system

Abstract

The system according to this embodiment aims to centrally manage children's health data and enable quick consultation with medical professionals. [Solution] The system according to this embodiment comprises a reception unit, an advice unit, a scheduling unit, a management unit, and a visualization unit. The reception unit receives input of the patient's condition. The advice unit analyzes the information received by the reception unit and provides advice on possible diagnoses and initial responses. The scheduling unit schedules online consultations with doctors based on the advice provided by the advice unit. The management unit centrally manages digitized health records of children. The visualization unit visualizes the information managed by the management unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to quickly consult a medical expert when a child suddenly gets sick or feels unwell, and there is also a problem that it is burdensome to manually manage the child's health data centrally.

[0005] The system according to the embodiment aims to centrally manage the health data of a child and enable quick consultation with a medical expert.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an advice unit, a scheduling unit, a management unit, and a visualization unit. The reception unit receives input of the patient's medical condition. The advice unit analyzes the information received by the reception unit and provides advice on possible diagnoses and initial responses. The scheduling unit schedules online consultations with doctors based on the advice provided by the advice unit. The management unit centrally manages digitized health records of children. The visualization unit visualizes the information managed by the management unit. [Effects of the Invention]

[0007] The system according to this embodiment can centrally manage children's health data and enable quick consultation with medical professionals. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The health management system according to an embodiment of the present invention is a system intended for families with children and their guardians. This health management system uses AI to input medical conditions and provides simple advice on possible diagnoses and initial responses. It is also possible to schedule online consultations with a doctor as needed. Furthermore, the AI ​​analyzes the latest medical information and research to provide individualized health information and childcare advice tailored to the child's age and health condition. This allows guardians to easily understand reliable information. In addition, the AI ​​centrally manages digitized health records of children, making all information easily accessible. This allows for the visualization of the child's health condition as it changes over time. For example, a guardian inputs their child's medical conditions. At this time, detailed information such as specific symptoms and the time of onset is entered. For example, information such as "the child has a fever and a cough" is entered. This information is input into the AI. Next, the AI ​​analyzes the input information and provides simple advice on possible diagnoses and initial responses. For example, it may provide advice such as "It is likely a cold, so ensure the child gets plenty of rest and hydration." It is also possible to schedule online consultations with a doctor as needed. For example, it may provide advice such as "If symptoms persist, we recommend consulting a doctor online." Furthermore, the AI ​​analyzes the latest medical information and research to provide personalized health information and parenting advice tailored to the child's age and health condition. For example, it might provide information such as, "A 2-year-old child needs this much vitamin D per day." This allows parents to easily understand reliable information. The AI ​​also centralizes the management of digitized health records of children, making all information easily accessible. For example, past medical records and vaccination history are centrally managed and can be accessed immediately when needed. This allows for the visualization of a child's health condition as it changes over time. For example, growth curves and weight trends can be displayed in graphs. This system allows parents to efficiently manage their child's health and always be aware of their child's health status. It also enables quick responses in case of sudden illness or poor health.For example, if a child develops a fever in the middle of the night, the AI ​​can immediately provide advice and, if necessary, schedule a consultation with a doctor. This reduces the burden on parents and helps protect the child's health. This allows the health management system to centrally manage a child's health status and provide parents with reliable information.

[0029] The health management system according to this embodiment comprises a reception unit, an advice unit, a scheduling unit, a management unit, and a visualization unit. The reception unit receives input of medical conditions. Input of medical conditions includes, but is not limited to, text input, voice input, and image input. For example, the reception unit receives medical conditions using text input. The reception unit can also receive medical conditions using voice input. For example, a parent describes the medical condition by voice, and it is converted into text and input. The reception unit can also receive medical conditions using image input. For example, a parent takes a picture of their child's symptoms and inputs the image. The advice unit analyzes the information received by the reception unit and provides advice on possible diagnoses and initial responses. The advice unit analyzes medical conditions and makes a diagnosis using, for example, AI. The advice unit can also provide advice on initial responses. For example, if there is a high possibility of a cold, the advice unit will advise getting enough rest and staying hydrated. The advice unit can also schedule an online consultation with a doctor if symptoms persist. The scheduling unit schedules online consultations with doctors based on advice provided by the advice unit. The scheduling unit can, for example, schedule consultations with doctors to suit the parents' convenience. The scheduling unit can also adjust the schedule considering the doctor's availability. The management unit centrally manages digitized health records of children. The management unit can, for example, centrally manage past medical records and vaccination history. The management unit can also digitize health records and make them easily accessible. The visualization unit visualizes the information managed by the management unit. The visualization unit can, for example, display growth curves and weight trends in graphs. The visualization unit can also visually display changes in health status. As a result, the health management system according to this embodiment can centrally perform everything from inputting medical conditions to diagnosis, consultations with doctors, and management and visualization of health records.

[0030] The reception desk accepts input of medical conditions. This input includes, but is not limited to, text input, voice input, and image input. For example, the reception desk accepts medical conditions using text input. Specifically, the user describes the medical condition in detail in a dedicated input form, and the system receives this information. The reception desk can also accept medical conditions using voice input. For example, a parent describes the medical condition verbally, and this is converted into text for input. In this case, voice recognition technology is used to convert the voice data into text data, and natural language processing technology is used to analyze the medical condition. Furthermore, the reception desk can accept medical conditions using image input. For example, a parent takes a picture of their child's symptoms and inputs the image. In this case, image recognition technology is used to analyze the image data and extract the characteristics of the symptoms. This allows the reception desk to provide diverse input methods, enabling users to input medical conditions in the most convenient way. The reception desk also centrally manages the input data and can process the information quickly and accurately in cooperation with other departments. For example, the input medical condition data is immediately sent to the advice department for analysis and diagnosis. This allows the reception department to enhance user convenience and improve the overall efficiency of the system.

[0031] The Advice Department analyzes information received by the Reception Department and provides advice on possible diagnoses and initial responses. For example, the Advice Department uses AI to analyze symptoms and make diagnoses. Specifically, it analyzes text data using natural language processing technology to extract characteristics of the symptoms. Similarly, it analyzes audio and image data to make a comprehensive diagnosis. The AI ​​refers to past medical data and a medical knowledge base to present the most likely diagnosis. For example, if a cold is highly probable, the AI ​​makes a diagnosis based on the symptoms and provides advice on initial responses. Specifically, it advises getting enough rest and staying hydrated. The Advice Department can also schedule online consultations with doctors if symptoms persist, allowing users to take prompt and appropriate action. Furthermore, the Advice Department can collect user feedback and continuously improve the accuracy of its advice. For example, it can adjust the AI's diagnostic algorithm based on user feedback to make more accurate diagnoses. This allows the Advice Department to provide users with quick and accurate advice, improving the quality of their health management.

[0032] The scheduling unit schedules online consultations with doctors based on advice provided by the advice unit. For example, the scheduling unit schedules consultations with doctors to suit the parents' convenience. Specifically, the user enters their desired date and time, and the system checks the doctor's availability based on that information. The scheduling unit can also adjust the schedule considering the doctor's availability. For example, it can compare the schedules of multiple doctors and set the earliest possible consultation date and time. Furthermore, the scheduling unit has a reminder function that can send a notification to the user the day before the consultation. This ensures that the user does not forget the date and time of the consultation and can be sure to have a consultation with a doctor. The scheduling unit also provides cancellation and rescheduling functions, allowing for flexible responses to the user's needs. For example, even if there is a sudden change in plans, it can be easily rescheduled. In this way, the scheduling unit provides users with flexible and efficient schedule management, enabling smooth consultations with doctors.

[0033] The management department centrally manages digitized children's health records. For example, it centrally manages past medical records and vaccination histories. Specifically, it uses an electronic medical record system to digitize medical records and vaccination histories and stores them in a central database. The management department can also digitize health records and make them easily accessible. For example, parents can access their child's health records and check necessary information through a dedicated application. Furthermore, the management department protects data using encryption technology to ensure data security. This prevents the leakage of personal information and allows users to use the system with peace of mind. The management department can also regularly back up data to prevent data loss. As a result, the management department can safely and efficiently manage children's health records and quickly provide necessary information.

