Program, method, and system

Abstract

By finding out the true feelings of employees who are at risk of mental health issues about their work, you can check and manage the mental state of employees. [Solution] A program to be executed by a computer having a processor and a memory, the program causes the processor to execute the following steps: acquiring first information on the working status of a user, determining whether the user is a target person based on the acquired first information and second information on the working status of other users who have been suspected of having mental health problems in the past, creating a question for the user based on the first information if the user is a target person, sending the created question to the user, and responding to a reply from the user to the question.

Description

[Technical field]

[0001] The present invention relates to a program, a method, and a system. [Background technology]

[0002] In recent years, flexible work styles that are not limited by time or place, such as teleworking, have become widespread. On the other hand, it has become difficult for managers to grasp the actual working conditions of employees (work load, etc.), and there is a risk that employees will develop health disorders due to overwork. For this reason, there is a need to check and manage the mental state of employees based on their actual working conditions. For example, the technology disclosed in Patent Document 1 describes a technique that extracts employees who may have mental health problems based on the actual working conditions of employees and those of people with mental health problems, and sets up interviews between the extracted employees and their superiors, etc. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2016-151979 A Summary of the Invention [Problem to be solved by the invention]

[0004] Patent Document 1 describes how to efficiently connect employees who may be experiencing mental health problems to an interview, but the person being interviewed may not always have an available schedule, and depending on the person being interviewed, it may be difficult to discuss the issue.

[0005] The purpose of this disclosure is to check and manage the mental state of employees by finding out their true feelings about work from employees who are suspected of having mental disorders. [Means for solving the problem]

[0006] A program to be executed by a computer having a processor and a memory, the program causing the processor to execute the steps of acquiring first information on the working status of a user, determining whether the user is a target person based on the acquired first information and second information on the working status of other users who have been suspected of having mental disorders in the past, creating a question for the user based on the first information if the user is a target person, sending the created question to the user, and responding to a reply from the user to the question. Effect of the Invention

[0007] According to the program of the present invention, it is possible to ascertain and manage the mental state of an employee by finding out the true feelings of the employee about work when the employee is suspected of having mental disorders. [Brief description of the drawings]

[0008] [Figure 1] 1 is a diagram showing the overall configuration of a system according to an embodiment of the present invention; [Diagram 2] FIG. 2 is a block diagram showing a configuration of a terminal device included in the system of the present embodiment. [Diagram 3] FIG. 2 is a block diagram showing the functional configuration of a server included in the system of the present embodiment. [Figure 4] FIG. 2 is a diagram showing the data structure of an employee information DB. [Diagram 5] FIG. 13 is a diagram illustrating the data structure of a log information DB. [Figure 6] FIG. 2 is a diagram showing the data structure of a work information DB. [Figure 7] FIG. 13 is a diagram for explaining an example of a flow for managing the mental state of an employee. [Figure 8] FIG. 4 is a schematic diagram illustrating a display example of a terminal device according to the embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an embodiment of the present invention will be described in detail with reference to the drawings. In the drawings for explaining the embodiment, the same components are generally designated by the same reference numerals, and the repeated description will be omitted.

[0010] <Summary> The system 1 according to this embodiment manages the working status of company employees. The working status indicates, for example, information related to attendance such as the time of arrival and departure of employees, absence, lateness, or early departure, work content, workload, overtime hours, working on holidays, etc. Based on the working status, the system 1 determines whether the employee is suffering from mental disorders, and if it is estimated that the employee is suffering from mental disorders, it provides care for the employee.

[0011] <Overall composition> Fig. 1 is a diagram showing the overall configuration of a system 1 according to the present embodiment. As shown in Fig. 1, the system 1 includes a terminal device 10 used by an employee, and a server 20. The terminal device 10 and the server 20 are connected to each other via a network 80 using a wired or wireless communication standard so as to be able to communicate with each other. In the illustrated example, a plurality of terminal devices 10 are included in the system 1. The number of terminal devices 10 can be changed arbitrarily according to the number of employees.

[0012] The terminal device 10 is a general-purpose computer used by an employee for business purposes. The terminal device 10 is realized, for example, by a stationary PC (Personal Computer), a laptop PC, a head-mounted display, etc. The terminal device 10 may also be a portable computer such as a smartphone or a tablet terminal.

[0013] As shown in FIG. 1, the terminal device 10 includes a communication IF (Interface) 12, an input device 13, an output device 14, a memory 15, a storage unit 16, and a processor 19.

[0014] The communication IF 12 is an interface for transmitting and receiving signals so that the terminal device 10 can communicate with external devices. The input device 13 is an input device for receiving input operations from users (employees). The input device 13 includes, for example, a touch panel, a touch pad, a pointing device such as a mouse, a keyboard, and the like.

[0015] The output device 14 is an output device for presenting information to a user. The output device 14 includes, for example, a display, a speaker, etc. The memory 15 is for temporarily storing a program and data processed by the program, etc., and is realized by, for example, a volatile memory such as a DRAM (Dynamic Random Access Memory). The storage unit 16 is a storage device for saving data, and is realized by, for example, a flash memory or an HDD (Hard Disc Drive). The processor 19 is hardware for executing an instruction set described in a program, and is composed of an arithmetic unit, a register, a peripheral circuit, etc.

[0016] The server 20 is a device that manages employee information. The server 20 is a computer connected to a network 80.

[0017] As shown in FIG. 1, the server 20 includes a communication IF 22, an input / output IF 23, a memory 25, a storage 26, and a processor 29.

[0018] The communication IF 22 is an interface for transmitting and receiving signals so that the server 20 can communicate with an external device. The input / output IF 23 functions as an interface with an input device for receiving input operations from a user and an output device for presenting information to the user. The memory 25 is for temporarily storing programs and data processed by the programs, etc., and is realized by a volatile memory such as a DRAM. The storage 26 is a storage device for saving data, and is realized by a flash memory or a HDD, for example. The processor 29 is hardware for executing an instruction set described in a program, and is composed of an arithmetic unit, a register, a peripheral circuit, etc.

[0019] <Terminal device configuration> Fig. 2 is a block diagram showing a configuration of the terminal device 10 included in the system 1 of the present embodiment. As shown in Fig. 2, the terminal device 10 includes a communication unit 121, an operation input unit 130 (including a touch-sensitive device 131 and a display 132), a voice processing unit 140, a microphone 141, a speaker 142, a position information sensor 150, a camera 160, a storage unit 170, and a control unit 180. The terminal device 10 also includes functions and configurations (e.g., a battery for storing power, a power supply circuit for controlling the supply of power from the battery to each circuit, etc.) that are not shown in Fig. 2. Each block included in the terminal device 10 is electrically connected by, for example, a bus or the like.

