System

Abstract

To provide a system according to an embodiment that efficiently performs procedures from a reservation of a health diagnosis to a result confirmation, and a re-examination.SOLUTION: A system according to an embodiment comprises: a search unit; a reservation unit; a result confirmation unit; and a re-examination reservation unit. The search unit is configured to search for a hospital. The reservation unit is configured to make a reservation of the hospital retrieved by the search unit. The result confirmation unit is configured to confirm a result of a health diagnosis in the hospital. The re-examination reservation unit is configured to, when a re-examination is required on the basis of the result of the health diagnosis by the result confirmation unit, make a reservation.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, the procedures for booking health checkups, checking results, and requesting re-examinations were complicated and difficult to carry out efficiently.

[0005] The system according to the embodiment aims to efficiently carry out procedures from booking medical checkups to checking results and requesting re-examinations. [Means for solving the problem]

[0006] The system according to the embodiment includes a search unit, a reservation unit, a result confirmation unit, and a re-examination reservation unit. The search unit searches for hospitals. The reservation unit makes a reservation at the hospital searched by the search unit. The result confirmation unit confirms the results of the health check at the hospital. The re-examination reservation unit searches for a hospital again and makes a reservation if a re-examination is necessary based on the results of the health check confirmed by the result confirmation unit. Effect of the Invention

[0007] The system according to the embodiment can efficiently carry out procedures from booking health checkups to checking results and requesting re-examinations. [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) The health checkup support system according to the embodiment of the present invention is a system that provides support from the reservation procedure at a hospital required for a health checkup, to confirmation of the results, and hospital search and reservation when retesting is required. This health checkup support system is designed to allow users to select a hospital suitable for them from multiple hospitals nationwide and perform the procedure smoothly. By using this system, the support required for health management can be completed with one service, so even busy people can easily receive a medical checkup. For example, a user can search for a hospital to receive a health checkup. The search unit (which may include AI processing) searches for a hospital that meets the user's conditions from a database of multiple hospitals nationwide. For example, when a user searches for "hospitals in Tokyo that are available on Saturdays and Sundays," a list of relevant hospitals is displayed. Next, the user can make a reservation at a hospital selected by the user from the searched hospitals. The reservation unit (which may include AI processing) supports the reservation procedure at a hospital selected by the user. For example, when a user inputs "I would like to make a reservation at XX hospital on Saturday morning," the reservation unit checks the reservation status of the hospital and confirms the reservation. After the health checkup is completed, the user can check the results of the health checkup through the result confirmation unit (which may include AI processing). The result confirmation unit displays the results of the health checkup provided by the hospital to the user. For example, when a user inputs "I want to check the results of a blood test," the result confirmation unit displays the results. Furthermore, if a retest is necessary based on the results of the health check, the retest reservation unit (which may include AI processing) searches for a hospital again and makes a reservation. The retest reservation unit searches for a hospital where a retest is necessary based on the results of the health check confirmed by the result confirmation unit, and supports the reservation procedure. For example, when a user inputs "I want to search for and make a reservation at a hospital where a retest is necessary," the retest reservation unit performs the procedure. With this system, users can receive consistent support from reserving a health check, checking the results, and retesting, making it easy for even busy people to manage their health. As a result, the health check support system allows users to receive consistent support from reserving a health check, checking the results, and retesting.

[0029] The health checkup support system according to the embodiment includes a search unit, a reservation unit, a result confirmation unit, and a re-examination reservation unit. The search unit searches for a hospital where the user can receive a health checkup. For example, the search unit searches for a hospital that meets the user's conditions from a database of multiple hospitals nationwide. For example, when a user searches for "hospitals in Tokyo that are available on Saturdays and Sundays," a list of corresponding hospitals is displayed. The search unit can analyze the hospital database using AI and select the hospital that best suits the user's conditions. The reservation unit makes a reservation at the hospital searched by the search unit. The reservation unit supports, for example, the reservation procedure for the hospital selected by the user. For example, when a user inputs "I would like to make a reservation at XX Hospital on Saturday morning," the reservation unit checks the reservation status of the hospital and confirms the reservation. The reservation unit uses AI to link with the hospital's reservation system, checks the reservation status in real time, and can suggest the optimal reservation time. The result confirmation unit displays the results of the health checkup provided by the hospital to the user. For example, when a user inputs "I would like to check the results of my blood test," the result confirmation unit displays the results. The result confirmation unit can analyze the results of the health checkup using AI and display them in a format that is easy for the user to understand. The re-examination reservation unit searches for a hospital again and makes a reservation if a re-examination is necessary based on the results of the health checkup confirmed by the result confirmation unit. For example, when a user inputs "I would like to search for and make a reservation at a hospital in case a re-examination is necessary," the re-examination reservation unit carries out the procedure. The re-examination reservation unit can search for a hospital where a re-examination is necessary using AI and suggest the optimal reservation time. As a result, the health checkup support system according to the embodiment allows the user to receive consistent support from reserving the health checkup to confirming the results and re-examination reservation.

[0030] The search unit searches for hospitals where users can receive health checkups. For example, the search unit searches for hospitals that meet the user's requirements from a database of multiple hospitals across the country. Specifically, the search unit refers to a database that includes information such as the hospital's location, medical specialty, consultation hours, available tests, level of facilities, and past patient evaluations. When a user searches for "hospitals in Tokyo that are available on weekends," a list of applicable hospitals is displayed. The search unit uses AI to analyze the hospital database and select the hospital that best suits the user's requirements. The AI ​​uses natural language processing technology to understand the user's search conditions and efficiently filter the information in the database. For example, when a user searches for "hospitals that can provide internal medicine and have the latest MRI equipment," the AI ​​quickly identifies and lists hospitals that meet these conditions. The search unit also has a function to recommend the most suitable hospital for each user based on the user's past search history and reservation history. This allows users to easily find the hospital that best suits their needs. In addition, the search unit also provides information on hospital congestion and waiting times, supporting users to visit the hospital at the best time. In this way, the search unit helps the user to efficiently and effectively find a hospital for a medical checkup.

[0031] The reservation unit makes a reservation at the hospital searched by the search unit. The reservation unit supports, for example, the reservation procedure at the hospital selected by the user. Specifically, the reservation unit provides an interface for the user to input the desired date and time and examination contents, and cooperates with the hospital's reservation system to confirm the reservation. When the user inputs "I would like to make a reservation at XX Hospital on Saturday morning," the reservation unit checks the reservation status of that hospital and confirms the reservation. The reservation unit cooperates with the hospital's reservation system using AI, checks the reservation status in real time, and can suggest the optimal reservation time. The AI ​​analyzes the data obtained from the hospital's reservation system and finds the available time that is closest to the user's preference. The reservation unit also has a function to respond when the user wants to change or cancel the reservation. For example, if the user inputs "I would like to cancel the reservation due to a sudden change in plans," the reservation unit cooperates with the hospital's system to carry out the cancellation procedure and propose the next optimal reservation time. In addition, the reservation unit also has a function to send reservation reminders to the user, and supports the user not to forget the reservation by notifying them when the reservation date approaches. This allows the reservation unit to enable the user to smoothly make reservations for health checkups and undergo the necessary tests.

[0032] The result confirmation unit displays the results of the health check provided by the hospital to the user. For example, when the user inputs "I want to check the results of the blood test," the result confirmation unit displays the results. Specifically, the result confirmation unit obtains the result data of the health check from the hospital's system and displays it by linking it to the user's account. The result confirmation unit can analyze the results of the health check using AI and display them in a format that is easy for the user to understand. The AI ​​analyzes the numerical values ​​and graphs of the test results, determines whether they are within the normal range, and explains the details of any abnormalities if there are any. The result confirmation unit also has a function that allows the user to compare with past health check results, allowing the user to visually confirm changes in health status. For example, it compares past blood test results with current results and indicates areas for improvement and points of attention. The result confirmation unit also provides advice for the user to decide the next step based on the results. For example, if a certain numerical value is abnormal, it displays a message recommending re-examination or consultation with a specialist. In addition, the result confirmation unit also has a function for the user to share the results with family and doctors, and the results can be downloaded in PDF format or sent by email as necessary. In this way, the result confirmation unit provides support for the user to quickly and accurately understand the results of the health check and take appropriate action.