[0034] The visualization unit visualizes information managed by the management unit. For example, the visualization unit displays growth curves and weight trends in graphs. Specifically, it creates and visually displays growth curves based on a child's height and weight data. The visualization unit can also visually display changes in health status. For example, it displays changes in health status in graphs and charts based on past medical records and vaccination history. This allows parents to grasp their child's health status at a glance. Furthermore, the visualization unit has an alert function that can send notifications when abnormal data is detected. For example, it sends notifications to parents if there is a sudden increase or decrease in weight or if a vaccination schedule is approaching. This allows parents to respond quickly. In addition, the visualization unit provides a data customization function, allowing users to freely select the type and format of data to display. This enables the visualization unit to provide users with a flexible and intuitive data display, supporting health management.

[0035] The advice unit can analyze the latest medical information and research to provide individualized health information and parenting advice tailored to the child's age and health condition. For example, the advice unit can analyze the latest medical journals and research papers to provide highly reliable information. The advice unit can also provide health information appropriate to the child's age. For example, the advice unit can provide the daily required intake of vitamin D for a two-year-old child. The advice unit can also provide parenting advice tailored to the child's health condition. For example, if there is a high possibility of a cold, the advice unit will advise ensuring sufficient rest and hydration. This allows parents to obtain highly reliable information by providing individualized health information and parenting advice based on the latest medical information. Some or all of the above processing in the advice unit may be performed using, for example, a generating AI, or without a generating AI. For example, the advice unit can input the latest medical information and research into a generating AI and output the analysis results to the generating AI.

[0036] The management department can centrally manage past medical records and vaccination histories. For example, the management department can digitize and centrally manage past medical records. The management department can also digitize and centrally manage vaccination histories. For example, the management department can store past medical records in a database and make them accessible when needed. The management department can also store vaccination histories in a database and make them accessible when needed. This allows for quick access to necessary information by centrally managing past medical records and vaccination histories. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can have the AI ​​input past medical records and vaccination histories and save them to a database.

[0037] The visualization unit can display growth curves and weight trends in graph form. For example, the visualization unit can display growth curves in graph form. It can also display weight trends in graph form. For example, the visualization unit can create a growth curve based on a child's height and weight data and display it in graph form. It can also create a weight trend graph based on a child's weight data and display it in graph form. This makes it easy to understand changes in a child's health by visually displaying growth curves and weight trends. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input growth curve and weight trend data into AI and have the AI ​​create the graphs.

[0038] The scheduling function can schedule online consultations with a doctor if symptoms persist. For example, the scheduling function can schedule consultations with a doctor to suit the guardian's convenience if symptoms persist. The scheduling function can also adjust the schedule considering the doctor's availability. For example, the scheduling function can refer to the guardian's calendar information and schedule a consultation with a doctor during an available time slot. The scheduling function can also suggest the optimal time slot based on the doctor's availability. This allows for prompt scheduling of consultations with a doctor when symptoms persist, ensuring appropriate medical care is received. Some or all of the above processes in the scheduling function may be performed using AI, for example, or not. For example, the scheduling function can input the guardian's calendar information and the doctor's availability into the AI ​​and have the AI ​​suggest the optimal schedule.

[0039] The advice unit can provide advice on ensuring sufficient rest and hydration when there is a high probability of a cold. For example, the advice unit can advise parents to ensure sufficient rest and hydration when there is a high probability of a cold. The advice unit can also provide appropriate advice for the initial stages of a cold. For example, the advice unit can advise parents on appropriate actions to take when cold symptoms appear. By providing appropriate advice for the initial stages of a cold, it is possible to prevent the symptoms from worsening. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input data on cold symptoms into AI and have the AI ​​output appropriate advice.

[0040] The reception desk can improve the accuracy of input by referring to past medical history data when a patient enters their medical condition. For example, the reception desk can automatically refer to past medical records and display relevant medical conditions as candidates. The reception desk can also analyze the frequency and patterns of specific symptoms from past medical history data and supplement the input content. Furthermore, the reception desk can detect inconsistencies in the entered medical condition based on past medical history data and suggest corrections. In this way, by referring to past medical history data, the accuracy of input can be improved and appropriate diagnoses can be supported. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input past medical history data into AI and have the AI ​​perform tasks such as supplementing input content and detecting inconsistencies.

[0041] The reception system can complete the input content when a child's medical condition is entered, taking into account the child's current activity level and environmental information. For example, the reception system can suggest relevant medical conditions by considering the child's current activity level (e.g., after exercise, during a meal). The reception system can also refer to the child's environmental information (e.g., temperature, humidity) to complete the possible medical conditions. Furthermore, the reception system can automatically complete the details of the entered medical condition based on the child's current activity level and environmental information. This allows for more accurate medical condition input by considering the child's current activity level and environmental information. Some or all of the above processing in the reception system may be performed using AI, for example, or without AI. For example, the reception system can input the child's activity level and environmental information into the AI ​​and have the AI ​​complete the input content.

[0042] The reception desk can prioritize inputting highly relevant information when entering medical conditions, taking into account the guardian's geographical location. For example, the reception desk can prioritize inputting region-specific diseases or prevalent medical conditions based on the guardian's current location. The reception desk can also refer to the guardian's geographical location and reflect information about nearby medical institutions in the input. Furthermore, the reception desk can prioritize inputting medical conditions related to the region's climate and environment based on the guardian's geographical location. In this way, by considering geographical location, information about region-specific medical conditions and medical institutions can be prioritized. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the guardian's geographical location into AI and have the AI ​​perform the process of prioritizing the input of highly relevant information.

[0043] The reception desk can analyze the parent's social media activity when entering medical information and input relevant medical information. For example, the reception desk can analyze the parent's social media posts and input medical information related to recent activities and events. The reception desk can also refer to posts from the parent's social media friends and followers and reflect prevalent medical conditions in the input. Furthermore, based on the parent's social media activity, the reception desk can prioritize inputting medical information related to specific events or locations. This allows for the rapid input of relevant medical information by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the parent's social media activity data into AI and have the AI ​​perform the process of inputting relevant medical information.

[0044] The advice unit can adjust the level of detail in its advice based on the severity of the patient's condition. For example, in the case of a mild condition, the advice unit can provide simple initial response advice. In the case of a moderate condition, the advice unit can also provide advice that includes detailed response methods and precautions. Furthermore, in the case of a severe condition, the advice unit can provide advice that strongly recommends emergency response or consultation with a doctor. This facilitates appropriate responses by providing advice according to the severity of the condition. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input disease severity data into the AI ​​and have the AI ​​perform the process of adjusting the level of detail in the advice.

[0045] The advice unit can apply different advice algorithms depending on the category of the medical condition when providing advice. For example, in the case of an infectious disease, the advice unit can provide advice on preventing the spread of infection. In the case of an allergy, the advice unit can also provide advice on allergen avoidance and coping methods. Furthermore, in the case of an injury, the advice unit can provide advice recommending first aid or seeking medical attention. By providing advice according to the category of the medical condition, appropriate responses can be promoted. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input medical condition category data into the AI ​​and have the AI ​​perform the process of applying different advice algorithms.

[0046] The advice unit can prioritize advice based on the onset of the illness when providing advice. For example, if the illness has suddenly developed, the advice unit will provide advice prioritizing emergency response. If the illness has persisted for several days, the advice unit can also provide advice recommending a doctor's visit. Furthermore, if the illness is chronic, the advice unit can provide advice on long-term management methods and lifestyle improvements. This facilitates appropriate responses by providing advice tailored to the onset of the illness. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input illness onset data into AI and have the AI ​​perform the process of determining the priority of advice.

[0047] The advice unit can adjust the order of advice based on the relationships between medical conditions when providing advice. For example, if there are multiple medical conditions, the advice unit will provide advice for the most serious condition first. The advice unit can also provide advice in order of relevance if the medical conditions are related. Furthermore, if the medical conditions are different, the advice unit can provide advice for each condition in order. This facilitates appropriate responses by providing advice according to the relationships between medical conditions. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input medical condition relationship data into AI and have the AI ​​perform the process of adjusting the order of advice.

[0048] The scheduling unit can suggest the optimal schedule by referring to past consultation history when setting a schedule. For example, the scheduling unit can suggest time slots that are convenient for parents based on past consultation history. The scheduling unit can also suggest the optimal schedule by considering the doctor's availability based on past consultation history. Furthermore, the scheduling unit can refer to past consultation history and suggest a schedule that suits the parents' preferences and circumstances. In this way, by referring to past consultation history, the system can provide the optimal schedule for parents. Some or all of the above processes in the scheduling unit may be performed using AI, for example, or not. For example, the scheduling unit can input past consultation history data into AI and have the AI ​​perform the process of suggesting the optimal schedule.