[0020] The communication unit 121 performs processing for the terminal device 10 to communicate with other devices. The communication unit 121 performs transmission processing on a signal generated by the control unit 180 and transmits the signal to the outside (for example, the server 20). The communication unit 121 performs reception processing on a signal received from the outside and outputs the signal to the control unit 180.

[0021] The operation input unit 130 has a mechanism for receiving an input operation from a user who owns the terminal device 10. Specifically, for example, when the operation input unit 130 is a touch screen, it includes a touch-sensitive device 131 and a display 132. The operation input unit 130 may also include a mouse, a keyboard, and the like.

[0022] Touch-sensitive device 131 detects the position of a user's touch on the touch panel by using, for example, a capacitive touch panel. Touch-sensitive device 131 outputs a signal indicating the position of a user's touch detected by the touch panel to control unit 180 as an input operation.

[0023] Display 132 displays data such as images, videos, and text under the control of control unit 180. Display 132 is realized by, for example, an LCD (Liquid Crystal Display) or an organic EL (Electro-Luminescence) display.

[0024] The audio processing unit 140 modulates and demodulates audio signals and is realized by, for example, a processor for audio processing. The audio processing unit 140 modulates an audio signal provided by the microphone 141, and outputs the modulated signal to the control unit 180. The audio processing unit 140 also demodulates an audio signal provided by the control unit 180, and provides the demodulated signal to the speaker 142.

[0025] The microphone 141 receives a voice input and outputs a voice signal corresponding to the voice input to the voice processing unit 140. The speaker 142 converts the voice signal provided from the voice processing unit 140 into a voice and outputs the voice to the outside of the terminal device 10.

[0026] The position information sensor 150 is a sensor that detects the position of the terminal device 10, and is, for example, a GPS (Global Positioning System) module. The GPS module is a receiving device used in a satellite positioning system. In the satellite positioning system, signals are received from at least three or four satellites, and the current position of the terminal device 10 equipped with the GPS module is detected based on the received signals.

[0027] Camera 160 is a device that receives light with an imaging element and outputs the light as an image signal. Camera 160 is disposed, for example, at a position facing a user who uses terminal device 10. Camera 160 captures an image of the user and outputs an image signal.

[0028] The storage unit 170 is configured with, for example, a flash memory, and stores data and programs used by the terminal device 10. For example, the storage unit 170 stores employee information 171.

[0029] The employee information 171 is information about an employee who owns the terminal device 10. The employee information 171 includes, for example, identification information (employee ID), name, identification information (terminal ID) of the terminal device 10, employee attributes, etc. The attributes indicate the employee's affiliation. The attributes include, for example, department, position, project, working style, etc.

[0030] The control unit 180 is realized by the processor 19 reading a program stored in the storage unit 170 and executing instructions included in the program. The control unit 180 controls the operation of the terminal device 10. Specifically, the control unit 180 performs the functions of an operation reception unit 181, a transmission / reception unit 182, an acquisition unit 183, and a presentation unit 184, for example.

[0031] The operation reception unit 181 performs processing for receiving a user's operation input from the input device 13. For example, the operation reception unit 181 determines the type of operation, such as whether the operation by the operator is a flick operation, a tap operation, or a drag (swipe) operation, based on information on the coordinates where the user touches the touch-sensitive device 131 with a finger or the like, and receives the operation.

[0032] The transmission / reception unit 182 performs processing for the terminal device 10 to transmit and receive data to and from an external device such as the server 20 in accordance with a communication protocol.

[0033] The acquisition unit 183 acquires information that allows understanding of the working status of the user who uses the terminal device 10. Examples of the acquired information include information input by the user to a predetermined application, information related to an operation history of the user operating the terminal device 10, and information based on an image signal of the user captured by the camera 160. The information acquired by the acquisition unit 183 is transmitted to the server 20 via the transmission / reception unit 182.

[0034] The information input to a specific application includes, for example, information input by a user to a time attendance management application indicating that the user has arrived at work, finished work, or taken a break, information indicating that the user has taken paid leave, or information regarding various reasons. The information also includes, for example, information regarding a schedule input by a user to a schedule management application. The information regarding a schedule includes, for example, the content of work tasks, such as the duration of meetings and the number of business trips, and the amount of work tasks.

[0035] The information on the operation history specifically includes a log of the user's operations on the input device 13 (hardware) of the terminal device 10, a log of the user's operations on an application, or a log of the user's operations on an OS (Operating System).

[0036] The hardware operation log includes, for example, information indicating which key was entered on the keyboard, the number of times a key was entered, etc. The hardware operation log also includes position information detected by a pointing device such as a mouse, touchpad, or touch-sensitive device, information on the user's operation determined based on the position information (for example, click, drag, swipe, flick, long tap, etc.), the number of operations, etc. The hardware operation log also includes voice information entered by a microphone, the number of utterances, etc.

[0037] The application operation log includes a log of operations input by a user when using an application to perform a task. Specifically, for example, if the application is a presentation material creation software, various information such as the application name, file name, item name in the file, information indicating which function was used, input character information, error information, information on the operation to add a new slide, etc. If the application is an email software, the information includes header information of emails sent and received, setting information, etc. If the application is a document creation software, the number of characters input, the number of times characters were converted, the number of pages created, the input contents (such as inputting characters, modifying characters, inserting images or videos), etc.

[0038] The OS operation log includes information on which applications have become active, information on changes to OS settings, information on putting the terminal device 10 into sleep mode, information on waking it up from sleep mode, and information on the usage status of hardware resources (e.g., CPU usage, memory usage, network usage, etc.).

[0039] Information based on an image signal of a user captured by a camera includes, for example, information about an operation of the user to start the camera, information about an image showing the orientation and expression of the user's face, or information about feature points that are obtained by extracting the configuration of the face through image analysis and expressing the movement of the feature points, etc. Note that the information based on the image signal may include information other than the above, or may include a part of the above information. The presentation unit 184 presents various information to the user.

