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 technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding the chatbot character, 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, when undergoing a health checkup, the process of selecting a medical institution, arranging a schedule, and linking health insurance card information was complicated, making it difficult to carry out the process efficiently.

[0005] The system according to the embodiment aims to efficiently select medical institutions for health checkups, arrange schedules, and link health insurance card information. [Means for solving the problem]

[0006] The system according to the embodiment includes a face authentication unit, an identification unit, a linking unit, a presentation unit, a suggestion unit, and an adjustment unit. The face authentication unit captures a face image of the employee and links the employee's personal information using a face recognition algorithm. The identification unit identifies a nearby affiliated medical institution based on the employee information linked by the face authentication unit. The linking unit automatically links health insurance card information to the medical institution identified by the identification unit. The presentation unit presents a medical examination course based on the employee's age information. The suggestion unit proposes recommended optional examinations based on the medical examination course presented by the presentation unit. The adjustment unit adjusts the schedule by matching the employee's calendar information with the medical institution's reservation history. Effect of the Invention

[0007] The system according to the embodiment can efficiently select medical institutions for health checkups, arrange schedules, and link health insurance card information. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

[0017] 1, a 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. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A medical examination reservation system according to an embodiment of the present invention is a system that makes it more efficient for employees to undergo medical examinations. This system links employee information by facial recognition, identifies nearby affiliated medical institutions, and automatically links the symbol and number information on the health insurance card. Furthermore, it presents an appropriate medical examination course based on the employee's age information, and suggests recommended optional examinations based on past examination results. Finally, it matches the employee's calendar information with the reservation history of medical institutions and automatically adjusts the optimal schedule. This mechanism automates the troublesome process of selecting a medical institution, adjusting the schedule, deciding the medical examination course, and linking with health insurance card information. For example, employee information is linked by facial recognition. At this time, an image of the employee's face is taken with a camera, and linked to the employee's personal information using a facial recognition algorithm using deep learning. For example, information such as the employee's name, employee number, and department is automatically obtained by facial recognition. Next, it identifies nearby affiliated medical institutions. For example, it uses an algorithm that lists the nearest affiliated medical institutions based on the employee's place of work and home address. This list is optimized based on information such as distance and transportation, taking into account the convenience of the employee. Furthermore, it automatically links the symbol and number information on the health insurance card. Employees do not need to bring their health insurance cards with them, and the system automatically obtains and provides the health insurance card information to the medical institution. This simplifies the procedures at the time of reception. Next, the system presents an appropriate medical examination course based on the employee's age information. For example, a specific medical examination course is proposed for employees over 40 years old, and a different course is proposed for employees in their 20s. In addition, recommended optional examinations are proposed based on past examination results. For example, optional examinations including blood pressure measurement are proposed for employees who have had a tendency toward high blood pressure in the past. Finally, the system matches the employee's calendar information with the medical institution's reservation history and automatically adjusts the optimal date. For example, an algorithm is used that takes into account the employee's work schedule and private plans, and suggests the optimal reservation date in comparison with the medical institution's availability. This allows employees to complete their health examination reservations without hassle. This system allows companies to efficiently manage health examinations based on the Industrial Safety and Health Act, and employees can receive health examinations without hassle.As a result, the health checkup reservation system can make it more efficient for employees to undergo health checkups and support companies in their role under the Industrial Safety and Health Act.

[0029] The medical examination reservation system according to the embodiment includes a face authentication unit, a specification unit, a linking unit, a presentation unit, a suggestion unit, and an adjustment unit. The face authentication unit captures a face image of an employee and links the employee's personal information using a face authentication algorithm. The face image of the employee includes, for example, information such as the employee's name, employee number, and department, but is not limited to such examples. The face authentication unit links the employee's personal information using, for example, a face authentication algorithm using deep learning. Deep learning is realized by, for example, a technology such as CNN (convolutional neural network) or RNN (recurrent neural network). The specification unit identifies a nearby affiliated medical institution based on the employee information linked by the face authentication unit. The specification unit uses, for example, an algorithm that lists the nearest affiliated medical institution based on the employee's place of work or home address. The listing algorithm is realized by, for example, a method such as distance calculation or prioritization. The specification unit automatically links the health insurance card information to the medical institution identified by the specification unit. The linking unit, for example, registers the employee's health insurance card information in a database in advance and collates it with the employee information linked by face recognition. The database may be, for example, an SQL database or a NoSQL database. The presentation unit presents an appropriate medical examination course based on the employee's age information. The presentation unit uses, for example, an algorithm that proposes a medical examination course based on the employee's age information. The proposal algorithm is realized, for example, by a rule-based or machine learning-based method. The proposal unit proposes a recommended optional examination based on the medical examination course presented by the presentation unit. The proposal unit uses, for example, an algorithm that analyzes past medical examination results and proposes a recommended optional examination. The analysis algorithm is realized, for example, by a data mining or statistical analysis method. 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, for example, an algorithm that compares the employee's calendar information with the medical institution's availability and proposes a reservation date. The matching algorithm is realized, for example, by a matching algorithm or a heuristic algorithm.As a result, the health checkup reservation system according to the embodiment can streamline the process for employees to undergo health checkups and support companies in fulfilling their roles under the Industrial Safety and Health Act.

[0030] The facial recognition unit takes a facial image of an employee and links it to the employee's personal information using a facial recognition algorithm. Specifically, the facial recognition unit uses a high-resolution camera to acquire the employee's facial image and inputs it into a facial recognition algorithm using deep learning. Deep learning models include CNN (convolutional neural network) and RNN (recurrent neural network). 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 trained in advance with a large amount of facial image data set, and can recognize the employee's facial image with high accuracy. In addition, the facial recognition unit performs the process from acquiring the facial image to authenticating it in real time, minimizing the waiting time when an employee accesses 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 necessary to always maintain high authentication accuracy. This allows the facial recognition unit to quickly and accurately link the personal information of employees, improving the efficiency and security of the entire system.

[0031] The identification unit identifies nearby affiliated medical institutions based on the employee information linked by the face recognition unit. Specifically, the identification unit receives the employee's workplace and home address as input data, and lists the nearest affiliated medical institutions based on this. A distance calculation algorithm and a prioritization algorithm 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 address of the medical institution to identify the nearest medical institution. The prioritization algorithm selects the optimal medical institution by taking into account factors such as the medical institution's evaluation, past usage history, and the richness of medical departments. By combining these algorithms, the identification unit can identify the medical institution that is most convenient for the employee. Furthermore, the identification unit regularly updates the database of affiliated medical institutions to reflect new affiliated medical institutions and changes in service content. This allows the identification unit to always identify medical institutions based on the latest information and provide the employee with the optimal option.

[0032] The linking unit automatically links the health insurance card information to the medical institution identified by the identifying unit. Specifically, the linking unit registers the employee's health insurance card information in a database in advance and collates it with the employee information linked by face recognition. The database may be a SQL database or a NoSQL database, and encryption technology is used to safely manage the health insurance card information. When the employee visits a medical institution, the linking unit automatically transmits the health insurance card information to the medical institution's system, simplifying the reception procedure. This eliminates the need for the employee to bring the health insurance card with them, and reduces the hassle of the procedure. Furthermore, the linking unit links data with the medical institution in real time, so that updates and changes to the health insurance card information are reflected immediately. This allows the linking unit to accurately and quickly provide the employee's health insurance card information to the medical institution, supporting smooth medical treatment.

[0033] The presentation unit presents an appropriate medical examination course based on the employee's age information. Specifically, the presentation unit receives the employee's age information as input data, and uses an algorithm that proposes an optimal medical examination course based on the input data. The proposed algorithm is realized by a rule-based method, a machine learning method, or the like. The rule-based algorithm pre-sets recommended health examination items for each age, and presents an appropriate medical examination course according to the employee's age. The machine learning-based algorithm learns past medical examination data and medical examination results, and predicts the optimal medical examination course according to age, gender, and health condition. The presentation unit can present the most appropriate medical examination course for the employee by combining these algorithms. Furthermore, the presentation unit can also propose an individually customized medical examination course taking into account the employee's health condition and past medical examination history. As a result, the presentation unit can make the employee's medical examination more efficient and improve the quality of health management.

[0034] The suggestion unit suggests optional tests based on the medical examination course suggested by the suggestion unit. Specifically, the suggestion unit uses an algorithm that analyzes past medical examination results and suggests optional tests necessary for employees. The analysis algorithm is realized by methods such as data mining and statistical analysis. The data mining algorithm extracts patterns and trends from past medical examination data and suggests optional tests according to the employee's health condition. The statistical analysis algorithm statistically analyzes the employee's medical examination results and identifies high-risk items and necessary tests. By combining these algorithms, the suggestion unit can suggest the most effective optional tests for employees. Furthermore, the suggestion unit can collect employee feedback and continuously improve the accuracy and effectiveness of the suggestions. In this way, the suggestion unit can support employees' health management and maximize the effectiveness of the medical examinations.

[0035] The coordination unit matches the employee's calendar information with the medical institution's reservation history to adjust the optimal schedule. Specifically, the coordination unit receives the employee's calendar information as input data, and uses an algorithm to match it with the availability of the medical institution based on this. The matching algorithm is realized by methods such as a matching algorithm and a heuristic algorithm. The matching algorithm compares the employee's desired date and time with the availability of the medical institution and proposes the optimal reservation date. The heuristic algorithm considers the employee's priority and the medical institution's congestion status to adjust the efficient reservation schedule. By combining these algorithms, the coordination unit can propose the most convenient reservation schedule for the employee. Furthermore, the coordination unit can flexibly respond to reservation changes and cancellations, and make adjustments to suit the employee's schedule. As a result, the coordination unit can make medical checkup reservations for employees more efficient and support smooth medical examinations.