[0033] The re-examination reservation unit searches for a hospital again and makes a reservation if a re-examination is necessary based on the results of the health check confirmed by the result confirmation unit. For example, when the user inputs "I want to search for a hospital in case a re-examination is necessary and make a reservation", the re-examination reservation unit performs the procedure. Specifically, the re-examination reservation unit identifies the items that need to be re-examined based on the data from the result confirmation unit and searches for a hospital corresponding to the item. The re-examination reservation unit can use AI to search for hospitals that need re-examination and propose the optimal reservation time. The AI ​​determines the need for re-examination and quickly identifies a hospital that meets the user's conditions. In addition, the re-examination reservation unit provides an interface that simplifies the reservation procedure so that the user can smoothly make a reservation for a re-examination. For example, when the user inputs "I want to make a reservation for a re-examination", the re-examination reservation unit proposes the optimal hospital and date and time based on the user's past reservation history and health check results. In addition, the re-examination reservation unit also has a function of explaining the importance of re-examination to the user and sending a reminder to take the re-examination at the appropriate time. In this way, the re-examination reservation unit supports the user so that he or she can undergo a re-examination promptly and appropriately based on the results of the health check.

[0034] The search unit can search for hospitals that meet the user's conditions from a database of multiple hospitals nationwide. For example, when a user searches for "hospitals in Tokyo that are open on weekends," a list of corresponding hospitals is displayed. The search unit can analyze the hospital database using AI and select the hospital that best suits the user's conditions. For example, the search unit receives the user's conditions such as geographical conditions, medical specialty, and consultation hours as input, searches the hospital database based on the conditions, and outputs the most suitable hospital. This allows efficient search for hospitals that meet the user's conditions.

[0035] The reservation unit can support the reservation procedure at a hospital selected by the user. For example, when a user inputs "I would like to make a reservation at XX Hospital on Saturday morning," the reservation unit checks the reservation status of the hospital and confirms the reservation. The reservation unit can use AI to link with the hospital's reservation system, check the reservation status in real time, and suggest the optimal reservation time. For example, the reservation unit receives the user's desired date and time as input, searches the hospital's reservation system based on that, checks the availability, and confirms the reservation. This allows efficient support of the reservation procedure at a hospital selected by the user.

[0036] The result confirmation unit can display the results of the health check provided by the hospital to the user. For example, when the user inputs "I want to check the results of my blood test," the result confirmation unit displays the results. The result confirmation unit can analyze the results of the health check using AI and display them in a format that is easy for the user to understand. For example, the result confirmation unit receives the results of the health check provided by the hospital as input, analyzes them, and displays them to the user. The result confirmation unit can display the results in the format of numerical data, graph display, diagnostic comments, etc. This allows the user to efficiently check the results of the health check.

[0037] The re-examination reservation unit can search for hospitals where re-examination is required based on the results of the health check confirmed by the result confirmation unit, and support the reservation procedure. For example, when a user inputs "I would like to search for and make a reservation at a hospital where re-examination is required," the re-examination reservation unit performs the procedure. The re-examination reservation unit can use AI to search for hospitals where re-examination is required and suggest the optimal reservation time. For example, the re-examination reservation unit receives as input the results of the health check confirmed by the result confirmation unit, searches for hospitals where re-examination is required based on the results, and performs the reservation procedure. The re-examination reservation unit searches for hospitals based on the specific conditions and criteria for which a re-examination is determined to be necessary, and confirms the reservation. This makes it possible to search for an appropriate hospital and support the reservation procedure when a re-examination is required.

[0038] The search unit can analyze the user's past hospital usage history and recommend appropriate hospitals. For example, the search unit recommends hospitals with similar ratings based on ratings of hospitals the user has used in the past. The search unit can also recommend hospitals with the same medical departments based on the medical departments of hospitals the user has used in the past. The search unit can also recommend hospitals that are about the same distance away based on the distance of the hospital the user has used in the past. The search unit can analyze the user's past hospital usage history using AI and recommend the most suitable hospital. This makes it possible to recommend the most suitable hospital based on the user's past hospital usage history.

[0039] The search unit can filter hospitals based on the user's current health condition or specific symptoms when searching for hospitals. For example, when a user inputs a specific symptom, the search unit preferentially displays hospitals that can handle that symptom. When the user's health condition is deteriorating, the search unit can also preferentially display hospitals that can handle emergency treatment. When the user desires regular health checkups, the search unit can also preferentially display general hospitals. The search unit can use AI to analyze the user's current health condition or specific symptoms and filter hospitals based on the results. This makes it possible to search for an appropriate hospital based on the user's health condition or specific symptoms.

[0040] When searching for a hospital, the search unit can give priority to displaying highly relevant hospitals in consideration of the user's geographical location information. For example, the search unit can give priority to displaying hospitals closest to the user's current location. The search unit can also give priority to displaying hospitals along the user's commuting route. The search unit can also give priority to displaying hospitals that are easily accessible from the user's home. The search unit can analyze the user's geographical location information using AI and select highly relevant hospitals based on the analysis. This makes it possible to give priority to displaying highly relevant hospitals based on the user's geographical location information.

[0041] When searching for a hospital, the search unit can analyze the user's social media activity and recommend relevant hospitals. For example, the search unit preferentially displays hospitals that the user follows on social media. The search unit can also preferentially display hospitals that the user has "liked" on social media. The search unit can also preferentially display hospitals that the user's friends have recommended on social media. The search unit can analyze the user's social media activity using AI and recommend relevant hospitals based on the analysis. This makes it possible to recommend relevant hospitals based on the user's social media activity.

[0042] When making a reservation, the reservation unit can analyze the user's past reservation history and suggest an appropriate reservation method. For example, the reservation unit preferentially suggests reservation methods (online, telephone, etc.) that the user has used in the past. The reservation unit can also suggest reservations for the same time slot based on the time slot that the user has previously reserved. The reservation unit can also suggest reservations for specific days of the week or time slots based on the user's past reservation history. The reservation unit can analyze the user's past reservation history using AI and suggest the optimal reservation method. This makes it possible to suggest the optimal reservation method based on the user's past reservation history.

[0043] The reservation unit can suggest an appropriate reservation time based on the user's current schedule and plans at the time of reservation. The reservation unit, for example, refers to the user's calendar information and suggests a reservation during an available time slot. The reservation unit can also suggest an optimal reservation time in accordance with the user's plans. The reservation unit can also adjust the reservation time taking into account the user's schedule. The reservation unit can use AI to analyze the user's current schedule and plans and suggest an optimal reservation time based on the results. This makes it possible to suggest an optimal reservation time based on the user's schedule and plans.

[0044] The reservation unit can propose an appropriate reservation time in consideration of the user's geographical location information at the time of making a reservation. The reservation unit, for example, proposes a reservation time at a hospital closest to the user's current location. The reservation unit can also propose a reservation time at a hospital along the user's commuting route. The reservation unit can also propose a reservation time at a hospital that is easily accessible from the user's home. The reservation unit can analyze the user's geographical location information using AI and propose an optimal reservation time based on the analysis. This makes it possible to propose an optimal reservation time based on the user's geographical location information.

[0045] At the time of making a reservation, the reservation unit can analyze the user's social media activity and provide relevant reservation information. The reservation unit can provide, for example, reservation information for hospitals that the user follows on social media. The reservation unit can also provide reservation information for hospitals that the user has "liked" on social media. The reservation unit can also provide reservation information for hospitals that the user's friends have recommended on social media. The reservation unit can use AI to analyze the user's social media activity and provide relevant reservation information based on that. This makes it possible to provide relevant reservation information based on the user's social media activity.