[0040] <Server Functional Configuration> 3 is a block diagram showing a functional configuration of the server 20 included in the system 1 of the present embodiment. As shown in FIG. 3, the server 20 fulfills the functions of a communication unit 201, a storage unit 202, and a control unit 203.

[0041] The communication unit 201 performs processing for the server 20 to communicate with external devices.

[0042] The storage unit 202 stores data and programs used by the server 20. For example, the storage unit 202 stores an employee information database (DB) 281, a log information database (DB) 282, and a work information database (DB) 283.

[0043] The employee information DB 281 is a database for holding information about employees, which will be described in detail later.

[0044] The log information DB 282 is a database for holding information about the operation history of the user operating the terminal device 10. Details will be described later.

[0045] The work information DB 283 is a database for storing the work details of the user. The details will be described later.

[0046] Further, the storage unit 202 stores a plurality of trained models generated by, for example, machine learning. The first trained model 284 is, for example, a model used to determine whether or not a user is a person who is suspected of having a mental disorder. The second trained model 285 is, for example, a model used when creating a question for a person who is suspected of having a mental disorder. The third trained model 286 is a model for realizing a chatbot that chats with a person who is suspected of having a mental disorder. The first trained model 284 to the third trained model 286 are stored in the storage unit 202, for example, before the provision of the corresponding service is started. In this embodiment, the person who is suspected of having a mental disorder is assumed to be, for example, the following. Those at risk of mental health problems Those at risk of losing motivation

[0047] The first trained model 284 to the third trained model 286 are obtained by making the machine learning model perform machine learning based on the learning data in accordance with a model learning program.

[0048] For example, in this embodiment, the first trained model 284 is trained to output whether or not a mental disorder is suspected for the input information on the user. At this time, the training data, for example, information on the past working conditions of the employee is used as input data, and the judgment of whether or not the person is suspected of having a mental disorder for this input data is used as the correct output data. The judgment of whether or not a person is suspected of having a mental disorder is made, for example, based on the opinion of an expert. That is, the first trained model 284 is trained based on information on the working conditions of an employee who was suspected of having a mental disorder in the past. The information on the past working conditions of the employee is acquired, for example, from the employee information DB 281, the log information DB 282, or the work information DB 283. Note that the training data may be, for example, a judgment of whether or not a person is suffering from a mental disorder as the correct output data. The judgment of whether or not a person is suffering from a mental disorder is made, for example, based on the opinion of an expert.

[0049] In addition, the second trained model 285 is trained to output a question according to the state of the user for the input information on the user. For example, when a person suspected of having a mental disorder is a person suspected of having a mental health disorder, the second trained model 285 is trained to output a question for relieving anxiety, stress, or the like. At this time, the learning data is information on the working conditions of an employee who was suspected of having a mental disorder in the past as input data, and a question sentence based on the input information is the correct answer output data. The question sentence as the correct answer output data is created, for example, by extracting a predetermined phrase from the information on the working conditions to relieve anxiety, stress, or the like. The question sentence may be rephrased as, for example, generated based on information on the working conditions of the user. In addition, the question sentence may be rephrased as, for example, assumed from information on the working conditions of the user. The question sentence for relieving anxiety or stress is a question for relieving the cause of the user's mental health disorder, such as the content of the work, human relationships, personal circumstances, daily anxiety, or the presence or absence of worries, for example.

[0050] In addition, when a person suspected of having mental disorders is a person suspected of having a decline in motivation, the second trained model 285 is trained to output a question for clarifying the reason for the decline in motivation. At this time, the learning data is information on the working conditions of an employee who was suspected of having mental disorders in the past as input data, and a question sentence based on the input information is the correct answer output data. The question sentence as the correct answer output data is created, for example, by extracting a predetermined phrase from the information on the working conditions to clarify the reason for the decline in motivation. The question for clarifying the reason for the decline in motivation is a question for clarifying the cause of the decline in motivation of the user, such as the content of the work, human relationships, personal circumstances, daily dissatisfaction, or the presence or absence of worries.

[0051] The third trained model 286 is trained to output a predetermined response to the input answer and information on the user's working situation. At this time, the learning data is, for example, an expected answer pattern and information on the working situation of an employee who was suspected of having mental disorders in the past, and the response pattern based on the input answer pattern and the information on the working situation is the correct output data. The response to the answer from the user may include, for example, a suggestion to consult with an industrial doctor, a suggestion to improve lifestyle habits, or a suggestion to take a (long) vacation.

[0052] The machine learning model according to the present embodiment is, for example, a parameterized composite function in which a plurality of functions are combined. The parameterized composite function is defined by a combination of a plurality of adjustable functions and parameters. The machine learning model according to the present embodiment may be any parameterized composite function that satisfies the above requirements, but is assumed to be a multi-layered network model (hereinafter referred to as a multi-layered network). A trained model using a multi-layered network has an input layer, an output layer, and at least one intermediate layer or hidden layer provided between the input layer and the output layer. The trained model is expected to be used as a program module that is a part of artificial intelligence software.

[0053] As the multi-layered network according to the present embodiment, for example, a deep neural network (DNN) that is a multi-layered neural network that is the subject of deep learning may be used. As the DNN, for example, a convolution neural network (CNN) that targets images may be used.

[0054] The control unit 203 is realized by the processor 29 reading a program stored in the storage unit 202 and executing instructions included in the program. The control unit 203 controls the operation of the server 20. Specifically, the control unit 203 exerts functions as a transmission / reception unit 2031, an acquisition unit 2032, a determination unit 2033, a dialogue processing unit 2034, and an execution unit 2035, for example.

[0055] The transmitting / receiving unit 2031 controls the process in which the server 20 transmits and receives data to and from external devices such as the terminal device 10 in accordance with a communication protocol. The received data is stored in the storage unit 202. For example, information transmitted from the terminal device 10 that the user inputs to a predetermined application, information related to the operation history of the user's operation of the terminal device 10, information based on an image signal of the user captured by the camera 160, and the like are stored in the employee information DB281, the log information DB282, or the work information DB283 of the storage unit 202.

[0056] The acquisition unit 2032 acquires information required for the determination from the information stored in the storage unit 202. For example, the acquisition unit 2032 acquires information related to the user's work status during a predetermined period from the employee information DB 281, the log information DB 282, or the work information DB 283. The information related to the work status includes, for example, information related to attendance, information related to schedule, information related to operation history, information based on an image signal, or work content inferred from information related to operation history and information based on an image signal. The acquisition unit 2032 acquires information required for the determination at a predetermined timing, for example.

