system

Abstract

The system according to this embodiment aims to distribute and streamline medical resources by utilizing AI agents. [Solution] The system according to the embodiment comprises a reception unit, a registration unit, a handover unit, a learning unit, a consultation unit, a provision unit, and a dialogue unit. The reception unit conducts an initial medical interview. The registration unit registers the medical record created by the reception unit into a database. The handover unit hands over the medical record registered by the registration unit to an AI agent. The learning unit learns cases similar to the medical record handed over by the handover unit. The consultation unit visits the user based on the information learned by the learning unit. The provision unit provides health management and medical advice to the user who has been visited by the consultation unit. The dialogue unit interacts with the user based on the advice provided by the provision 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, there are problems such as a shortage of doctors in an aging society, an increase in the burden on medical staff, and overcrowding in the waiting rooms of hospitals.

[0005] The system according to the embodiment aims to utilize an AI agent to decentralize and streamline medical resources.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a registration unit, a handover unit, a learning unit, a consultation unit, a provision unit, and a dialogue unit. The reception unit conducts an initial medical interview. The registration unit registers the medical record created by the reception unit into a database. The handover unit hands over the medical record registered by the registration unit to an AI agent. The learning unit learns cases similar to the medical record handed over by the handover unit. The consultation unit visits the user based on the information learned by the learning unit. The provision unit provides health management and medical advice to the user who has been visited by the consultation unit. The dialogue unit interacts with the user based on the advice provided by the provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can utilize an AI agent to distribute and streamline medical resources. [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 signed communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied 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 reception 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 reception 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 smartphone application system using an AI agent according to an embodiment of the present invention is a system aimed at addressing the shortage of medical resources and reducing the burden on medical professionals in a super-aging society. In this system, the actual primary care physician conducts an interview during the initial consultation and creates a patient's medical record. This medical record is registered in a database and handed over to the AI ​​agent. The AI ​​agent learns from the registered medical record and similar cases, enabling personalized consultations. Users can consult with the AI ​​agent through the smartphone app and receive health management and medical advice 24 hours a day. Furthermore, the AI ​​agent distributes resources by acting as a substitute for the primary care physician, handling elderly patients and non-urgent matters, while actual doctors handle emergencies. The AI ​​agent also acts as a conversational partner, alleviating the user's loneliness. This mechanism helps to reduce rising medical costs, doctor shortages, and the burden on medical professionals, and can prevent hospital waiting rooms from becoming mere social spaces. By creating a database of users' hobbies and preferences and providing personalized service, it can offer a source of emotional support. For example, an AI agent can provide appropriate topics of conversation based on the user's hobbies and interests, and build trust through dialogue with the user. Furthermore, the AI ​​agent can constantly monitor the user's health and respond quickly if any abnormalities are detected. This allows users to live their daily lives with peace of mind. In addition, the AI ​​agent can provide advice that takes into account the user's lifestyle and environmental information. For example, it can provide specific advice for a healthy lifestyle based on the user's diet and exercise habits. The AI ​​agent can also monitor the user's stress levels and sleep patterns, and provide advice on relaxation methods and sleep improvement as needed. This allows for comprehensive support of the user's health. As a result, smartphone application systems using AI agents can address the shortage of medical resources and alleviate the burden on healthcare professionals, while providing user-centric support.

[0029] The smartphone application system using an AI agent according to this embodiment comprises a reception unit, a registration unit, a handover unit, a learning unit, a consultation unit, a provision unit, and a dialogue unit. The reception unit conducts an initial medical interview. The reception unit, for example, confirms the user's basic information and symptoms and creates a medical record. The reception unit can also ask questions about the user's past medical history and lifestyle habits to collect detailed information. The reception unit can also use AI to estimate the user's emotions and adjust the content of the medical interview questions based on the estimated emotions. For example, if the user is feeling anxious, the reception unit can ask questions in a gentle tone to provide reassurance. Also, if the user is relaxed, the reception unit can ask detailed questions to collect deeper information. Furthermore, if the user is in a hurry, the reception unit can ask concise and quick questions to save time. The registration unit registers the medical record created by the reception unit into a database. The registration unit saves the contents of the medical record as digital data, for example, using an electronic medical record system. The registration unit can also use AI to optimize the contents of the medical record. For example, the registration unit can refer to the user's past medical data and prioritize the registration of relevant information. The registration unit can also register medical records while reflecting the user's lifestyle and environmental information. The handover unit hands over the medical records registered by the registration unit to an AI agent. For example, the handover unit transfers the contents of the medical records to the AI ​​agent, which then uses this data to learn about the user. The handover unit can also use AI to optimize the method of handing over medical records. For example, the handover unit can estimate the user's emotions and adjust the method of handing over the medical records based on the estimated emotions. The learning unit learns cases similar to the medical records handed over by the handover unit. For example, the learning unit uses AI to analyze large amounts of medical data and learn appropriate examination methods for the user. The learning unit can also estimate the user's emotions and select learning data based on the estimated emotions. The consultation unit visits the user based on the information learned by the learning unit. For example, the consultation unit uses an AI agent to confirm the user's symptoms and provide appropriate medical advice.The consultation unit can estimate the user's emotions and adjust the consultation method based on the estimated emotions. The provision unit provides health management and medical advice to users who have been consulted by the consultation unit. The provision unit can, for example, use AI to monitor the user's health status and provide appropriate advice. The provision unit can also estimate the user's emotions and adjust the way the advice is expressed based on the estimated emotions. The dialogue unit interacts with the user based on the advice provided by the provision unit. The dialogue unit can, for example, use AI to build a relationship of trust with the user through dialogue. The dialogue unit can also estimate the user's emotions and adjust the method of dialogue based on the estimated emotions. As a result, the smartphone application system using the AI ​​agent according to this embodiment can address the shortage of medical resources and reduce the burden on medical professionals, while realizing a user-centric approach.

