system

Abstract

To efficiently conduct selection of a medical institution and schedule adjustment in receiving a health examination and linkage with insurance certificate information.SOLUTION: A system according to an embodiment of the present invention comprises a face authentication unit, an identification unit, a linkage unit, a presentation unit, a proposition unit, and an adjustment unit. The face authentication unit captures a face image of a personnel and associates the personal information of the personnel using a face authentication algorithm. The identification unit identifies a nearby affiliated medical institution on the basis of the personnel information associated by the face authentication unit. The linkage unit automatically links insurance certification information to the medical institution identified by the identification unit. The presentation unit presents an examination reception course on the basis of the age information of the personnel. The proposition unit proposes a recommended optional inspection on the basis of the examination reception course proposed by the proposition unit. The adjustment unit matches the personnel's calendar information with the reservation history of the medical institution and adjusts schedules.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that in the case of undergoing a health checkup, the selection of a medical institution, the scheduling, and the linkage of insurance card information are complicated and difficult to perform efficiently.

[0005] The system according to the embodiment aims to efficiently perform the selection of a medical institution, the scheduling, and the linkage of insurance card information in the case of undergoing a health checkup.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a facial recognition unit, an identification unit, a linking unit, a presentation unit, a proposal unit, and an adjustment unit. The facial recognition unit captures an image of the employee's face and links it to the employee's personal information using a facial recognition algorithm. The identification unit identifies nearby affiliated medical institutions based on the employee information linked by the facial recognition unit. The linking unit automatically links insurance card information to the medical institutions identified by the identification unit. The presentation unit presents a medical examination course based on the employee's age information. The proposal unit suggests recommended optional tests based on the medical examination course presented by the presentation unit. The adjustment unit matches the employee's calendar information with the medical institution's reservation history to adjust the schedule. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently select a medical institution, schedule appointments, and link insurance card information when undergoing a health checkup. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The health checkup reservation system according to an embodiment of the present invention is a system that streamlines the process of employees undergoing health checkups. This system links employee information through facial recognition, identifies nearby affiliated medical institutions, and automatically links insurance card symbols and numbers. Furthermore, it presents an appropriate examination course based on the employee's age information and suggests recommended optional tests based on past examination results. Finally, it matches the employee's calendar information with the medical institution's reservation history and automatically adjusts the optimal schedule. This mechanism automates the tedious tasks of selecting a medical institution, scheduling, deciding on an examination course, and linking with insurance card information. For example, employee information is linked through facial recognition. In this process, the employee's face image is captured by a camera, and a facial recognition algorithm using deep learning is used to link it with the employee's personal information. For example, information such as the employee's name, employee number, and department is automatically obtained through facial recognition. Next, nearby affiliated medical institutions are identified. For example, an algorithm is used to list the nearest affiliated medical institutions based on the employee's workplace or home address. This list is optimized based on information such as distance and means of transportation, taking into consideration the employee's convenience. Furthermore, insurance card symbols and numbers are automatically linked. Employees do not need to bring their health insurance cards, as the system automatically retrieves and provides insurance information to medical institutions. This simplifies the registration process. Next, the system suggests appropriate examination courses based on the employee's age. For example, it suggests a specific health checkup course for employees over 40 and a different course for employees in their 20s. It also suggests recommended optional tests based on past examination results. For example, it suggests optional tests including blood pressure measurement for employees with a history of high blood pressure. Finally, it matches the employee's calendar information with the medical institution's appointment history and automatically adjusts the optimal date. For example, it uses an algorithm that considers the employee's work schedule and personal plans, and compares them with the medical institution's availability to suggest the best appointment date. This allows employees to complete their health checkup reservations without any hassle. This system allows companies to efficiently manage health checkups in accordance with the Industrial Safety and Health Act, and employees can receive health checkups without any inconvenience.This allows the health checkup reservation system to streamline employee health checkups and support companies' roles under the Industrial Safety and Health Act.

[0029] The health checkup reservation system according to this embodiment comprises a facial recognition unit, an identification unit, a linking unit, a presentation unit, a proposal unit, and an adjustment unit. The facial recognition unit captures an image of an employee's face and links it to the employee's personal information using a facial recognition algorithm. The employee's facial image includes, but is not limited to, information such as the employee's name, employee number, and department. The facial recognition unit links the employee's personal information using, for example, a facial recognition algorithm using deep learning. Deep learning is implemented using, for example, technologies such as CNN (Convolutional Neural Network) or RNN (Recurrent Neural Network). The identification unit identifies nearby affiliated medical institutions based on the employee information linked by the facial recognition unit. The identification unit uses, for example, an algorithm to list the nearest affiliated medical institutions based on the employee's workplace and home address. The listing algorithm is implemented using, for example, distance calculation and prioritization. The linking unit automatically links insurance card information to the medical institutions identified by the identification unit. The linking unit, for example, pre-registers employees' insurance card information in a database and matches it with employee information linked via facial recognition. The database can be of various types, such as SQL or NoSQL databases. The presentation unit presents an appropriate medical examination course based on the employee's age information. The presentation unit uses an algorithm to suggest a medical examination course based on the employee's age information. The suggestion algorithm can be implemented using methods such as rule-based or machine learning-based approaches. The suggestion unit suggests recommended optional tests based on the medical examination course presented by the presentation unit. The suggestion unit uses an algorithm to suggest recommended optional tests by analyzing past medical examination results. The analysis algorithm can be implemented using methods such as data mining or statistical analysis. The adjustment unit matches the employee's calendar information with the medical institution's reservation history to adjust the optimal schedule. The adjustment unit uses an algorithm to match the employee's calendar information with the medical institution's availability and suggest a reservation date. The matching algorithm can be implemented using methods such as matching algorithms or heuristic algorithms.As a result, the health checkup reservation system according to this embodiment can streamline the process of employees undergoing health checkups and support companies' roles under the Industrial Safety and Health Act.

[0030] The facial recognition unit captures images of employees' faces and links them to their personal information using a facial recognition algorithm. Specifically, the facial recognition unit uses a high-resolution camera to acquire images of employees' faces and inputs them into a facial recognition algorithm that uses deep learning. Deep learning models such as CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network) are used. These models extract facial feature points with high accuracy and link them to personal information such as the employee's name, employee number, and department. The facial recognition algorithm is pre-trained on a large dataset of facial images, enabling it to recognize employee faces with high accuracy. Furthermore, the facial recognition unit performs the process from facial image acquisition to authentication in real time, minimizing waiting times for employees to access the system. As a security measure, the facial recognition unit encrypts the acquired facial image data and securely stores it in a database. In addition, the facial recognition unit regularly verifies the accuracy of the algorithm and retrains the model as needed to maintain consistently high authentication accuracy. As a result, the facial recognition unit can quickly and accurately link employees' personal information, improving the overall efficiency and security of the system.

[0031] The identification department identifies nearby affiliated medical institutions based on employee information linked by the facial recognition department. Specifically, the identification department receives the employee's work location and home address as input data and uses this to list the nearest affiliated medical institutions. Distance calculation algorithms and prioritization algorithms are used in the listing process. The distance calculation algorithm calculates the straight-line distance and travel time between the employee's address and the medical institution's address to identify the closest medical institution. The prioritization algorithm selects the most suitable medical institution by considering factors such as the medical institution's rating, past usage history, and the comprehensiveness of its medical specialties. By combining these algorithms, the identification department can identify the most convenient medical institution for the employee. Furthermore, the identification department regularly updates the database of affiliated medical institutions to reflect newly affiliated medical institutions and changes in service content. This allows the identification department to always identify medical institutions based on the latest information and provide employees with the best possible options.

[0032] The Linkage Unit automatically links insurance information to medical institutions identified by the Designation Unit. Specifically, the Linkage Unit pre-registers employees' insurance information in a database and matches it with linked employee information via facial recognition. The database can be of various types, such as SQL or NoSQL databases, and encryption technology is used to securely manage insurance information. When an employee visits a medical institution, the Linkage Unit automatically transmits the insurance information to the medical institution's system, simplifying the check-in process. This eliminates the need for employees to bring their insurance cards, saving them time and effort. Furthermore, the Linkage Unit performs real-time data exchange with medical institutions, ensuring that updates and changes to insurance information are reflected immediately. This allows the Linkage Unit to provide employees' insurance information accurately and quickly to medical institutions, supporting smooth medical care.

[0033] The presentation unit suggests appropriate health checkup courses based on the employee's age information. Specifically, the presentation unit receives the employee's age information as input data and uses an algorithm to propose the optimal health checkup course based on this information. The suggestion algorithm is implemented using methods such as rule-based or machine learning-based approaches. Rule-based algorithms pre-set recommended health checkup items for each age group and present appropriate courses according to the employee's age. Machine learning-based algorithms learn from past health checkup data and results to predict the optimal health checkup course based on age, gender, and health status. By combining these algorithms, the presentation unit can present the most appropriate health checkup course for each employee. Furthermore, the presentation unit can also propose individually customized health checkup courses, taking into account the employee's health status and past health checkup history. This allows the presentation unit to streamline employee health checkups and improve the quality of health management.

[0034] The Proposal Department suggests recommended optional tests based on the examination course presented by the Presentation Department. Specifically, the Proposal Department uses an algorithm that analyzes past examination results and suggests necessary optional tests for employees. The analysis algorithm is implemented using methods such as data mining and statistical analysis. The data mining algorithm extracts patterns and trends from past examination data and suggests optional tests according to the employee's health status. The statistical analysis algorithm statistically analyzes the employee's health check results to identify high-risk items and necessary tests. By combining these algorithms, the Proposal Department can suggest the most effective optional tests for employees. Furthermore, the Proposal Department can collect employee feedback and continuously improve the accuracy and effectiveness of its suggestions. In this way, the Proposal Department can support employee health management and maximize the effectiveness of health checkups.