[0057] The determination unit 2033 executes a process of determining whether the user is a person who is suspected of having a mental disorder and is a person to be monitored. Specifically, for example, the determination unit 2033 inputs the information acquired by the acquisition unit 2032 to the first trained model 284, and causes the first trained model 284 to output whether the user is a person who is suspected of having a mental disorder.

[0058] The dialogue processing unit 2034 creates a question using the second trained model 285 stored in the storage unit 202, and automatically responds to the answer from the user in a dialogue format using the third trained model 286. The dialogue processing unit 2034 is realized, for example, by utilizing a chat system used in company business.

[0059] Specifically, for example, the dialogue processing unit 2034 inputs information on the working situation of a user who is determined to be at risk of mental illness to the second trained model 285, and outputs a question sentence for relieving anxiety, stress, or the like. The dialogue processing unit 2034 transmits the output question sentence from the communication unit 201 to the terminal device 10. At this time, the dialogue processing unit 2034 transmits the question sentence at a timing when the user can easily answer, for example. The timing when the user can easily answer is preferably a timing when the user is not highly concentrated while operating the terminal device 10. The timing when the user is not highly concentrated is determined, for example, based on information on the operation history, that is, information such as a slow input speed and a large number of erroneous conversions. In addition, the timing when the user is not highly concentrated is determined, for example, based on the direction of the user's face or the behavior of the user's gaze determined by an image signal. The timing when the user can easily answer is determined, for example, based on information on the working situation, and includes immediately after a task is switched, immediately after a break ends, immediately before the lunch break ends, and the like.

[0060] The dialogue processing unit 2034 receives the answer sent from the terminal device 10 via the communication unit 201. The answer can be acquired in the form of natural sentences. By making it possible to reply in the form of natural sentences, the user can chat with the same feeling as if he or she were chatting with a person.

[0061] The dialogue processing unit 2034 inputs the acquired answer sentence and information on the work situation to the third trained model 286, and causes it to output a response sentence according to the input answer. The dialogue processing unit 2034 transmits the output response sentence from the communication unit 201 to the terminal device 10.

[0062] The execution unit 2035 executes a procedure according to the contents of the chat with the user. For example, when the user agrees to a proposal for vacation or leaving early transmitted by the dialogue processing unit 2034, the execution unit 2035 executes attendance procedures by the attendance app.

[0063] <Data Structure> Figures 4 to 6 are diagrams showing the data structures of the databases stored in the server 20. Note that Figures 4 to 6 are merely examples, and do not exclude data that is not shown. Fig. 4 is a diagram showing the data structure of the employee information DB 281. As shown in Fig. 4, each record of the employee information DB 281 includes an item "employee ID", an item "employee name", an item "attributes", and the like.

[0064] The item "employee ID" stores an ID for identifying an employee.

[0065] The item "employee name" stores the name of the employee.

[0066] The item "attribute" stores the attributes of an employee. The attributes include various systems to which an employee belongs. For example, the item "attribute" includes sub-items such as the item "department", the item "position", the item "project", and the item "work style".

[0067] The item "department" stores the division to which the user belongs, for example, the department. The item "job title" stores the user's position in the organization to which the user belongs, for example, the job title. If there are multiple job titles, multiple job titles may be registered. The item "project" stores information on a collective task to which the user is assigned, for example, a project. If there are multiple projects, multiple projects may be registered. The item "work style" stores the work style set for the user.

[0068] The item "Work style" stores, for example, proposed work, where the employee works at fixed hours, flextime, where the employee works at different times, discretionary work, where the employee's working hours are left to the discretion of the user, etc. The item "Attributes" may also include further sub-items (for example, year of joining the company, employment style, etc.) as necessary.

[0069] The employee information DB281 may store information other than the information shown in Fig. 4. For example, the employee information DB281 may include quotas imposed within a predetermined period. The employee information DB281 may also include information on the place of work, information on client companies, information on superiors or subordinates, and the like.

[0070] Furthermore, information indicating the health condition of the user may be stored in the employee information DB 281. The information indicating the health condition may include, for example, information indicating a concern of mental illness or information related to the diagnosis result of a general health check.

[0071] Fig. 5 is a diagram showing the data structure of the log information DB 282. As shown in Fig. 5, each record of the log information DB 282 includes an item "employee ID", an item "date", an item "operation log", and the like.

[0072] The item "date" stores the date when the operation log was acquired.

[0073] The item "operation log" stores a log of information related to operation history. The item "operation log" includes sub-items such as an item "time", an item "terminal ID", an item "log", etc. as information related to operation history.

[0074] The item "time" stores the time (timestamp) when a user operation occurs. Specifically, the item "time" includes, for example, the time when hardware is operated, the time when an application is operated, or the time when an OS is operated.

[0075] The item "Terminal ID" stores the ID of the terminal device 10 operated by the user.

[0076] The item "log" stores the contents of operations performed by a user. Specifically, the item "log" stores, for example, the contents of operations performed on hardware, applications, or OS.

[0077] Fig. 6 is a diagram showing the data structure of the work information DB 283. As shown in Fig. 6, each record of the work information DB 283 includes an item "employee ID", an item "date", an item "duty content", an item "time", and the like.

[0078] The item "date" stores the date on which the user performed the task.

[0079] The item "task content" stores the content of the task performed by the user. The item "task content" is identified and stored by the control unit 203, for example, based on information input to an application. For example, the control unit 203 identifies attendance information such as attendance, finishing, rest, lateness, leaving early, absence, etc., based on attendance information such as attendance, finishing, rest, etc., input by the user to an attendance app, and stores the information in the item "task content". In addition, the control unit 203 identifies meetings, business trips, etc., based on schedule information input by the user to a schedule management app, and stores the information in the item "task content".

[0080] Furthermore, the item "business content" is identified and stored, for example, by the control unit 203 based on information related to the operation history. For example, the control unit 203 identifies email, document creation, work, research, etc. based on the application used by the user, the file name created by the used application, the operation performed on the file, etc., and stores them in the item "business content".

[0081] The item "time" stores the time when the work described in the item "work content" was performed. Note that the work information DB283 is not limited to the item "time" and may store the time when the work was performed. Also, it is not limited to only the time when the specified work started, but may store the time when the specified work started and the time when the specified work ended.