[0046] The result confirmation unit can provide an interpretation of the results by referring to the user's past health check results when confirming the results. The result confirmation unit, for example, interprets the current results by comparing them with the user's past health check results. The result confirmation unit can also provide improvements and points to note based on the user's past health check results. The result confirmation unit can also explain changes in health status by referring to the user's past health check results. The result confirmation unit can analyze the user's past health check results using AI and interpret the current results based on the results. This makes it possible to provide an interpretation of the results based on the user's past health check results.

[0047] The result confirmation unit can emphasize the importance of the result based on the user's current health condition when confirming the result. For example, if the user's current health condition is deteriorating, the result confirmation unit highlights and displays important results. If the user's current health condition is good, the result confirmation unit can also provide a reassuring display method. The result confirmation unit can also adjust the interpretation of the result based on the user's current health condition. The result confirmation unit can analyze the user's current health condition using AI and emphasize the importance of the result based on that. This makes it possible to emphasize the importance of the result based on the user's current health condition.

[0048] The result confirmation unit can provide information on related medical institutions taking into consideration the user's geographical location information when confirming the result. The result confirmation unit can provide, for example, information on medical institutions closest to the user's current location. The result confirmation unit can also provide information on medical institutions along the user's commute route. The result confirmation unit can also provide information on medical institutions that are easily accessible from the user's home. The result confirmation unit can analyze the user's geographical location information using AI and provide information on related medical institutions based on the analysis. This makes it possible to provide information on related medical institutions based on the user's geographical location information.

[0049] The result confirmation unit can analyze the user's social media activity at the time of result confirmation and provide relevant health information. The result confirmation unit can provide, for example, information on medical institutions that the user follows on social media. The result confirmation unit can also provide health information that the user has "liked" on social media. The result confirmation unit can also provide health information that the user's friends have recommended on social media. The result confirmation unit can use AI to analyze the user's social media activity and provide relevant health information based thereon. This makes it possible to provide relevant health information based on the user's social media activity.

[0050] When reexamination booking is made, the reexamination booking unit can analyze the user's past reexamination history and suggest the optimal reexamination method. For example, the reexamination booking unit suggests a similar reexamination method based on the method of reexamination that the user has previously undergone. The reexamination booking unit can also suggest the most effective reexamination method from the user's past reexamination history. The reexamination booking unit can also analyze the user's past reexamination history and suggest the frequency and timing of reexamination. The reexamination booking unit can analyze the user's past reexamination history using AI and suggest the optimal reexamination method based on that. This makes it possible to suggest the optimal reexamination method based on the user's past reexamination history.

[0051] The reexamination reservation unit can propose an appropriate reexamination time based on the user's current health condition when reexamination is scheduled. For example, if the user's current health condition is deteriorating, the reexamination reservation unit proposes a time when the reexamination can be taken promptly. If the user's current health condition is good, the reexamination reservation unit can also propose a reexamination time that matches the user's schedule. The reexamination reservation unit can also adjust the timing of the reexamination based on the user's current health condition. The reexamination reservation unit can analyze the user's current health condition using AI and propose an optimal reexamination time based on the analysis. This makes it possible to propose an optimal reexamination time based on the user's current health condition.

[0052] The reexamination reservation unit can propose an optimal reexamination time in consideration of the user's geographical location information when reexamination is scheduled. The reexamination reservation unit, for example, proposes a reexamination time at a hospital closest to the user's current location. The reexamination reservation unit can also propose a reexamination time at a hospital along the user's commuting route. The reexamination reservation unit can also propose a reexamination time at a hospital that is easily accessible from the user's home. The reexamination reservation unit can analyze the user's geographical location information using AI and propose an optimal reexamination time based on the analysis. This makes it possible to propose an optimal reexamination time based on the user's geographical location information.

[0053] The reexamination reservation unit can analyze the user's social media activity at the time of reexamination reservation and provide related reexamination information. The reexamination reservation unit can provide, for example, reexamination information of hospitals that the user follows on social media. The reexamination reservation unit can also provide reexamination information of hospitals that the user has "liked" on social media. The reexamination reservation unit can also provide reexamination information of hospitals that the user's friends have recommended on social media. The reexamination reservation unit can analyze the user's social media activity using AI and provide related reexamination information based on the analysis. This makes it possible to provide related reexamination information based on the user's social media activity.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The health checkup support system may further include a health advice providing unit based on the user's health checkup results. The health advice providing unit may, for example, analyze the user's health checkup results and provide advice on lifestyle improvements and diet. For example, if the user's blood pressure is high, diet advice such as limiting salt intake may be provided. Also, if it is determined that the user is not getting enough exercise, an appropriate exercise plan may be proposed. Furthermore, if the user's stress level is high, advice on relaxation methods and stress management may be provided. This allows the user to obtain specific health improvement measures based on the health checkup results.

[0056] The health checkup support system may further include a health reminder unit based on the user's health checkup results. The health reminder unit, for example, reminds the user to undergo regular health checkups. For example, if one year has passed since the user's last health checkup, a reminder can be sent to encourage the user to make an appointment for the next health checkup. Also, if the user needs to undergo a specific test, a reminder for that test can be sent. Furthermore, if the user needs to undergo a specific treatment or follow-up based on the health checkup results, a reminder for that can be sent. This allows the user to continuously manage their health.

[0057] The health checkup support system may further include a health community section based on the user's health checkup results. The health community section may provide, for example, a platform where the user can interact with other users who have the same health goal. For example, if the user is trying to lose weight, a community may be provided where the user can exchange information and encourage each other with users who have the same goal. Also, if the user is undergoing treatment for a particular illness, a community may be provided where the user can share experiences with users who have the same illness. Furthermore, a forum may be provided where the user can post questions or inquiries about health. This allows the user to manage their health while interacting with other users.

[0058] The health checkup support system may further include a health goal setting unit based on the user's health checkup results. The health goal setting unit, for example, supports the user in setting a specific health goal based on the health checkup results. For example, when the user sets a goal to lose weight, a specific weight loss goal and period can be set. When the user sets a goal to lower blood pressure, a specific numerical goal and a method for achieving the goal can also be set. Furthermore, when the user sets a goal to develop an exercise habit, a specific exercise plan and frequency can also be set. This allows the user to set a specific health goal based on the health checkup results and work toward it.

[0059] The health checkup support system may further include a health data sharing unit based on the user's health checkup results. The health data sharing unit, for example, supports the user in sharing his or her health checkup results with family members or doctors. For example, when the user shares his or her health checkup results with family members, the family members can check the results and provide appropriate support. When the user shares his or her health checkup results with a doctor, the doctor can make an appropriate diagnosis or treatment based on the results. Furthermore, when the user shares his or her health checkup results with a specialist, the specialist can provide advice based on the results. This allows the user to share his or her health checkup results with others and receive appropriate support.

[0060] The process flow of the first embodiment will be briefly described below.

[0061] Step 1: The search unit searches for hospitals where the user can receive a health checkup. The search unit searches a database of multiple hospitals across the country for hospitals that meet the user's criteria. For example, if a user searches for "hospitals in Tokyo that are open on weekends," a list of relevant hospitals is displayed. The search unit uses AI to analyze the hospital database and select the hospital that best suits the user's criteria. Step 2: The reservation unit makes a reservation at the hospital searched by the search unit. The reservation unit supports the reservation procedure at the hospital selected by the user. For example, if a user inputs "I would like to make a reservation at X hospital on Saturday morning," the reservation unit checks the reservation status of that hospital and confirms the reservation. The reservation unit uses AI to link with the hospital's reservation system, check the reservation status in real time, and can suggest the optimal reservation time. Step 3: The result confirmation unit displays the results of the health check provided by the hospital to the user. When the user inputs "I would like to check the results of my blood test," the result confirmation unit displays the results. The result confirmation unit can use AI to analyze the results of the health check and display them in a format that is easy for the user to understand. Step 4: The re-examination reservation unit searches for hospitals again and makes a reservation if re-examination is necessary based on the health check results confirmed by the result confirmation unit. When the user inputs "I would like to search for hospitals where re-examination is necessary and make a reservation," the re-examination reservation unit carries out the procedure. The re-examination reservation unit can use AI to search for hospitals where re-examination is necessary and suggest the optimal reservation time.