[0035] The scheduling unit matches employees' calendar information with medical institution appointment history to arrange the optimal schedule. Specifically, the scheduling unit receives employees' calendar information as input data and uses an algorithm to compare it with the availability of medical institutions. This matching algorithm is implemented using methods such as matching algorithms and heuristic algorithms. The matching algorithm compares the employee's desired date and time with the medical institution's availability and proposes the optimal appointment date. The heuristic algorithm adjusts the appointment schedule efficiently, taking into account the employee's priorities and the medical institution's congestion. By combining these algorithms, the scheduling unit can propose the most convenient appointment schedule for the employee. Furthermore, the scheduling unit can flexibly handle changes and cancellations of appointments and make adjustments to match the employee's schedule. As a result, the scheduling unit can streamline employee health check-up appointments and support smooth consultations.

[0036] The facial recognition unit can link employees' personal information using a facial recognition algorithm based on deep learning. Deep learning is implemented using technologies such as CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network). The facial recognition unit links employees' personal information using a facial recognition algorithm based on deep learning. This improves the accuracy of facial recognition by using deep learning. Some or all of the above processing in the facial recognition unit may be performed using AI, for example, or without AI. For example, the facial recognition unit can input an employee's facial image into a generating AI and have the generating AI perform training on the facial recognition algorithm.

[0037] The identification unit can use an algorithm to list affiliated medical institutions based on the employee's work location and home address. The listing algorithm can be implemented, for example, by methods such as distance calculation and prioritization. The identification unit can use an algorithm to list the nearest affiliated medical institutions based on the employee's work location and home address. This makes it possible to identify medical institutions that take the employee's convenience into consideration. Some or all of the above processing in the identification unit may be performed using AI, for example, or not using AI. For example, the identification unit can input employee work location and home address data into a generating AI and have the generating AI perform the task of listing the most suitable medical institutions.

[0038] The linking unit can register employees' insurance card information in a database in advance and compare it with employee information linked via facial recognition. The database can be of any type, such as an SQL database or a NoSQL database. The linking unit, for example, registers employees' insurance card information in a database in advance and compares it with employee information linked via facial recognition. This simplifies the registration process through the automatic linking of insurance card information. Some or all of the above processing in the linking unit may be performed using AI, for example, or without AI. For example, the linking unit can input employee insurance card information into a generating AI and have the generating AI perform the registration and matching in the database.

[0039] The presentation unit can use an algorithm that suggests medical treatment courses based on the employee's age information. The suggestion algorithm can be implemented using methods such as rule-based or machine learning-based approaches. The presentation unit uses, for example, an algorithm that suggests medical treatment courses based on the employee's age information. This makes it possible to suggest appropriate medical treatment courses according to age. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the employee's age information into a generating AI and have the generating AI perform the medical treatment course suggestion.

[0040] The suggestion unit can use an algorithm that analyzes past medical examination results and suggests recommended optional tests. The algorithm for analysis can be implemented using methods such as data mining or statistical analysis. The suggestion unit uses an algorithm that analyzes past medical examination results and suggests recommended optional tests. This makes it possible to suggest optional tests based on past medical examination results. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input past medical examination result data into a generating AI and have the generating AI execute the suggestion of optional tests.

[0041] The scheduling unit can use an algorithm that matches employee calendar information with the availability of medical institutions and proposes appointment dates. The matching algorithm can be implemented using methods such as a matching algorithm or a heuristic algorithm. The scheduling unit uses an algorithm that matches employee calendar information with the availability of medical institutions and proposes appointment dates. This makes it possible to propose the optimal appointment date based on the employee's schedule. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input employee calendar information into a generating AI and have the generating AI propose the optimal appointment date.

[0042] The facial recognition unit can improve the accuracy of authentication by referring to past facial image data of employees during facial recognition. For example, the facial recognition unit can retrieve previously taken facial images of employees from a database and perform authentication by comparing them with current facial images. The facial recognition unit can also improve authentication accuracy by training the facial recognition algorithm using past facial image data. Furthermore, the facial recognition unit can improve authentication accuracy by analyzing facial feature points in detail based on past facial image data. In this way, the accuracy of facial recognition is improved by referring to past facial image data. Some or all of the above processes in the facial recognition unit may be performed using AI, for example, or without AI. For example, the facial recognition unit can input past facial image data into a generating AI and have the generating AI perform the authentication accuracy improvement.

[0043] The facial recognition unit can improve the accuracy of linking personal information by analyzing the facial feature points of employees in detail during facial recognition. For example, the facial recognition unit can analyze facial feature points in detail and accurately link them to personal information. The facial recognition unit can also retrieve the employee's personal information from a database based on the facial feature points and perform authentication. Furthermore, the facial recognition unit can analyze facial feature points in detail and perform filtering to prevent misrecognition. This improves the accuracy of linking personal information by analyzing facial feature points in detail. Some or all of the above processing in the facial recognition unit may be performed using AI, for example, or without AI. For example, the facial recognition unit can input facial feature point data into a generating AI and have the generating AI perform the task of improving the accuracy of linking personal information.

[0044] The facial recognition unit can determine the authentication priority by considering the employee's work status during facial recognition. For example, the facial recognition unit can prioritize facial recognition for employees who are on duty, completing the authentication quickly. The facial recognition unit can also lower the priority of facial recognition for employees who are not on duty. Furthermore, the facial recognition unit can dynamically adjust the facial recognition priority according to the work status. This makes it possible to adjust the facial recognition priority according to the work status. Some or all of the above processing in the facial recognition unit may be performed using AI, for example, or without AI. For example, the facial recognition unit can input employee work status data into a generating AI and have the generating AI perform the authentication priority determination.

[0045] The facial recognition unit can improve the accuracy of facial recognition by referring to the facial images of employees on social media during the facial recognition process. For example, the facial recognition unit can acquire facial images from employees on social media and add them to the authentication database. The facial recognition unit can also improve the accuracy of facial recognition by training the facial recognition algorithm based on the facial images from social media. Furthermore, the facial recognition unit can refer to facial images from social media and use them as auxiliary data to improve the accuracy of facial recognition. In this way, the accuracy of facial recognition is improved by referring to facial images from social media. Some or all of the above processes in the facial recognition unit may be performed using AI, for example, or without AI. For example, the facial recognition unit can input facial image data from social media into a generating AI and have the generating AI perform the task of improving the accuracy of authentication.

[0046] The identification unit can select the most suitable medical institution by referring to an employee's past medical visit history when identifying a medical institution. For example, the identification unit can select the most suitable medical institution based on evaluations of medical institutions visited in the past. The identification unit can also refer to past medical visit history and prioritize the selection of the same medical institution. Furthermore, the identification unit can select a medical institution strong in a specific specialty based on past medical visit history. In this way, it becomes possible to select the most suitable medical institution by referring to past medical visit history. Some or all of the above processing in the identification unit may be performed using AI, for example, or without using AI. For example, the identification unit can input past medical visit history data into a generating AI and have the generating AI perform the selection of the most suitable medical institution.

[0047] The identification unit can prioritize specific medical institutions when identifying medical institutions, taking into account the employee's health condition. For example, the identification unit can prioritize identifying medical institutions with specialists for employees whose health is deteriorating. It can also prioritize identifying general medical institutions for employees in good health. Furthermore, the identification unit can dynamically prioritize specific medical institutions according to the employee's health condition. This makes it possible to prioritize medical institutions according to the employee's health condition. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input employee health condition data into a generating AI and have the generating AI perform the priority identification of medical institutions.

[0048] The selection department can choose the most suitable medical institution by considering the employee's mode of transportation when identifying medical institutions. For example, for employees who use public transportation, the selection department can select a medical institution close to the nearest train station or bus stop. For employees who use private cars, the selection department can also select a medical institution with ample parking facilities. Furthermore, for employees who commute on foot, the selection department can select a medical institution within walking distance. This makes it possible to select the most suitable medical institution according to the mode of transportation. Some or all of the above processing in the selection department may be performed using AI, for example, or not using AI. For example, the selection department can input employee transportation data into a generating AI and have the generating AI perform the selection of the most suitable medical institution.

[0049] The identification unit can prioritize specific medical institutions when identifying them, taking into account the employee's work schedule. For example, the identification unit can prioritize identifying medical institutions that can be visited during working hours. It can also prioritize identifying medical institutions that can be visited outside of working hours. Furthermore, the identification unit can dynamically prioritize specific medical institutions according to the work schedule. This makes it possible to prioritize medical institutions according to the work schedule. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input employee work schedule data into a generating AI and have the generating AI perform the priority identification of medical institutions.

[0050] The linking unit can improve the accuracy of linking insurance card information by referring to the employee's past insurance usage history. For example, the linking unit links insurance card information based on past insurance usage history. The linking unit can also refer to past insurance usage history to improve the accuracy of the linking. Furthermore, the linking unit can optimize the linking procedure based on past insurance usage history. As a result, the accuracy of linking insurance card information is improved by referring to past insurance usage history. Some or all of the above processing in the linking unit may be performed using AI, for example, or without using AI. For example, the linking unit can input past insurance usage history data into a generating AI and have the generating AI perform the linking accuracy improvement.

[0051] The linking unit can determine the priority of linking insurance card information by considering the health status of the employees. For example, the linking unit can prioritize linking insurance card information for employees whose health is deteriorating. It can also postpone linking insurance card information for employees in good health. Furthermore, the linking unit can dynamically adjust the priority of linking according to the health status. This makes it possible to determine the priority of linking insurance card information according to the health status. Some or all of the above processing in the linking unit may be performed using AI, for example, or without using AI. For example, the linking unit can input employee health status data into a generating AI and have the generating AI perform the determination of the linking priority.

[0052] The linking unit can determine the priority of linking insurance card information by considering the employee's work status when linking insurance card information. For example, the linking unit can prioritize linking insurance card information for employees who are on duty. The linking unit can also postpone linking insurance card information for employees who are not on duty. Furthermore, the linking unit can dynamically adjust the priority of linking according to the work status. This makes it possible to determine the priority of linking insurance card information according to the work status. Some or all of the above processing in the linking unit may be performed using AI, for example, or without using AI. For example, the linking unit can input employee work status data into a generating AI and have the generating AI perform the determination of the linking priority.