[0082] The work information DB283 may store information other than the information shown in FIG. 6. For example, the work information DB283 may include an evaluation of the work situation. For example, the work information DB283 may include an evaluation for each task. The evaluation for each task is evaluated by the control unit 203, for example, based on information on the operation history or information based on the image signal of the user. For example, the control unit 203 estimates the processing speed of the task, the degree of concentration on the task, the degree of participation in the meeting, etc., based on information on the operation history or information based on the image signal of the user, and evaluates the task content of the user. The work information DB283 may also include an evaluation for each predetermined period. The evaluation for each predetermined period represents, for example, the user's evaluation for each day, week, month, etc. The evaluation for each predetermined period is evaluated by the control unit 203, for example, based on the accumulation of the evaluation for each task.

[0083] Furthermore, the total number of working hours for each predetermined period may be stored in the work information DB 283. The total number of working hours for each predetermined period may represent, for example, the total number of working hours for each day, week, month, etc. good.

[0084] <Operation> Hereinafter, the process of managing the mental state of employees by the system 1 will be described with reference to Fig. 7. In the explanation of Fig. 7, for example, a case will be described where an individual suspected of having a mental disorder is an individual suspected of having a mental health disorder.

[0085] FIG. 7 is a diagram for explaining an example of a flow of managing the mental state of an employee by the system 1. 7, the terminal device 10 transmits information enabling the user's working status to be ascertained to the server 20. Specifically, for example, when the user inputs information to a predetermined application, the terminal device 10 transmits the input information to the server 20. Also, for example, when the user operates the terminal device 10, the terminal device 10 transmits information on the operation history to the server 20. Also, the terminal device 10 transmits information based on an image signal of the user captured by the camera 160 to the server 20 at a predetermined cycle.

[0086] The transmitting / receiving unit 2031 of the server 20 receives the information about the multiple employees transmitted from the terminal device 10, and stores the received information in a new record in the database stored in the storage unit 202.

[0087] The acquisition unit 2032 of the server 20 acquires information required for judgment from the database of the storage unit 202 at a predetermined timing (step S201). Specifically, for example, the acquisition unit 2032 acquires information required for judgment as to whether or not the employee P1 is suspected of having a mental disorder from the employee information DB281, the log information DB282, or the work information DB283 of the storage unit 202. In the case where the employee P1 is not suspected of having a mental disorder, the predetermined timing is, for example, as follows. When you arrive at work on a preset day (when you press the attendance object in the attendance management app) -Pre-set time on a pre-set day When data is obtained that suggests a mental disorder Data that may indicate a mental disorder includes, for example, inconsistent arrival times at work, continuous long hours of overtime, or not being able to take vacations.

[0088] Furthermore, in the case where there is concern that employee P1 is in a mental state of distress, the specified timing is, for example, as follows. When you arrive at work -Pre-set time In this explanation, the process for employee P1 will be described, but the process may be performed in parallel for other employees.

[0089] When the information required for the judgment is acquired, the judgment unit 2033 judges whether the employee P1 is suspected of having a mental disorder (step S202). Specifically, the judgment unit 2033 inputs the acquired information on the working situation to the first trained model 284, and outputs whether the employee P1 is suspected of having a mental disorder. In the following, a person suspected of having a mental disorder is referred to as a subject as a person to be monitored for health management. When the judgment unit 2033 judges that the employee P1 is a subject, the judgment result is stored in, for example, the employee information DB 281 of the storage unit 202, and the subsequent process is executed. When the employee P1 is judged to be a subject, there are cases where the employee is judged to be a subject from a state where there is no concern, and cases where the judgment that the employee is a subject is maintained.

[0090] On the other hand, if the determination unit 2033 determines that the employee P1 is not a target person, the process ends. Note that, if it is determined that the employee P1 is not a target person, the state of concern may be eliminated or the state of concern may be maintained.

[0091] When it is determined that the employee P1 is a target person, the dialogue processing unit 2034 creates a question for the employee P1 (step S203). Specifically, the dialogue processing unit 2034 inputs information about the working situation of the employee P1, who has been determined to be at risk of mental illness, to the second trained model 285, and outputs a question for relieving the anxiety or stress of the employee P1. The question for relieving the anxiety or stress of the employee P1 is created based on the information about the working situation of the employee P1.

[0092] For example, if employee P1 finished work late yesterday, a question is created to thank employee P1, such as "You worked late last night. Did you have time to rest after you got home?"

[0093] Also, for example, if employee P1, whose arrival time at work is normally regular, suddenly becomes irregular, a question that shows concern for employee P1 is created, such as, "Your arrival time at work has been irregular recently. Are you worried?"

[0094] Also, for example, if employee P1, who normally comes to work on time, suddenly starts being late, a question that shows concern for employee P1 can be created, such as, "You've been coming in late a lot lately. Is there anything you're worried about?"

[0095] In addition, for example, if meetings with the same person are held multiple times over a long period of time, a question that shows concern for employee P1 can be created, such as, "Long meetings must be tough. Have any unpleasant things been said in the meetings?"

[0096] The dialogue processing unit 2034 transmits the output question from the communication unit 201 to the terminal device 10 (step S204). At this time, the dialogue processing unit 2034 transmits the question at a timing when it is easy for the user to answer, for example. The timing when it is easy for the user to answer is preferably a timing when the employee P1 is not highly concentrated while operating the terminal device 10. The dialogue processing unit 2034 refers to information on the working situation and determines a timing when it is easy for the employee P1 to answer the question. At the determined timing, the dialogue processing unit 2034 transmits the created question from the communication unit 201 to the terminal device 10. The dialogue processing unit 2034 transmits the created question to the terminal device 10 in a format of being inserted into a part of a chat message.

[0097] When a question is transmitted from the server 20, the presentation unit 184 of the terminal device 10 displays the received question on the display 132 (step S101). The operation reception unit 181 receives a response from the employee P1 who operates the terminal device 10. The presentation unit 184 displays the input response on the display 132. The transmission / reception unit 182 transmits the input response to the server 20 (step S102).

[0098] Fig. 8 is a schematic diagram showing a display example of the terminal device 10 according to this embodiment. As shown in Fig. 8, a chat area 1321 is displayed on the display 132 of the terminal device 10. A question 1321A from the server 20 is displayed in the left column of the chat area 1321, and an answer 1321B input by the employee P1 is displayed in the right column. When the presentation unit 184 receives a question from the server 20, it may display an object indicating that a message has been received at a predetermined position (e.g., a bar) on the display 132, and display the chat area 1321 in response to a press on this object by the employee P1.