[0049] The scheduling unit can customize the schedule when setting it, taking into account the parent's current schedule and living situation. For example, the scheduling unit can refer to the parent's calendar information and schedule consultations with a doctor during available time slots. The scheduling unit can also suggest the optimal time slot considering the parent's living situation (e.g., work, household chores). Furthermore, the scheduling unit can suggest a manageable schedule based on the parent's current schedule. This allows for manageable medical consultations by providing a schedule that suits the parent's living situation. Some or all of the above processing in the scheduling unit may be performed using AI, or not. For example, the scheduling unit can input the parent's calendar information and living situation data into the AI ​​and have the AI ​​perform the process of customizing the schedule.

[0050] The scheduling unit can select the most suitable doctor when setting a schedule, taking into account the parent's geographical location. For example, the scheduling unit can prioritize selecting nearby doctors based on the parent's current location. It can also refer to the parent's geographical location and suggest doctors that are easily accessible. Furthermore, the scheduling unit can select the most suitable doctor by considering transportation methods and travel time based on the parent's geographical location. This allows for the selection of easily accessible doctors by considering geographical location. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input the parent's geographical location into AI and have the AI ​​perform the process of selecting the most suitable doctor.

[0051] The scheduling unit can analyze parents' social media activity when setting schedules and propose the optimal schedule. For example, the scheduling unit can analyze parents' social media posts and propose a schedule based on recent activities and events. It can also refer to posts from parents' social media friends and followers and propose a schedule based on relevant events and activities. Furthermore, based on parents' social media activity, the scheduling unit can prioritize suggesting schedules related to specific events or locations. This allows the system to provide schedules based on relevant events and activities by analyzing social media activity. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or not. For example, the scheduling unit can input parents' social media activity data into an AI and have the AI ​​perform the process of proposing the optimal schedule.

[0052] The management department can optimize its management algorithm by referring to past medical records when managing health records. For example, the management department can automatically refer to past medical records and display relevant health records as candidates. The management department can also analyze the frequency and patterns of specific symptoms from past medical records to supplement the management content. Furthermore, the management department can detect inconsistencies in managed health records based on past medical records and propose corrections. In this way, by referring to past medical records, the management algorithm can be optimized and accurate health record management can be achieved. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input past medical record data into AI and have the AI ​​perform the process of optimizing the management algorithm.

[0053] The management department can customize health records when managing them, taking into account the child's current health status and lifestyle. For example, the management department can suggest relevant health records based on the child's current health status (e.g., fever, cough). The management department can also supplement the content of health records by referring to the child's lifestyle (e.g., school, exercise). Furthermore, the management department can automatically supplement the details of managed health records based on the child's current health status and lifestyle. This enables more accurate health record management by taking into account the child's current health status and lifestyle. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input the child's health status and lifestyle data into the AI ​​and have the AI ​​perform the process of customizing the records.

[0054] The management department can select the optimal recording method when managing health records, taking into account the geographical location information of the guardian. For example, the management department can prioritize recording region-specific diseases and prevalent illnesses based on the guardian's current location. The management department can also refer to the guardian's geographical location information and reflect information about nearby medical institutions in the records. Furthermore, the management department can prioritize managing health records related to the local climate and environment based on the guardian's geographical location information. In this way, by considering geographical location information, information about region-specific illnesses and medical institutions can be prioritized for recording. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input the guardian's geographical location information into AI and have the AI ​​perform the process of selecting the optimal recording method.

[0055] The management department can improve the accuracy of health records by analyzing parents' social media activity when managing health records. For example, the management department can analyze parents' social media posts and manage health records related to recent activities and events. The management department can also refer to posts from parents' social media friends and followers and reflect prevalent illnesses in the records. Furthermore, the management department can prioritize the management of health records related to specific events or locations based on parents' social media activity. This allows for the rapid management of relevant health records by analyzing social media activity. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input parents' social media activity data into AI and have the AI ​​perform processes to improve the accuracy of the records.

[0056] The visualization unit can optimize its visualization algorithm by referring to past health data during the visualization process. For example, the visualization unit can display growth curves and weight trends in graphs based on past health data. The visualization unit can also analyze the frequency and patterns of specific symptoms from past health data and supplement the visualization content. Furthermore, the visualization unit can detect inconsistencies in the visualized information based on past health data and propose corrections. This allows for the optimization of the visualization algorithm by referring to past health data, thereby enabling the provision of accurate information. Some or all of the above-described processes in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input past health data into AI and have the AI ​​perform the process of optimizing the visualization algorithm.

[0057] The visualization unit can customize the visualization content by considering the child's current health status and living situation during the visualization process. For example, the visualization unit visualizes relevant health data by considering the child's current health status (e.g., fever, cough). The visualization unit can also supplement the visualization content by referring to the child's living situation (e.g., school, exercise). Furthermore, the visualization unit can automatically supplement the details of the visualized information based on the child's current health status and living situation. This enables the provision of more accurate information by considering the child's current health status and living situation. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the child's health status and living situation data into the AI ​​and have the AI ​​perform the process of customizing the visualization content.

[0058] The visualization unit can select the optimal visualization method when creating visualizations, taking into account the geographical location information of the guardian. For example, the visualization unit can prioritize the visualization of region-specific diseases and prevalent medical conditions based on the guardian's current location. The visualization unit can also refer to the guardian's geographical location information and reflect information on nearby medical institutions in the visualization. Furthermore, the visualization unit can prioritize the visualization of health data related to the region's climate and environment based on the guardian's geographical location information. In this way, by considering geographical location information, information on region-specific medical conditions and medical institutions can be prioritized for visualization. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the guardian's geographical location information into AI and have the AI ​​perform the process of selecting the optimal visualization method.

[0059] The visualization unit can analyze parents' social media activity during visualization to improve the accuracy of the visualization. For example, the visualization unit can analyze parents' social media posts and visualize health data related to recent activities and events. The visualization unit can also refer to posts from parents' social media friends and followers and reflect prevalent illnesses in the visualization. Furthermore, based on parents' social media activity, the visualization unit can prioritize the visualization of health data related to specific events or locations. This allows for the rapid visualization of relevant health data by analyzing social media activity. Some or all of the above processing in the visualization unit may be performed using AI, for example, or not. For example, the visualization unit can input parents' social media activity data into AI and have the AI ​​perform processing to improve the accuracy of the visualization.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The reception desk can improve the accuracy of input by referring to past medical history data when patients enter their medical conditions. For example, it can automatically refer to past medical records and display relevant medical conditions as candidates. It can also analyze the frequency and patterns of specific symptoms from past medical history data and supplement the input content. Furthermore, it can detect inconsistencies in the entered medical conditions based on past medical history data and suggest corrections. In this way, by referring to past medical history data, the accuracy of input can be improved and appropriate diagnoses can be supported. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input past medical history data into AI and have the AI ​​perform tasks such as supplementing the input content and detecting inconsistencies.

[0062] The advice unit can adjust the level of detail in the advice provided based on the severity of the patient's condition. For example, in the case of a mild condition, it can provide simple initial response advice. In the case of a moderate condition, it can also provide advice that includes detailed response methods and precautions. Furthermore, in the case of a severe condition, it can provide advice that strongly recommends emergency response or consultation with a doctor. This allows for appropriate responses by providing advice according to the severity of the condition. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input disease severity data into the AI ​​and have the AI ​​perform the process of adjusting the level of detail in the advice.

[0063] The scheduling unit can suggest the optimal schedule by referring to past consultation history when setting a schedule. For example, it can suggest a time slot that is convenient for the parent based on past consultation history. It can also suggest the optimal schedule by considering the doctor's availability based on past consultation history. Furthermore, it can suggest a schedule that is tailored to the parent's preferences and circumstances by referring to past consultation history. In this way, by referring to past consultation history, the optimal schedule can be provided to the parent. Some or all of the above processes in the scheduling unit may be performed using AI, for example, or not using AI. For example, the scheduling unit can input past consultation history data into AI and have the AI ​​perform the process of suggesting the optimal schedule.

[0064] The management department can optimize its management algorithm by referring to past medical records when managing health records. For example, it can automatically refer to past medical records and display relevant health records as candidates. It can also analyze the frequency and patterns of specific symptoms from past medical records to supplement the management content. Furthermore, it can detect inconsistencies in managed health records based on past medical records and suggest corrections. In this way, by referring to past medical records, the management algorithm can be optimized, and accurate health record management can be achieved. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input past medical record data into AI and have the AI ​​perform the process of optimizing the management algorithm.