[0062] (Example 2) The health checkup support system according to the embodiment of the present invention is a system that provides support from the reservation procedure at a hospital required for a health checkup, to confirmation of the results, and hospital search and reservation when retesting is required. This health checkup support system is designed to allow users to select a hospital suitable for them from multiple hospitals nationwide and perform the procedure smoothly. By using this system, the support required for health management can be completed with one service, so even busy people can easily receive a medical checkup. For example, a user can search for a hospital to receive a health checkup. The search unit (which may include AI processing) searches for a hospital that meets the user's conditions from a database of multiple hospitals nationwide. For example, when a user searches for "hospitals in Tokyo that are available on Saturdays and Sundays," a list of relevant hospitals is displayed. Next, the user can make a reservation at a hospital selected by the user from the searched hospitals. The reservation unit (which may include AI processing) supports the reservation procedure at a hospital selected by the user. For example, when a user inputs "I would like to make a reservation at XX hospital on Saturday morning," the reservation unit checks the reservation status of the hospital and confirms the reservation. After the health checkup is completed, the user can check the results of the health checkup through the result confirmation unit (which may include AI processing). The result confirmation unit displays the results of the health checkup provided by the hospital to the user. For example, when a user inputs "I want to check the results of a blood test," the result confirmation unit displays the results. Furthermore, if a retest is necessary based on the results of the health check, the retest reservation unit (which may include AI processing) searches for a hospital again and makes a reservation. The retest reservation unit searches for a hospital where a retest is necessary based on the results of the health check confirmed by the result confirmation unit, and supports the reservation procedure. For example, when a user inputs "I want to search for and make a reservation at a hospital where a retest is necessary," the retest reservation unit performs the procedure. With this system, users can receive consistent support from reserving a health check, checking the results, and retesting, making it easy for even busy people to manage their health. As a result, the health check support system allows users to receive consistent support from reserving a health check, checking the results, and retesting.

[0063] The health checkup support system according to the embodiment includes a search unit, a reservation unit, a result confirmation unit, and a re-examination reservation unit. The search unit searches for a hospital where the user can receive a health checkup. For example, the search unit searches for a hospital that meets the user's conditions from a database of multiple hospitals nationwide. For example, when a user searches for "hospitals in Tokyo that are available on Saturdays and Sundays," a list of corresponding hospitals is displayed. The search unit can analyze the hospital database using AI and select the hospital that best suits the user's conditions. The reservation unit makes a reservation at the hospital searched by the search unit. The reservation unit supports, for example, the reservation procedure for the hospital selected by the user. For example, when a user inputs "I would like to make a reservation at XX Hospital on Saturday morning," the reservation unit checks the reservation status of the hospital and confirms the reservation. The reservation unit uses AI to link with the hospital's reservation system, checks the reservation status in real time, and can suggest the optimal reservation time. The result confirmation unit displays the results of the health checkup provided by the hospital to the user. For example, when a user inputs "I would like to check the results of my blood test," the result confirmation unit displays the results. The result confirmation unit can analyze the results of the health checkup using AI and display them in a format that is easy for the user to understand. The re-examination reservation unit searches for a hospital again and makes a reservation if a re-examination is necessary based on the results of the health checkup confirmed by the result confirmation unit. For example, when a user inputs "I would like to search for and make a reservation at a hospital in case a re-examination is necessary," the re-examination reservation unit carries out the procedure. The re-examination reservation unit can search for a hospital where a re-examination is necessary using AI and suggest the optimal reservation time. As a result, the health checkup support system according to the embodiment allows the user to receive consistent support from reserving the health checkup to confirming the results and re-examination reservation.

[0064] The search unit searches for hospitals where users can receive health checkups. For example, the search unit searches for hospitals that meet the user's requirements from a database of multiple hospitals across the country. Specifically, the search unit refers to a database that includes information such as the hospital's location, medical specialty, consultation hours, available tests, level of facilities, and past patient evaluations. When a user searches for "hospitals in Tokyo that are available on weekends," a list of applicable hospitals is displayed. The search unit uses AI to analyze the hospital database and select the hospital that best suits the user's requirements. The AI ​​uses natural language processing technology to understand the user's search conditions and efficiently filter the information in the database. For example, when a user searches for "hospitals that can provide internal medicine and have the latest MRI equipment," the AI ​​quickly identifies and lists hospitals that meet these conditions. The search unit also has a function to recommend the most suitable hospital for each user based on the user's past search history and reservation history. This allows users to easily find the hospital that best suits their needs. In addition, the search unit also provides information on hospital congestion and waiting times, supporting users to visit the hospital at the best time. In this way, the search unit helps the user to efficiently and effectively find a hospital for a medical checkup.

[0065] The reservation unit makes a reservation at the hospital searched by the search unit. The reservation unit supports, for example, the reservation procedure at the hospital selected by the user. Specifically, the reservation unit provides an interface for the user to input the desired date and time and examination contents, and cooperates with the hospital's reservation system to confirm the reservation. When the user inputs "I would like to make a reservation at XX Hospital on Saturday morning," the reservation unit checks the reservation status of that hospital and confirms the reservation. The reservation unit cooperates with the hospital's reservation system using AI, checks the reservation status in real time, and can suggest the optimal reservation time. The AI ​​analyzes the data obtained from the hospital's reservation system and finds the available time that is closest to the user's preference. The reservation unit also has a function to respond when the user wants to change or cancel the reservation. For example, if the user inputs "I would like to cancel the reservation due to a sudden change in plans," the reservation unit cooperates with the hospital's system to carry out the cancellation procedure and propose the next optimal reservation time. In addition, the reservation unit also has a function to send reservation reminders to the user, and supports the user not to forget the reservation by notifying them when the reservation date approaches. This allows the reservation unit to enable the user to smoothly make reservations for health checkups and undergo the necessary tests.

[0066] The result confirmation unit displays the results of the health check provided by the hospital to the user. For example, when the user inputs "I want to check the results of the blood test," the result confirmation unit displays the results. Specifically, the result confirmation unit obtains the result data of the health check from the hospital's system and displays it by linking it to the user's account. The result confirmation unit can analyze the results of the health check using AI and display them in a format that is easy for the user to understand. The AI ​​analyzes the numerical values ​​and graphs of the test results, determines whether they are within the normal range, and explains the details of any abnormalities if there are any. The result confirmation unit also has a function that allows the user to compare with past health check results, allowing the user to visually confirm changes in health status. For example, it compares past blood test results with current results and indicates areas for improvement and points of attention. The result confirmation unit also provides advice for the user to decide the next step based on the results. For example, if a certain numerical value is abnormal, it displays a message recommending re-examination or consultation with a specialist. In addition, the result confirmation unit also has a function for the user to share the results with family and doctors, and the results can be downloaded in PDF format or sent by email as necessary. In this way, the result confirmation unit provides support for the user to quickly and accurately understand the results of the health check and take appropriate action.

[0067] The re-examination reservation unit searches for a hospital again and makes a reservation if a re-examination is necessary based on the results of the health check confirmed by the result confirmation unit. For example, when the user inputs "I want to search for a hospital in case a re-examination is necessary and make a reservation", the re-examination reservation unit performs the procedure. Specifically, the re-examination reservation unit identifies the items that need to be re-examined based on the data from the result confirmation unit and searches for a hospital corresponding to the item. The re-examination reservation unit can use AI to search for hospitals that need re-examination and propose the optimal reservation time. The AI ​​determines the need for re-examination and quickly identifies a hospital that meets the user's conditions. In addition, the re-examination reservation unit provides an interface that simplifies the reservation procedure so that the user can smoothly make a reservation for a re-examination. For example, when the user inputs "I want to make a reservation for a re-examination", the re-examination reservation unit proposes the optimal hospital and date and time based on the user's past reservation history and health check results. In addition, the re-examination reservation unit also has a function of explaining the importance of re-examination to the user and sending a reminder to take the re-examination at the appropriate time. In this way, the re-examination reservation unit supports the user so that he or she can undergo a re-examination promptly and appropriately based on the results of the health check.