[0053] The linking unit can improve the accuracy of linking insurance card information by referring to employee social media information. For example, the linking unit can obtain insurance card information from employee social media and add it to the linking database. The linking unit can also improve the accuracy of linking insurance card information based on social media information. Furthermore, the linking unit can refer to social media information and use it as auxiliary data to improve the accuracy of linking. In this way, the accuracy of linking insurance card information is improved by referring to social media information. Some or all of the above processing in the linking unit may be performed using AI, for example, or without AI. For example, the linking unit can input social media information into a generating AI and have the generating AI perform the linking accuracy improvement.

[0054] The presentation unit can suggest the most suitable course when presenting a medical examination course, by referring to the employee's past medical examination history. For example, the presentation unit can suggest the most suitable course based on past medical examination history. The presentation unit can also improve the accuracy of the medical examination course by referring to past medical examination history. Furthermore, the presentation unit can determine the priority of medical examination courses based on past medical examination history. This makes it possible to suggest the most suitable medical examination course by referring to past medical examination history. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without using AI. For example, the presentation unit can input past medical examination history data into a generating AI and have the generating AI execute the suggestion of the most suitable medical examination course.

[0055] The presentation unit can prioritize medical examination courses by considering the employee's health condition when presenting them. For example, the presentation unit may prioritize suggesting specialized medical examination courses to employees whose health is deteriorating. Conversely, it may prioritize suggesting general medical examination courses to employees in good health. The presentation unit can also dynamically adjust the priority of medical examination courses according to the employee's health condition. This makes it possible to determine the priority of medical examination courses according to the employee's health condition. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input employee health condition data into a generating AI and have the generating AI perform the determination of the priority of medical examination courses.

[0056] The presentation unit can propose the most suitable medical treatment course when presenting treatment courses, taking into account the employee's work situation. For example, the presentation unit can prioritize proposing courses that can be taken during working hours. It can also prioritize proposing courses that can be taken outside of working hours. Furthermore, the presentation unit can dynamically propose the most suitable medical treatment course according to the work situation. This makes it possible to propose the most suitable medical treatment course according to the work situation. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without using AI. For example, the presentation unit can input employee work situation data into a generating AI and have the generating AI execute the proposal of the most suitable medical treatment course.

[0057] The presentation unit can improve the accuracy of the suggested medical treatment courses by referring to information from employees' social media when presenting them. For example, the presentation unit can obtain health-related information from employees' social media and reflect it in the suggested medical treatment courses. The presentation unit can also improve the accuracy of the medical treatment courses based on the information from social media. Furthermore, the presentation unit can refer to the information from social media to determine the priority of the medical treatment courses. In this way, the accuracy of the medical treatment courses is improved by referring to the information from social media. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without using AI. For example, the presentation unit can input social media information into a generating AI and have the generating AI perform the task of improving the accuracy of the medical treatment courses.

[0058] The proposal department can suggest the most suitable optional tests by referring to an employee's past medical history when proposing optional tests. For example, the proposal department can suggest the most suitable optional tests based on past medical history. The proposal department can also improve the accuracy of optional tests by referring to past medical history. Furthermore, the proposal department can determine the priority of optional tests based on past medical history. This makes it possible to suggest the most suitable optional tests by referring to past medical history. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input past medical history data into a generating AI and have the generating AI execute a proposal for the most suitable optional tests.

[0059] The proposal department can prioritize optional tests when proposing them, taking into account the health status of the employees. For example, the proposal department can prioritize proposing specialized optional tests to employees whose health is deteriorating. Conversely, the proposal department can also prioritize proposing general optional tests to employees who are in good health. Furthermore, the proposal department can dynamically adjust the priority of optional tests according to the health status. This makes it possible to determine the priority of optional tests according to the health status. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input employee health status data into a generating AI and have the generating AI perform the determination of the priority of optional tests.

[0060] The proposal department can suggest the most suitable optional tests, taking into account the employee's work situation, when proposing optional tests. For example, the proposal department can prioritize suggesting optional tests that can be taken during working hours. It can also prioritize suggesting optional tests that can be taken outside of working hours. Furthermore, the proposal department can dynamically suggest the most suitable optional tests according to the work situation. This makes it possible to suggest the most suitable optional tests according to the work situation. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input employee work situation data into a generating AI and have the generating AI execute a suggestion of the most suitable optional tests.

[0061] The proposal department can improve the accuracy of optional tests by referring to employee social media information when proposing them. For example, the proposal department can obtain health information from employees' social media and reflect it in the optional test proposals. The proposal department can also improve the accuracy of optional tests based on social media information. Furthermore, the proposal department can refer to social media information to determine the priority of optional tests. In this way, the accuracy of optional tests is improved by referring to social media information. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input social media information into a generating AI and have the generating AI perform the process of improving the accuracy of optional tests.

[0062] The scheduling unit can propose the optimal schedule by referring to an employee's past schedule history during scheduling. For example, the scheduling unit proposes the optimal schedule based on past schedule history. The scheduling unit can also improve the accuracy of scheduling by referring to past schedule history. Furthermore, the scheduling unit can determine the priority of dates based on past schedule history. This makes it possible to propose the optimal schedule by referring to past schedule history. Some or all of the above processes in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input past schedule history data into a generating AI and have the generating AI propose the optimal schedule.

[0063] The scheduling unit can determine scheduling priorities by considering the health status of employees during scheduling. For example, the scheduling unit can expedite scheduling for employees whose health is deteriorating. Conversely, the scheduling unit can postpone scheduling for employees in good health. Furthermore, the scheduling unit can dynamically adjust scheduling priorities according to health status. This makes it possible to determine scheduling priorities according to health status. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input employee health status data into a generating AI and have the generating AI perform scheduling priority determination.

[0064] The scheduling unit can propose the optimal schedule when scheduling appointments, taking into account the employee's work situation. For example, the scheduling unit can prioritize proposing appointments that can be made during working hours. It can also prioritize proposing appointments that can be made outside of working hours. Furthermore, the scheduling unit can dynamically propose the optimal schedule according to the work situation. This makes it possible to propose the optimal schedule according to the work situation. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input employee work situation data into a generating AI and have the generating AI propose the optimal schedule.

[0065] The scheduling unit can improve the accuracy of scheduling by referring to employees' social media information during scheduling. For example, the scheduling unit can obtain schedule information from employees' social media and reflect it in scheduling. The scheduling unit can also improve the accuracy of scheduling based on social media information. Furthermore, the scheduling unit can refer to social media information to determine the priority of dates. In this way, the accuracy of scheduling is improved by referring to social media information. Some or all of the above processes in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input social media information into a generating AI and have the generating AI perform the task of improving the accuracy of scheduling.

[0066] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0067] The health checkup reservation system can also be equipped with a health monitoring unit that monitors employees' health status in real time. The health monitoring unit acquires data such as heart rate, blood pressure, and body temperature from employees' wearable devices and can immediately notify if an abnormality is detected. For example, if the heart rate is abnormally high, it will send a notification recommending an emergency visit to a medical institution. If blood pressure is high, it can also set a reminder to encourage regular blood pressure measurement. Furthermore, if body temperature is high, it can suggest the possibility of a cold or influenza and recommend a visit to an appropriate medical institution. This allows for constant monitoring of employees' health status and enables early intervention.

[0068] The health checkup reservation system can also include a lifestyle management section to manage employees' diet and exercise records. The lifestyle management section allows employees to input their daily diet and exercise levels, providing health improvement advice linked to their health checkup results. For example, it can detect nutritional imbalances from diet records and suggest balanced meal plans. If exercise levels are insufficient, it can also suggest appropriate exercise plans and send reminders to encourage implementation. Furthermore, the lifestyle management section can support employees in setting goals and visualize their progress to help maintain motivation. This enables comprehensive support for maintaining and improving employee health.

[0069] The health checkup reservation system can also include a report generation unit that analyzes employees' health checkup results and generates individual health reports. Based on the employee's past health checkup results and current health status, the report generation unit assesses health risks and proposes specific improvement measures. For example, if past results indicate a tendency towards high blood sugar levels, it will suggest improvements to diet and exercise. If cholesterol levels are high, it can also recommend appropriate meal plans and supplements. Furthermore, if health risks are high, it can recommend regular follow-up examinations to encourage early detection and treatment. This makes it possible to manage employee health more effectively.

[0070] The health checkup reservation system can also include an education department that provides health education content based on employees' health checkup results. The education department selects and provides appropriate health education content according to each employee's health condition. For example, employees with high blood pressure can be provided with videos and articles on blood pressure management. Employees at risk of diabetes can be provided with information on diabetes prevention. Furthermore, based on health checkup results, the system can suggest health topics that employees might be interested in and encourage continuous learning. This makes it possible to raise employees' health awareness and improve their self-management skills.

[0071] The following briefly describes the processing flow for example form 1.

[0072] Step 1: The facial recognition unit captures images of employees' faces and uses a facial recognition algorithm to link them to the employees' personal information. The employee's facial image includes information such as the employee's name, employee number, and department. The facial recognition unit uses a facial recognition algorithm based on deep learning to link the employees' personal information. Deep learning is implemented using technologies such as CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network). Step 2: The identification unit identifies nearby affiliated medical institutions based on employee information linked by the facial recognition unit. The identification unit uses an algorithm to list the nearest affiliated medical institutions based on the employee's work location and home address. The listing algorithm is implemented using methods such as distance calculation and prioritization. Step 3: The linking unit automatically links insurance card information to the medical institutions identified by the identification unit. The linking unit pre-registers employee insurance card information in a database and compares it with employee information linked via facial recognition. The database can be of any type, such as an SQL database or a NoSQL database. Step 4: The presentation unit presents an appropriate medical examination course based on the employee's age information. The presentation unit uses an algorithm to suggest a medical examination course based on the employee's age information. The suggestion algorithm is implemented using methods such as rule-based or machine learning-based approaches. Step 5: The Proposal Unit suggests recommended optional tests based on the examination course presented by the Presentation Unit. The Proposal Unit uses an algorithm that analyzes past examination results to suggest recommended optional tests. The analysis algorithm is implemented using methods such as data mining and statistical analysis. Step 6: The scheduling unit matches employee calendar information with medical institution appointment history to arrange the optimal schedule. The scheduling unit uses an algorithm to compare employee calendar information with medical institution availability and suggest appointment dates. The matching algorithm is implemented using methods such as matching algorithms or heuristic algorithms.