[0065] The visualization unit can optimize its visualization algorithm by referring to past health data during visualization. For example, it can display growth curves and weight trends in graphs based on past health data. It can also analyze the frequency and patterns of specific symptoms from past health data to supplement the visualization content. Furthermore, it can detect inconsistencies in the visualized information based on past health data and suggest corrections. In this way, by referring to past health data, the visualization algorithm can be optimized, enabling the provision of accurate information. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input past health data into AI and have the AI ​​perform the process of optimizing the visualization algorithm.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The reception desk accepts input of the patient's symptoms. This input includes text input, voice input, and image input. For example, a parent can describe the symptoms verbally and have it converted into text for input, or they can take a picture of their child's symptoms and input that image. Step 2: The advice department analyzes the information received by the reception department and provides advice on possible diagnoses and initial responses. For example, it uses AI to analyze the patient's condition, make a diagnosis, and advises them to get plenty of rest and stay hydrated if a cold is likely. Step 3: The scheduling department schedules online consultations with doctors based on the advice provided by the advice department. For example, they schedule consultations with doctors to suit the parents' convenience and adjust the schedule to take into account the doctors' availability. Step 4: The administration department centrally manages the digitized children's health records. For example, it centrally manages past medical records and vaccination histories, and digitizes health records to make them easily accessible. Step 5: The visualization unit visualizes the information managed by the management unit. For example, it displays growth curves and weight trends in graphs to visually show changes in health status.

[0068] (Example of form 2) The health management system according to an embodiment of the present invention is a system intended for families with children and their guardians. This health management system uses AI to input medical conditions and provides simple advice on possible diagnoses and initial responses. It is also possible to schedule online consultations with a doctor as needed. Furthermore, the AI ​​analyzes the latest medical information and research to provide individualized health information and childcare advice tailored to the child's age and health condition. This allows guardians to easily understand reliable information. In addition, the AI ​​centrally manages digitized health records of children, making all information easily accessible. This allows for the visualization of the child's health condition as it changes over time. For example, a guardian inputs their child's medical conditions. At this time, detailed information such as specific symptoms and the time of onset is entered. For example, information such as "the child has a fever and a cough" is entered. This information is input into the AI. Next, the AI ​​analyzes the input information and provides simple advice on possible diagnoses and initial responses. For example, it may provide advice such as "It is likely a cold, so ensure the child gets plenty of rest and hydration." It is also possible to schedule online consultations with a doctor as needed. For example, it may provide advice such as "If symptoms persist, we recommend consulting a doctor online." Furthermore, the AI ​​analyzes the latest medical information and research to provide personalized health information and parenting advice tailored to the child's age and health condition. For example, it might provide information such as, "A 2-year-old child needs this much vitamin D per day." This allows parents to easily understand reliable information. The AI ​​also centralizes the management of digitized health records of children, making all information easily accessible. For example, past medical records and vaccination history are centrally managed and can be accessed immediately when needed. This allows for the visualization of a child's health condition as it changes over time. For example, growth curves and weight trends can be displayed in graphs. This system allows parents to efficiently manage their child's health and always be aware of their child's health status. It also enables quick responses in case of sudden illness or poor health.For example, if a child develops a fever in the middle of the night, the AI ​​can immediately provide advice and, if necessary, schedule a consultation with a doctor. This reduces the burden on parents and helps protect the child's health. This allows the health management system to centrally manage a child's health status and provide parents with reliable information.

[0069] The health management system according to this embodiment comprises a reception unit, an advice unit, a scheduling unit, a management unit, and a visualization unit. The reception unit receives input of medical conditions. Input of medical conditions includes, but is not limited to, text input, voice input, and image input. For example, the reception unit receives medical conditions using text input. The reception unit can also receive medical conditions using voice input. For example, a parent describes the medical condition by voice, and it is converted into text and input. The reception unit can also receive medical conditions using image input. For example, a parent takes a picture of their child's symptoms and inputs the image. The advice unit analyzes the information received by the reception unit and provides advice on possible diagnoses and initial responses. The advice unit analyzes medical conditions and makes a diagnosis using, for example, AI. The advice unit can also provide advice on initial responses. For example, if there is a high possibility of a cold, the advice unit will advise getting enough rest and staying hydrated. The advice unit can also schedule an online consultation with a doctor if symptoms persist. The scheduling unit schedules online consultations with doctors based on advice provided by the advice unit. The scheduling unit can, for example, schedule consultations with doctors to suit the parents' convenience. The scheduling unit can also adjust the schedule considering the doctor's availability. The management unit centrally manages digitized health records of children. The management unit can, for example, centrally manage past medical records and vaccination history. The management unit can also digitize health records and make them easily accessible. The visualization unit visualizes the information managed by the management unit. The visualization unit can, for example, display growth curves and weight trends in graphs. The visualization unit can also visually display changes in health status. As a result, the health management system according to this embodiment can centrally perform everything from inputting medical conditions to diagnosis, consultations with doctors, and management and visualization of health records.

[0070] The reception desk accepts input of medical conditions. This input includes, but is not limited to, text input, voice input, and image input. For example, the reception desk accepts medical conditions using text input. Specifically, the user describes the medical condition in detail in a dedicated input form, and the system receives this information. The reception desk can also accept medical conditions using voice input. For example, a parent describes the medical condition verbally, and this is converted into text for input. In this case, voice recognition technology is used to convert the voice data into text data, and natural language processing technology is used to analyze the medical condition. Furthermore, the reception desk can accept medical conditions using image input. For example, a parent takes a picture of their child's symptoms and inputs the image. In this case, image recognition technology is used to analyze the image data and extract the characteristics of the symptoms. This allows the reception desk to provide diverse input methods, enabling users to input medical conditions in the most convenient way. The reception desk also centrally manages the input data and can process the information quickly and accurately in cooperation with other departments. For example, the input medical condition data is immediately sent to the advice department for analysis and diagnosis. This allows the reception department to enhance user convenience and improve the overall efficiency of the system.

[0071] The Advice Department analyzes information received by the Reception Department and provides advice on possible diagnoses and initial responses. For example, the Advice Department uses AI to analyze symptoms and make diagnoses. Specifically, it analyzes text data using natural language processing technology to extract characteristics of the symptoms. Similarly, it analyzes audio and image data to make a comprehensive diagnosis. The AI ​​refers to past medical data and a medical knowledge base to present the most likely diagnosis. For example, if a cold is highly probable, the AI ​​makes a diagnosis based on the symptoms and provides advice on initial responses. Specifically, it advises getting enough rest and staying hydrated. The Advice Department can also schedule online consultations with doctors if symptoms persist, allowing users to take prompt and appropriate action. Furthermore, the Advice Department can collect user feedback and continuously improve the accuracy of its advice. For example, it can adjust the AI's diagnostic algorithm based on user feedback to make more accurate diagnoses. This allows the Advice Department to provide users with quick and accurate advice, improving the quality of their health management.

[0072] The scheduling unit schedules online consultations with doctors based on advice provided by the advice unit. For example, the scheduling unit schedules consultations with doctors to suit the parents' convenience. Specifically, the user enters their desired date and time, and the system checks the doctor's availability based on that information. The scheduling unit can also adjust the schedule considering the doctor's availability. For example, it can compare the schedules of multiple doctors and set the earliest possible consultation date and time. Furthermore, the scheduling unit has a reminder function that can send a notification to the user the day before the consultation. This ensures that the user does not forget the date and time of the consultation and can be sure to have a consultation with a doctor. The scheduling unit also provides cancellation and rescheduling functions, allowing for flexible responses to the user's needs. For example, even if there is a sudden change in plans, it can be easily rescheduled. In this way, the scheduling unit provides users with flexible and efficient schedule management, enabling smooth consultations with doctors.

[0073] The management department centrally manages digitized children's health records. For example, it centrally manages past medical records and vaccination histories. Specifically, it uses an electronic medical record system to digitize medical records and vaccination histories and stores them in a central database. The management department can also digitize health records and make them easily accessible. For example, parents can access their child's health records and check necessary information through a dedicated application. Furthermore, the management department protects data using encryption technology to ensure data security. This prevents the leakage of personal information and allows users to use the system with peace of mind. The management department can also regularly back up data to prevent data loss. As a result, the management department can safely and efficiently manage children's health records and quickly provide necessary information.