[0036] The face authentication unit can link personal information of employees using a face authentication algorithm using deep learning. Deep learning is realized by technologies such as CNN (convolutional neural network) and RNN (recurrent neural network). The face authentication unit can link personal information of employees using a face authentication algorithm using deep learning. As a result, the accuracy of face authentication is improved by using deep learning. Some or all of the above-mentioned processing in the face authentication unit may be performed using AI, for example, or may be performed without using AI. For example, the face authentication unit can input a face image of an employee to a generation AI and cause the generation AI to learn the face authentication algorithm.

[0037] The identification unit can use an algorithm that lists affiliated medical institutions based on the employee's workplace and home address. The listing algorithm is realized, for example, by a method such as distance calculation or prioritization. The identification unit can use an algorithm that lists the nearest affiliated medical institutions based on the employee's workplace 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-mentioned processing in the identification unit can be performed, for example, using AI or without AI. For example, the identification unit can input the employee's workplace and home address data into the generation AI and cause the generation AI to list the most suitable medical institutions.

[0038] The linking unit can register the employee's health insurance card information in a database in advance and compare it with the employee information linked by facial recognition. The database can be, for example, a SQL database or a NoSQL database. The linking unit, for example, registers the employee's health insurance card information in a database in advance and compares it with the employee information linked by facial recognition. This simplifies the reception procedure by automatically linking the health insurance card information. Some or all of the above-mentioned processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the linking unit can input the employee's health insurance card information to the generation AI and cause the generation AI to register it in the database and compare it.

[0039] The presentation unit can use an algorithm that suggests a medical examination course based on the employee's age information. The proposed algorithm is realized, for example, by a rule-based or machine learning-based method. The presentation unit can use, for example, an algorithm that suggests a medical examination course based on the employee's age information. This makes it possible to suggest an appropriate medical examination course according to age. Some or all of the above-mentioned processing in the presentation unit can be performed, for example, using AI, or can be performed without using AI. For example, the presentation unit can input the employee's age information to the generation AI and cause the generation AI to suggest a medical examination course.

[0040] The suggestion unit can use an algorithm that analyzes past medical examination results and suggests recommended optional examinations. The analysis algorithm is realized, for example, by a method such as data mining or statistical analysis. The suggestion unit can use, for example, an algorithm that analyzes past medical examination results and suggests recommended optional examinations. This makes it possible to suggest optional examinations based on past medical examination results. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input past medical examination result data to a generation AI and cause the generation AI to suggest optional examinations.

[0041] The adjustment unit can use an algorithm that compares the employee's calendar information with the availability of the medical institution and suggests an appointment date. The comparison algorithm is realized, for example, by a matching algorithm, a heuristic algorithm, or the like. The adjustment unit can use, for example, an algorithm that compares the employee's calendar information with the availability of the medical institution and suggests an appointment date. This makes it possible to suggest an optimal appointment date based on the employee's schedule. Some or all of the above-mentioned processing in the adjustment unit can be performed, for example, using AI, or can be performed without using AI. For example, the adjustment unit can input the employee's calendar information to the generation AI and cause the generation AI to suggest an optimal appointment date.

[0042] The face authentication unit can improve the accuracy of authentication by referring to the employee's past face image data during face authentication. For example, the face authentication unit obtains a face image of the employee taken in the past from a database and compares it with a current face image to perform authentication. The face authentication unit can also use the past face image data to learn a face authentication algorithm and improve the authentication accuracy. The face authentication unit can also analyze facial feature points in detail based on the past face image data and improve the authentication accuracy. As a result, the accuracy of face authentication is improved by referring to the past face image data. A part or all of the above-mentioned processing in the face authentication unit may be performed using, for example, AI, or may be performed without using AI. For example, the face authentication unit can input the past face image data to the generation AI and cause the generation AI to execute the improvement of the authentication accuracy.

[0043] The face authentication unit can analyze the facial feature points of the employee in detail during face authentication, and improve the accuracy of linking personal information. For example, the face authentication unit can analyze the facial feature points in detail and link them accurately with personal information. The face authentication unit can also obtain the employee's personal information from a database based on the facial feature points and perform authentication. The face authentication unit can also analyze the facial feature points in detail and perform filtering to prevent erroneous recognition. As a result, the accuracy of linking personal information is improved by analyzing the facial feature points in detail. A part or all of the above-mentioned processing in the face authentication unit may be performed using, for example, AI, or may be performed without using AI. For example, the face authentication unit can input facial feature point data to the generation AI and cause the generation AI to execute improvement of the accuracy of linking personal information.

[0044] The face authentication unit can determine the authentication priority in consideration of the working status of the employee during face authentication. For example, the face authentication unit performs face authentication by giving priority to employees who are working, and quickly completes the authentication. In addition, the face authentication unit can perform authentication by lowering the priority of face authentication for employees who are not working. In addition, the face authentication unit can dynamically adjust the priority of face authentication according to the working status. This makes it possible to adjust the priority of face authentication according to the working status. A part or all of the above-mentioned processing in the face authentication unit may be performed using, for example, AI, or may be performed without using AI. For example, the face authentication unit can input the working status data of employees to the generation AI and cause the generation AI to execute the authentication priority determination.

[0045] The face authentication unit can improve the accuracy of authentication by referring to the face image of the employee on social media during face authentication. For example, the face authentication unit acquires face images from the employee's social media and adds them to the authentication database. The face authentication unit can also improve the authentication accuracy by learning a face authentication algorithm based on the face image on social media. The face authentication unit can also refer to the face image on social media and use it as auxiliary data for improving the accuracy of face authentication. As a result, the accuracy of face authentication is improved by referring to the face image on social media. A part or all of the above-mentioned processing in the face authentication unit may be performed, for example, using AI or may be performed without using AI. For example, the face authentication unit can input face image data from social media to the generation AI and cause the generation AI to improve the accuracy of authentication.

[0046] When identifying a medical institution, the identification unit can select the most suitable medical institution by referring to the employee's past medical history. The identification unit selects the most suitable medical institution based on, for example, an evaluation of a medical institution visited in the past. The identification unit can also refer to the past medical history and select the same medical institution with priority. The identification unit can also select a medical institution that is strong in a specific specialty field based on the past medical history. This makes it possible to select the most suitable medical institution by referring to the past medical history. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input past medical history data to the generation AI and cause the generation AI to select the most suitable medical institution.

[0047] The identification unit can prioritize a specific medical institution in consideration of the employee's health condition when identifying a medical institution. For example, the identification unit prioritizes identifying a medical institution with a specialist for an employee whose health condition is deteriorating. The identification unit can also prioritize identifying a general medical institution for an employee whose health condition is good. The identification unit can also dynamically prioritize a specific medical institution according to the health condition. This makes it possible to prioritize identifying a medical institution according to the health condition. A part or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the employee's health condition data to the generation AI and cause the generation AI to execute the priority identification of a medical institution.

[0048] When identifying a medical institution, the identification unit can select an optimal medical institution taking into consideration the employee's means of transportation. For example, for an employee who uses public transportation, the identification unit selects a medical institution close to the nearest station or bus stop. In addition, for an employee who uses a private car, the identification unit can also select a medical institution with a parking lot. In addition, for an employee who commutes by foot, the identification unit can also select a medical institution within walking distance. This makes it possible to select an optimal medical institution according to the means of transportation. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the employee's means of transportation data into the generation AI and cause the generation AI to select an optimal medical institution.

[0049] The identification unit can prioritize a specific medical institution in consideration of the employee's work schedule when identifying a medical institution. For example, the identification unit prioritizes identifying a medical institution that can be visited during work hours. The identification unit can also prioritize identifying a medical institution that can be visited outside work hours. The identification unit can also dynamically prioritize a specific medical institution according to the work schedule. This makes it possible to prioritize identification of a medical institution according to the work schedule. A part or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the employee's work schedule data to the generation AI and cause the generation AI to execute the priority identification of a medical institution.

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

[0051] The linking unit can determine the priority of linking in consideration of the employee's health condition when linking the health insurance card information. For example, the linking unit prioritizes linking of health insurance card information for an employee whose health condition is deteriorating. The linking unit can also postpone linking of health insurance card information for an employee whose health condition is good. The linking unit can also dynamically adjust the priority of linking according to the health condition. This makes it possible to determine the priority of linking of health insurance card information according to the health condition. Some or all of the above-mentioned processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the linking unit can input the employee's health condition data to the generation AI and cause the generation AI to determine the priority of linking.

[0052] The linking unit can determine the priority of linking in consideration of the working status of employees when linking health insurance card information. For example, the linking unit prioritizes employees who are working when linking health insurance card information. The linking unit can also postpone linking of health insurance card information for employees who are not working. The linking unit can also dynamically adjust the priority of linking according to the working status. This makes it possible to determine the priority of linking of health insurance card information according to the working status. Some or all of the above-mentioned processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input the working status data of employees to the generation AI and cause the generation AI to determine the priority of linking.

[0053] The linking unit can improve the accuracy of linking by referring to the employee's social media information when linking the health insurance card information. The linking unit, for example, acquires the health insurance card information from the employee's social media and adds it to the linking database. The linking unit can also improve the accuracy of linking the health insurance card information based on the social media information. The linking unit can also refer to the social media information and use it as auxiliary data for improving the accuracy of linking. In this way, the accuracy of linking the health insurance card information is improved by referring to the social media information. Some or all of the above-mentioned processing in the linking unit may be performed, for example, using AI or may be performed without using AI. For example, the linking unit can input social media information to the generation AI and cause the generation AI to improve the accuracy of linking.

[0054] When presenting the medical examination course, the presentation unit can suggest the optimal course by referring to the employee's past medical examination history. The presentation unit, for example, proposes the optimal medical examination course based on the past medical examination history. The presentation unit can also improve the accuracy of the medical examination course by referring to the past medical examination history. The presentation unit can also determine the priority order of the medical examination course based on the past medical examination history. This makes it possible to suggest the optimal medical examination course by referring to the past medical examination history. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input past medical examination history data to the generation AI and cause the generation AI to suggest the optimal medical examination course.