[0099] Upon receiving the response sentence from the terminal device 10, the dialogue processing unit 2034 creates a response sentence to the employee P1 (step S205). Specifically, the dialogue processing unit 2034 inputs the received response sentence and information on the working situation to the third trained model 286, and causes the third trained model 286 to output a response sentence according to the input answer. The dialogue processing unit 2034 transmits the output response sentence from the communication unit 201 to the terminal device 10 (step S206).

[0100] When the response text is transmitted from the server 20, the presentation unit 184 of the terminal device 10 displays the received response text on the display 132 (step S103). The operation reception unit 181 receives a response from the employee P1 who operates the terminal device 10. The presentation unit 184 displays the input response text on the display 132. The transmission / reception unit 182 transmits the input response text to the server 20 (step S104). The chat between the employee P1 and the server 20 continues, for example, until no response is input from the employee P1. The contents of the chat between the employee P1 and the server 20 (question text, response text, and response text) are stored in the storage unit 202. Note that the contents of the chat between the employee P1 and the server 20 are processed (for example, deleted) and stored so that it cannot be identified as a chat with the employee P1. For example, if the superior of the department to which employee P1 belongs responds that he or she is under excessive stress through work, this response is stored in the storage unit 202 with information such as the name that may identify the superior obscured. The stored information is used statistically, for example, as work style improvement data.

[0101] Also, for example, if employee P1 has no plans such as a meeting the next day, the server 20 may respond with a message such as, "How about taking tomorrow off?" If employee P1 responds to such a message with a message such as, "Yes, I'll take the day off," the execution unit 2035 of the server 20 executes a vacation request for the next day to the attendance app.

[0102] The process is not limited to the above, and each step of the process can be changed as desired as long as the flow of each process is not inconsistent. For example, there may be cases where employee P1 does not reply to the automatic response.

[0103] As described above, in this embodiment, the acquisition unit 2032 of the server 20 acquires first information on the working status of the user. The determination unit 2033 determines whether the user is a target person based on the acquired first information and second information on the working status of other users who have been suspected of having mental disorders in the past. If the user is a target person, the dialogue processing unit 2034 creates a question for the user based on the first information. The dialogue processing unit 2034 transmits the created question to the user. Then, the dialogue processing unit 2034 responds to the user's reply to the question. This allows the server 20 to accurately grasp that the user is suspected of having mental disorders. In addition, the server 20 can find out the user's true feelings about anxiety or stress. Therefore, according to the system 1 according to the present embodiment, it is possible to ascertain and manage the mental state of an employee by finding out the employee's true feelings about work when he or she is suspected of having mental disorders.

[0104] In this embodiment, the determination unit 2033 determines whether or not the user is a target person by inputting the first information to the first trained model 284 trained using the second information. This makes it possible to quickly and highly accurately determine whether or not the user is a target person based on the first information about the user.

[0105] In this embodiment, the dialogue processing unit 2034 creates a question by inputting the first information to the second trained model 285 trained using the second information and a question expected from the second information. This makes it possible to quickly and accurately create a question that can relieve the anxiety or stress of the user based on the first information about the user.

[0106] In this embodiment, the execution unit 2035 executes the attendance procedure in response to the user's reply to the question. This allows the attendance procedure to be performed automatically without the user having to perform the procedure, eliminating the situation where a person cannot take a day off even if he or she wants to.

[0107] In this embodiment, the dialogue processor 2034 refers to the first information and transmits the question at a timing that is easy for the user to answer, thereby increasing the probability of obtaining an answer from the user.

[0108] In this embodiment, the control unit 203 processes the questions to the user and the responses from the user, and stores the processed questions and responses related to personal information. This makes it possible to appropriately protect the personal information of the user and the employees around him / her. It also makes it possible to prevent the user from being put in a disadvantageous situation due to the content entered in the chat.

[0109] In this embodiment, the dialogue processing unit 2034 creates a new response to the user by inputting the first information and a response from the user to the third trained model 286 trained using the second information, an expected response sentence, and a response sentence expected from the response sentence and the second information. This makes it possible to quickly and accurately create a response sentence in response to the answer from the user.

[0110] <Modification> In the above embodiment, an example has been described in which the first trained model 284 is trained to output whether or not a user is at risk of mental illness, based on information about the input user. However, the learning of the first trained model 284 is not limited to this.

[0111] For example, the first trained model 284 may be trained to output the degree of mental illness for input information about a user. The degree of mental illness includes, for example, that mental illness is feared, that mental illness is mild, or that mental illness is severe. In this case, the learning data is, for example, information about the employee's past work situation as input data, and the degree of mental illness for this input data is the correct output data.

[0112] In step S202, the determination unit 2033 inputs information related to the working situation into the first trained model 284 and outputs the degree of mental illness. If it is output that mental illness is suspected, that the mental illness is mild, or that the mental illness is severe, this indicates that the user is a target person. When the determination unit 2033 determines that the user is a target person, it stores the determination result including the degree of mental illness in the memory unit 202, for example, in the employee information DB 281. By determining the degree of mental illness, it becomes possible to manage the mental state of the employee in more detail.

[0113] The first trained model 284 may be trained to output whether or not a user is at risk of mental illness and to output stress factors in response to input information about the user. Stress factors are elements that cause stress, and include, for example, a lot of overtime, superiors, subordinates, customers, life rhythms, etc. In this case, the learning data is, for example, information about the employee's past working conditions as input data, and the judgment of whether or not a user is at risk of mental illness and the stress factors are output as correct answer data in response to the input data.

[0114] In step S202, the determination unit 2033 inputs information about the working situation into the first trained model 284, and outputs the stress factors together with whether or not the user is suspected of having mental illness. When the determination unit 2033 determines that the user is a target person, the determination result including the stress factors is stored in the storage unit 202, for example, in the employee information DB 281. By determining the stress factors in addition to the mental illness, it becomes possible to grasp the cause of the employee's mental illness.

[0115] In the above embodiment, an example has been described in which the second trained model 285 is trained to output questions for relieving anxiety, stress, etc., in response to input information about a user. However, the learning of the second trained model 285 is not limited to this.