[0073] (Example of form 2) The health checkup reservation system according to an embodiment of the present invention is a system that streamlines the process of employees undergoing health checkups. This system links employee information through facial recognition, identifies nearby affiliated medical institutions, and automatically links insurance card symbols and numbers. Furthermore, it presents an appropriate examination course based on the employee's age information and suggests recommended optional tests based on past examination results. Finally, it matches the employee's calendar information with the medical institution's reservation history and automatically adjusts the optimal schedule. This mechanism automates the tedious tasks of selecting a medical institution, scheduling, deciding on an examination course, and linking with insurance card information. For example, employee information is linked through facial recognition. In this process, the employee's face image is captured by a camera, and a facial recognition algorithm using deep learning is used to link it with the employee's personal information. For example, information such as the employee's name, employee number, and department is automatically obtained through facial recognition. Next, nearby affiliated medical institutions are identified. For example, an algorithm is used to list the nearest affiliated medical institutions based on the employee's workplace or home address. This list is optimized based on information such as distance and means of transportation, taking into consideration the employee's convenience. Furthermore, insurance card symbols and numbers are automatically linked. Employees do not need to bring their health insurance cards, as the system automatically retrieves and provides insurance information to medical institutions. This simplifies the registration process. Next, the system suggests appropriate examination courses based on the employee's age. For example, it suggests a specific health checkup course for employees over 40 and a different course for employees in their 20s. It also suggests recommended optional tests based on past examination results. For example, it suggests optional tests including blood pressure measurement for employees with a history of high blood pressure. Finally, it matches the employee's calendar information with the medical institution's appointment history and automatically adjusts the optimal date. For example, it uses an algorithm that considers the employee's work schedule and personal plans, and compares them with the medical institution's availability to suggest the best appointment date. This allows employees to complete their health checkup reservations without any hassle. This system allows companies to efficiently manage health checkups in accordance with the Industrial Safety and Health Act, and employees can receive health checkups without any inconvenience.This allows the health checkup reservation system to streamline employee health checkups and support companies' roles under the Industrial Safety and Health Act.

[0074] The health checkup reservation system according to this embodiment comprises a facial recognition unit, an identification unit, a linking unit, a presentation unit, a proposal unit, and an adjustment unit. The facial recognition unit captures an image of an employee's face and links it to the employee's personal information using a facial recognition algorithm. The employee's facial image includes, but is not limited to, information such as the employee's name, employee number, and department. The facial recognition unit links the employee's personal information using, for example, a facial recognition algorithm using deep learning. Deep learning is implemented using, for example, technologies such as CNN (Convolutional Neural Network) or RNN (Recurrent Neural Network). The identification unit identifies nearby affiliated medical institutions based on the employee information linked by the facial recognition unit. The identification unit uses, for example, an algorithm to list the nearest affiliated medical institutions based on the employee's workplace and home address. The listing algorithm is implemented using, for example, distance calculation and prioritization. The linking unit automatically links insurance card information to the medical institutions identified by the identification unit. The linking unit, for example, pre-registers employees' insurance card information in a database and matches it with employee information linked via facial recognition. The database can be of various types, such as SQL or NoSQL databases. The presentation unit presents an appropriate medical examination course based on the employee's age information. The presentation unit uses an algorithm to suggest a medical examination course based on the employee's age information. The suggestion algorithm can be implemented using methods such as rule-based or machine learning-based approaches. The suggestion unit suggests recommended optional tests based on the medical examination course presented by the presentation unit. The suggestion unit uses an algorithm to suggest recommended optional tests by analyzing past medical examination results. The analysis algorithm can be implemented using methods such as data mining or statistical analysis. The adjustment unit matches the employee's calendar information with the medical institution's reservation history to adjust the optimal schedule. The adjustment unit uses an algorithm to match the employee's calendar information with the medical institution's availability and suggest a reservation date. The matching algorithm can be implemented using methods such as matching algorithms or heuristic algorithms.As a result, the health checkup reservation system according to this embodiment can streamline the process of employees undergoing health checkups and support companies' roles under the Industrial Safety and Health Act.

[0075] The facial recognition unit captures images of employees' faces and links them to their personal information using a facial recognition algorithm. Specifically, the facial recognition unit uses a high-resolution camera to acquire images of employees' faces and inputs them into a facial recognition algorithm that uses deep learning. Deep learning models such as CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network) are used. These models extract facial feature points with high accuracy and link them to personal information such as the employee's name, employee number, and department. The facial recognition algorithm is pre-trained on a large dataset of facial images, enabling it to recognize employee faces with high accuracy. Furthermore, the facial recognition unit performs the process from facial image acquisition to authentication in real time, minimizing waiting times for employees to access the system. As a security measure, the facial recognition unit encrypts the acquired facial image data and securely stores it in a database. In addition, the facial recognition unit regularly verifies the accuracy of the algorithm and retrains the model as needed to maintain consistently high authentication accuracy. As a result, the facial recognition unit can quickly and accurately link employees' personal information, improving the overall efficiency and security of the system.

[0076] The identification department identifies nearby affiliated medical institutions based on employee information linked by the facial recognition department. Specifically, the identification department receives the employee's work location and home address as input data and uses this to list the nearest affiliated medical institutions. Distance calculation algorithms and prioritization algorithms are used in the listing process. The distance calculation algorithm calculates the straight-line distance and travel time between the employee's address and the medical institution's address to identify the closest medical institution. The prioritization algorithm selects the most suitable medical institution by considering factors such as the medical institution's rating, past usage history, and the comprehensiveness of its medical specialties. By combining these algorithms, the identification department can identify the most convenient medical institution for the employee. Furthermore, the identification department regularly updates the database of affiliated medical institutions to reflect newly affiliated medical institutions and changes in service content. This allows the identification department to always identify medical institutions based on the latest information and provide employees with the best possible options.

[0077] The Linkage Unit automatically links insurance information to medical institutions identified by the Designation Unit. Specifically, the Linkage Unit pre-registers employees' insurance information in a database and matches it with linked employee information via facial recognition. The database can be of various types, such as SQL or NoSQL databases, and encryption technology is used to securely manage insurance information. When an employee visits a medical institution, the Linkage Unit automatically transmits the insurance information to the medical institution's system, simplifying the check-in process. This eliminates the need for employees to bring their insurance cards, saving them time and effort. Furthermore, the Linkage Unit performs real-time data exchange with medical institutions, ensuring that updates and changes to insurance information are reflected immediately. This allows the Linkage Unit to provide employees' insurance information accurately and quickly to medical institutions, supporting smooth medical care.

[0078] The presentation unit suggests appropriate health checkup courses based on the employee's age information. Specifically, the presentation unit receives the employee's age information as input data and uses an algorithm to propose the optimal health checkup course based on this information. The suggestion algorithm is implemented using methods such as rule-based or machine learning-based approaches. Rule-based algorithms pre-set recommended health checkup items for each age group and present appropriate courses according to the employee's age. Machine learning-based algorithms learn from past health checkup data and results to predict the optimal health checkup course based on age, gender, and health status. By combining these algorithms, the presentation unit can present the most appropriate health checkup course for each employee. Furthermore, the presentation unit can also propose individually customized health checkup courses, taking into account the employee's health status and past health checkup history. This allows the presentation unit to streamline employee health checkups and improve the quality of health management.

[0079] The Proposal Department suggests recommended optional tests based on the examination course presented by the Presentation Department. Specifically, the Proposal Department uses an algorithm that analyzes past examination results and suggests necessary optional tests for employees. The analysis algorithm is implemented using methods such as data mining and statistical analysis. The data mining algorithm extracts patterns and trends from past examination data and suggests optional tests according to the employee's health status. The statistical analysis algorithm statistically analyzes the employee's health check results to identify high-risk items and necessary tests. By combining these algorithms, the Proposal Department can suggest the most effective optional tests for employees. Furthermore, the Proposal Department can collect employee feedback and continuously improve the accuracy and effectiveness of its suggestions. In this way, the Proposal Department can support employee health management and maximize the effectiveness of health checkups.

[0080] The scheduling unit matches employees' calendar information with medical institution appointment history to arrange the optimal schedule. Specifically, the scheduling unit receives employees' calendar information as input data and uses an algorithm to compare it with the availability of medical institutions. This matching algorithm is implemented using methods such as matching algorithms and heuristic algorithms. The matching algorithm compares the employee's desired date and time with the medical institution's availability and proposes the optimal appointment date. The heuristic algorithm adjusts the appointment schedule efficiently, taking into account the employee's priorities and the medical institution's congestion. By combining these algorithms, the scheduling unit can propose the most convenient appointment schedule for the employee. Furthermore, the scheduling unit can flexibly handle changes and cancellations of appointments and make adjustments to match the employee's schedule. As a result, the scheduling unit can streamline employee health check-up appointments and support smooth consultations.

[0081] The facial recognition unit can link employees' personal information using a facial recognition algorithm based on deep learning. Deep learning is implemented using technologies such as CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network). The facial recognition unit links employees' personal information using a facial recognition algorithm based on deep learning. This improves the accuracy of facial recognition by using deep learning. Some or all of the above processing in the facial recognition unit may be performed using AI, for example, or without AI. For example, the facial recognition unit can input an employee's facial image into a generating AI and have the generating AI perform training on the facial recognition algorithm.

[0082] The identification unit can use an algorithm to list affiliated medical institutions based on the employee's work location and home address. The listing algorithm can be implemented, for example, by methods such as distance calculation and prioritization. The identification unit can use an algorithm to list the nearest affiliated medical institutions based on the employee's work location and home address. This makes it possible to identify medical institutions that take the employee's convenience into consideration. Some or all of the above processing in the identification unit may be performed using AI, for example, or not using AI. For example, the identification unit can input employee work location and home address data into a generating AI and have the generating AI perform the task of listing the most suitable medical institutions.