[0055] The presentation unit can determine the priority of the medical examination courses when presenting the medical examination courses, taking into consideration the employee's health condition. For example, the presentation unit preferentially proposes a specialized medical examination course to an employee whose health condition is deteriorating. The presentation unit can also preferentially propose a general medical examination course to an employee whose health condition is good. The presentation unit can also dynamically adjust the priority of the medical examination courses according to the health condition. This makes it possible to determine the priority of the medical examination courses according to the health condition. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input the employee's health condition data to the generation AI and cause the generation AI to execute the priority determination of the medical examination courses.

[0056] When presenting the medical examination course, the presentation unit can propose the optimal course taking into consideration the working conditions of the employee. For example, the presentation unit preferentially proposes a course that can be examined during working hours. The presentation unit can also preferentially propose a course that can be examined outside working hours. The presentation unit can also dynamically propose the optimal medical examination course according to the working conditions. This makes it possible to propose the optimal medical examination course according to the working conditions. A part or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the working conditions data of the employee to the generation AI and cause the generation AI to execute the proposal of the optimal medical examination course.

[0057] The presentation unit can improve the accuracy of the medical examination course by referring to the employee's social media information when presenting the medical examination course. The presentation unit, for example, acquires health-related information from the employee's social media and reflects it in the proposal of the medical examination course. The presentation unit can also improve the accuracy of the medical examination course based on the social media information. The presentation unit can also determine the priority of the medical examination course by referring to the social media information. As a result, the accuracy of the medical examination course is improved by referring to the social media information. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input social media information to the generation AI and cause the generation AI to improve the accuracy of the medical examination course.

[0058] When proposing an optional test, the suggestion unit can suggest the most suitable test by referring to the employee's past medical examination history. The suggestion unit can, for example, suggest the most suitable optional test based on the past medical examination history. The suggestion unit can also improve the accuracy of the optional test by referring to the past medical examination history. The suggestion unit can also determine the priority order of the optional test based on the past medical examination history. This makes it possible to suggest the most suitable optional test by referring to the past medical examination history. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input past medical examination history data to the generation AI and cause the generation AI to suggest the most suitable optional test.

[0059] When proposing optional tests, the suggestion unit can determine the priority of the tests taking into account the employee's health condition. For example, the suggestion unit preferentially suggests specialized optional tests to employees whose health condition is deteriorating. The suggestion unit can also preferentially suggest general optional tests to employees whose health condition is good. The suggestion unit can also dynamically adjust the priority of optional tests according to the health condition. This makes it possible to determine the priority of optional tests according to the health condition. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the employee's health condition data to the generation AI and cause the generation AI to execute the priority determination of optional tests.

[0060] When proposing an optional test, the suggestion unit can suggest an optimal test taking into consideration the working conditions of the employee. For example, the suggestion unit preferentially suggests optional tests that can be taken during working hours. The suggestion unit can also preferentially suggest optional tests that can be taken outside working hours. The suggestion unit can also dynamically suggest optimal optional tests according to the working conditions. This makes it possible to suggest optimal optional tests according to the working conditions. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the working conditions data of the employee to the generation AI and cause the generation AI to suggest optimal optional tests.

[0061] The suggestion unit can improve the accuracy of the test by referring to the employee's social media information when proposing an optional test. The suggestion unit, for example, acquires health information from the employee's social media and reflects it in the suggestion of the optional test. The suggestion unit can also improve the accuracy of the optional test based on the social media information. The suggestion unit can also determine the priority of the optional test by referring to the social media information. As a result, the accuracy of the optional test is improved by referring to the social media information. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input social media information to the generation AI and cause the generation AI to improve the accuracy of the optional test.

[0062] When adjusting a schedule, the adjustment unit can propose an optimal schedule by referring to the employee's past schedule history. The adjustment unit can propose an optimal schedule based on, for example, the past schedule history. The adjustment unit can also improve the accuracy of schedule adjustment by referring to the past schedule history. The adjustment unit can also determine the priority order of the schedule based on the past schedule history. This makes it possible to propose an optimal schedule by referring to the past schedule history. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the adjustment unit can input past schedule history data to the generation AI and cause the generation AI to propose an optimal schedule.

[0063] The adjustment unit can determine the priority of schedules in consideration of the health condition of employees when adjusting schedules. For example, the adjustment unit adjusts schedules promptly for employees whose health condition is deteriorating. The adjustment unit can also postpone schedule adjustment for employees whose health condition is good. The adjustment unit can also dynamically adjust the priority of schedules according to the health condition. This makes it possible to determine the priority of schedules according to the health condition. A part or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the health condition data of employees to the generation AI and cause the generation AI to execute the priority of schedules.

[0064] The adjustment unit can propose an optimal schedule taking into consideration the working conditions of employees when adjusting the schedule. For example, the adjustment unit prioritizes and proposes a schedule in which the patient can be examined during working hours. The adjustment unit can also prioritize and propose a schedule in which the patient can be examined outside working hours. The adjustment unit can also dynamically propose an optimal schedule according to the working conditions. This makes it possible to propose an optimal schedule according to the working conditions. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the working conditions data of employees to the generation AI and cause the generation AI to execute the proposal of an optimal schedule.

[0065] The adjustment unit can improve the accuracy of the schedule by referring to the employee's social media information when adjusting the schedule. The adjustment unit, for example, acquires information about the schedule from the employee's social media and reflects it in the schedule adjustment. The adjustment unit can also improve the accuracy of the schedule adjustment based on the social media information. The adjustment unit can also determine the priority of the schedule by referring to the social media information. In this way, the accuracy of the schedule is improved by referring to the social media information. A part or all of the above-mentioned processing in the adjustment unit may be performed, for example, using AI, or may be performed without using AI. For example, the adjustment unit can input social media information to the generation AI and cause the generation AI to improve the accuracy of the schedule.

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

[0067] The health checkup reservation system can further include a health condition monitoring unit that monitors the health condition of employees in real time. The health condition monitoring unit can obtain data such as heart rate, blood pressure, and body temperature from employees' wearable devices, and can immediately notify employees if an abnormality is detected. For example, if the heart rate is abnormally high, a notification can be sent recommending an emergency visit to a medical institution. Also, if blood pressure is high, a reminder can be set to encourage regular blood pressure measurements. Furthermore, if the body temperature is high, it can suggest the possibility of a cold or flu and recommend a visit to an appropriate medical institution. This allows employees' health conditions to be constantly monitored and early action can be taken.

[0068] The health checkup reservation system can also be equipped with a lifestyle management section that manages the employee's diet and exercise records. The lifestyle management section provides advice on improving health in conjunction with the results of the health checkup, by having employees input their daily dietary intake and amount of exercise. For example, it can detect nutritional imbalances from the dietary records and suggest balanced meal menus. In addition, if the amount of exercise is insufficient, it can present an appropriate exercise plan and send reminders to encourage the employee to carry it out. Furthermore, the lifestyle management section can help employees set goals and help them maintain their motivation by visualizing their achievements. This makes it possible to provide comprehensive support for maintaining and improving the health of employees.

[0069] The health checkup reservation system can further include a report generation unit that analyzes the employee's health checkup results and generates individual health reports. The report generation unit evaluates health risks based on the employee's past health checkup results and current health condition, and presents specific improvement measures. For example, if past results indicate a tendency for blood sugar levels to be high, improvement measures for diet and exercise can be suggested. In addition, if cholesterol levels are high, appropriate meal menus and supplements can be recommended. Furthermore, if health risks are high, regular follow-up examinations can be recommended to encourage early detection and early treatment. This makes it possible to manage the health of employees more effectively.

[0070] The health checkup reservation system can further include an education provision unit that provides health education content based on the results of employees' health checkups. The education provision unit selects and provides appropriate health education content according to the employee's health condition. For example, videos and articles about blood pressure management can be provided to employees with high blood pressure. Information on diabetes prevention can also be provided to employees at risk of diabetes. Furthermore, it can suggest health topics that employees may be interested in based on the results of their health checkups, encouraging continuous learning. This can increase employees' health awareness and improve their self-management capabilities.

[0071] The process flow of the first embodiment will be briefly described below.

[0072] Step 1: The facial recognition unit takes a photograph of the employee's face and links it to the employee's personal information using a facial recognition algorithm. The employee's facial image includes information such as the employee's name, employee number, and department. The facial recognition unit links the employee's personal information using a facial recognition algorithm that uses deep learning. Deep learning is achieved 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 the employee information linked by the face 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 realized by methods such as distance calculation and prioritization. Step 3: The linking unit automatically links the health insurance card information to the medical institution identified by the identification unit. The linking unit registers the employee's health insurance card information in a database in advance and compares it with the employee information linked by facial recognition. There are various types of databases, such as SQL databases and NoSQL databases. Step 4: The suggestion unit suggests an appropriate medical examination course based on the employee's age information. The suggestion unit uses an algorithm that suggests a medical examination course based on the employee's age information. The proposed algorithm is realized by a method such as a rule-based method or a machine learning-based method. Step 5: The suggestion unit suggests optional examinations based on the examination course suggested by the suggestion unit. The suggestion unit uses an algorithm that analyzes past examination results and suggests recommended optional examinations. The analysis algorithm is realized by methods such as data mining and statistical analysis. Step 6: The adjustment unit adjusts the optimal schedule by matching the employee's calendar information with the medical institution's reservation history. The adjustment unit uses an algorithm that compares the employee's calendar information with the medical institution's availability and suggests a reservation date. The matching algorithm is realized by methods such as a matching algorithm or a heuristic algorithm.