[0116] For example, the second trained model 285 may be trained to output a question for relieving anxiety, stress, etc., in response to input of information about the user and the degree of mental illness. In this case, the learning data is input with information about the user's past work situation for each degree of mental illness as input data, and a question created according to the degree is the correct answer output data. The question as the correct answer output data is created, for example, by extracting a predetermined phrase from the information about the work situation and taking into account the degree of mental illness, so as to relieve anxiety, stress, etc.

[0117] In step S203, the dialogue processing unit 2034 inputs information about the working conditions of the employee whose degree of mental illness has been determined to the second trained model 285, and outputs questions for relieving the employee's anxiety or stress. Since questions are created taking into account the degree of mental illness, it becomes possible to more effectively relieve the employee's anxiety or stress.

[0118] Also, for example, the second trained model 285 may be trained to output a question for relieving anxiety or stress, etc., in response to input of information about the user and stress factors. In this case, the learning data is input with information about the working situation of a user who was previously suspected of having mental disorders and stress factors as input data, and a question sentence based on the input information is set as correct answer output data. The question sentence as the correct answer output data is created by extracting a predetermined wording from the information about the working situation and the stress factors, for example, so as to relieve anxiety or stress, etc.

[0119] In step S203, the dialogue processing unit 2034 inputs information about the working conditions of the employee determined to be at risk of mental illness and the stress factors estimated by the determination unit 2033 to the second trained model 285, and outputs questions for relieving the employee's anxiety or stress. Since questions taking into account the stress factors are created, it becomes possible to more effectively relieve the employee's anxiety or stress.

[0120] In the above embodiment, the first trained model 284 to the third trained model 286 may be retrained based on feedback information. For example, the control unit 203 acquires feedback information on the judgment result by the judgment unit 2033. The feedback information may be, for example, an evaluation from the user on the judgment, or an evaluation based on the content of a chat established between the user and the server 20. The control unit 203 retrains the first trained model 284 using information on the working situation weighted based on the feedback information and the judgment result. This adds new teacher data, making it possible to improve the accuracy of judgment using the first trained model 284 over the course of the usage period.

[0121] Also, for example, the control unit 203 acquires feedback information on the question created by the dialogue processing unit 2034. The feedback information may be, for example, a user's evaluation of the created question, or an evaluation based on the content of a chat established between the user and the server 20. The control unit 203 re-trains the second trained model 285 using information on the working situation weighted based on the feedback information and the created question. This adds new teacher data, and the content of the question created using the second trained model 285 becomes more appropriate as the usage period elapses.

[0122] Also, for example, the control unit 203 acquires feedback information on a response to a reply sentence from a user, created by the dialogue processing unit 2034. The feedback information may be, for example, an evaluation from the user on the response to the reply sentence, or an evaluation based on the content of a chat established between the user and the server 20. The control unit 203 re-trains the third trained model 286 using information on the working situation weighted based on the feedback information, the input reply sentence, and the created response sentence. This adds new teacher data, and the content of the response sentence created using the third trained model 286 becomes more appropriate as the usage period elapses.

[0123] In the above embodiment, the degree of the user's mental illness is not determined based on the content of the chat between the user and the server 20. However, the degree of the user's mental illness may be determined based on the content of the chat between the user and the server 20. In this case, the storage unit 202 stores, for example, a fourth trained model. The fourth trained model is a model used when determining the degree of the user's mental illness based on, for example, the content of the chat between the user and the server 20. The fourth trained model is trained to output the degree of mental illness in response to an answer input by the user. In this case, the learning data is, for example, a past answer pattern input by an employee who is suspected of having a mental illness, and the judgment of the degree of mental illness in response to this input data is the correct answer output data. By determining the degree of the user's mental illness from the content of the chat, it is possible to respond according to the determined degree.

[0124] The fourth trained model may be trained to determine whether or not the user has a mental disorder in addition to the degree of the mental disorder. In this case, the learning data may be, for example, past answer patterns input by employees who are suspected of having a mental disorder, and the determination that the employee has no mental disorder in response to the input data is the correct output data.

[0125] The control unit 203 may re-train the fourth trained model using the feedback information.

[0126] In the above embodiment, the storage unit 202 stores a plurality of trained models, but the models stored in the storage unit 202 do not have to be trained models. A predetermined calculation table or a predetermined function may be stored in the storage unit 202, and the data may be used in the processing in the control unit 203.

[0127] Also, in the above embodiment, an example has been described in which the determination unit 2033 determines whether or not the user is a person to be monitored who is suspected of having mental health problems, using the first trained model 284. However, in this embodiment, the determination unit 2033 may use the first trained model 284 to determine whether or not the user is a person who is suspected of having mental health problems and whether or not the user is a person who is suspected of having a decrease in motivation.

[0128] At this time, for example, the first trained model 284 is trained to output a numerical value representing a concern of mental health problems and a numerical value representing a concern of decreased motivation in response to input information about a user. For example, the learning data is information about the past working conditions of employees as input data, and the judgment of whether or not the person is at risk of mental health problems and whether or not the person is at risk of decreased motivation in response to this input data is the correct output data.

[0129] The judgment unit 2033 judges, for example, based on a numerical value representing a concern about mental health problems and a numerical value representing a concern about a decline in motivation, whether the user is a person who is not mentally ill, a person who is concerned about a mental health problem, or a person who is concerned about a decline in motivation.

[0130] When the determination unit 2033 determines whether the user is a person without mental disorders, a person suspected of having mental health disorders, or a person suspected of having a decline in motivation, the dialogue processing unit 2034 creates questions according to the determination result. Specifically, for example, the dialogue processing unit 2034 uses the second trained model 285 for people suspected of having mental health disorders to create questions for people suspected of having a decline in mental health, and uses the second trained model 285 for people suspected of having a decline in motivation to create questions for people suspected of having a decline in motivation.

[0131] In the above embodiment, a case has been described in which a question sentence appropriate to the target person is transmitted from the dialogue processing unit 2034 to the target person based on the determination result of the determination unit 2033. However, in this embodiment, a predetermined notification may be transmitted to the target person's manager based on the determination result of the determination unit 2033. The manager is, for example, the target person's superior, a medical specialist, or a predetermined person in charge. The predetermined notification is, for example, a notification that the target person is a target person, or a notification of predetermined information. The notification of predetermined information includes, for example, a notification that a face-to-face meeting is necessary, a notification of information regarding working hours, or a notification that overtime should be reduced.