[0074] The visualization unit visualizes information managed by the management unit. For example, the visualization unit displays growth curves and weight trends in graphs. Specifically, it creates and visually displays growth curves based on a child's height and weight data. The visualization unit can also visually display changes in health status. For example, it displays changes in health status in graphs and charts based on past medical records and vaccination history. This allows parents to grasp their child's health status at a glance. Furthermore, the visualization unit has an alert function that can send notifications when abnormal data is detected. For example, it sends notifications to parents if there is a sudden increase or decrease in weight or if a vaccination schedule is approaching. This allows parents to respond quickly. In addition, the visualization unit provides a data customization function, allowing users to freely select the type and format of data to display. This enables the visualization unit to provide users with a flexible and intuitive data display, supporting health management.

[0075] The advice unit can analyze the latest medical information and research to provide individualized health information and parenting advice tailored to the child's age and health condition. For example, the advice unit can analyze the latest medical journals and research papers to provide highly reliable information. The advice unit can also provide health information appropriate to the child's age. For example, the advice unit can provide the daily required intake of vitamin D for a two-year-old child. The advice unit can also provide parenting advice tailored to the child's health condition. For example, if there is a high possibility of a cold, the advice unit will advise ensuring sufficient rest and hydration. This allows parents to obtain highly reliable information by providing individualized health information and parenting advice based on the latest medical information. Some or all of the above processing in the advice unit may be performed using, for example, a generating AI, or without a generating AI. For example, the advice unit can input the latest medical information and research into a generating AI and output the analysis results to the generating AI.

[0076] The management department can centrally manage past medical records and vaccination histories. For example, the management department can digitize and centrally manage past medical records. The management department can also digitize and centrally manage vaccination histories. For example, the management department can store past medical records in a database and make them accessible when needed. The management department can also store vaccination histories in a database and make them accessible when needed. This allows for quick access to necessary information by centrally managing past medical records and vaccination histories. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can have the AI ​​input past medical records and vaccination histories and save them to a database.

[0077] The visualization unit can display growth curves and weight trends in graph form. For example, the visualization unit can display growth curves in graph form. It can also display weight trends in graph form. For example, the visualization unit can create a growth curve based on a child's height and weight data and display it in graph form. It can also create a weight trend graph based on a child's weight data and display it in graph form. This makes it easy to understand changes in a child's health by visually displaying growth curves and weight trends. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input growth curve and weight trend data into AI and have the AI ​​create the graphs.

[0078] The scheduling function can schedule online consultations with a doctor if symptoms persist. For example, the scheduling function can schedule consultations with a doctor to suit the guardian's convenience if symptoms persist. The scheduling function can also adjust the schedule considering the doctor's availability. For example, the scheduling function can refer to the guardian's calendar information and schedule a consultation with a doctor during an available time slot. The scheduling function can also suggest the optimal time slot based on the doctor's availability. This allows for prompt scheduling of consultations with a doctor when symptoms persist, ensuring appropriate medical care is received. Some or all of the above processes in the scheduling function may be performed using AI, for example, or not. For example, the scheduling function can input the guardian's calendar information and the doctor's availability into the AI ​​and have the AI ​​suggest the optimal schedule.

[0079] The advice unit can provide advice on ensuring sufficient rest and hydration when there is a high probability of a cold. For example, the advice unit can advise parents to ensure sufficient rest and hydration when there is a high probability of a cold. The advice unit can also provide appropriate advice for the initial stages of a cold. For example, the advice unit can advise parents on appropriate actions to take when cold symptoms appear. By providing appropriate advice for the initial stages of a cold, it is possible to prevent the symptoms from worsening. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input data on cold symptoms into AI and have the AI ​​output appropriate advice.

[0080] The reception desk can estimate the parent's emotions and adjust the method of inputting medical conditions based on the estimated emotions. For example, if the parent is feeling anxious, the reception desk can provide a simple and intuitive interface and minimize the input steps. If the parent is relaxed, the reception desk can also provide detailed input options and suggest a customizable input method. Furthermore, if the parent is in a hurry, the reception desk can prioritize voice input to allow for quick input of medical conditions. This reduces stress and promotes accurate input of medical conditions by providing an input method that is tailored to the parent's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input the parent's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0081] The reception desk can improve the accuracy of input by referring to past medical history data when a patient enters their medical condition. For example, the reception desk can automatically refer to past medical records and display relevant medical conditions as candidates. The reception desk can also analyze the frequency and patterns of specific symptoms from past medical history data and supplement the input content. Furthermore, the reception desk can detect inconsistencies in the entered medical condition based on past medical history data and suggest corrections. In this way, by referring to past medical history data, the accuracy of input can be improved and appropriate diagnoses can be supported. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input past medical history data into AI and have the AI ​​perform tasks such as supplementing input content and detecting inconsistencies.

[0082] The reception system can complete the input content when a child's medical condition is entered, taking into account the child's current activity level and environmental information. For example, the reception system can suggest relevant medical conditions by considering the child's current activity level (e.g., after exercise, during a meal). The reception system can also refer to the child's environmental information (e.g., temperature, humidity) to complete the possible medical conditions. Furthermore, the reception system can automatically complete the details of the entered medical condition based on the child's current activity level and environmental information. This allows for more accurate medical condition input by considering the child's current activity level and environmental information. Some or all of the above processing in the reception system may be performed using AI, for example, or without AI. For example, the reception system can input the child's activity level and environmental information into the AI ​​and have the AI ​​complete the input content.

[0083] The reception desk can estimate the parent's emotions and determine the priority of medical conditions to be entered based on the estimated emotions. For example, if the parent is feeling anxious, the reception desk may prompt them to prioritize entering urgent medical conditions. If the parent is relaxed, the reception desk may also suggest entering detailed medical conditions in order. Furthermore, if the parent is in a hurry, the reception desk may adjust the interface to prioritize entering important medical conditions. This allows for the rapid entry of important medical conditions by setting priorities according to the parent's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk may input the parent's emotion data into a generative AI and have the generative AI perform the process of determining the priority of medical conditions.

[0084] The reception desk can prioritize inputting highly relevant information when entering medical conditions, taking into account the guardian's geographical location. For example, the reception desk can prioritize inputting region-specific diseases or prevalent medical conditions based on the guardian's current location. The reception desk can also refer to the guardian's geographical location and reflect information about nearby medical institutions in the input. Furthermore, the reception desk can prioritize inputting medical conditions related to the region's climate and environment based on the guardian's geographical location. In this way, by considering geographical location, information about region-specific medical conditions and medical institutions can be prioritized. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the guardian's geographical location into AI and have the AI ​​perform the process of prioritizing the input of highly relevant information.

[0085] The reception desk can analyze the parent's social media activity when entering medical information and input relevant medical information. For example, the reception desk can analyze the parent's social media posts and input medical information related to recent activities and events. The reception desk can also refer to posts from the parent's social media friends and followers and reflect prevalent medical conditions in the input. Furthermore, based on the parent's social media activity, the reception desk can prioritize inputting medical information related to specific events or locations. This allows for the rapid input of relevant medical information by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the parent's social media activity data into AI and have the AI ​​perform the process of inputting relevant medical information.

[0086] The advice unit can estimate the parent's emotions and adjust the way it expresses advice based on those emotions. For example, if the parent is feeling anxious, the advice unit will provide advice in a reassuring way. If the parent is relaxed, the advice unit can also provide detailed and specific advice. Furthermore, if the parent is in a hurry, the advice unit can provide concise and quick advice. By providing advice that is tailored to the parent's emotions, it is possible to provide reassurance and facilitate appropriate responses. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI or not. For example, the advice unit can input the parent's emotion data into the generative AI and have the generative AI perform the process of adjusting the way it expresses advice.

[0087] The advice unit can adjust the level of detail in its advice based on the severity of the patient's condition. For example, in the case of a mild condition, the advice unit can provide simple initial response advice. In the case of a moderate condition, the advice unit can also provide advice that includes detailed response methods and precautions. Furthermore, in the case of a severe condition, the advice unit can provide advice that strongly recommends emergency response or consultation with a doctor. This facilitates appropriate responses by providing advice according to the severity of the condition. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input disease severity data into the AI ​​and have the AI ​​perform the process of adjusting the level of detail in the advice.