[0073] (Example 2) A medical examination reservation system according to an embodiment of the present invention is a system that makes it more efficient for employees to undergo medical examinations. This system links employee information by facial recognition, identifies nearby affiliated medical institutions, and automatically links the symbol and number information on the health insurance card. Furthermore, it presents an appropriate medical examination course based on the employee's age information, and suggests recommended optional examinations based on past examination results. Finally, it matches the employee's calendar information with the reservation history of medical institutions and automatically adjusts the optimal schedule. This mechanism automates the troublesome process of selecting a medical institution, adjusting the schedule, deciding the medical examination course, and linking with health insurance card information. For example, employee information is linked by facial recognition. At this time, an image of the employee's face is taken with a camera, and linked to the employee's personal information using a facial recognition algorithm using deep learning. For example, information such as the employee's name, employee number, and department is automatically obtained by facial recognition. Next, it identifies nearby affiliated medical institutions. For example, it uses an algorithm that lists the nearest affiliated medical institutions based on the employee's place of work and home address. This list is optimized based on information such as distance and transportation, taking into account the convenience of the employee. Furthermore, it automatically links the symbol and number information on the health insurance card. Employees do not need to bring their health insurance cards with them, and the system automatically obtains and provides the health insurance card information to the medical institution. This simplifies the procedures at the time of reception. Next, the system presents an appropriate medical examination course based on the employee's age information. For example, a specific medical examination course is proposed for employees over 40 years old, and a different course is proposed for employees in their 20s. In addition, recommended optional examinations are proposed based on past examination results. For example, optional examinations including blood pressure measurement are proposed for employees who have had a tendency toward high blood pressure in the past. Finally, the system matches the employee's calendar information with the medical institution's reservation history and automatically adjusts the optimal date. For example, an algorithm is used that takes into account the employee's work schedule and private plans, and suggests the optimal reservation date in comparison with the medical institution's availability. This allows employees to complete their health examination reservations without hassle. This system allows companies to efficiently manage health examinations based on the Industrial Safety and Health Act, and employees can receive health examinations without hassle.As a result, the health checkup reservation system can make it more efficient for employees to undergo health checkups and support companies in their role under the Industrial Safety and Health Act.

[0074] The medical examination reservation system according to the embodiment includes a face authentication unit, a specification unit, a linking unit, a presentation unit, a suggestion unit, and an adjustment unit. The face authentication unit captures a face image of an employee and links the employee's personal information using a face authentication algorithm. The face image of the employee includes, for example, information such as the employee's name, employee number, and department, but is not limited to such examples. The face authentication unit links the employee's personal information using, for example, a face authentication algorithm using deep learning. Deep learning is realized by, for example, a technology such as CNN (convolutional neural network) or RNN (recurrent neural network). The specification unit identifies a nearby affiliated medical institution based on the employee information linked by the face authentication unit. The specification unit uses, for example, an algorithm that lists the nearest affiliated medical institution based on the employee's place of work or home address. The listing algorithm is realized by, for example, a method such as distance calculation or prioritization. The specification unit automatically links the health insurance card information to the medical institution identified by the specification unit. The linking unit, for example, registers the employee's health insurance card information in a database in advance and collates it with the employee information linked by face recognition. The database may be, for example, an SQL database or a NoSQL database. The presentation unit presents an appropriate medical examination course based on the employee's age information. The presentation unit uses, for example, an algorithm that proposes a medical examination course based on the employee's age information. The proposal algorithm is realized, for example, by a rule-based or machine learning-based method. The proposal unit proposes a recommended optional examination based on the medical examination course presented by the presentation unit. The proposal unit uses, for example, an algorithm that analyzes past medical examination results and proposes a recommended optional examination. The analysis algorithm is realized, for example, by a data mining or statistical analysis method. 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, for example, an algorithm that compares the employee's calendar information with the medical institution's availability and proposes a reservation date. The matching algorithm is realized, for example, by a matching algorithm or a heuristic algorithm.As a result, the health checkup reservation system according to the embodiment can streamline the process for employees to undergo health checkups and support companies in fulfilling their roles under the Industrial Safety and Health Act.

[0075] The facial recognition unit takes a facial image of an employee and links it to the employee's personal information using a facial recognition algorithm. Specifically, the facial recognition unit uses a high-resolution camera to acquire the employee's facial image and inputs it into a facial recognition algorithm using deep learning. Deep learning models include CNN (convolutional neural network) and RNN (recurrent neural network). 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 trained in advance with a large amount of facial image data set, and can recognize the employee's facial image with high accuracy. In addition, the facial recognition unit performs the process from acquiring the facial image to authenticating it in real time, minimizing the waiting time when an employee accesses 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 necessary to always maintain high authentication accuracy. This allows the facial recognition unit to quickly and accurately link the personal information of employees, improving the efficiency and security of the entire system.

[0076] The identification unit identifies nearby affiliated medical institutions based on the employee information linked by the face recognition unit. Specifically, the identification unit receives the employee's workplace and home address as input data, and lists the nearest affiliated medical institutions based on this. A distance calculation algorithm and a prioritization algorithm 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 address of the medical institution to identify the nearest medical institution. The prioritization algorithm selects the optimal medical institution by taking into account factors such as the medical institution's evaluation, past usage history, and the richness of medical departments. By combining these algorithms, the identification unit can identify the medical institution that is most convenient for the employee. Furthermore, the identification unit regularly updates the database of affiliated medical institutions to reflect new affiliated medical institutions and changes in service content. This allows the identification unit to always identify medical institutions based on the latest information and provide the employee with the optimal option.

[0077] The linking unit automatically links the health insurance card information to the medical institution identified by the identifying unit. Specifically, the linking unit registers the employee's health insurance card information in a database in advance and collates it with the employee information linked by face recognition. The database may be a SQL database or a NoSQL database, and encryption technology is used to safely manage the health insurance card information. When the employee visits a medical institution, the linking unit automatically transmits the health insurance card information to the medical institution's system, simplifying the reception procedure. This eliminates the need for the employee to bring the health insurance card with them, and reduces the hassle of the procedure. Furthermore, the linking unit links data with the medical institution in real time, so that updates and changes to the health insurance card information are reflected immediately. This allows the linking unit to accurately and quickly provide the employee's health insurance card information to the medical institution, supporting smooth medical treatment.

[0078] The presentation unit presents an appropriate medical examination course based on the employee's age information. Specifically, the presentation unit receives the employee's age information as input data, and uses an algorithm that proposes an optimal medical examination course based on the input data. The proposed algorithm is realized by a rule-based method, a machine learning method, or the like. The rule-based algorithm pre-sets recommended health examination items for each age, and presents an appropriate medical examination course according to the employee's age. The machine learning-based algorithm learns past medical examination data and medical examination results, and predicts the optimal medical examination course according to age, gender, and health condition. The presentation unit can present the most appropriate medical examination course for the employee by combining these algorithms. Furthermore, the presentation unit can also propose an individually customized medical examination course taking into account the employee's health condition and past medical examination history. As a result, the presentation unit can make the employee's medical examination more efficient and improve the quality of health management.

[0079] The suggestion unit suggests optional tests based on the medical examination course suggested by the suggestion unit. Specifically, the suggestion unit uses an algorithm that analyzes past medical examination results and suggests optional tests necessary for employees. The analysis algorithm is realized by methods such as data mining and statistical analysis. The data mining algorithm extracts patterns and trends from past medical examination data and suggests optional tests according to the employee's health condition. The statistical analysis algorithm statistically analyzes the employee's medical examination results and identifies high-risk items and necessary tests. By combining these algorithms, the suggestion unit can suggest the most effective optional tests for employees. Furthermore, the suggestion unit can collect employee feedback and continuously improve the accuracy and effectiveness of the suggestions. In this way, the suggestion unit can support employees' health management and maximize the effectiveness of the medical examinations.

[0080] The coordination unit matches the employee's calendar information with the medical institution's reservation history to adjust the optimal schedule. Specifically, the coordination unit receives the employee's calendar information as input data, and uses an algorithm to match it with the availability of the medical institution based on this. The matching algorithm is realized by methods such as a matching algorithm and a heuristic algorithm. The matching algorithm compares the employee's desired date and time with the availability of the medical institution and proposes the optimal reservation date. The heuristic algorithm considers the employee's priority and the medical institution's congestion status to adjust the efficient reservation schedule. By combining these algorithms, the coordination unit can propose the most convenient reservation schedule for the employee. Furthermore, the coordination unit can flexibly respond to reservation changes and cancellations, and make adjustments to suit the employee's schedule. As a result, the coordination unit can make medical checkup reservations for employees more efficient and support smooth medical examinations.

[0081] The face authentication unit can link personal information of employees using a face authentication algorithm using deep learning. Deep learning is realized by technologies such as CNN (convolutional neural network) and RNN (recurrent neural network). The face authentication unit can link personal information of employees using a face authentication algorithm using deep learning. As a result, the accuracy of face authentication is improved by using deep learning. Some or all of the above-mentioned processing in the face authentication unit may be performed using AI, for example, or may be performed without using AI. For example, the face authentication unit can input a face image of an employee to a generation AI and cause the generation AI to learn the face authentication algorithm.

[0082] The identification unit can use an algorithm that lists affiliated medical institutions based on the employee's workplace and home address. The listing algorithm is realized, for example, by a method such as distance calculation or prioritization. The identification unit can use an algorithm that lists the nearest affiliated medical institutions based on the employee's workplace 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-mentioned processing in the identification unit can be performed, for example, using AI or without AI. For example, the identification unit can input the employee's workplace and home address data into the generation AI and cause the generation AI to list the most suitable medical institutions.

[0083] The linking unit can register the employee's health insurance card information in a database in advance and compare it with the employee information linked by facial recognition. The database can be, for example, a SQL database or a NoSQL database. The linking unit, for example, registers the employee's health insurance card information in a database in advance and compares it with the employee information linked by facial recognition. This simplifies the reception procedure by automatically linking the health insurance card information. Some or all of the above-mentioned processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the linking unit can input the employee's health insurance card information to the generation AI and cause the generation AI to register it in the database and compare it.