[0068] The search unit can search for hospitals that meet the user's conditions from a database of multiple hospitals nationwide. For example, when a user searches for "hospitals in Tokyo that are open on weekends," a list of corresponding hospitals is displayed. The search unit can analyze the hospital database using AI and select the hospital that best suits the user's conditions. For example, the search unit receives the user's conditions such as geographical conditions, medical specialty, and consultation hours as input, searches the hospital database based on the conditions, and outputs the most suitable hospital. This allows efficient search for hospitals that meet the user's conditions.

[0069] The reservation unit can support the reservation procedure at a hospital selected by the user. For example, when a user inputs "I would like to make a reservation at XX Hospital on Saturday morning," the reservation unit checks the reservation status of the hospital and confirms the reservation. The reservation unit can use AI to link with the hospital's reservation system, check the reservation status in real time, and suggest the optimal reservation time. For example, the reservation unit receives the user's desired date and time as input, searches the hospital's reservation system based on that, checks the availability, and confirms the reservation. This allows efficient support of the reservation procedure at a hospital selected by the user.

[0070] The result confirmation unit can display the results of the health check provided by the hospital to the user. For example, when the user inputs "I want to check the results of my blood test," the result confirmation unit displays the results. The result confirmation unit can analyze the results of the health check using AI and display them in a format that is easy for the user to understand. For example, the result confirmation unit receives the results of the health check provided by the hospital as input, analyzes them, and displays them to the user. The result confirmation unit can display the results in the format of numerical data, graph display, diagnostic comments, etc. This allows the user to efficiently check the results of the health check.

[0071] The re-examination reservation unit can search for hospitals where re-examination is required based on the results of the health check confirmed by the result confirmation unit, and support the reservation procedure. For example, when a user inputs "I would like to search for and make a reservation at a hospital where re-examination is required," the re-examination reservation unit performs the procedure. The re-examination reservation unit can use AI to search for hospitals where re-examination is required and suggest the optimal reservation time. For example, the re-examination reservation unit receives as input the results of the health check confirmed by the result confirmation unit, searches for hospitals where re-examination is required based on the results, and performs the reservation procedure. The re-examination reservation unit searches for hospitals based on the specific conditions and criteria for which a re-examination is determined to be necessary, and confirms the reservation. This makes it possible to search for an appropriate hospital and support the reservation procedure when a re-examination is required.

[0072] The search unit can estimate the user's emotion and adjust the priority of the hospital search based on the estimated user's emotion. For example, when the user feels anxious, the search unit preferentially displays hospitals with good reputations. When the user is relaxed, the search unit can also preferentially display hospitals that emphasize distance and cost. When the user is in a hurry, the search unit can also preferentially display hospitals that are easy to make an appointment with. 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. This allows the priority of the hospital search to be adjusted according to the user's emotion.

[0073] The search unit can analyze the user's past hospital usage history and recommend appropriate hospitals. For example, the search unit recommends hospitals with similar ratings based on ratings of hospitals the user has used in the past. The search unit can also recommend hospitals with the same medical departments based on the medical departments of hospitals the user has used in the past. The search unit can also recommend hospitals that are about the same distance away based on the distance of the hospital the user has used in the past. The search unit can analyze the user's past hospital usage history using AI and recommend the most suitable hospital. This makes it possible to recommend the most suitable hospital based on the user's past hospital usage history.

[0074] The search unit can filter hospitals based on the user's current health condition or specific symptoms when searching for hospitals. For example, when a user inputs a specific symptom, the search unit preferentially displays hospitals that can handle that symptom. When the user's health condition is deteriorating, the search unit can also preferentially display hospitals that can handle emergency treatment. When the user desires regular health checkups, the search unit can also preferentially display general hospitals. The search unit can use AI to analyze the user's current health condition or specific symptoms and filter hospitals based on the results. This makes it possible to search for an appropriate hospital based on the user's health condition or specific symptoms.

[0075] The search unit can estimate the user's emotion and adjust the display method of the search results based on the estimated user's emotion. For example, when the user feels anxious, the search unit provides a simple and highly visible display method. When the user is relaxed, the search unit can also provide a display method including detailed information. When the user is in a hurry, the search unit can also provide a display method that focuses on the main points. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This makes it possible to adjust the display method of the search results according to the user's emotion.

[0076] When searching for a hospital, the search unit can give priority to displaying highly relevant hospitals in consideration of the user's geographical location information. For example, the search unit can give priority to displaying hospitals closest to the user's current location. The search unit can also give priority to displaying hospitals along the user's commuting route. The search unit can also give priority to displaying hospitals that are easily accessible from the user's home. The search unit can analyze the user's geographical location information using AI and select highly relevant hospitals based on the analysis. This makes it possible to give priority to displaying highly relevant hospitals based on the user's geographical location information.

[0077] When searching for a hospital, the search unit can analyze the user's social media activity and recommend relevant hospitals. For example, the search unit preferentially displays hospitals that the user follows on social media. The search unit can also preferentially display hospitals that the user has "liked" on social media. The search unit can also preferentially display hospitals that the user's friends have recommended on social media. The search unit can analyze the user's social media activity using AI and recommend relevant hospitals based on the analysis. This makes it possible to recommend relevant hospitals based on the user's social media activity.

[0078] The reservation unit can estimate the user's emotion and adjust the priority of the reservation procedure based on the estimated user's emotion. For example, when the user feels anxious, the reservation unit simplifies the reservation procedure and quickly confirms the reservation. When the user is relaxed, the reservation unit can also provide detailed reservation options and suggest a customizable reservation method. When the user is in a hurry, the reservation unit can also preferentially display hospitals that can make reservations the soonest. 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 (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This makes it possible to adjust the priority of the reservation procedure according to the user's emotion.

[0079] When making a reservation, the reservation unit can analyze the user's past reservation history and suggest an appropriate reservation method. For example, the reservation unit preferentially suggests reservation methods (online, telephone, etc.) that the user has used in the past. The reservation unit can also suggest reservations for the same time slot based on the time slot that the user has previously reserved. The reservation unit can also suggest reservations for specific days of the week or time slots based on the user's past reservation history. The reservation unit can analyze the user's past reservation history using AI and suggest the optimal reservation method. This makes it possible to suggest the optimal reservation method based on the user's past reservation history.

[0080] The reservation unit can suggest an appropriate reservation time based on the user's current schedule and plans at the time of reservation. The reservation unit, for example, refers to the user's calendar information and suggests a reservation during an available time slot. The reservation unit can also suggest an optimal reservation time in accordance with the user's plans. The reservation unit can also adjust the reservation time taking into account the user's schedule. The reservation unit can use AI to analyze the user's current schedule and plans and suggest an optimal reservation time based on the results. This makes it possible to suggest an optimal reservation time based on the user's schedule and plans.

[0081] The reservation unit can estimate the user's emotion and adjust the notification method of the reservation confirmation based on the estimated user's emotion. For example, when the user feels anxious, the reservation unit provides a simple and highly visible notification method. When the user is relaxed, the reservation unit can also provide a notification method including detailed information. When the user is in a hurry, the reservation unit can also provide a notification method that focuses on the main points. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This makes it possible to adjust the notification method of the reservation confirmation according to the user's emotion.