[0088] The advice unit can apply different advice algorithms depending on the category of the medical condition when providing advice. For example, in the case of an infectious disease, the advice unit can provide advice on preventing the spread of infection. In the case of an allergy, the advice unit can also provide advice on allergen avoidance and coping methods. Furthermore, in the case of an injury, the advice unit can provide advice recommending first aid or seeking medical attention. By providing advice according to the category of the medical condition, appropriate responses can be promoted. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input medical condition category data into the AI ​​and have the AI ​​perform the process of applying different advice algorithms.

[0089] The advice unit can estimate the parent's emotions and adjust the length of the advice based on the estimated emotions. For example, if the parent is feeling anxious, the advice unit can provide detailed advice to reassure them. If the parent is relaxed, the advice unit can also provide longer advice containing more detailed information. Furthermore, if the parent is in a hurry, the advice unit can provide concise, to-the-point advice. This facilitates appropriate responses by providing advice of a length that matches the parent's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI or not. For example, the advice unit can input parent emotion data into a generative AI and have the generative AI perform the process of adjusting the length of the advice.

[0090] The advice unit can prioritize advice based on the onset of the illness when providing advice. For example, if the illness has suddenly developed, the advice unit will provide advice prioritizing emergency response. If the illness has persisted for several days, the advice unit can also provide advice recommending a doctor's visit. Furthermore, if the illness is chronic, the advice unit can provide advice on long-term management methods and lifestyle improvements. This facilitates appropriate responses by providing advice tailored to the onset of the illness. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input illness onset data into AI and have the AI ​​perform the process of determining the priority of advice.

[0091] The advice unit can adjust the order of advice based on the relationships between medical conditions when providing advice. For example, if there are multiple medical conditions, the advice unit will provide advice for the most serious condition first. The advice unit can also provide advice in order of relevance if the medical conditions are related. Furthermore, if the medical conditions are different, the advice unit can provide advice for each condition in order. This facilitates appropriate responses by providing advice according to the relationships between medical conditions. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input medical condition relationship data into AI and have the AI ​​perform the process of adjusting the order of advice.

[0092] The scheduling unit can estimate the parent's emotions and adjust the doctor's consultation schedule based on the estimated emotions. For example, if the parent is feeling anxious, the scheduling unit will schedule an immediate consultation with a doctor. If the parent is relaxed, the scheduling unit can also suggest a schedule that suits the parent's needs. Furthermore, if the parent is in a hurry, the scheduling unit can prioritize scheduling the earliest possible consultation. By providing a schedule that aligns with the parent's emotions, it is possible to provide reassurance and promote appropriate medical consultation. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the scheduling unit may be performed using AI or not. For example, the scheduling unit can input the parent's emotion data into the generative AI and have the generative AI perform the process of adjusting the doctor's consultation schedule.

[0093] The scheduling unit can suggest the optimal schedule by referring to past consultation history when setting a schedule. For example, the scheduling unit can suggest time slots that are convenient for parents based on past consultation history. The scheduling unit can also suggest the optimal schedule by considering the doctor's availability based on past consultation history. Furthermore, the scheduling unit can refer to past consultation history and suggest a schedule that suits the parents' preferences and circumstances. In this way, by referring to past consultation history, the system can provide the optimal schedule for parents. Some or all of the above processes in the scheduling unit may be performed using AI, for example, or not. For example, the scheduling unit can input past consultation history data into AI and have the AI ​​perform the process of suggesting the optimal schedule.

[0094] The scheduling unit can customize the schedule when setting it, taking into account the parent's current schedule and living situation. For example, the scheduling unit can refer to the parent's calendar information and schedule consultations with a doctor during available time slots. The scheduling unit can also suggest the optimal time slot considering the parent's living situation (e.g., work, household chores). Furthermore, the scheduling unit can suggest a manageable schedule based on the parent's current schedule. This allows for manageable medical consultations by providing a schedule that suits the parent's living situation. Some or all of the above processing in the scheduling unit may be performed using AI, or not. For example, the scheduling unit can input the parent's calendar information and living situation data into the AI ​​and have the AI ​​perform the process of customizing the schedule.

[0095] The scheduling unit can estimate the parent's emotions and determine schedule priorities based on those emotions. For example, if the parent is feeling anxious, the scheduling unit will prioritize urgent appointments. If the parent is relaxed, the scheduling unit can also suggest schedules that suit the parent's needs. Furthermore, if the parent is in a hurry, the scheduling unit can prioritize appointments for the earliest possible time slot. This allows for prompt important medical consultations by prioritizing appointments according to the parent's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the scheduling unit may be performed using AI or not. For example, the scheduling unit can input parent emotion data into a generative AI and have the generative AI perform the process of determining schedule priorities.

[0096] The scheduling unit can select the most suitable doctor when setting a schedule, taking into account the parent's geographical location. For example, the scheduling unit can prioritize selecting nearby doctors based on the parent's current location. It can also refer to the parent's geographical location and suggest doctors that are easily accessible. Furthermore, the scheduling unit can select the most suitable doctor by considering transportation methods and travel time based on the parent's geographical location. This allows for the selection of easily accessible doctors by considering geographical location. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input the parent's geographical location into AI and have the AI ​​perform the process of selecting the most suitable doctor.

[0097] The scheduling unit can analyze parents' social media activity when setting schedules and propose the optimal schedule. For example, the scheduling unit can analyze parents' social media posts and propose a schedule based on recent activities and events. It can also refer to posts from parents' social media friends and followers and propose a schedule based on relevant events and activities. Furthermore, based on parents' social media activity, the scheduling unit can prioritize suggesting schedules related to specific events or locations. This allows the system to provide schedules based on relevant events and activities by analyzing social media activity. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or not. For example, the scheduling unit can input parents' social media activity data into an AI and have the AI ​​perform the process of proposing the optimal schedule.

[0098] The management unit can estimate the parent's emotions and adjust the health record management method based on the estimated parent's emotions. For example, if the parent is feeling anxious, the management unit can provide a simple and intuitive interface and minimize management procedures. If the parent is relaxed, the management unit can also provide detailed management options and suggest a customizable management method. Furthermore, if the parent is in a hurry, the management unit can prioritize voice input to enable quick management of health records. This reduces stress and promotes accurate health record management by providing a management method that is tailored to the parent's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI or not. For example, the management unit can input parent emotion data into a generative AI and have the generative AI perform the process of adjusting the health record management method.

[0099] The management department can optimize its management algorithm by referring to past medical records when managing health records. For example, the management department can automatically refer to past medical records and display relevant health records as candidates. The management department can also analyze the frequency and patterns of specific symptoms from past medical records to supplement the management content. Furthermore, the management department can detect inconsistencies in managed health records based on past medical records and propose corrections. In this way, by referring to past medical records, the management algorithm can be optimized and accurate health record management can be achieved. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input past medical record data into AI and have the AI ​​perform the process of optimizing the management algorithm.

[0100] The management department can customize health records when managing them, taking into account the child's current health status and lifestyle. For example, the management department can suggest relevant health records based on the child's current health status (e.g., fever, cough). The management department can also supplement the content of health records by referring to the child's lifestyle (e.g., school, exercise). Furthermore, the management department can automatically supplement the details of managed health records based on the child's current health status and lifestyle. This enables more accurate health record management by taking into account the child's current health status and lifestyle. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input the child's health status and lifestyle data into the AI ​​and have the AI ​​perform the process of customizing the records.

[0101] The management unit can estimate the parent's emotions and prioritize health records based on those emotions. For example, if the parent is feeling anxious, the management unit may prompt them to prioritize managing urgent health records. Conversely, if the parent is relaxed, the management unit may suggest managing detailed health records in order. Furthermore, if the parent is in a hurry, the management unit can adjust the interface to prioritize important health records. This allows for the rapid management of important health records by setting priorities according to the parent's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI or not. For example, the management unit can input parent emotion data into a generative AI and have the generative AI perform the process of determining the priority of health records.

[0102] The management department can select the optimal recording method when managing health records, taking into account the geographical location information of the guardian. For example, the management department can prioritize recording region-specific diseases and prevalent illnesses based on the guardian's current location. The management department can also refer to the guardian's geographical location information and reflect information about nearby medical institutions in the records. Furthermore, the management department can prioritize managing health records related to the local climate and environment based on the guardian's geographical location information. In this way, by considering geographical location information, information about region-specific illnesses and medical institutions can be prioritized for recording. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input the guardian's geographical location information into AI and have the AI ​​perform the process of selecting the optimal recording method.