[0084] The presentation unit can use an algorithm that suggests a medical examination course based on the employee's age information. The proposed algorithm is realized, for example, by a rule-based or machine learning-based method. The presentation unit can use, for example, an algorithm that suggests a medical examination course based on the employee's age information. This makes it possible to suggest an appropriate medical examination course according to age. Some or all of the above-mentioned processing in the presentation unit can be performed, for example, using AI, or can be performed without using AI. For example, the presentation unit can input the employee's age information to the generation AI and cause the generation AI to suggest a medical examination course.

[0085] The suggestion unit can use an algorithm that analyzes past medical examination results and suggests recommended optional examinations. The analysis algorithm is realized, for example, by a method such as data mining or statistical analysis. The suggestion unit can use, for example, an algorithm that analyzes past medical examination results and suggests recommended optional examinations. This makes it possible to suggest optional examinations based on past medical examination results. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input past medical examination result data to a generation AI and cause the generation AI to suggest optional examinations.

[0086] The adjustment unit can use an algorithm that compares the employee's calendar information with the availability of the medical institution and suggests an appointment date. The comparison algorithm is realized, for example, by a matching algorithm, a heuristic algorithm, or the like. The adjustment unit can use, for example, an algorithm that compares the employee's calendar information with the availability of the medical institution and suggests an appointment date. This makes it possible to suggest an optimal appointment date based on the employee's schedule. Some or all of the above-mentioned processing in the adjustment unit can be performed, for example, using AI, or can be performed without using AI. For example, the adjustment unit can input the employee's calendar information to the generation AI and cause the generation AI to suggest an optimal appointment date.

[0087] The face authentication unit can estimate the emotion of the employee and adjust the accuracy of the face authentication based on the estimated emotion of the employee. For example, when the employee is nervous, the face authentication unit widens the tolerance range of face authentication to increase the success rate of authentication. In addition, when the employee is relaxed, the face authentication unit can increase the accuracy of face authentication to prevent erroneous recognition. In addition, when the employee is tired, the face authentication unit can prioritize the speed of face authentication and perform authentication quickly. This makes it possible to adjust the accuracy of face authentication according to the emotion of the employee. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the face authentication unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the face authentication unit can input the emotion data of the employee to the generation AI and cause the generation AI to adjust the accuracy of face authentication.

[0088] The face authentication unit can improve the accuracy of authentication by referring to the employee's past face image data during face authentication. For example, the face authentication unit obtains a face image of the employee taken in the past from a database and compares it with a current face image to perform authentication. The face authentication unit can also use the past face image data to learn a face authentication algorithm and improve the authentication accuracy. The face authentication unit can also analyze facial feature points in detail based on the past face image data and improve the authentication accuracy. As a result, the accuracy of face authentication is improved by referring to the past face image data. A part or all of the above-mentioned processing in the face authentication unit may be performed using, for example, AI, or may be performed without using AI. For example, the face authentication unit can input the past face image data to the generation AI and cause the generation AI to execute the improvement of the authentication accuracy.

[0089] The face authentication unit can analyze the facial feature points of the employee in detail during face authentication, and improve the accuracy of linking personal information. For example, the face authentication unit can analyze the facial feature points in detail and link them accurately with personal information. The face authentication unit can also obtain the employee's personal information from a database based on the facial feature points and perform authentication. The face authentication unit can also analyze the facial feature points in detail and perform filtering to prevent erroneous recognition. As a result, the accuracy of linking personal information is improved by analyzing the facial feature points in detail. A part or all of the above-mentioned processing in the face authentication unit may be performed using, for example, AI, or may be performed without using AI. For example, the face authentication unit can input facial feature point data to the generation AI and cause the generation AI to execute improvement of the accuracy of linking personal information.

[0090] The face authentication unit can estimate the emotion of the employee and adjust the timing of the face authentication based on the estimated emotion of the employee. For example, when the employee is relaxed, the face authentication unit delays the timing of the face authentication to perform natural authentication. Also, when the employee is in a hurry, the face authentication unit can also advance the timing of the face authentication to perform quick authentication. Also, when the employee is nervous, the face authentication unit can adjust the timing of the face authentication to increase the success rate of authentication. This makes it possible to adjust the timing of the face authentication according to the emotion of the employee. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the face authentication unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the face authentication unit can input the emotion data of the employee to the generation AI and cause the generation AI to adjust the timing of the face authentication.

[0091] The face authentication unit can determine the authentication priority in consideration of the working status of the employee during face authentication. For example, the face authentication unit performs face authentication by giving priority to employees who are working, and quickly completes the authentication. In addition, the face authentication unit can perform authentication by lowering the priority of face authentication for employees who are not working. In addition, the face authentication unit can dynamically adjust the priority of face authentication according to the working status. This makes it possible to adjust the priority of face authentication according to the working status. A part or all of the above-mentioned processing in the face authentication unit may be performed using, for example, AI, or may be performed without using AI. For example, the face authentication unit can input the working status data of employees to the generation AI and cause the generation AI to execute the authentication priority determination.

[0092] The face authentication unit can improve the accuracy of authentication by referring to the face image of the employee on social media during face authentication. For example, the face authentication unit acquires face images from the employee's social media and adds them to the authentication database. The face authentication unit can also improve the authentication accuracy by learning a face authentication algorithm based on the face image on social media. The face authentication unit can also refer to the face image on social media and use it as auxiliary data for improving the accuracy of face authentication. As a result, the accuracy of face authentication is improved by referring to the face image on social media. A part or all of the above-mentioned processing in the face authentication unit may be performed, for example, using AI or may be performed without using AI. For example, the face authentication unit can input face image data from social media to the generation AI and cause the generation AI to improve the accuracy of authentication.

[0093] The identification unit can estimate the employee's emotion and adjust the method of identifying a medical institution based on the estimated employee's emotion. For example, when the employee is relaxed, the identification unit provides detailed medical institution information to expand the options. In addition, when the employee is in a hurry, the identification unit can also prioritize identifying the nearest medical institution. In addition, when the employee is nervous, the identification unit can also prioritize identifying a highly reliable medical institution. This makes it possible to adjust the method of identifying a medical institution according to the employee's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the identification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the identification unit can input the employee's emotion data to the generation AI and cause the generation AI to adjust the method of identifying a medical institution.

[0094] When identifying a medical institution, the identification unit can select the most suitable medical institution by referring to the employee's past medical history. The identification unit selects the most suitable medical institution based on, for example, an evaluation of a medical institution visited in the past. The identification unit can also refer to the past medical history and select the same medical institution with priority. The identification unit can also select a medical institution that is strong in a specific specialty field based on the past medical history. This makes it possible to select the most suitable medical institution by referring to the past medical history. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input past medical history data to the generation AI and cause the generation AI to select the most suitable medical institution.

[0095] The identification unit can prioritize a specific medical institution in consideration of the employee's health condition when identifying a medical institution. For example, the identification unit prioritizes identifying a medical institution with a specialist for an employee whose health condition is deteriorating. The identification unit can also prioritize identifying a general medical institution for an employee whose health condition is good. The identification unit can also dynamically prioritize a specific medical institution according to the health condition. This makes it possible to prioritize identifying a medical institution according to the health condition. A part or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the employee's health condition data to the generation AI and cause the generation AI to execute the priority identification of a medical institution.

[0096] The identification unit can estimate the employee's emotion and adjust the identification order of medical institutions based on the estimated employee's emotion. For example, when the employee is relaxed, the identification unit provides detailed information to expand the options. In addition, when the employee is in a hurry, the identification unit can also prioritize identifying the nearest medical institution. In addition, when the employee is nervous, the identification unit can also prioritize identifying a highly reliable medical institution. This makes it possible to adjust the identification order of medical institutions according to the employee's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the identification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the identification unit can input the employee's emotion data to the generation AI and cause the generation AI to adjust the identification order of medical institutions.

[0097] When identifying a medical institution, the identification unit can select an optimal medical institution taking into consideration the employee's means of transportation. For example, for an employee who uses public transportation, the identification unit selects a medical institution close to the nearest station or bus stop. In addition, for an employee who uses a private car, the identification unit can also select a medical institution with a parking lot. In addition, for an employee who commutes by foot, the identification unit can also select a medical institution within walking distance. This makes it possible to select an optimal medical institution according to the means of transportation. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the employee's means of transportation data into the generation AI and cause the generation AI to select an optimal medical institution.

[0098] The identification unit can prioritize a specific medical institution in consideration of the employee's work schedule when identifying a medical institution. For example, the identification unit prioritizes identifying a medical institution that can be visited during work hours. The identification unit can also prioritize identifying a medical institution that can be visited outside work hours. The identification unit can also dynamically prioritize a specific medical institution according to the work schedule. This makes it possible to prioritize identification of a medical institution according to the work schedule. A part or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the employee's work schedule data to the generation AI and cause the generation AI to execute the priority identification of a medical institution.

[0099] The linking unit can estimate the employee's emotion and adjust the linking method of the health insurance card information based on the estimated employee's emotion. For example, when the employee is relaxed, the linking unit can provide a detailed linking procedure. Also, when the employee is in a hurry, the linking unit can also adjust the linking procedure to increase the success rate when the employee is nervous. This makes it possible to adjust the linking method of the health insurance card information according to the employee's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the linking unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the linking unit can input the employee's emotion data to the generation AI and cause the generation AI to adjust the linking method of the health insurance card information.

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

[0101] The linking unit can determine the priority of linking in consideration of the employee's health condition when linking the health insurance card information. For example, the linking unit prioritizes linking of health insurance card information for an employee whose health condition is deteriorating. The linking unit can also postpone linking of health insurance card information for an employee whose health condition is good. The linking unit can also dynamically adjust the priority of linking according to the health condition. This makes it possible to determine the priority of linking of health insurance card information according to the health condition. Some or all of the above-mentioned processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the linking unit can input the employee's health condition data to the generation AI and cause the generation AI to determine the priority of linking.