[0082] The reservation unit can propose an appropriate reservation time in consideration of the user's geographical location information at the time of making a reservation. The reservation unit, for example, proposes a reservation time at a hospital closest to the user's current location. The reservation unit can also propose a reservation time at a hospital along the user's commuting route. The reservation unit can also propose a reservation time at a hospital that is easily accessible from the user's home. The reservation unit can analyze the user's geographical location information using AI and propose an optimal reservation time based on the analysis. This makes it possible to propose an optimal reservation time based on the user's geographical location information.

[0083] At the time of making a reservation, the reservation unit can analyze the user's social media activity and provide relevant reservation information. The reservation unit can provide, for example, reservation information for hospitals that the user follows on social media. The reservation unit can also provide reservation information for hospitals that the user has "liked" on social media. The reservation unit can also provide reservation information for hospitals that the user's friends have recommended on social media. The reservation unit can use AI to analyze the user's social media activity and provide relevant reservation information based on that. This makes it possible to provide relevant reservation information based on the user's social media activity.

[0084] The result confirmation unit can estimate the user's emotion and adjust the method of displaying the results based on the estimated emotion of the user. For example, when the user feels anxious, the result confirmation unit provides a simple and highly visible display method. When the user is relaxed, the result confirmation unit can also provide a display method including detailed information. When the user is in a hurry, the result confirmation unit can also provide a display method that focuses on the main points. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This allows the method of displaying the results to be adjusted according to the user's emotion.

[0085] The result confirmation unit can provide an interpretation of the results by referring to the user's past health check results when confirming the results. The result confirmation unit, for example, interprets the current results by comparing them with the user's past health check results. The result confirmation unit can also provide improvements and points to note based on the user's past health check results. The result confirmation unit can also explain changes in health status by referring to the user's past health check results. The result confirmation unit can analyze the user's past health check results using AI and interpret the current results based on the results. This makes it possible to provide an interpretation of the results based on the user's past health check results.

[0086] The result confirmation unit can emphasize the importance of the result based on the user's current health condition when confirming the result. For example, if the user's current health condition is deteriorating, the result confirmation unit highlights and displays important results. If the user's current health condition is good, the result confirmation unit can also provide a reassuring display method. The result confirmation unit can also adjust the interpretation of the result based on the user's current health condition. The result confirmation unit can analyze the user's current health condition using AI and emphasize the importance of the result based on that. This makes it possible to emphasize the importance of the result based on the user's current health condition.

[0087] The result confirmation unit can estimate the user's emotion and adjust the result notification method based on the estimated user's emotion. For example, when the user feels anxious, the result confirmation unit provides a simple and highly visible notification method. When the user is relaxed, the result confirmation unit can also provide a notification method including detailed information. When the user is in a hurry, the result confirmation unit can also provide a notification method that focuses on the main points. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This makes it possible to adjust the result notification method according to the user's emotion.

[0088] The result confirmation unit can provide information on related medical institutions taking into consideration the user's geographical location information when confirming the result. The result confirmation unit can provide, for example, information on medical institutions closest to the user's current location. The result confirmation unit can also provide information on medical institutions along the user's commute route. The result confirmation unit can also provide information on medical institutions that are easily accessible from the user's home. The result confirmation unit can analyze the user's geographical location information using AI and provide information on related medical institutions based on the analysis. This makes it possible to provide information on related medical institutions based on the user's geographical location information.

[0089] The result confirmation unit can analyze the user's social media activity at the time of result confirmation and provide relevant health information. The result confirmation unit can provide, for example, information on medical institutions that the user follows on social media. The result confirmation unit can also provide health information that the user has "liked" on social media. The result confirmation unit can also provide health information that the user's friends have recommended on social media. The result confirmation unit can use AI to analyze the user's social media activity and provide relevant health information based thereon. This makes it possible to provide relevant health information based on the user's social media activity.

[0090] The reexamination reservation unit can estimate the user's emotions and adjust the priority of reexamination based on the estimated user's emotions. For example, when the user feels anxious, the reexamination reservation unit preferentially displays hospitals where reexamination can be performed immediately. When the user is relaxed, the reexamination reservation unit can also preferentially display hospitals that prioritize cost and distance. When the user is in a hurry, the reexamination reservation unit can also preferentially display hospitals where it is easy to make an appointment. 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. This makes it possible to adjust the priority of reexamination according to the user's emotions.

[0091] When reexamination booking is made, the reexamination booking unit can analyze the user's past reexamination history and suggest the optimal reexamination method. For example, the reexamination booking unit suggests a similar reexamination method based on the method of reexamination that the user has previously undergone. The reexamination booking unit can also suggest the most effective reexamination method from the user's past reexamination history. The reexamination booking unit can also analyze the user's past reexamination history and suggest the frequency and timing of reexamination. The reexamination booking unit can analyze the user's past reexamination history using AI and suggest the optimal reexamination method based on that. This makes it possible to suggest the optimal reexamination method based on the user's past reexamination history.

[0092] The reexamination reservation unit can propose an appropriate reexamination time based on the user's current health condition when reexamination is scheduled. For example, if the user's current health condition is deteriorating, the reexamination reservation unit proposes a time when the reexamination can be taken promptly. If the user's current health condition is good, the reexamination reservation unit can also propose a reexamination time that matches the user's schedule. The reexamination reservation unit can also adjust the timing of the reexamination based on the user's current health condition. The reexamination reservation unit can analyze the user's current health condition using AI and propose an optimal reexamination time based on the analysis. This makes it possible to propose an optimal reexamination time based on the user's current health condition.

[0093] The reexamination reservation unit can estimate the user's emotion and adjust the notification method of the reexamination based on the estimated user's emotion. For example, when the user feels anxious, the reexamination reservation unit provides a notification method that is simple and highly visible. When the user is relaxed, the reexamination reservation unit can also provide a notification method that includes detailed information. When the user is in a hurry, the reexamination reservation unit can also provide a notification method that focuses on the main points. The estimation of emotion is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This makes it possible to adjust the notification method of the reexamination according to the user's emotion.

[0094] The reexamination reservation unit can propose an optimal reexamination time in consideration of the user's geographical location information when reexamination is scheduled. The reexamination reservation unit, for example, proposes a reexamination time at a hospital closest to the user's current location. The reexamination reservation unit can also propose a reexamination time at a hospital along the user's commuting route. The reexamination reservation unit can also propose a reexamination time at a hospital that is easily accessible from the user's home. The reexamination reservation unit can analyze the user's geographical location information using AI and propose an optimal reexamination time based on the analysis. This makes it possible to propose an optimal reexamination time based on the user's geographical location information.

[0095] The reexamination reservation unit can analyze the user's social media activity at the time of reexamination reservation and provide related reexamination information. The reexamination reservation unit can provide, for example, reexamination information of hospitals that the user follows on social media. The reexamination reservation unit can also provide reexamination information of hospitals that the user has "liked" on social media. The reexamination reservation unit can also provide reexamination information of hospitals that the user's friends have recommended on social media. The reexamination reservation unit can analyze the user's social media activity using AI and provide related reexamination information based on the analysis. This makes it possible to provide related reexamination information based on the user's social media activity.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The health checkup support system may further include a health advice providing unit based on the user's health checkup results. The health advice providing unit may, for example, analyze the user's health checkup results and provide advice on lifestyle improvements and diet. For example, if the user's blood pressure is high, diet advice such as limiting salt intake may be provided. Also, if it is determined that the user is not getting enough exercise, an appropriate exercise plan may be proposed. Furthermore, if the user's stress level is high, advice on relaxation methods and stress management may be provided. This allows the user to obtain specific health improvement measures based on the health checkup results.

[0098] The health checkup support system may further include a health reminder unit based on the user's health checkup results. The health reminder unit, for example, reminds the user to undergo regular health checkups. For example, if one year has passed since the user's last health checkup, a reminder can be sent to encourage the user to make an appointment for the next health checkup. Also, if the user needs to undergo a specific test, a reminder for that test can be sent. Furthermore, if the user needs to undergo a specific treatment or follow-up based on the health checkup results, a reminder for that can be sent. This allows the user to continuously manage their health.