[0103] The management department can improve the accuracy of health records by analyzing parents' social media activity when managing health records. For example, the management department can analyze parents' social media posts and manage health records related to recent activities and events. The management department can also refer to posts from parents' social media friends and followers and reflect prevalent illnesses in the records. Furthermore, the management department can prioritize the management of health records related to specific events or locations based on parents' social media activity. This allows for the rapid management of relevant health records by analyzing social media activity. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input parents' social media activity data into AI and have the AI ​​perform processes to improve the accuracy of the records.

[0104] The visualization unit can estimate the parent's emotions and adjust the display method of the visualization based on the estimated emotions of the parent. For example, if the parent is feeling anxious, the visualization unit can provide a simple and highly visible display method. If the parent is relaxed, the visualization unit can also provide a display method that includes detailed information. Furthermore, if the parent is in a hurry, the visualization unit can provide a concise display method. This improves visibility and enables the provision of appropriate information by providing a display method that matches the parent's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the visualization unit may be performed using AI, or not. For example, the visualization unit can input parent's emotion data into the generative AI and have the generative AI perform the process of adjusting the display method.

[0105] The visualization unit can optimize its visualization algorithm by referring to past health data during the visualization process. For example, the visualization unit can display growth curves and weight trends in graphs based on past health data. The visualization unit can also analyze the frequency and patterns of specific symptoms from past health data and supplement the visualization content. Furthermore, the visualization unit can detect inconsistencies in the visualized information based on past health data and propose corrections. This allows for the optimization of the visualization algorithm by referring to past health data, thereby enabling the provision of accurate information. Some or all of the above-described processes in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input past health data into AI and have the AI ​​perform the process of optimizing the visualization algorithm.

[0106] The visualization unit can customize the visualization content by considering the child's current health status and living situation during the visualization process. For example, the visualization unit visualizes relevant health data by considering the child's current health status (e.g., fever, cough). The visualization unit can also supplement the visualization content by referring to the child's living situation (e.g., school, exercise). Furthermore, the visualization unit can automatically supplement the details of the visualized information based on the child's current health status and living situation. This enables the provision of more accurate information by considering the child's current health status and living situation. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the child's health status and living situation data into the AI ​​and have the AI ​​perform the process of customizing the visualization content.

[0107] The visualization unit can estimate the parent's emotions and determine visualization priorities based on the estimated emotions. For example, if the parent is feeling anxious, the visualization unit will prioritize visualizing urgent health data. If the parent is relaxed, the visualization unit can also visualize detailed health data sequentially. Furthermore, if the parent is in a hurry, the visualization unit can adjust the interface to prioritize visualizing important health data. This allows for the rapid visualization of important information by setting priorities according to the parent's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the visualization unit may be performed using AI or not. For example, the visualization unit can input parent emotion data into the generative AI and have the generative AI perform the process of determining visualization priorities.

[0108] The visualization unit can select the optimal visualization method when creating visualizations, taking into account the geographical location information of the guardian. For example, the visualization unit can prioritize the visualization of region-specific diseases and prevalent medical conditions based on the guardian's current location. The visualization unit can also refer to the guardian's geographical location information and reflect information on nearby medical institutions in the visualization. Furthermore, the visualization unit can prioritize the visualization of health data related to the region's climate and environment based on the guardian's geographical location information. In this way, by considering geographical location information, information on region-specific medical conditions and medical institutions can be prioritized for visualization. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the guardian's geographical location information into AI and have the AI ​​perform the process of selecting the optimal visualization method.

[0109] The visualization unit can analyze parents' social media activity during visualization to improve the accuracy of the visualization. For example, the visualization unit can analyze parents' social media posts and visualize health data related to recent activities and events. The visualization unit can also refer to posts from parents' social media friends and followers and reflect prevalent illnesses in the visualization. Furthermore, based on parents' social media activity, the visualization unit can prioritize the visualization of health data related to specific events or locations. This allows for the rapid visualization of relevant health data by analyzing social media activity. Some or all of the above processing in the visualization unit may be performed using AI, for example, or not. For example, the visualization unit can input parents' social media activity data into AI and have the AI ​​perform processing to improve the accuracy of the visualization.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The reception desk can estimate the parent's emotions and adjust the method of inputting medical information based on the estimated emotions. For example, if the parent is feeling anxious, it can provide a simple and intuitive interface and minimize the input steps. If the parent is relaxed, it can provide detailed input options and suggest a customizable input method. Furthermore, if the parent is in a hurry, it can prioritize voice input to allow for quick input of medical information. This reduces stress and promotes accurate medical information input by providing an input method that is appropriate to the parent's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the parent's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0112] The advice unit can estimate the parent's emotions and adjust the way it expresses advice based on those emotions. For example, if the parent is feeling anxious, it can provide advice in a reassuring way. If the parent is relaxed, it can provide detailed and specific advice. Furthermore, if the parent is in a hurry, it can provide concise and quick advice. By providing advice that is tailored to the parent's emotions, it is possible to provide reassurance and facilitate appropriate responses. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI or not. For example, the advice unit can input the parent's emotion data into the generative AI and have the generative AI perform the process of adjusting the way it expresses advice.

[0113] The scheduling unit can estimate the parent's emotions and adjust the doctor's consultation schedule based on the estimated emotions. For example, if the parent is feeling anxious, it can schedule an urgent consultation with the doctor. If the parent is relaxed, it can suggest a schedule that suits the parent's needs. Furthermore, if the parent is in a hurry, it can prioritize scheduling the earliest possible consultation. By providing a schedule that matches the parent's emotions, it can provide reassurance and promote appropriate medical consultation. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the scheduling unit may be performed using AI or not. For example, the scheduling unit can input the parent's emotion data into the generative AI and have the generative AI perform the process of adjusting the doctor's consultation schedule.

[0114] The management unit can estimate the parent's emotions and adjust the health record management method based on the estimated parent's emotions. For example, if the parent is feeling anxious, it can provide a simple and intuitive interface and minimize management procedures. If the parent is relaxed, it can provide detailed management options and suggest a customizable management method. Furthermore, if the parent is in a hurry, it can prioritize voice input to enable quick management of health records. This reduces stress and promotes accurate health record management by providing a management method that is tailored to the parent's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI or not. For example, the management unit can input parent emotion data into a generative AI and have the generative AI perform the process of adjusting the health record management method.

[0115] The visualization unit can estimate the parent's emotions and adjust the display method of the visualization based on the estimated parent's emotions. For example, if the parent is feeling anxious, it can provide a simple and highly visible display method. If the parent is relaxed, it can also provide a display method that includes detailed information. Furthermore, if the parent is in a hurry, it can provide a display method that gets straight to the point. By providing a display method that matches the parent's emotions, visibility can be improved and appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the visualization unit may be performed using AI, for example, or not using AI. For example, the visualization unit can input the parent's emotion data into the generative AI and have the generative AI perform the process of adjusting the display method.

[0116] The reception desk can improve the accuracy of input by referring to past medical history data when patients enter their medical conditions. For example, it can automatically refer to past medical records and display relevant medical conditions as candidates. It can also analyze the frequency and patterns of specific symptoms from past medical history data and supplement the input content. Furthermore, it can detect inconsistencies in the entered medical conditions based on past medical history data and suggest corrections. In this way, by referring to past medical history data, the accuracy of input can be improved and appropriate diagnoses can be supported. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input past medical history data into AI and have the AI ​​perform tasks such as supplementing the input content and detecting inconsistencies.

[0117] The advice unit can adjust the level of detail in the advice provided based on the severity of the patient's condition. For example, in the case of a mild condition, it can provide simple initial response advice. In the case of a moderate condition, it can also provide advice that includes detailed response methods and precautions. Furthermore, in the case of a severe condition, it can provide advice that strongly recommends emergency response or consultation with a doctor. This allows for appropriate responses by providing advice according to the severity of the condition. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input disease severity data into the AI ​​and have the AI ​​perform the process of adjusting the level of detail in the advice.

[0118] The scheduling unit can suggest the optimal schedule by referring to past consultation history when setting a schedule. For example, it can suggest a time slot that is convenient for the parent based on past consultation history. It can also suggest the optimal schedule by considering the doctor's availability based on past consultation history. Furthermore, it can suggest a schedule that is tailored to the parent's preferences and circumstances by referring to past consultation history. In this way, by referring to past consultation history, the optimal schedule can be provided to the parent. Some or all of the above processes in the scheduling unit may be performed using AI, for example, or not using AI. For example, the scheduling unit can input past consultation history data into AI and have the AI ​​perform the process of suggesting the optimal schedule.