[0102] The linking unit can estimate the employee's emotion and adjust the linking timing of the health insurance card information based on the estimated employee's emotion. For example, when the employee is relaxed, the linking unit can delay the linking timing to perform natural linking. Also, when the employee is in a hurry, the linking unit can advance the linking timing to perform quick linking. Also, when the employee is nervous, the linking unit can adjust the linking timing to increase the success rate. This makes it possible to adjust the linking timing of the health insurance card information according to the employee's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the linking unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the linking unit can input the employee's emotion data to the generation AI and cause the generation AI to adjust the linking timing of the health insurance card information.

[0103] The linking unit can determine the priority of linking in consideration of the working status of employees when linking health insurance card information. For example, the linking unit prioritizes employees who are working when linking health insurance card information. The linking unit can also postpone linking of health insurance card information for employees who are not working. The linking unit can also dynamically adjust the priority of linking according to the working status. This makes it possible to determine the priority of linking of health insurance card information according to the working status. Some or all of the above-mentioned processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input the working status data of employees to the generation AI and cause the generation AI to determine the priority of linking.

[0104] The linking unit can improve the accuracy of linking by referring to the employee's social media information when linking the health insurance card information. The linking unit, for example, acquires the health insurance card information from the employee's social media and adds it to the linking database. The linking unit can also improve the accuracy of linking the health insurance card information based on the social media information. The linking unit can also refer to the social media information and use it as auxiliary data for improving the accuracy of linking. In this way, the accuracy of linking the health insurance card information is improved by referring to the social media information. Some or all of the above-mentioned processing in the linking unit may be performed, for example, using AI or may be performed without using AI. For example, the linking unit can input social media information to the generation AI and cause the generation AI to improve the accuracy of linking.

[0105] The presentation unit can estimate the employee's emotion and adjust the presentation method of the medical examination course based on the estimated emotion of the employee. For example, when the employee is relaxed, the presentation unit can provide detailed medical examination course information. Also, when the employee is in a hurry, the presentation unit can also provide simplified medical examination course information. Also, when the employee is nervous, the presentation unit can adjust the presentation method of the medical examination course to increase the success rate. This makes it possible to adjust the presentation method of the medical examination course according to the employee's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the presentation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the presentation unit can input the employee's emotion data to the generation AI and cause the generation AI to adjust the presentation method of the medical examination course.

[0106] When presenting the medical examination course, the presentation unit can suggest the optimal course by referring to the employee's past medical examination history. The presentation unit, for example, proposes the optimal medical examination course based on the past medical examination history. The presentation unit can also improve the accuracy of the medical examination course by referring to the past medical examination history. The presentation unit can also determine the priority order of the medical examination course based on the past medical examination history. This makes it possible to suggest the optimal medical examination course by referring to the past medical examination history. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input past medical examination history data to the generation AI and cause the generation AI to suggest the optimal medical examination course.

[0107] The presentation unit can determine the priority of the medical examination courses when presenting the medical examination courses, taking into consideration the employee's health condition. For example, the presentation unit preferentially proposes a specialized medical examination course to an employee whose health condition is deteriorating. The presentation unit can also preferentially propose a general medical examination course to an employee whose health condition is good. The presentation unit can also dynamically adjust the priority of the medical examination courses according to the health condition. This makes it possible to determine the priority of the medical examination courses according to the health condition. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input the employee's health condition data to the generation AI and cause the generation AI to execute the priority determination of the medical examination courses.

[0108] The presentation unit can estimate the employee's emotions and adjust the presentation order of medical examination courses based on the estimated employee's emotions. For example, when the employee is relaxed, the presentation unit provides detailed information to expand options. In addition, when the employee is in a hurry, the presentation unit can also prioritize and present the most important medical examination course. In addition, when the employee is nervous, the presentation unit can also prioritize and present the most reliable medical examination course. This makes it possible to adjust the presentation order of medical examination courses according to the employee's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the presentation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the presentation unit can input the employee's emotion data to the generation AI and cause the generation AI to adjust the presentation order of medical examination courses.

[0109] When presenting the medical examination course, the presentation unit can propose the optimal course taking into consideration the working conditions of the employee. For example, the presentation unit preferentially proposes a course that can be examined during working hours. The presentation unit can also preferentially propose a course that can be examined outside working hours. The presentation unit can also dynamically propose the optimal medical examination course according to the working conditions. This makes it possible to propose the optimal medical examination course according to the working conditions. A part or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the working conditions data of the employee to the generation AI and cause the generation AI to execute the proposal of the optimal medical examination course.

[0110] The presentation unit can improve the accuracy of the medical examination course by referring to the employee's social media information when presenting the medical examination course. The presentation unit, for example, acquires health-related information from the employee's social media and reflects it in the proposal of the medical examination course. The presentation unit can also improve the accuracy of the medical examination course based on the social media information. The presentation unit can also determine the priority of the medical examination course by referring to the social media information. As a result, the accuracy of the medical examination course is improved by referring to the social media information. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input social media information to the generation AI and cause the generation AI to improve the accuracy of the medical examination course.

[0111] The suggestion unit can estimate the employee's emotions and adjust the optional test proposal method based on the estimated employee's emotions. For example, when the employee is relaxed, the suggestion unit can provide detailed optional test information. Also, when the employee is in a hurry, the suggestion unit can also provide simplified optional test information. Also, when the employee is nervous, the suggestion unit can adjust the optional test proposal method to increase the success rate. This makes it possible to adjust the optional test proposal method according to the employee's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the employee's emotion data to the generation AI and cause the generation AI to execute adjustment of the optional test proposal method.

[0112] When proposing an optional test, the suggestion unit can suggest the most suitable test by referring to the employee's past medical examination history. The suggestion unit can, for example, suggest the most suitable optional test based on the past medical examination history. The suggestion unit can also improve the accuracy of the optional test by referring to the past medical examination history. The suggestion unit can also determine the priority order of the optional test based on the past medical examination history. This makes it possible to suggest the most suitable optional test by referring to the past medical examination history. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input past medical examination history data to the generation AI and cause the generation AI to suggest the most suitable optional test.

[0113] When proposing optional tests, the suggestion unit can determine the priority of the tests taking into account the employee's health condition. For example, the suggestion unit preferentially suggests specialized optional tests to employees whose health condition is deteriorating. The suggestion unit can also preferentially suggest general optional tests to employees whose health condition is good. The suggestion unit can also dynamically adjust the priority of optional tests according to the health condition. This makes it possible to determine the priority of optional tests according to the health condition. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the employee's health condition data to the generation AI and cause the generation AI to execute the priority determination of optional tests.

[0114] The suggestion unit can estimate the employee's emotions and adjust the proposed order of optional tests based on the estimated employee's emotions. For example, when the employee is relaxed, the suggestion unit provides detailed information to expand the options. In addition, when the employee is in a hurry, the suggestion unit can also prioritize and propose the most important optional test. In addition, when the employee is nervous, the suggestion unit can also prioritize and propose the most reliable optional test. This makes it possible to adjust the proposed order of optional tests according to the employee's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the employee's emotion data to the generation AI and cause the generation AI to adjust the proposed order of optional tests.

[0115] When proposing an optional test, the suggestion unit can suggest an optimal test taking into consideration the working conditions of the employee. For example, the suggestion unit preferentially suggests optional tests that can be taken during working hours. The suggestion unit can also preferentially suggest optional tests that can be taken outside working hours. The suggestion unit can also dynamically suggest optimal optional tests according to the working conditions. This makes it possible to suggest optimal optional tests according to the working conditions. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the working conditions data of the employee to the generation AI and cause the generation AI to suggest optimal optional tests.

[0116] The suggestion unit can improve the accuracy of the test by referring to the employee's social media information when proposing an optional test. The suggestion unit, for example, acquires health information from the employee's social media and reflects it in the suggestion of the optional test. The suggestion unit can also improve the accuracy of the optional test based on the social media information. The suggestion unit can also determine the priority of the optional test by referring to the social media information. As a result, the accuracy of the optional test is improved by referring to the social media information. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input social media information to the generation AI and cause the generation AI to improve the accuracy of the optional test.

[0117] The adjustment unit can estimate the employee's emotion and adjust the schedule adjustment method based on the estimated employee's emotion. For example, when the employee is relaxed, the adjustment unit can provide detailed schedule adjustment information. Also, when the employee is in a hurry, the adjustment unit can also provide simplified schedule adjustment information. Also, when the employee is nervous, the adjustment unit can adjust the schedule adjustment method to increase the success rate. This makes it possible to adjust the schedule adjustment method according to the employee's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the adjustment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the adjustment unit can input the employee's emotion data to the generation AI and cause the generation AI to execute the adjustment of the schedule adjustment method.

[0118] When adjusting a schedule, the adjustment unit can propose an optimal schedule by referring to the employee's past schedule history. The adjustment unit can propose an optimal schedule based on, for example, the past schedule history. The adjustment unit can also improve the accuracy of schedule adjustment by referring to the past schedule history. The adjustment unit can also determine the priority order of the schedule based on the past schedule history. This makes it possible to propose an optimal schedule by referring to the past schedule history. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the adjustment unit can input past schedule history data to the generation AI and cause the generation AI to propose an optimal schedule.

[0119] The adjustment unit can determine the priority of schedules in consideration of the health condition of employees when adjusting schedules. For example, the adjustment unit adjusts schedules promptly for employees whose health condition is deteriorating. The adjustment unit can also postpone schedule adjustment for employees whose health condition is good. The adjustment unit can also dynamically adjust the priority of schedules according to the health condition. This makes it possible to determine the priority of schedules according to the health condition. A part or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the health condition data of employees to the generation AI and cause the generation AI to execute the priority of schedules.

[0120] The adjustment unit can estimate the employee's emotions and adjust the order of schedule adjustment based on the estimated employee's emotions. For example, when the employee is relaxed, the adjustment unit provides detailed information to expand options. In addition, when the employee is in a hurry, the adjustment unit can also adjust the most important schedule with priority. In addition, when the employee is nervous, the adjustment unit can also adjust the schedule with priority to a highly reliable schedule. This makes it possible to adjust the order of schedule adjustment according to the employee's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the adjustment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the adjustment unit can input the employee's emotion data to the generation AI and cause the generation AI to adjust the order of schedule adjustment.