[0099] The health checkup support system may further include a health community section based on the user's health checkup results. The health community section may provide, for example, a platform where the user can interact with other users who have the same health goal. For example, if the user is trying to lose weight, a community may be provided where the user can exchange information and encourage each other with users who have the same goal. Also, if the user is undergoing treatment for a particular illness, a community may be provided where the user can share experiences with users who have the same illness. Furthermore, a forum may be provided where the user can post questions or inquiries about health. This allows the user to manage their health while interacting with other users.

[0100] The health checkup support system may further include a health goal setting unit based on the user's health checkup results. The health goal setting unit, for example, supports the user in setting a specific health goal based on the health checkup results. For example, when the user sets a goal to lose weight, a specific weight loss goal and period can be set. When the user sets a goal to lower blood pressure, a specific numerical goal and a method for achieving the goal can also be set. Furthermore, when the user sets a goal to develop an exercise habit, a specific exercise plan and frequency can also be set. This allows the user to set a specific health goal based on the health checkup results and work toward it.

[0101] The health checkup support system may further include a health data sharing unit based on the user's health checkup results. The health data sharing unit, for example, supports the user in sharing his or her health checkup results with family members or doctors. For example, when the user shares his or her health checkup results with family members, the family members can check the results and provide appropriate support. When the user shares his or her health checkup results with a doctor, the doctor can make an appropriate diagnosis or treatment based on the results. Furthermore, when the user shares his or her health checkup results with a specialist, the specialist can provide advice based on the results. This allows the user to share his or her health checkup results with others and receive appropriate support.

[0102] The search unit can estimate the user's emotions and adjust the priority of the hospital search based on the estimated user's emotions. For example, if the user feels anxious, hospitals with good reputations are preferentially displayed. If the user is relaxed, the search unit can also preferentially display hospitals that emphasize distance and cost. If the user is in a hurry, the search unit can also preferentially display hospitals that are easy to make reservations at. 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 (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This allows the priority of the hospital search to be adjusted according to the user's emotions.

[0103] The reservation unit can estimate the user's emotions and adjust the priority of the reservation procedure based on the estimated user's emotions. For example, if the user feels anxious, the reservation procedure is simplified and the reservation is quickly confirmed. If the user is relaxed, the reservation unit can also provide detailed reservation options and suggest a customizable reservation method. If the user is in a hurry, the reservation unit can also preferentially display hospitals where reservations can be made the soonest. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This makes it possible to adjust the priority of the reservation procedure according to the user's emotions.

[0104] The result confirmation unit can estimate the user's emotion and adjust the method of displaying the results based on the estimated user's emotion. For example, if the user feels anxious, a simple and highly visible display method is provided. If the user is relaxed, the result confirmation unit can also provide a display method including detailed information. If the user is in a hurry, the result confirmation unit can also provide a display method that focuses on the main points. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This makes it possible to adjust the method of displaying the results according to the user's emotion.

[0105] The reexamination reservation unit can estimate the user's emotions and adjust the priority of reexamination based on the estimated user's emotions. For example, when the user feels anxious, hospitals where reexamination can be performed immediately are preferentially displayed. When the user is relaxed, the reexamination reservation unit can also preferentially display hospitals that emphasize cost and distance. When the user is in a hurry, the reexamination reservation unit can also preferentially display hospitals where it is easy to make an appointment. 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 (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This makes it possible to adjust the priority of reexamination according to the user's emotions.

[0106] The result confirmation unit can estimate the user's emotion and adjust the result notification method based on the estimated user's emotion. For example, if the user feels anxious, a simple and highly visible notification method is provided. If the user is relaxed, the result confirmation unit can also provide a notification method including detailed information. If the user is in a hurry, the result confirmation unit can also provide a notification method that focuses on the main points. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This makes it possible to adjust the result notification method according to the user's emotion.

[0107] The process flow of the second embodiment will be briefly described below.