[0132] Although the preferred embodiment of the present disclosure has been described above, the present disclosure is not limited to the specific embodiment, and the present disclosure includes the invention described in the claims and its equivalents. In addition, the configurations of the devices described in the above embodiments and modifications can be combined as appropriate as long as no technical contradiction occurs.

[0133] <Additional Notes> The matters described in the above embodiments will be supplemented below.

[0134] (Appendix 1) A program for causing a computer having a processor and a memory to execute the program, the program causing the processor to: A step of acquiring first information regarding a user's working situation (step S201); A step of determining whether the user is a target person based on the acquired first information and second information related to the working conditions of other users who have been suspected of having mental disorders in the past (step S202); If the user is a target person, a step of creating a question for the user based on the first information (step S203); A step of transmitting the created question to the user (step S204); A step of responding to a reply from the user to the question (step S206); A program that executes the following.

[0135] (Appendix 2) In the determining step (step S202) The program described in (Appendix 1) determines whether the user is a target person by inputting the first information into a first trained model trained using the second information.

[0136] (Appendix 3) The first trained model is trained using stress factors that other users who have been concerned about mental disorders in the past found stressful, In the determining step (step S202), The program described in (Appendix 2), which estimates a stress factor by inputting the first information to the first trained model.

[0137] (Appendix 4) The program described in (Appendix 2) or (Appendix 3), which causes the processor to execute a step of re-learning the first trained model using information fed back from a user who has been determined to be the target person.

[0138] (Appendix 5) In the step of creating the question (step S203), The program described in any one of (Appendix 1) to (Appendix 4), which creates the question by inputting the first information into a second trained model trained using the second information and a question expected from the second information.

[0139] (Appendix 6) The second trained model is trained using stress factors that other users who have been concerned about mental disorders in the past found stressful, In the step of creating the question (step S203), The program described in (Appendix 5), wherein the question sentence is created by inputting the first information and the stress factor estimated in the determining step to the second trained model.

[0140] (Appendix 7) The program described in (Appendix 5) or (Appendix 6), which causes the processor to execute a step of re-learning the second trained model using information provided in response to the question from the user.

[0141] (Appendix 8) The program described in any one of (Appendix 1) to (Appendix 7), which causes the processor to execute a step of estimating the degree of mental illness of the user based on a response from the user to the question.

[0142] (Appendix 9) The program according to any one of (Appendix 1) to (Appendix 8), which causes the processor to execute a step of executing attendance procedures in response to a response from the user to the question.

[0143] (Appendix 10) In a step of transmitting the created question to the user (step S204), The program according to any one of (Supplementary Note 1) to (Supplementary Note 9), which refers to the first information and transmits the question at a timing that is easy for the user to answer.

[0144] (Appendix 11) The program described in (Appendix 10), wherein the timing when the user is likely to respond is a timing when the user is not highly concentrated while operating the terminal.

[0145] (Appendix 12) The program described in any one of (Appendix 1) to (Appendix 11), which causes the processor to execute a step of processing the questions to the user and the responses from the user into personal information and storing the processed content.

[0146] (Appendix 13) In the step of responding (step S206) The program described in any one of (Appendix 1) to (Appendix 12), which creates a new response to the user by inputting the first information and a response from the user to a third trained model trained using the second information, an expected response sentence, and a response sentence expected from the response sentence and the second information.

[0147] (Appendix 14) A step of acquiring first information regarding a user's working situation (step S201); A step of determining whether the user is a target person based on the acquired first information and second information related to the working conditions of other users who have been suspected of having mental disorders in the past (step S202); If the user is a target person, a step of creating a question for the user based on the first information (step S203); A step of transmitting the created question to the user (step S204); A step of responding to a reply from the user to the question (step S206); How to implement this.

[0148] (Appendix 15) A means for acquiring first information regarding a user's working status; A means for determining whether the user is a target person based on the acquired first information and second information on the working conditions of other users who have been suspected of having mental disorders in the past; means for generating a question for the user based on the first information if the user is a target person; means for transmitting the created question text to the user; means for responding to a reply from the user to the question; A system comprising: [Explanation of symbols]

[0149] 1. System 10...Terminal device 121…Communications Department 13...Input device 130...Operation input section 131...Touch-sensitive devices 132…Display 1321…Chat area 1321A…Question text 1321B…Answer text 14...Output device 140...Audio processing unit 141…Mike 142…Speaker 15…Memory 150...Location information sensor 16...Storage section 160…Camera 170...Storage section 171...Employee information 180...Control unit 181...Operation reception section 182...Transmitter / receiver 183…Acquisition Department 184…Presentation part 19…Processor 20…Server 201…Communications Department 202...Storage section 203...Control section 2031…Transmitter / receiver 2032…Acquisition Department 2033...Judgment section 2034…Dialogue processing unit 2035…Executive Division 25…Memory 26…Storage 281…Employee information DB 282…Log information DB 283...Work information DB 284…First trained model 285…Second trained model 286…Third trained model 29…Processor

Claims

1. A program for causing a computer having a processor and a memory to execute the program, the program causing the processor to: Obtaining first information regarding a user's working status; determining whether the user is a person who is suspected of having a mental disorder based on the acquired first information, information on operation history of other users who have been suspected of having a mental disorder in the past, information based on image signals of the other users, and second information on work conditions including work content inferred from the information on the operation history and the information based on the image signals; If the user is a person who is suspected of having a mental disorder, extracting a predetermined phrase from the first information, and creating a question for the user to alleviate a cause of the mental disorder related to a working situation using the extracted phrase; transmitting the created question to the user; Responding to a response from the user to the question; Run the command, In the step of determining, A program that outputs whether or not the user is a person at risk of mental illness by inputting the first information into a first trained model trained using the second information.

2. In the step of creating a question, The program according to claim 1 , wherein the question is created by inputting the first information into a second trained model trained using the second information and a question expected from the second information.

3. In the step of responding, The program according to claim 1 or claim 2, further comprising: inputting the first information and a response from the user into a third trained model trained using the second information, an expected response sentence, and a response sentence expected from the response sentence and the second information, thereby creating a new response to the user.

4. In the step of responding, A program as described in any one of claims 1 to 3, which determines the degree of mental illness of a user by inputting a reply from the user and the created response to a fourth trained model trained to output the degree of mental illness in response to an answer input by the user.

5. A method implemented on a computer having a processor and a memory, the processor performing all of the steps performed in any one of claims 1 to 4.

6. A system comprising means for executing all the steps performed in any one of claims 1 to 4.

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