[0121] The adjustment unit can propose an optimal schedule taking into consideration the working conditions of employees when adjusting the schedule. For example, the adjustment unit prioritizes and proposes a schedule in which the patient can be examined during working hours. The adjustment unit can also prioritize and propose a schedule in which the patient can be examined outside working hours. The adjustment unit can also dynamically propose an optimal schedule according to the working conditions. This makes it possible to propose an optimal schedule according to the working conditions. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the working conditions data of employees to the generation AI and cause the generation AI to execute the proposal of an optimal schedule.

[0122] The adjustment unit can improve the accuracy of the schedule by referring to the employee's social media information when adjusting the schedule. The adjustment unit, for example, acquires information about the schedule from the employee's social media and reflects it in the schedule adjustment. The adjustment unit can also improve the accuracy of the schedule adjustment based on the social media information. The adjustment unit can also determine the priority of the schedule by referring to the social media information. In this way, the accuracy of the schedule is improved by referring to the social media information. A part or all of the above-mentioned processing in the adjustment unit may be performed, for example, using AI, or may be performed without using AI. For example, the adjustment unit can input social media information to the generation AI and cause the generation AI to improve the accuracy of the schedule.

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

[0124] The health checkup reservation system can further include a health condition monitoring unit that monitors the health condition of employees in real time. The health condition monitoring unit can obtain data such as heart rate, blood pressure, and body temperature from employees' wearable devices, and can immediately notify employees if an abnormality is detected. For example, if the heart rate is abnormally high, a notification can be sent recommending an emergency visit to a medical institution. Also, if blood pressure is high, a reminder can be set to encourage regular blood pressure measurements. Furthermore, if the body temperature is high, it can suggest the possibility of a cold or flu and recommend a visit to an appropriate medical institution. This allows employees' health conditions to be constantly monitored and early action can be taken.

[0125] The health checkup reservation system can also be equipped with a lifestyle management section that manages the employee's diet and exercise records. The lifestyle management section provides advice on improving health in conjunction with the results of the health checkup, by having employees input their daily dietary intake and amount of exercise. For example, it can detect nutritional imbalances from the dietary records and suggest balanced meal menus. In addition, if the amount of exercise is insufficient, it can present an appropriate exercise plan and send reminders to encourage the employee to carry it out. Furthermore, the lifestyle management section can help employees set goals and help them maintain their motivation by visualizing their achievements. This makes it possible to provide comprehensive support for maintaining and improving the health of employees.

[0126] The health checkup reservation system can further include a stress evaluation unit that evaluates the stress level of employees. The stress evaluation unit estimates the stress level based on the employee's heart rate variability, sleep patterns, questionnaire results, etc., and proposes appropriate measures. For example, if there is little heart rate variability, it can provide a guide to breathing techniques and meditation to help employees relax. In addition, if the sleep pattern is disrupted, it can provide advice on improving the sleep environment and ensuring adequate sleep hours. Furthermore, it can identify psychological stress factors from the questionnaire results and recommend that employees seek counseling. This makes it possible to support the mental health of employees and reduce stress.

[0127] The health checkup reservation system can further include a report generation unit that analyzes the employee's health checkup results and generates individual health reports. The report generation unit evaluates health risks based on the employee's past health checkup results and current health condition, and presents specific improvement measures. For example, if past results indicate a tendency for blood sugar levels to be high, improvement measures for diet and exercise can be suggested. In addition, if cholesterol levels are high, appropriate meal menus and supplements can be recommended. Furthermore, if health risks are high, regular follow-up examinations can be recommended to encourage early detection and early treatment. This makes it possible to manage the health of employees more effectively.

[0128] The health checkup reservation system can further include an education provision unit that provides health education content based on the results of employees' health checkups. The education provision unit selects and provides appropriate health education content according to the employee's health condition. For example, videos and articles about blood pressure management can be provided to employees with high blood pressure. Information on diabetes prevention can also be provided to employees at risk of diabetes. Furthermore, it can suggest health topics that employees may be interested in based on the results of their health checkups, encouraging continuous learning. This can increase employees' health awareness and improve their self-management capabilities.

[0129] The health checkup reservation system can further estimate the emotions of employees and provide motivational messages to increase their motivation to undergo health checkups based on the estimated emotions. For example, if an employee is feeling anxious, a message emphasizing the importance and benefits of health checkups can be sent. If an employee is relaxed, advice on how to enjoy the health checkup can be provided. Furthermore, if an employee is tired, a message emphasizing that undergoing a health checkup is useful for maintaining health can be sent to increase motivation to undergo the checkup. This makes it possible to provide appropriate motivation according to employees' emotions, and improve the rate at which employees undergo health checkups.

[0130] The health checkup reservation system can also estimate the employee's emotions and adjust the way in which the health checkup results are notified based on the estimated emotions. For example, if an employee is nervous, the results can be notified in a gentle manner to reassure them. If the employee is relaxed, detailed results can be provided to raise awareness of self-management. Furthermore, if an employee is feeling anxious, appropriate follow-up advice can be provided at the same time as the results. This makes it possible to notify employees of results according to their emotions and raise awareness of health management.

[0131] The health checkup reservation system can further estimate the employee's emotions and adjust the health checkup schedule based on the estimated emotions. For example, if an employee is feeling stressed, the checkup can be scheduled for a time when the employee is able to relax. If the employee is busy, a schedule that allows for a short checkup can be suggested. Furthermore, if the employee is relaxed, a schedule that includes a detailed examination can be suggested. This makes it possible to adjust the checkup schedule according to the employee's emotions, reducing the burden of checkups.

[0132] The health checkup reservation system can further estimate the employee's emotions and provide support before and after the health checkup based on the estimated emotions. For example, if an employee feels anxious, advice on how to relax before the checkup can be provided. Also, if an employee feels tense, a method for relaxing after the checkup can be suggested. Furthermore, if the employee feels relaxed, information on health management after the checkup can be provided to support ongoing health management. This makes it possible to provide support before and after the checkup according to the employee's emotions, improving the health checkup experience.

[0133] The health checkup reservation system can further estimate the employee's emotions and provide feedback on the results of the health checkup based on the estimated emotions. For example, if the employee is feeling anxious, the system can emphasize positive feedback on the results. If the employee is relaxed, the system can provide detailed feedback to raise awareness of self-management. Furthermore, if the employee is nervous, the system can suggest specific measures to improve the results and provide a sense of security. This makes it possible to provide appropriate feedback according to the employee's emotions and raise awareness of health management.

[0134] The process flow of the second embodiment will be briefly described below.

[0135] Step 1: The facial recognition unit takes a photograph of the employee's face and links it to the employee's personal information using a facial recognition algorithm. The employee's facial image includes information such as the employee's name, employee number, and department. The facial recognition unit links the employee's personal information using a facial recognition algorithm that uses deep learning. Deep learning is achieved 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 the employee information linked by the face 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 realized by methods such as distance calculation and prioritization. Step 3: The linking unit automatically links the health insurance card information to the medical institution identified by the identification unit. The linking unit registers the employee's health insurance card information in a database in advance and compares it with the employee information linked by facial recognition. There are various types of databases, such as SQL databases and NoSQL databases. Step 4: The suggestion unit suggests an appropriate medical examination course based on the employee's age information. The suggestion unit uses an algorithm that suggests a medical examination course based on the employee's age information. The proposed algorithm is realized by a method such as a rule-based method or a machine learning-based method. Step 5: The suggestion unit suggests optional examinations based on the examination course suggested by the suggestion unit. The suggestion unit uses an algorithm that analyzes past examination results and suggests recommended optional examinations. The analysis algorithm is realized by methods such as data mining and statistical analysis. Step 6: The adjustment unit adjusts the optimal schedule by matching the employee's calendar information with the medical institution's reservation history. The adjustment unit uses an algorithm that compares the employee's calendar information with the medical institution's availability and suggests a reservation date. The matching algorithm is realized by methods such as a matching algorithm or a heuristic algorithm.

[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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the 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 voice data.

[0137] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0138] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

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

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

[0141] 3, the data processing system 210 includes the 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 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0147] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0150] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0153] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0155] Each of the multiple elements including the face authentication unit, the identification unit, the linking unit, the presentation unit, the suggestion unit, and the adjustment unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the face authentication unit takes a face image of the employee using the camera 42 of the smart glasses 214, and executes a face recognition algorithm using deep learning by the control unit 46A. The identification unit, for example, by the specific processing unit 290 of the data processing device 12, lists the nearest affiliated medical institutions based on the employee's place of work or home address. The linking unit, for example, by the specific processing unit 290 of the data processing device 12, automatically links health insurance card information. The presentation unit, for example, by the specific processing unit 290 of the data processing device 12, presents an appropriate medical examination course based on the employee's age information. The suggestion unit, for example, by the specific processing unit 290 of the data processing device 12, analyzes past medical examination results and suggests recommended optional examinations. The adjustment unit, for example, by the specific processing unit 290 of the data processing device 12, matches the employee's calendar information with the medical institution's reservation history to adjust the optimal schedule. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

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

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

[0158] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0162] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0163] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0164] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0165] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0166] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0167] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

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

[0169] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0171] Each of the multiple elements including the face authentication unit, the identification unit, the linking unit, the presentation unit, the suggestion unit, and the adjustment unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the face authentication unit takes a face image of the employee using the camera 42 of the headset type terminal 314, and executes a face recognition algorithm using deep learning by the control unit 46A. The identification unit, for example, by the specific processing unit 290 of the data processing device 12, lists the nearest affiliated medical institutions based on the employee's place of work or home address. The linking unit, for example, by the specific processing unit 290 of the data processing device 12, automatically links health insurance card information. The presentation unit, for example, by the specific processing unit 290 of the data processing device 12, presents an appropriate medical examination course based on the employee's age information. The suggestion unit, for example, by the specific processing unit 290 of the data processing device 12, analyzes past medical examination results and suggests recommended optional examinations. The adjustment unit, for example, by the specific processing unit 290 of the data processing device 12, matches the employee's calendar information with the medical institution's reservation history to adjust the optimal schedule. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