[0108] Step 1: The search unit searches for hospitals where the user can receive a health checkup. The search unit searches a database of multiple hospitals across the country for hospitals that meet the user's criteria. For example, if a user searches for "hospitals in Tokyo that are open on weekends," a list of relevant hospitals is displayed. The search unit uses AI to analyze the hospital database and select the hospital that best suits the user's criteria. Step 2: The reservation unit makes a reservation at the hospital searched by the search unit. The reservation unit supports the reservation procedure at the hospital selected by the user. For example, if a user inputs "I would like to make a reservation at X hospital on Saturday morning," the reservation unit checks the reservation status of that hospital and confirms the reservation. The reservation unit uses AI to link with the hospital's reservation system, check the reservation status in real time, and can suggest the optimal reservation time. Step 3: The result confirmation unit displays the results of the health check provided by the hospital to the user. When the user inputs "I would like to check the results of my blood test," the result confirmation unit displays the results. The result confirmation unit can use AI to analyze the results of the health check and display them in a format that is easy for the user to understand. Step 4: The re-examination reservation unit searches for hospitals again and makes a reservation if re-examination is necessary based on the health check results confirmed by the result confirmation unit. When the user inputs "I would like to search for hospitals where re-examination is necessary and make a reservation," the re-examination reservation unit carries out the procedure. The re-examination reservation unit can use AI to search for hospitals where re-examination is necessary and suggest the optimal reservation time.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] For example, the search unit is realized by at least one of the smart device 14 and the data processing device 12. For example, the search unit is realized by the control unit 46A of the smart device 14, and inputs the user's conditions using the camera 42 and the microphone 38B of the smart device 14, and searches for a hospital by the control unit 46A. Also, the search unit is realized by the specific processing unit 290 of the data processing device 12, and acquires hospital information from the database 24, and searches for a hospital that meets the user's conditions. The reservation unit is realized by, for example, the control unit 46A of the smart device 14, and performs a reservation procedure using the display 40A of the smart device 14. Also, the reservation unit is realized by the specific processing unit 290 of the data processing device 12, and confirms the reservation in cooperation with the hospital's reservation system. The result confirmation unit is realized by, for example, the control unit 46A of the smart device 14, and displays the results of the health check using the display 40A of the smart device 14. Also, the result confirmation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the results provided by the hospital and displays them to the user. The reexamination reservation unit is realized, for example, by the control unit 46A of the smart device 14, and performs the reservation procedure for the reexamination using the display 40A of the smart device 14. The reexamination reservation unit is realized by the specific processing unit 290 of the data processing device 12, and searches for a hospital where a reexamination is required and confirms the reservation. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] For example, the search unit is realized by at least one of the smart glasses 214 and the data processing device 12. For example, the search unit is realized by the control unit 46A of the smart glasses 214, and inputs the user's conditions using the camera 42 and the microphone 238 of the smart glasses 214, and searches for a hospital by the control unit 46A. Also, the search unit is realized by the specific processing unit 290 of the data processing device 12, and acquires hospital information from the database 24, and searches for a hospital that meets the user's conditions. The reservation unit is realized by, for example, the control unit 46A of the smart glasses 214, and performs a reservation procedure using the display of the smart glasses 214. Also, the reservation unit is realized by the specific processing unit 290 of the data processing device 12, and confirms the reservation in cooperation with the hospital's reservation system. The result confirmation unit is realized by, for example, the control unit 46A of the smart glasses 214, and displays the results of the health check using the display of the smart glasses 214. Also, the result confirmation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the results provided by the hospital and displays them to the user. The reexamination reservation unit is realized, for example, by the control unit 46A of the smart glasses 214, and performs the reservation procedure for the reexamination using the display of the smart glasses 214. In addition, the reexamination reservation unit is realized by the specific processing unit 290 of the data processing device 12, and searches for a hospital where a reexamination is required and confirms the reservation. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] For example, the search unit is realized by at least one of the headset type terminal 314 and the data processing device 12. For example, the search unit is realized by the control unit 46A of the headset type terminal 314, and the user's conditions are input using the camera 42 and the microphone 238 of the headset type terminal 314, and the control unit 46A searches for a hospital. The search unit is realized by the specific processing unit 290 of the data processing device 12, and obtains hospital information from the database 24, and searches for a hospital that meets the user's conditions. The reservation unit is realized by the control unit 46A of the headset type terminal 314, and performs a reservation procedure using the display 343 of the headset type terminal 314. The reservation unit is realized by the specific processing unit 290 of the data processing device 12, and confirms the reservation in cooperation with the hospital's reservation system. The result confirmation unit is realized by the control unit 46A of the headset type terminal 314, and displays the results of the medical examination using the display 343 of the headset type terminal 314. The result confirmation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the results provided by the hospital and displays them to the user. The reexamination reservation unit is realized by, for example, the control unit 46A of the headset type terminal 314, and performs the reservation procedure for the reexamination using the display 343 of the headset type terminal 314. The reexamination reservation unit is realized by the specific processing unit 290 of the data processing device 12, and searches for a hospital where a reexamination is required and confirms the reservation. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] For example, the search unit is realized by at least one of the robot 414 and the data processing device 12. For example, the search unit is realized by the control unit 46A of the robot 414, and inputs the user's conditions using the camera 42 and microphone 238 of the robot 414, and searches for a hospital by the control unit 46A. Also, the search unit is realized by the specific processing unit 290 of the data processing device 12, and obtains hospital information from the database 24, and searches for a hospital that meets the user's conditions. The reservation unit is realized by, for example, the control unit 46A of the robot 414, and performs a reservation procedure using the display of the robot 414. Also, the reservation unit is realized by the specific processing unit 290 of the data processing device 12, and confirms the reservation in cooperation with the reservation system of the hospital. The result confirmation unit is realized by, for example, the control unit 46A of the robot 414, and displays the results of the medical examination using the display of the robot 414. Also, the result confirmation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the results provided by the hospital and displays them to the user. The reexamination reservation unit is realized, for example, by the control unit 46A of the robot 414, and performs the reservation procedure for the reexamination using the display of the robot 414. The reexamination reservation unit is realized by the specific processing unit 290 of the data processing device 12, and searches for a hospital where a reexamination is required and confirms the reservation. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Appendix 1) A search section for searching hospitals; a reservation unit for making a reservation at a hospital searched for by the search unit; A result confirmation unit for confirming the results of the medical examination at the hospital; A re-examination reservation unit is provided, which searches for a hospital again and makes a reservation if a re-examination is necessary based on the result of the health check confirmed by the result confirmation unit. A system characterized by: (Appendix 2) The search unit is A system that searches for hospitals that meet the user's criteria from a database of multiple hospitals across the country. (Appendix 3) The reservation unit: A system that supports the reservation process at a hospital of the user's choice. (Appendix 4) The result confirmation unit: A system that displays the results of health checkups provided by a hospital to a user. (Appendix 5) The re-examination reservation unit, This system searches for hospitals where re-examination is required based on the health check results confirmed by the result confirmation department, and supports the reservation process. (Appendix 6) The search unit is Estimating user sentiment and adjusting hospital search priorities based on the estimated user sentiment 2. The system of claim 1, (Appendix 7) The search unit is A system that analyzes a user's past hospital usage history and recommends appropriate hospitals. (Appendix 8) The search unit is Filtering hospital searches based on the user's current health status or specific symptoms 2. The system of claim 1, (Appendix 9) The search unit is Infer user sentiment and adjust how search results are displayed based on the inferred user sentiment 2. The system of claim 1, (Appendix 10) The search unit is When searching for a hospital, the system prioritizes the display of hospitals that are most relevant to the user based on the user's geographic location. 2. The system of claim 1, (Appendix 11) The search unit is When searching for a hospital, the system analyzes the user's social media activity and recommends relevant hospitals. 2. The system of claim 1, (Appendix 12) The reservation unit: Infer user emotions and adjust reservation procedure priorities based on the estimated user emotions 2. The system of claim 1, (Appendix 13) The reservation unit: A system that analyzes a user's past reservation history and suggests an appropriate reservation method when making a reservation. (Appendix 14) The reservation unit: A system that suggests suitable reservation times based on the user's current schedule and plans. (Appendix 15) The reservation unit: To estimate a user's emotion and adjust a notification method of a reservation confirmation based on the estimated user's emotion. 2. The system of claim 1, (Appendix 16) The reservation unit: A system that suggests an appropriate reservation time by taking into account the user's geographical location information when making a reservation. (Appendix 17) The reservation unit: When making a reservation, analyze your social media activity to provide you with relevant reservation information 2. The system of claim 1, (Appendix 18) The result confirmation unit: Inferring user emotion and adjusting the way results are displayed based on the estimated user emotion 2. The system of claim 1, (Appendix 19) The result confirmation unit: When checking results, provide interpretation of the results by referring to the user's past medical examination results 2. The system of claim 1, (Appendix 20) The result confirmation unit: When reviewing results, emphasize the importance of the results based on the user's current health status 2. The system of claim 1, (Appendix 21) The result confirmation unit: The method of inferring the user's emotion and adjusting the method of notifying the result based on the inferred user's emotion 2. The system of claim 1, (Appendix 22) The result confirmation unit: When checking results, provide relevant medical institution information taking into account the user's geographic location information 2. The system of claim 1, (Appendix 23) The result confirmation unit: Upon checking the results, the system will analyze users' social media activity and provide relevant health information 2. The system of claim 1, (Appendix 24) The re-examination reservation unit, Inferring user sentiment and adjusting retest priorities based on the estimated user sentiment 2. The system of claim 1, (Appendix 25) The re-examination reservation unit, When re-examination is scheduled, the system analyzes the user's past re-examination history and suggests the optimal re-examination method. 2. The system of claim 1, (Appendix 26) The re-examination reservation unit, A system that suggests an appropriate re-examination time based on the user's current health condition when re-examination is scheduled. (Appendix 27) The re-examination reservation unit, Inferring a user's emotion and adjusting a re-examination notification method based on the estimated user's emotion 2. The system of claim 1, (Appendix 28) The re-examination reservation unit, When re-scheduling a re-examination, the system will suggest the optimal re-examination time based on the user's geographic location. 2. The system of claim 1, (Appendix 29) The re-examination reservation unit, Analyze users' social media activity to provide relevant re-examination information when re-examination reservations are made 2. The system of claim 1, [Explanation of symbols]

[0181] 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 search section for searching hospitals; a reservation unit for making a reservation at a hospital searched for by the search unit; A result confirmation unit for confirming the results of the medical examination at the hospital; A re-examination reservation unit is provided, which searches for a hospital again and makes a reservation if a re-examination is necessary based on the result of the health check confirmed by the result confirmation unit. A system characterized by:

2. The search unit is Search for hospitals that meet the user's criteria from a database of multiple hospitals across the country 2. The system of claim 1 .

3. The reservation unit: Help users make appointments with the hospital of their choice 2. The system of claim 1 .

4. The result confirmation unit: Display the medical examination results provided by the hospital to the user 2. The system of claim 1 .

5. The re-examination reservation unit, Based on the medical examination results confirmed by the result confirmation unit, the system searches for hospitals where re-examination is required and supports reservation procedures 2. The system of claim 1 .

6. The search unit is Estimating user sentiment and adjusting hospital search priorities based on the estimated user sentiment 2. The system of claim 1 .

7. The search unit is Analyze the user's past hospital usage history and recommend appropriate hospitals 2. The system of claim 1 .

8. The search unit is Filtering hospital searches based on the user's current health status or specific symptoms 2. The system of claim 1 .

9. The search unit is Infer user sentiment and adjust how search results are displayed based on the inferred user sentiment 2. The system of claim 1 .

10. The search unit is When searching for a hospital, the system prioritizes the display of hospitals that are most relevant to the user based on the user's geographic location.

2. The system of claim 1 .

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