[0083] The linking unit can register employees' insurance card information in a database in advance and compare it with employee information linked via facial recognition. The database can be of any type, such as an SQL database or a NoSQL database. The linking unit, for example, registers employees' insurance card information in a database in advance and compares it with employee information linked via facial recognition. This simplifies the registration process through the automatic linking of insurance card information. Some or all of the above processing in the linking unit may be performed using AI, for example, or without AI. For example, the linking unit can input employee insurance card information into a generating AI and have the generating AI perform the registration and matching in the database.

[0084] The presentation unit can use an algorithm that suggests medical treatment courses based on the employee's age information. The suggestion algorithm can be implemented using methods such as rule-based or machine learning-based approaches. The presentation unit uses, for example, an algorithm that suggests medical treatment courses based on the employee's age information. This makes it possible to suggest appropriate medical treatment courses according to age. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the employee's age information into a generating AI and have the generating AI perform the medical treatment course suggestion.

[0085] The suggestion unit can use an algorithm that analyzes past medical examination results and suggests recommended optional tests. The algorithm for analysis can be implemented using methods such as data mining or statistical analysis. The suggestion unit uses an algorithm that analyzes past medical examination results and suggests recommended optional tests. This makes it possible to suggest optional tests based on past medical examination results. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input past medical examination result data into a generating AI and have the generating AI execute the suggestion of optional tests.

[0086] The scheduling unit can use an algorithm that matches employee calendar information with the availability of medical institutions and proposes appointment dates. The matching algorithm can be implemented using methods such as a matching algorithm or a heuristic algorithm. The scheduling unit uses an algorithm that matches employee calendar information with the availability of medical institutions and proposes appointment dates. This makes it possible to propose the optimal appointment date based on the employee's schedule. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input employee calendar information into a generating AI and have the generating AI propose the optimal appointment date.

[0087] The facial recognition unit can estimate an employee's emotions and adjust the accuracy of facial recognition based on the estimated emotions. For example, if an employee is nervous, the facial recognition unit can broaden the tolerance range for facial recognition to increase the success rate of authentication. Conversely, if an employee is relaxed, the facial recognition unit can improve the accuracy of facial recognition to prevent misrecognition. Furthermore, if an employee is tired, the facial recognition unit can prioritize the speed of facial recognition and perform authentication quickly. This allows for adjustment of facial recognition accuracy according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the facial recognition unit may be performed using AI, or not. For example, the facial recognition unit can input employee emotion data into a generative AI and have the generative AI perform the adjustment of facial recognition accuracy.

[0088] The facial recognition unit can improve the accuracy of authentication by referring to past facial image data of employees during facial recognition. For example, the facial recognition unit can retrieve previously taken facial images of employees from a database and perform authentication by comparing them with current facial images. The facial recognition unit can also improve authentication accuracy by training the facial recognition algorithm using past facial image data. Furthermore, the facial recognition unit can improve authentication accuracy by analyzing facial feature points in detail based on past facial image data. In this way, the accuracy of facial recognition is improved by referring to past facial image data. Some or all of the above processes in the facial recognition unit may be performed using AI, for example, or without AI. For example, the facial recognition unit can input past facial image data into a generating AI and have the generating AI perform the authentication accuracy improvement.

[0089] The facial recognition unit can improve the accuracy of linking personal information by analyzing the facial feature points of employees in detail during facial recognition. For example, the facial recognition unit can analyze facial feature points in detail and accurately link them to personal information. The facial recognition unit can also retrieve the employee's personal information from a database based on the facial feature points and perform authentication. Furthermore, the facial recognition unit can analyze facial feature points in detail and perform filtering to prevent misrecognition. This improves the accuracy of linking personal information by analyzing facial feature points in detail. Some or all of the above processing in the facial recognition unit may be performed using AI, for example, or without AI. For example, the facial recognition unit can input facial feature point data into a generating AI and have the generating AI perform the task of improving the accuracy of linking personal information.

[0090] The facial recognition unit can estimate the employee's emotions and adjust the timing of facial recognition based on the estimated emotions. For example, if the employee is relaxed, the facial recognition unit can delay the timing of facial recognition to perform natural authentication. If the employee is in a hurry, the facial recognition unit can speed up the timing of facial recognition to perform authentication quickly. Furthermore, if the employee is nervous, the facial recognition unit can adjust the timing of facial recognition to increase the success rate of authentication. This makes it possible to adjust the timing of facial recognition according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the facial recognition unit may be performed using AI, for example, or not using AI. For example, the facial recognition unit can input employee emotion data into the generative AI and have the generative AI perform the adjustment of the facial recognition timing.

[0091] The facial recognition unit can determine the authentication priority by considering the employee's work status during facial recognition. For example, the facial recognition unit can prioritize facial recognition for employees who are on duty, completing the authentication quickly. The facial recognition unit can also lower the priority of facial recognition for employees who are not on duty. Furthermore, the facial recognition unit can dynamically adjust the facial recognition priority according to the work status. This makes it possible to adjust the facial recognition priority according to the work status. Some or all of the above processing in the facial recognition unit may be performed using AI, for example, or without AI. For example, the facial recognition unit can input employee work status data into a generating AI and have the generating AI perform the authentication priority determination.

[0092] The facial recognition unit can improve the accuracy of facial recognition by referring to the facial images of employees on social media during the facial recognition process. For example, the facial recognition unit can acquire facial images from employees on social media and add them to the authentication database. The facial recognition unit can also improve the accuracy of facial recognition by training the facial recognition algorithm based on the facial images from social media. Furthermore, the facial recognition unit can refer to facial images from social media and use them as auxiliary data to improve the accuracy of facial recognition. In this way, the accuracy of facial recognition is improved by referring to facial images from social media. Some or all of the above processes in the facial recognition unit may be performed using AI, for example, or without AI. For example, the facial recognition unit can input facial image data from social media into a generating AI and have the generating AI perform the task of improving the accuracy of authentication.

[0093] The identification unit can estimate an employee's emotions and adjust the method of identifying a medical institution based on the estimated emotions. For example, if an employee is relaxed, the identification unit can provide detailed medical institution information to broaden the options. If an employee is in a hurry, the identification unit can prioritize identifying the nearest medical institution. If an employee is stressed, the identification unit can prioritize identifying a highly reliable medical institution. This allows for adjustment of the medical institution identification method according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the identification unit may be performed using AI or not using AI. For example, the identification unit can input employee emotion data into a generative AI and have the generative AI perform the adjustment of the medical institution identification method.

[0094] The identification unit can select the most suitable medical institution by referring to an employee's past medical visit history when identifying a medical institution. For example, the identification unit can select the most suitable medical institution based on evaluations of medical institutions visited in the past. The identification unit can also refer to past medical visit history and prioritize the selection of the same medical institution. Furthermore, the identification unit can select a medical institution strong in a specific specialty based on past medical visit history. In this way, it becomes possible to select the most suitable medical institution by referring to past medical visit history. Some or all of the above processing in the identification unit may be performed using AI, for example, or without using AI. For example, the identification unit can input past medical visit history data into a generating AI and have the generating AI perform the selection of the most suitable medical institution.

[0095] The identification unit can prioritize specific medical institutions when identifying medical institutions, taking into account the employee's health condition. For example, the identification unit can prioritize identifying medical institutions with specialists for employees whose health is deteriorating. It can also prioritize identifying general medical institutions for employees in good health. Furthermore, the identification unit can dynamically prioritize specific medical institutions according to the employee's health condition. This makes it possible to prioritize medical institutions according to the employee's health condition. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input employee health condition data into a generating AI and have the generating AI perform the priority identification of medical institutions.

[0096] The identification unit can estimate an employee's emotions and adjust the order in which medical institutions are identified based on the estimated emotions. For example, if an employee is relaxed, the identification unit can provide detailed information to broaden the options. If an employee is in a hurry, the identification unit can prioritize identifying the nearest medical institution. If an employee is stressed, the identification unit can prioritize identifying a highly reliable medical institution. This makes it possible to adjust the order in which medical institutions are identified according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the identification unit may be performed using AI or not using AI. For example, the identification unit can input employee emotion data into a generative AI and have the generative AI perform the adjustment of the order in which medical institutions are identified.

[0097] The selection department can choose the most suitable medical institution by considering the employee's mode of transportation when identifying medical institutions. For example, for employees who use public transportation, the selection department can select a medical institution close to the nearest train station or bus stop. For employees who use private cars, the selection department can also select a medical institution with ample parking facilities. Furthermore, for employees who commute on foot, the selection department can select a medical institution within walking distance. This makes it possible to select the most suitable medical institution according to the mode of transportation. Some or all of the above processing in the selection department may be performed using AI, for example, or not using AI. For example, the selection department can input employee transportation data into a generating AI and have the generating AI perform the selection of the most suitable medical institution.

[0098] The identification unit can prioritize specific medical institutions when identifying them, taking into account the employee's work schedule. For example, the identification unit can prioritize identifying medical institutions that can be visited during working hours. It can also prioritize identifying medical institutions that can be visited outside of working hours. Furthermore, the identification unit can dynamically prioritize specific medical institutions according to the work schedule. This makes it possible to prioritize medical institutions according to the work schedule. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input employee work schedule data into a generating AI and have the generating AI perform the priority identification of medical institutions.

[0099] The linking unit can estimate an employee's emotions and adjust the method of linking insurance card information based on the estimated emotions. For example, if an employee is relaxed, the linking unit can provide a detailed linking procedure. If an employee is in a hurry, the linking unit can also provide a simplified linking procedure. Furthermore, if an employee is stressed, the linking unit can adjust the linking procedure to increase the success rate. This makes it possible to adjust the method of linking insurance card information according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the linking unit may be performed using AI, for example, or not using AI. For example, the linking unit can input employee emotion data into a generative AI and have the generative AI perform the adjustment of the method of linking insurance card information.