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

[0173] 7, a 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 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

[0177] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0178] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0179] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0180] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0181] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0182] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0183] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0184] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[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 control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0186] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0188] Each of the multiple elements including the face authentication unit, the identification unit, the linking unit, the presentation unit, the suggestion unit, and the adjustment unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the face authentication unit takes a face image of the employee using the camera 42 of the robot 414, and executes a face recognition algorithm using deep learning by the control unit 46A. The identification unit, for example, by the specific processing unit 290 of the data processing device 12, lists the nearest affiliated medical institutions based on the employee's place of work or home address. The linking unit, for example, by the specific processing unit 290 of the data processing device 12, automatically links health insurance card information. The presentation unit, for example, by the specific processing unit 290 of the data processing device 12, presents an appropriate medical examination course based on the employee's age information. The suggestion unit, for example, by the specific processing unit 290 of the data processing device 12, analyzes past medical examination results and suggests recommended optional examinations. The adjustment unit, for example, by the specific processing unit 290 of the data processing device 12, matches the employee's calendar information with the medical institution's reservation history to adjust the optimal schedule. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

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

[0190] FIG. 9 is a diagram showing an emotion map 400 on 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 emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0191] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0192] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0193] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0194] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this 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 the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0196] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

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

[0198] In addition, 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 upon request from the data processing device 12.

[0199] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0200] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0201] The hardware resource that executes the specific process may be one of these various processors, or may be 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). Also, the hardware resource that executes the specific process may be a single processor.

[0202] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0203] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0204] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0205] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0206] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0207] (Appendix 1) A facial recognition unit that takes a picture of the employee's face and uses a facial recognition algorithm to link the employee's personal information; An identification unit that identifies a nearby affiliated medical institution based on the employee information linked by the face authentication unit; a linking unit that automatically links health insurance card information to the medical institution identified by the identifying unit; a presentation unit that presents a medical examination course based on the employee's age information; A suggestion unit that suggests recommended optional examinations based on the consultation course suggested by the suggestion unit; A coordination unit that adjusts schedules by matching employee calendar information with medical institution reservation history. A system characterized by: (Appendix 2) The face authentication unit is Use deep learning facial recognition algorithms to link employee personal information 2. The system of claim 1, (Appendix 3) The identification unit is Uses an algorithm to provide a list of partner medical facilities based on employees' work location or home address. 2. The system of claim 1, (Appendix 4) The linking unit is Employee health insurance card information is registered in a database in advance and matched with linked employee information using facial recognition. 2. The system of claim 1, (Appendix 5) The presentation unit is Use an algorithm to suggest medical examination courses based on employee age information 2. The system of claim 1, (Appendix 6) The suggestion unit, Uses an algorithm that analyzes past medical examination results and suggests optional tests 2. The system of claim 1, (Appendix 7) The adjustment unit is Use an algorithm that matches employees' calendar information with medical facility availability and suggests appointment dates 2. The system of claim 1, (Appendix 8) The face authentication unit is Estimate employee emotions and adjust the accuracy of facial recognition based on the estimated employee emotions 2. The system of claim 1, (Appendix 9) The face authentication unit is When performing face recognition, the accuracy of the recognition is improved by referencing the employee's past face image data. 2. The system of claim 1, (Appendix 10) The face authentication unit is When face recognition is performed, the facial features of employees are analyzed in detail to improve the accuracy of linking personal information. 2. The system of claim 1, (Appendix 11) The face authentication unit is Estimate employee emotions and adjust the timing of facial recognition based on the estimated employee emotions 2. The system of claim 1, (Appendix 12) The face authentication unit is When performing face recognition, the authentication priority is determined based on the employee's work status. 2. The system of claim 1, (Appendix 13) The face authentication unit is When performing facial recognition, the accuracy of the recognition can be improved by referencing facial images on employees' social media accounts. 2. The system of claim 1, (Appendix 14) The identification unit is Infer employee sentiment and adjust the identification method for medical institutions based on the inferred employee sentiment 2. The system of claim 1, (Appendix 15) The identification unit is When identifying medical institutions, select the most appropriate medical institution by referring to employees' past medical records. 2. The system of claim 1, (Appendix 16) The identification unit is When identifying medical institutions, give priority to certain medical institutions based on employees' health conditions. 2. The system of claim 1, (Appendix 17) The identification unit is Estimate employee sentiment and adjust the identification order of medical institutions based on the estimated employee sentiment 2. The system of claim 1, (Appendix 18) The identification unit is When identifying medical institutions, select the most suitable one taking into consideration employees' means of transportation. 2. The system of claim 1, (Appendix 19) The identification unit is When identifying medical institutions, prioritize certain medical institutions taking into account employees' work schedules. 2. The system of claim 1, (Appendix 20) The linking unit is Infer employee emotions and adjust the method of linking health insurance card information based on the inferred employee emotions 2. The system of claim 1, (Appendix 21) The linking unit is When linking insurance card information, improve the accuracy of linking by referring to employees' past insurance usage history 2. The system of claim 1, (Appendix 22) The linking unit is When linking health insurance card information, determine the priority of linking based on the employee's health condition 2. The system of claim 1, (Appendix 23) The linking unit is Estimate employee emotions and adjust the timing of sharing health insurance card information based on the estimated employee emotions 2. The system of claim 1, (Appendix 24) The linking unit is When linking health insurance card information, determine the priority of linking based on employee working status 2. The system of claim 1, (Appendix 25) The linking unit is When linking health insurance card information, improve the accuracy of linking by referencing employees' social media information 2. The system of claim 1, (Appendix 26) The presentation unit is Estimate the employee's emotions and adjust the presentation method for medical treatment courses based on the estimated employee emotions 2. The system of claim 1, (Appendix 27) The presentation unit is When presenting medical examination courses, the system suggests the most suitable course by referring to the employee's past medical examination history. 2. The system of claim 1, (Appendix 28) The presentation unit is When presenting medical examination courses, prioritize courses taking into account employees' health conditions 2. The system of claim 1, (Appendix 29) The presentation unit is Estimate the employee's emotions and adjust the order of presentation of medical treatment courses based on the estimated employee emotions 2. The system of claim 1, (Appendix 30) The presentation unit is When presenting consultation courses, the system will suggest the most appropriate course taking into account the employee's work situation. 2. The system of claim 1, (Appendix 31) The presentation unit is When presenting consultation courses, the accuracy of the courses can be improved by referring to employees' social media information. 2. The system of claim 1, (Appendix 32) The suggestion unit, Estimate employee sentiment and adjust optional test suggestions based on the estimated employee sentiment 2. The system of claim 1, (Appendix 33) The suggestion unit, When proposing optional tests, the system will refer to employees' past medical history to suggest the most appropriate test 2. The system of claim 1, (Appendix 34) The suggestion unit, When proposing optional tests, prioritize them taking into account employees' health conditions. 2. The system of claim 1, (Appendix 35) The suggestion unit, Infer employee sentiment and adjust the order of optional tests based on the inferred sentiment 2. The system of claim 1, (Appendix 36) The suggestion unit, When proposing optional tests, propose the most appropriate test taking into account the employee's working conditions 2. The system of claim 1, (Appendix 37) The suggestion unit, When proposing optional tests, improve the accuracy of the tests by referring to employees' social media information. 2. The system of claim 1, (Appendix 38) The adjustment unit is Estimate employee sentiment and adjust schedules based on the estimated sentiment 2. The system of claim 1, (Appendix 39) The adjustment unit is When adjusting schedules, the system refers to employees' past schedule history to suggest optimal dates 2. The system of claim 1, (Appendix 40) The adjustment unit is When adjusting schedules, prioritize schedules taking into account employees' health conditions. 2. The system of claim 1, (Appendix 41) The adjustment unit is Estimate employee emotions and adjust the order of schedule adjustments based on the estimated employee emotions 2. The system of claim 1, (Appendix 42) The adjustment unit is When adjusting schedules, propose the best schedule taking into account employees' work schedules 2. The system of claim 1, (Appendix 43) The adjustment unit is When adjusting schedules, refer to employees' social media information to improve the accuracy of the schedule. 2. The system of claim 1, [Explanation of symbols]

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

Claims

1. A facial recognition unit that takes a picture of the employee's face and uses a facial recognition algorithm to link the employee's personal information; An identification unit that identifies a nearby affiliated medical institution based on the employee information linked by the face authentication unit; a linking unit that automatically links health insurance card information to the medical institution identified by the identifying unit; a presentation unit that presents a medical examination course based on the employee's age information; A suggestion unit that suggests recommended optional examinations based on the consultation course suggested by the suggestion unit; A coordination unit that adjusts schedules by matching employee calendar information with medical institution reservation history. A system characterized by:

2. The face authentication unit is Use deep learning facial recognition algorithms to link employee personal information 2. The system of claim 1 .

3. The identification unit is Uses an algorithm to provide a list of partner medical facilities based on employees' work location or home address.

2. The system of claim 1 .

4. The linking unit is Employee health insurance card information is registered in a database in advance and matched with linked employee information using facial recognition.

2. The system of claim 1 .

5. The presentation unit is Use an algorithm to suggest medical examination courses based on employee age information 2. The system of claim 1 .

6. The suggestion unit, Uses an algorithm that analyzes past medical examination results and suggests optional tests 2. The system of claim 1 .

7. The adjustment unit is Use an algorithm that matches employees' calendar information with medical facility availability and suggests appointment dates 2. The system of claim 1 .

8. The face authentication unit is Estimate employee emotions and adjust the accuracy of facial recognition based on the estimated employee emotions 2. The system of claim 1 .

Images