject from a device attached to the subject, A step of calculating load data indicating the load on the subject based on the activity data, The process of sending a message to the user-accessible device used by the user, in accordance with the load data, to receive comments from the user regarding the load, When the user-accessible device receives the comment regarding the load from the user, the user-accessible device is configured to retrieve the comment. A step of estimating the factors of the load based on the comments obtained, A step of determining whether the load data and the load factors satisfy the first condition, A step of generating a first measure for the load when it is determined that the load data and the load factors satisfy the first condition, The process of generating a report that includes the first measure mentioned above, A program to achieve this.

15. A control method for a user-accessible device that communicates with a device attached to the user, The aforementioned device used by the target person, A step of obtaining activity data of the subject from the device, A step of calculating load data indicating the load on the subject based on the activity data, The process of outputting a message to receive comments from the target person regarding the load, depending on the load, When the aforementioned comments regarding the load are received from the aforementioned subject, the process involves estimating the factors of the load based on the acquired comments, A step of determining whether the load data and the load factors satisfy the first condition, A step of generating a first measure for the load when it is determined that the load data and the load factors satisfy the first condition, The process of generating a report that includes the first measure mentioned above, A control method comprising:

16. On the computer, A step of acquiring activity data of the subject from a device attached to the subject, A step of calculating load data indicating the load on the subject based on the activity data, The process of outputting a message to receive comments from the target person regarding the load, depending on the load, When the aforementioned comments regarding the load are received from the aforementioned subject, the process involves estimating the factors of the load based on the acquired comments, A step of determining whether the load data and the load factors satisfy the first condition, A step of generating a first measure for the load when it is determined that the load data and the load factors satisfy the first condition, The process of generating a report that includes the first measure mentioned above, A program to achieve this.

17. A control method for a device attached to a person, The aforementioned device, A step of obtaining activity data of the aforementioned subject, A step of calculating load data indicating the load on the subject based on the activity data, The process of having an external device output a message to receive comments from the target person regarding the load, depending on the load, When the external device receives the comments regarding the load from the subject, the process involves estimating the factors of the load based on the acquired comments. A step of determining whether the load data and the load factors satisfy the first condition, A step of generating a first measure for the load when it is determined that the load data and the load factors satisfy the first condition, The process of generating a report that includes the first measure mentioned above, A control method comprising:

18. On the computer attached to the subject, A step of obtaining activity data of the aforementioned subject, A step of calculating load data indicating the load on the subject based on the activity data, The process of having an external device output a message to receive comments from the target person regarding the load, depending on the load, When the external device receives the comments regarding the load from the subject, the process involves estimating the factors of the load based on the acquired comments. A step of determining whether the load data and the load factors satisfy the first condition, A step of generating a first measure for the load when it is determined that the load data and the load factors satisfy the first condition, The process of generating a report that includes the first measure mentioned above, A program to achieve this.

19. A method for providing reports to users that include measures to reduce the load, using a report delivery system, The report is output to the display of the user's terminal by the report provision system, The report includes load data indicating the load based on activity data of high-load individuals, and the factors of the load estimated based on comments received from the high-load individuals, and the measures proposed based on these, The aforementioned high-load individuals are those who, among the subjects from whom load data based on the activity level data is acquired, whose load index satisfies predetermined conditions for the aforementioned high-load individuals. How to submit the report.

20. A report provision system that outputs a report on the workload of a target person using an information processing device, a target person user device used by the target person, and a device attached to the target person, Based on the activity data of the subject measured by the device, load data indicating the subject's load is acquired. Based on comments from the subject regarding the load, the factors of the load are estimated and obtained. Determine whether the load data and the load factors satisfy the first condition. When it is determined that the load data and the load factors satisfy the first condition, the report including the first measure for the load is output. Report delivery system.

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