[0100] The linking unit can improve the accuracy of linking insurance card information by referring to the employee's past insurance usage history. For example, the linking unit links insurance card information based on past insurance usage history. The linking unit can also refer to past insurance usage history to improve the accuracy of the linking. Furthermore, the linking unit can optimize the linking procedure based on past insurance usage history. As a result, the accuracy of linking insurance card information is improved by referring to past insurance usage history. Some or all of the above processing in the linking unit may be performed using AI, for example, or without using AI. For example, the linking unit can input past insurance usage history data into a generating AI and have the generating AI perform the linking accuracy improvement.

[0101] The linking unit can determine the priority of linking insurance card information by considering the health status of the employees. For example, the linking unit can prioritize linking insurance card information for employees whose health is deteriorating. It can also postpone linking insurance card information for employees in good health. Furthermore, the linking unit can dynamically adjust the priority of linking according to the health status. This makes it possible to determine the priority of linking insurance card information according to the health status. Some or all of the above processing in the linking unit may be performed using AI, for example, or without using AI. For example, the linking unit can input employee health status data into a generating AI and have the generating AI perform the determination of the linking priority.

[0102] The linking unit can estimate an employee's emotions and adjust the timing of linking insurance card information based on the estimated emotions. For example, if an employee is relaxed, the linking unit can delay the linking timing for a more natural connection. If an employee is in a hurry, the linking unit can also speed up the linking timing for a quicker connection. Furthermore, if an employee is stressed, the linking unit can adjust the linking timing to increase the success rate. This makes it possible to adjust the timing of linking insurance card information according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the linking unit may be performed using AI, for example, or not using AI. For example, the linking unit can input employee emotion data into a generative AI and have the generative AI perform the adjustment of the timing of linking insurance card information.

[0103] The linking unit can determine the priority of linking insurance card information by considering the employee's work status when linking insurance card information. For example, the linking unit can prioritize linking insurance card information for employees who are on duty. The linking unit can also postpone linking insurance card information for employees who are not on duty. Furthermore, the linking unit can dynamically adjust the priority of linking according to the work status. This makes it possible to determine the priority of linking insurance card information according to the work status. Some or all of the above processing in the linking unit may be performed using AI, for example, or without using AI. For example, the linking unit can input employee work status data into a generating AI and have the generating AI perform the determination of the linking priority.

[0104] The linking unit can improve the accuracy of linking insurance card information by referring to employee social media information. For example, the linking unit can obtain insurance card information from employee social media and add it to the linking database. The linking unit can also improve the accuracy of linking insurance card information based on social media information. Furthermore, the linking unit can refer to social media information and use it as auxiliary data to improve the accuracy of linking. In this way, the accuracy of linking insurance card information is improved by referring to social media information. Some or all of the above processing in the linking unit may be performed using AI, for example, or without AI. For example, the linking unit can input social media information into a generating AI and have the generating AI perform the linking accuracy improvement.

[0105] The presentation unit can estimate an employee's emotions and adjust the presentation method of the consultation course based on the estimated emotions. For example, if an employee is relaxed, the presentation unit can provide detailed consultation course information. If an employee is in a hurry, the presentation unit can also provide simplified consultation course information. Furthermore, if an employee is nervous, the presentation unit can adjust the presentation method of the consultation course to increase the success rate. This makes it possible to adjust the presentation method of the consultation course according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presentation unit may be performed using AI, for example, or not using AI. For example, the presentation unit can input employee emotion data into a generative AI and have the generative AI adjust the presentation method of the consultation course.

[0106] The presentation unit can suggest the most suitable course when presenting a medical examination course, by referring to the employee's past medical examination history. For example, the presentation unit can suggest the most suitable course based on past medical examination history. The presentation unit can also improve the accuracy of the medical examination course by referring to past medical examination history. Furthermore, the presentation unit can determine the priority of medical examination courses based on past medical examination history. This makes it possible to suggest the most suitable medical examination course by referring to past medical examination history. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without using AI. For example, the presentation unit can input past medical examination history data into a generating AI and have the generating AI execute the suggestion of the most suitable medical examination course.

[0107] The presentation unit can prioritize medical examination courses by considering the employee's health condition when presenting them. For example, the presentation unit may prioritize suggesting specialized medical examination courses to employees whose health is deteriorating. Conversely, it may prioritize suggesting general medical examination courses to employees in good health. The presentation unit can also dynamically adjust the priority of medical examination courses according to the employee's health condition. This makes it possible to determine the priority of medical examination courses according to the employee's health condition. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input employee health condition data into a generating AI and have the generating AI perform the determination of the priority of medical examination courses.

[0108] The presentation unit can estimate an employee's emotions and adjust the order in which consultation courses are presented based on the estimated emotions. For example, if an employee is relaxed, the presentation unit can provide more detailed information to broaden the options. If an employee is in a hurry, the presentation unit can prioritize presenting the most important consultation courses. If an employee is stressed, the presentation unit can prioritize presenting the most reliable consultation courses. This allows for adjustment of the order in which consultation courses are presented according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presentation unit may be performed using AI or not using AI. For example, the presentation unit can input employee emotion data into a generative AI and have the generative AI adjust the order in which consultation courses are presented.

[0109] The presentation unit can propose the most suitable medical treatment course when presenting treatment courses, taking into account the employee's work situation. For example, the presentation unit can prioritize proposing courses that can be taken during working hours. It can also prioritize proposing courses that can be taken outside of working hours. Furthermore, the presentation unit can dynamically propose the most suitable medical treatment course according to the work situation. This makes it possible to propose the most suitable medical treatment course according to the work situation. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without using AI. For example, the presentation unit can input employee work situation data into a generating AI and have the generating AI execute the proposal of the most suitable medical treatment course.

[0110] The presentation unit can improve the accuracy of the suggested medical treatment courses by referring to information from employees' social media when presenting them. For example, the presentation unit can obtain health-related information from employees' social media and reflect it in the suggested medical treatment courses. The presentation unit can also improve the accuracy of the medical treatment courses based on the information from social media. Furthermore, the presentation unit can refer to the information from social media to determine the priority of the medical treatment courses. In this way, the accuracy of the medical treatment courses is improved by referring to the information from social media. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without using AI. For example, the presentation unit can input social media information into a generating AI and have the generating AI perform the task of improving the accuracy of the medical treatment courses.

[0111] The suggestion department can estimate an employee's emotions and adjust the suggestion method for optional tests based on the estimated emotions. For example, if an employee is relaxed, the suggestion department can provide detailed optional test information. If an employee is in a hurry, the suggestion department can also provide simplified optional test information. Furthermore, if an employee is nervous, the suggestion department can adjust the suggestion method for optional tests to increase the success rate. This makes it possible to adjust the suggestion method for optional tests according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion department may be performed using AI or not using AI. For example, the suggestion department can input employee emotion data into a generative AI and have the generative AI adjust the suggestion method for optional tests.

[0112] The proposal department can suggest the most suitable optional tests by referring to an employee's past medical history when proposing optional tests. For example, the proposal department can suggest the most suitable optional tests based on past medical history. The proposal department can also improve the accuracy of optional tests by referring to past medical history. Furthermore, the proposal department can determine the priority of optional tests based on past medical history. This makes it possible to suggest the most suitable optional tests by referring to past medical history. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input past medical history data into a generating AI and have the generating AI execute a proposal for the most suitable optional tests.

[0113] The proposal department can prioritize optional tests when proposing them, taking into account the health status of the employees. For example, the proposal department can prioritize proposing specialized optional tests to employees whose health is deteriorating. Conversely, the proposal department can also prioritize proposing general optional tests to employees who are in good health. Furthermore, the proposal department can dynamically adjust the priority of optional tests according to the health status. This makes it possible to determine the priority of optional tests according to the health status. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input employee health status data into a generating AI and have the generating AI perform the determination of the priority of optional tests.

[0114] The suggestion department can estimate an employee's emotions and adjust the order of suggested optional tests based on the estimated emotions. For example, if an employee is relaxed, the suggestion department can provide more detailed information to broaden the options. If an employee is in a hurry, the suggestion department can prioritize suggesting the most important optional tests. If an employee is stressed, the suggestion department can prioritize suggesting the most reliable optional tests. This allows for adjustment of the suggested order of optional tests according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion department may be performed using AI or not. For example, the suggestion department can input employee emotion data into a generative AI and have the generative AI adjust the suggested order of optional tests.

[0115] The proposal department can suggest the most suitable optional tests, taking into account the employee's work situation, when proposing optional tests. For example, the proposal department can prioritize suggesting optional tests that can be taken during working hours. It can also prioritize suggesting optional tests that can be taken outside of working hours. Furthermore, the proposal department can dynamically suggest the most suitable optional tests according to the work situation. This makes it possible to suggest the most suitable optional tests according to the work situation. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input employee work situation data into a generating AI and have the generating AI execute a suggestion of the most suitable optional tests.

[0116] The proposal department can improve the accuracy of optional tests by referring to employee social media information when proposing them. For example, the proposal department can obtain health information from employees' social media and reflect it in the optional test proposals. The proposal department can also improve the accuracy of optional tests based on social media information. Furthermore, the proposal department can refer to social media information to determine the priority of optional tests. In this way, the accuracy of optional tests is improved by referring to social media information. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input social media information into a generating AI and have the generating AI perform the process of improving the accuracy of optional tests.

[0117] The scheduling unit can estimate an employee's emotions and adjust the scheduling method based on the estimated emotions. For example, if an employee is relaxed, the scheduling unit can provide detailed scheduling information. If an employee is in a hurry, the scheduling unit can also provide simplified scheduling information. Furthermore, if an employee is stressed, the scheduling unit can adjust the scheduling method to increase the success rate. This makes it possible to adjust the scheduling method according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the scheduling unit may be performed using AI or not using AI. For example, the scheduling unit can input employee emotion data into a generative AI and have the generative AI perform the adjustment of the scheduling method.

[0118] The scheduling unit can propose the optimal schedule by referring to an employee's past schedule history during scheduling. For example, the scheduling unit proposes the optimal schedule based on past schedule history. The scheduling unit can also improve the accuracy of scheduling by referring to past schedule history. Furthermore, the scheduling unit can determine the priority of dates based on past schedule history. This makes it possible to propose the optimal schedule by referring to past schedule history. Some or all of the above processes in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input past schedule history data into a generating AI and have the generating AI propose the optimal schedule.