[0119] The management department can optimize its management algorithm by referring to past medical records when managing health records. For example, it can automatically refer to past medical records and display relevant health records as candidates. It can also analyze the frequency and patterns of specific symptoms from past medical records to supplement the management content. Furthermore, it can detect inconsistencies in managed health records based on past medical records and suggest corrections. In this way, by referring to past medical records, the management algorithm can be optimized, and accurate health record management can be achieved. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input past medical record data into AI and have the AI ​​perform the process of optimizing the management algorithm.

[0120] The visualization unit can optimize its visualization algorithm by referring to past health data during visualization. For example, it can display growth curves and weight trends in graphs based on past health data. It can also analyze the frequency and patterns of specific symptoms from past health data to supplement the visualization content. Furthermore, it can detect inconsistencies in the visualized information based on past health data and suggest corrections. In this way, by referring to past health data, the visualization algorithm can be optimized, enabling the provision of accurate information. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input past health data into AI and have the AI ​​perform the process of optimizing the visualization algorithm.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The reception desk accepts input of the patient's symptoms. This input includes text input, voice input, and image input. For example, a parent can describe the symptoms verbally and have it converted into text for input, or they can take a picture of their child's symptoms and input that image. Step 2: The advice department analyzes the information received by the reception department and provides advice on possible diagnoses and initial responses. For example, it uses AI to analyze the patient's condition, make a diagnosis, and advises them to get plenty of rest and stay hydrated if a cold is likely. Step 3: The scheduling department schedules online consultations with doctors based on the advice provided by the advice department. For example, they schedule consultations with doctors to suit the parents' convenience and adjust the schedule to take into account the doctors' availability. Step 4: The administration department centrally manages the digitized children's health records. For example, it centrally manages past medical records and vaccination histories, and digitizes health records to make them easily accessible. Step 5: The visualization unit visualizes the information managed by the management unit. For example, it displays growth curves and weight trends in graphs to visually show changes in health status.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the reception unit, advice unit, scheduling unit, management unit, and visualization unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and provides an interface for parents to input medical conditions. The advice unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the input medical condition information and provides appropriate advice. The scheduling unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and schedules online consultations with doctors. The management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and centrally manages digitized health records of children. The visualization unit is implemented by, for example, the control unit 46A of the smart device 14 and visually displays changes in health status. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the reception unit, advice unit, scheduling unit, management unit, and visualization unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and provides an interface for parents to input medical conditions. The advice unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the input medical condition information and provides appropriate advice. The scheduling unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and schedules online consultations with doctors. The management unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and centrally manages digitized health records of children. The visualization unit is implemented, for example, by the control unit 46A of the smart glasses 214 and visually displays changes in health status. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the reception unit, advice unit, scheduling unit, management unit, and visualization unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and provides an interface for parents to input medical conditions. The advice unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the input medical condition information and provides appropriate advice. The scheduling unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and schedules online consultations with doctors. The management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and centrally manages digitized health records of children. The visualization unit is implemented by, for example, the control unit 46A of the headset terminal 314 and visually displays changes in health status. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the reception unit, advice unit, scheduling unit, management unit, and visualization unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and provides an interface for parents to input medical information. The advice unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the input medical information and provides appropriate advice. The scheduling unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and schedules online consultations with doctors. The management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and centrally manages digitized health records of children. The visualization unit is implemented by, for example, the control unit 46A of the robot 414 and visually displays changes in health status. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0176] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0179] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0185] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0186] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0187] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0188] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0189] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] (Note 1) The reception area accepts entries regarding the patient's condition, The information received by the reception department is analyzed by the advice department, and advice is provided on possible diagnoses and initial responses. A scheduling unit that schedules online consultations with a doctor based on the advice provided by the aforementioned advice unit, A management department that centrally manages digitized children's health records, The system includes a visualization unit that visualizes the information managed by the aforementioned management unit. A system characterized by the following features. (Note 2) The aforementioned advice section, We analyze the latest medical information and research to provide personalized health information and parenting advice tailored to the child's age and health condition. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned management department, Centralized management of past medical records and vaccination history. The system described in Appendix 1, characterized by the features described herein. (Note 4) The visualization unit is, Display growth curves and weight changes in graphs. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned scheduling unit is If symptoms persist, schedule an online consultation with a doctor. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned advice section, If there is a high possibility of a cold, advise getting plenty of rest and staying hydrated. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the parents' emotions and adjusts the method of inputting medical conditions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When entering medical conditions, past medical history data is referenced to improve the accuracy of the input. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering medical conditions, the system will supplement the input by considering the child's current activity level and environmental information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the parent's emotions and determines the priority of the medical conditions to input based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering medical information, the system prioritizes inputting highly relevant information, taking into account the guardian's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When entering medical information, the system analyzes the parents' social media activity and inputs relevant medical information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned advice section, The system estimates the parents' emotions and adjusts the way advice is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned advice section, When providing advice, adjust the level of detail based on the severity of the patient's condition. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned advice section, When providing advice, different advice algorithms are applied depending on the category of the medical condition. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned advice section, The system estimates the parent's emotions and adjusts the length of the advice based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned advice section, When providing advice, prioritize the advice based on when the symptoms began. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned advice section, When providing advice, adjust the order of advice based on the relevance of the medical condition. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned scheduling unit is We estimate the parents' emotions and adjust the doctor's consultation schedule based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned scheduling unit is When setting a schedule, we will refer to your past consultation history to suggest the most suitable schedule. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned scheduling unit is When setting a schedule, customize it to take into account the parent's current schedule and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned scheduling unit is It estimates the parents' emotions and prioritizes the schedule based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned scheduling unit is When scheduling appointments, the system selects the most suitable doctor by taking into account the parents' geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned scheduling unit is When setting a schedule, we analyze parents' social media activity and suggest the optimal schedule. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned management department, Estimate the emotions of parents and adjust the management method of health records based on the estimated emotions of parents. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned management department, When managing health records, the management algorithm is optimized by referring to past medical records. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned management department, When managing health records, customize the records to take into account the child's current health status and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned management department, The system estimates the parents' emotions and prioritizes health records based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned management department, When managing health records, the optimal recording method should be selected considering the geographical location information of the guardian. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned management department, Analyzing parents' social media activity during health record management improves the accuracy of the records. The system described in Appendix 1, characterized by the features described herein. (Note 31) The visualization unit is, The system estimates the parent's emotions and adjusts the visualization display based on the estimated parent's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The visualization unit is, During visualization, the visualization algorithm is optimized by referencing past health data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The visualization unit is, When creating visualizations, customize the content to take into account the child's current health status and living situation. The system described in Appendix 1, characterized by the features described herein. (Note 34) The visualization unit is, The system estimates the parents' emotions and determines the priority of visualizations based on the estimated parents' emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The visualization unit is, When creating visualizations, the optimal visualization method is selected by considering the geographical location information of the guardians. The system described in Appendix 1, characterized by the features described herein. (Note 36) The visualization unit is, During visualization, we analyze parents' social media activity to improve the accuracy of the visualization. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception area accepts entries regarding the patient's condition, The information received by the reception department is analyzed by the advice department, and advice is provided on possible diagnoses and initial responses. A scheduling unit that schedules online consultations with a doctor based on the advice provided by the aforementioned advice unit, A management department that centrally manages digitized children's health records, The system includes a visualization unit that visualizes the information managed by the aforementioned management unit. A system characterized by the following features.

2. The aforementioned advice section, We analyze the latest medical information and research to provide personalized health information and parenting advice tailored to the child's age and health condition. The system according to feature 1.

3. The aforementioned management department, Centralized management of past medical records and vaccination history. The system according to feature 1.

4. The visualization unit is, Display growth curves and weight changes in graphs. The system according to feature 1.

5. The aforementioned scheduling unit is If symptoms persist, schedule an online consultation with a doctor. The system according to feature 1.

6. The aforementioned advice section, If there is a high possibility of a cold, advise getting plenty of rest and staying hydrated. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the parents' emotions and adjusts the method of inputting medical conditions based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is When entering medical conditions, past medical history data is referenced to improve the accuracy of the input. The system according to feature 1.

9. The aforementioned reception unit is When entering medical conditions, the system will supplement the input by considering the child's current activity level and environmental information. The system according to feature 1.

10. The aforementioned reception unit is The system estimates the parent's emotions and determines the priority of the medical conditions to input based on those estimated emotions. The system according to feature 1.

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