[0119] The scheduling unit can determine scheduling priorities by considering the health status of employees during scheduling. For example, the scheduling unit can expedite scheduling for employees whose health is deteriorating. Conversely, the scheduling unit can postpone scheduling for employees in good health. Furthermore, the scheduling unit can dynamically adjust scheduling priorities according to health status. This makes it possible to determine scheduling priorities according to health status. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input employee health status data into a generating AI and have the generating AI perform scheduling priority determination.

[0120] The scheduling unit can estimate employees' emotions and adjust the scheduling order based on those emotions. For example, if an employee is relaxed, the scheduling unit can provide more detailed information to broaden their options. If an employee is in a hurry, the scheduling unit can prioritize scheduling the most important dates. If an employee is stressed, the scheduling unit can prioritize scheduling the most reliable dates. This allows for scheduling order adjustments to be made according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the scheduling unit may be performed using AI or not. For example, the scheduling unit can input employee emotion data into a generative AI and have the generative AI perform the scheduling order adjustment.

[0121] The scheduling unit can propose the optimal schedule when scheduling appointments, taking into account the employee's work situation. For example, the scheduling unit can prioritize proposing appointments that can be made during working hours. It can also prioritize proposing appointments that can be made outside of working hours. Furthermore, the scheduling unit can dynamically propose the optimal schedule according to the work situation. This makes it possible to propose the optimal schedule according to the work situation. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input employee work situation data into a generating AI and have the generating AI propose the optimal schedule.

[0122] The scheduling unit can improve the accuracy of scheduling by referring to employees' social media information during scheduling. For example, the scheduling unit can obtain schedule information from employees' social media and reflect it in scheduling. The scheduling unit can also improve the accuracy of scheduling based on social media information. Furthermore, the scheduling unit can refer to social media information to determine the priority of dates. In this way, the accuracy of scheduling is improved by referring to social media information. Some or all of the above processes in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input social media information into a generating AI and have the generating AI perform the task of improving the accuracy of scheduling.

[0123] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0124] The health checkup reservation system can also be equipped with a health monitoring unit that monitors employees' health status in real time. The health monitoring unit acquires data such as heart rate, blood pressure, and body temperature from employees' wearable devices and can immediately notify if an abnormality is detected. For example, if the heart rate is abnormally high, it will send a notification recommending an emergency visit to a medical institution. If blood pressure is high, it can also set a reminder to encourage regular blood pressure measurement. Furthermore, if body temperature is high, it can suggest the possibility of a cold or influenza and recommend a visit to an appropriate medical institution. This allows for constant monitoring of employees' health status and enables early intervention.

[0125] The health checkup reservation system can also include a lifestyle management section to manage employees' diet and exercise records. The lifestyle management section allows employees to input their daily diet and exercise levels, providing health improvement advice linked to their health checkup results. For example, it can detect nutritional imbalances from diet records and suggest balanced meal plans. If exercise levels are insufficient, it can also suggest appropriate exercise plans and send reminders to encourage implementation. Furthermore, the lifestyle management section can support employees in setting goals and visualize their progress to help maintain motivation. This enables comprehensive support for maintaining and improving employee health.

[0126] The health checkup reservation system can also include a stress assessment unit that evaluates employees' stress levels. This unit estimates stress levels based on employee heart rate variability, sleep patterns, and survey results, and proposes appropriate countermeasures. For example, if heart rate variability is low, it can provide guidance on breathing exercises and meditation for relaxation. If sleep patterns are disrupted, it can also advise on improving the sleep environment and ensuring adequate sleep duration. Furthermore, it can identify psychological stressors from survey results and recommend counseling. This makes it possible to support employees' mental health and reduce stress.

[0127] The health checkup reservation system can also include a report generation unit that analyzes employees' health checkup results and generates individual health reports. Based on the employee's past health checkup results and current health status, the report generation unit assesses health risks and proposes specific improvement measures. For example, if past results indicate a tendency towards high blood sugar levels, it will suggest improvements to diet and exercise. If cholesterol levels are high, it can also recommend appropriate meal plans and supplements. Furthermore, if health risks are high, it can recommend regular follow-up examinations to encourage early detection and treatment. This makes it possible to manage employee health more effectively.

[0128] The health checkup reservation system can also include an education department that provides health education content based on employees' health checkup results. The education department selects and provides appropriate health education content according to each employee's health condition. For example, employees with high blood pressure can be provided with videos and articles on blood pressure management. Employees at risk of diabetes can be provided with information on diabetes prevention. Furthermore, based on health checkup results, the system can suggest health topics that employees might be interested in and encourage continuous learning. This makes it possible to raise employees' health awareness and improve their self-management skills.

[0129] The health checkup reservation system can also estimate employees' emotions and provide motivational messages based on those emotions to increase their willingness to undergo health checkups. For example, if an employee is feeling anxious, it can send a message emphasizing the importance and benefits of the health checkup. If an employee is relaxed, it can also provide advice on how to enjoy the health checkup. Furthermore, if an employee is tired, it can send a message emphasizing how the health checkup helps maintain good health, thereby increasing their willingness to undergo it. This enables appropriate motivation tailored to employees' emotions, which can improve the health checkup participation rate.

[0130] The health checkup reservation system can also estimate employees' emotions and adjust the method of notifying them of their health checkup results based on those emotions. For example, if an employee is feeling anxious, the results notification can be delivered in a gentle tone to provide reassurance. If an employee is relaxed, detailed results can be provided to raise their awareness of self-care. Furthermore, if an employee is feeling anxious, appropriate follow-up advice can be provided along with the results notification. This enables result notifications that are tailored to the employee's emotions, thereby raising their awareness of health management.

[0131] The health checkup reservation system can also estimate employees' emotions and adjust their health checkup schedules based on those emotions. For example, if an employee is feeling stressed, the system can schedule their checkup during a time when they can relax. If an employee is busy, it can suggest a shorter checkup schedule. Furthermore, if an employee is relaxed, it can suggest a schedule that includes more detailed examinations. This allows for adjustments to checkup schedules based on employees' emotions, reducing the burden of undergoing health checkups.

[0132] The health checkup reservation system can further estimate employees' emotions and provide support before and after the health checkup based on those estimated emotions. For example, if an employee is feeling anxious, it can provide advice on how to relax before the checkup. If an employee is nervous, it can also suggest ways to relax after the checkup. Furthermore, if an employee is relaxed, it can provide information on post-checkup health management to support ongoing health care. This enables support before and after the checkup that is tailored to the employee's emotions, improving the health checkup experience.

[0133] The health checkup reservation system can also estimate employees' emotions and provide feedback on the health checkup results based on those emotions. For example, if an employee is feeling anxious, it can emphasize positive feedback on the results. If an employee is relaxed, it can provide detailed feedback to raise their awareness of self-care. Furthermore, if an employee is stressed, it can suggest specific improvement measures based on the results to provide reassurance. This enables appropriate feedback tailored to employees' emotions, thereby raising their awareness of health management.

[0134] The following briefly describes the processing flow for example form 2.

[0135] Step 1: The facial recognition unit captures images of employees' faces and uses a facial recognition algorithm to link them to the employees' personal information. The employee's facial image includes information such as the employee's name, employee number, and department. The facial recognition unit uses a facial recognition algorithm based on deep learning to link the employees' personal information. Deep learning is implemented using technologies such as CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network). Step 2: The identification unit identifies nearby affiliated medical institutions based on employee information linked by the facial recognition unit. The identification unit uses an algorithm to list the nearest affiliated medical institutions based on the employee's work location and home address. The listing algorithm is implemented using methods such as distance calculation and prioritization. Step 3: The linking unit automatically links insurance card information to the medical institutions identified by the identification unit. The linking unit pre-registers employee insurance card information in a database and compares it with employee information linked via facial recognition. The database can be of any type, such as an SQL database or a NoSQL database. Step 4: The presentation unit presents an appropriate medical examination course based on the employee's age information. The presentation unit uses an algorithm to suggest a medical examination course based on the employee's age information. The suggestion algorithm is implemented using methods such as rule-based or machine learning-based approaches. Step 5: The Proposal Unit suggests recommended optional tests based on the examination course presented by the Presentation Unit. The Proposal Unit uses an algorithm that analyzes past examination results to suggest recommended optional tests. The analysis algorithm is implemented using methods such as data mining and statistical analysis. Step 6: The scheduling unit matches employee calendar information with medical institution appointment history to arrange the optimal schedule. The scheduling unit uses an algorithm to compare employee calendar information with medical institution availability and suggest appointment dates. The matching algorithm is implemented using methods such as matching algorithms or heuristic algorithms.

[0136] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0138] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0139] Each of the multiple elements described above, including the facial recognition unit, identification unit, linking unit, presentation unit, proposal unit, and adjustment unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the facial recognition unit uses the camera 42 of the smart device 14 to capture an image of the employee's face and the control unit 46A executes a facial recognition algorithm using deep learning. The identification unit, for example, uses the identification processing unit 290 of the data processing unit 12 to list the nearest affiliated medical institutions based on the employee's workplace and home address. The linking unit, for example, uses the identification processing unit 290 of the data processing unit 12 to automatically link insurance card information. The presentation unit, for example, uses the identification processing unit 290 of the data processing unit 12 to present an appropriate medical examination course based on the employee's age information. The proposal unit, for example, uses the identification processing unit 290 of the data processing unit 12 to analyze past medical examination results and propose recommended optional examinations. The adjustment unit, for example, uses the identification processing unit 290 of the data processing unit 12 to match the employee's calendar information with the medical institution's reservation history and adjust the optimal schedule. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0140] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0141] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0142] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0143] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0144] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0146] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0147] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0148] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0149] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0150] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0151] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0152] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0154] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0155] Each of the multiple elements described above, including the facial recognition unit, identification unit, linking unit, presentation unit, proposal unit, and adjustment unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the facial recognition unit uses the camera 42 of the smart glasses 214 to capture an image of the employee's face and the control unit 46A executes a facial recognition algorithm using deep learning. The identification unit, for example, uses the identification processing unit 290 of the data processing unit 12 to list the nearest affiliated medical institutions based on the employee's workplace and home address. The linking unit, for example, uses the identification processing unit 290 of the data processing unit 12 to automatically link insurance card information. The presentation unit, for example, uses the identification processing unit 290 of the data processing unit 12 to present an appropriate medical examination course based on the employee's age information. The proposal unit, for example, uses the identification processing unit 290 of the data processing unit 12 to analyze past medical examination results and propose recommended optional examinations. The adjustment unit, for example, uses the identification processing unit 290 of the data processing unit 12 to match the employee's calendar information with the medical institution's reservation history and adjust the optimal schedule. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0156] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0157] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0158] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0159] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0160] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0162] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0163] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0164] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0165] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0166] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0167] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0168] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0169] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0170] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0171] Each of the multiple elements described above, including the facial recognition unit, identification unit, linking unit, presentation unit, proposal unit, and adjustment unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the facial recognition unit uses the camera 42 of the headset terminal 314 to capture an image of the employee's face and the control unit 46A executes a facial recognition algorithm using deep learning. The identification unit, for example, uses the identification processing unit 290 of the data processing unit 12 to list the nearest affiliated medical institutions based on the employee's workplace and home address. The linking unit, for example, uses the identification processing unit 290 of the data processing unit 12 to automatically link insurance card information. The presentation unit, for example, uses the identification processing unit 290 of the data processing unit 12 to present an appropriate medical examination course based on the employee's age information. The proposal unit, for example, uses the identification processing unit 290 of the data processing unit 12 to analyze past medical examination results and propose recommended optional examinations. The adjustment unit, for example, uses the identification processing unit 290 of the data processing unit 12 to match the employee's calendar information with the medical institution's reservation history and adjust the optimal schedule. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0172] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0173] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0174] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0175] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0176] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0177] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0178] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0179] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0180] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0181] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0182] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0183] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0184] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0185] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0186] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0187] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0188] Each of the multiple elements described above, including the facial recognition unit, identification unit, linking unit, presentation unit, proposal unit, and adjustment unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the facial recognition unit uses the camera 42 of the robot 414 to capture an image of the employee's face and the control unit 46A executes a facial recognition algorithm using deep learning. The identification unit, for example, uses the identification processing unit 290 of the data processing unit 12 to list the nearest affiliated medical institutions based on the employee's workplace and home address. The linking unit, for example, uses the identification processing unit 290 of the data processing unit 12 to automatically link insurance card information. The presentation unit, for example, uses the identification processing unit 290 of the data processing unit 12 to present an appropriate medical examination course based on the employee's age information. The proposal unit, for example, uses the identification processing unit 290 of the data processing unit 12 to analyze past medical examination results and propose recommended optional examinations. The adjustment unit, for example, uses the identification processing unit 290 of the data processing unit 12 to match the employee's calendar information with the medical institution's reservation history and adjust the optimal schedule. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0189] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0190] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0191] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0192] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0193] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0194] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0195] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0196] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0197] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0198] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0199] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0200] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0201] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0202] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0203] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0204] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0205] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0206] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0207] (Note 1) The facial recognition department takes images of employees' faces and uses a facial recognition algorithm to link them to their personal information. Based on the employee information linked by the aforementioned facial recognition unit, the identification unit identifies nearby affiliated medical institutions, A linking unit that automatically links insurance card information to medical institutions identified by the aforementioned identification unit, A display unit that suggests examination courses based on the employee's age information, A suggestion unit that proposes recommended optional tests based on the examination course presented by the aforementioned presentation unit, It includes a scheduling unit that matches employee calendar information with medical institution appointment history to adjust schedules. A system characterized by the following features. (Note 2) The aforementioned facial recognition unit is We will use a deep learning-based facial recognition algorithm to link employees' personal information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The specified part is, The system uses an algorithm to list affiliated medical institutions based on employees' work locations and home addresses. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned linkage unit is, Employees' insurance card information is registered in a database in advance and then matched against linked employee information using facial recognition. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned display unit is, We use an algorithm that suggests medical treatment courses based on employees' age information. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We use an algorithm that analyzes past medical records to suggest recommended optional tests. The system described in Appendix 1, characterized by the features described herein. (Note 7) The adjustment unit is, We use an algorithm that matches employee calendar information with the availability of medical facilities and suggests appointment dates. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned facial recognition unit is The system estimates the emotions of employees and adjusts the accuracy of facial recognition based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned facial recognition unit is To improve the accuracy of facial recognition, past facial image data of employees is referenced during the facial recognition process. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned facial recognition unit is During facial recognition, the system analyzes the facial features of employees in detail to improve the accuracy of linking them to personal information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned facial recognition unit is The system estimates the emotions of employees and adjusts the timing of facial recognition based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned facial recognition unit is When performing facial recognition, the system prioritizes authentication based on the employee's work status. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned facial recognition unit is To improve the accuracy of facial recognition, the system references employees' social media profile pictures. The system described in Appendix 1, characterized by the features described herein. (Note 14) The specified part is, We estimate the emotions of our employees and adjust the method of identifying healthcare institutions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The specified part is, When selecting a medical institution, the system will refer to the employee's past medical history to choose the most suitable institution. The system described in Appendix 1, characterized by the features described herein. (Note 16) The specified part is, When selecting a medical institution, we will prioritize certain institutions based on the health status of our employees. The system described in Appendix 1, characterized by the features described herein. (Note 17) The specified part is, The system estimates employees' emotions and adjusts the order in which healthcare institutions are identified based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The specified part is, When selecting a medical institution, the most suitable one will be chosen considering the transportation options available to employees. The system described in Appendix 1, characterized by the features described herein. (Note 19) The specified part is, When identifying medical institutions, we prioritize certain institutions by taking into account the employees' work schedules. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned linkage unit is, The system estimates employees' emotions and adjusts the method of linking insurance card information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned linkage unit is, When linking insurance card information, we improve the accuracy of the linking process by referring to employees' past insurance usage history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned linkage unit is, When linking insurance card information, the priority of linking is determined by considering the employee's health status. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned linkage unit is, The system estimates employees' emotions and adjusts the timing of linking insurance card information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned linkage unit is, When linking insurance card information, the priority of the linking process is determined by considering the employee's work status. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned linkage unit is, When linking insurance card information, we will improve the accuracy of the linking process by referencing employees' social media information. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned display unit is, We estimate employees' emotions and adjust the way we present consultation courses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned display unit is, When presenting a medical examination course, we refer to the employee's past medical history to suggest the most suitable course. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned display unit is, When presenting a list of medical examination courses, we prioritize the courses based on the employee's health condition. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned display unit is, The system estimates employees' emotions and adjusts the order in which consultation courses are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned display unit is, When presenting a medical consultation course, we propose the most suitable course considering the employee's work situation. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned display unit is, When presenting medical treatment courses, we refer to employees' social media information to improve the accuracy of the courses. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned proposal section is, We estimate employees' emotions and adjust the method of suggesting optional tests based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned proposal section is, When proposing optional tests, we refer to the employee's past medical history to suggest the most suitable tests. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned proposal section is, When proposing optional tests, we prioritize tests based on the health status of the employees. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned proposal section is, The system estimates employees' emotions and adjusts the suggested order of optional tests based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned proposal section is, When proposing optional inspections, we will propose the most suitable inspections considering the employees' work conditions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned proposal section is, When proposing optional tests, we refer to employees' social media information to improve the accuracy of the tests. The system described in Appendix 1, characterized by the features described herein. (Note 38) The adjustment unit is, We estimate employees' emotions and adjust scheduling methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The adjustment unit is, When scheduling, we refer to the employee's past schedule history to suggest the most suitable dates. The system described in Appendix 1, characterized by the features described herein. (Note 40) The adjustment unit is, When scheduling, we prioritize dates while taking into account the health status of the employees. The system described in Appendix 1, characterized by the features described herein. (Note 41) The adjustment unit is, The system estimates employees' emotions and adjusts the scheduling order based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The adjustment unit is, When scheduling, we will propose the most suitable date considering the employees' work schedules. The system described in Appendix 1, characterized by the features described herein. (Note 43) The adjustment unit is, When scheduling, we refer to employees' social media information to improve the accuracy of scheduling. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0208] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A facial recognition unit that performs facial recognition by comparing a first facial image captured by a camera with a second facial image registered for each employee in a first database using a facial recognition algorithm, and associates the first facial image with the employee who has been authenticated as the person by the facial recognition, A second identification unit identifies a medical institution to which an employee should receive treatment, based on the address of the medical institution registered in the second database and the address of the employee linked by the facial recognition unit. A linking unit performs data linkage with the medical institution's system by transmitting the employee's insurance card information, which is linked by the facial recognition unit, from the insurance card information registered for each employee in the third database, to the medical institution's system identified by the specified unit. A presentation unit that presents to the employee a medical examination course to be performed at the medical institution based on the employee's age information linked by the facial recognition unit, A suggestion unit analyzes the employee's past medical examination results and proposes to the employee recommended optional tests to be added to the medical examination course presented by the presentation unit. The system includes an adjustment unit that matches the employee's calendar information with the medical institution's reservation history to adjust the schedule, The specified part is, The facial recognition unit estimates the emotions of the employees linked to it. If the estimated emotion category of the employee is urgent, the system prioritizes identifying medical institutions that are close to the employee's address. If the estimated emotion category of the employee is stressed, the system prioritizes identifying highly reliable medical institutions. A system characterized by the following features.

2. The aforementioned facial recognition unit is We use a deep learning-based facial recognition algorithm to link employee information. The system according to feature 1.

3. The aforementioned display unit is, The algorithm that proposes the aforementioned medical examination course based on the age information of the aforementioned employee is used. The system according to feature 1.

4. The adjustment unit is, An algorithm is used that matches the employee's calendar information with the availability of the medical institution and suggests an appointment date. The system according to feature 1.

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