[0030] The reception desk conducts the initial consultation. For example, the reception desk verifies the user's basic information and symptoms and creates a medical record. Specifically, it inputs basic information such as the user's name, age, gender, and contact information, and records details such as the current symptoms, their history, onset date, and changes in symptoms. Furthermore, the reception desk can gather detailed information by asking questions about the user's past medical history and lifestyle. For example, it can inquire about past illnesses and treatments, current medications, allergies, smoking and drinking habits, exercise habits, and diet. This allows the reception desk to understand the user's overall health status and collect basic data for appropriate examination and treatment. The reception desk can also use AI to estimate the user's emotions and adjust the consultation questions based on the estimated emotions. For example, the AI ​​analyzes the user's emotions from their facial expressions, tone of voice, and word choice to estimate states such as anxiety, tension, and relaxation. If the user is feeling anxious, the AI ​​will ask reassuring questions in a gentle tone; if the user is relaxed, it will ask detailed questions to gather deeper information. Furthermore, if the user is in a hurry, the AI ​​can ask concise and quick questions, saving time. This allows the reception staff to respond flexibly to the user's emotions, resulting in a less stressful consultation experience for the user.

[0031] The registration department registers medical records created by the reception department into a database. The registration department uses, for example, an electronic medical record system to save the contents of the medical records as digital data. Specifically, it inputs data such as the user's basic information, symptoms, medical history, and lifestyle collected by the reception department into the electronic medical record system and saves it to the database. This allows for centralized management of user information and quick access when needed. The registration department can also use AI to optimize the content of medical records. For example, AI can refer to the user's past medical data and prioritize the registration of relevant information. This ensures that important information is not overlooked and allows for efficient medical record creation. Furthermore, the registration department can incorporate the user's lifestyle and environmental information into the medical records. For example, if a user lives in a specific region, the medical record can be created considering the environmental factors of that region (e.g., climate and air pollution). This allows for a comprehensive understanding of factors that may affect the user's health. In addition, the registration department can use AI to verify and correct data to maintain data integrity and consistency. This allows the registration department to create accurate and reliable medical records, providing a foundation for healthcare professionals to provide appropriate examinations and treatments.

[0032] The handover unit transfers medical records registered by the registration unit to the AI ​​agent. For example, the handover unit transfers the contents of the medical record to the AI ​​agent, which then uses this data to learn about the user. Specifically, it extracts the necessary data from the electronic medical record system and provides it to the AI ​​agent in an appropriate format. This allows the AI ​​agent to learn about the user's health status and risk factors based on data such as the user's basic information, symptoms, medical history, and lifestyle. The handover unit can also use AI to optimize the method of handing over medical records. For example, the AI ​​can estimate the user's emotions and adjust the method of handing over the medical record based on the estimated emotions. If the user is feeling anxious, the AI ​​can hand over the medical record in a way that provides reassurance; if the user is relaxed, it can hand over a medical record containing detailed information. Also, if the user is in a hurry, the AI ​​can hand over the medical record in a concise and quick way, saving time. This allows the handover unit to respond flexibly to the user's emotions, resulting in a less stressful handover for the user. Furthermore, the data transfer unit can use AI to verify and correct data in order to maintain data integrity and consistency. This allows the data transfer unit to provide accurate and reliable data to the AI ​​agent, creating a foundation for the AI ​​agent to perform appropriate diagnoses and treatments.

[0033] The learning unit learns from medical records and similar cases handed over by the handover unit. For example, the learning unit uses AI to analyze large amounts of medical data and learn appropriate examination methods for the user. Specifically, the AI ​​searches for cases similar to the user's symptoms and medical history based on past case data, and analyzes the examination methods and treatment results for those cases. This allows the AI ​​to propose the most suitable examination method and treatment plan for the user. The learning unit can also estimate the user's emotions and select learning data based on those estimated emotions. For example, if the user is feeling anxious, the AI ​​prioritizes learning examination methods and treatment plans that provide reassurance; if the user is relaxed, it can learn examination methods and treatment plans that include detailed information. Furthermore, if the user is in a hurry, the AI ​​can learn concise and rapid examination methods and treatment plans, saving time. This allows the learning unit to respond flexibly to the user's emotions, resulting in less stressful consultations for the user. In addition, the learning unit can use AI to verify and correct data to maintain data integrity and consistency. This allows the learning unit to learn based on accurate and reliable data, providing a foundation for AI agents to perform appropriate diagnoses and treatments.

[0034] The consultation unit provides consultations to users based on information learned by the learning unit. For example, the consultation unit uses an AI agent to confirm the user's symptoms and provide appropriate medical advice. Specifically, the AI ​​agent evaluates the user's health status based on data such as symptoms, medical history, and lifestyle, and proposes appropriate examination methods and treatment plans. The consultation unit can also estimate the user's emotions and adjust the consultation method based on those emotions. For example, if the user is feeling anxious, the AI ​​agent can conduct the consultation in a reassuring way; if the user is relaxed, it can conduct a consultation that includes detailed information. Furthermore, if the user is in a hurry, the AI ​​agent can conduct the consultation in a concise and rapid manner, saving time. This allows the consultation unit to respond flexibly to the user's emotions, resulting in a less stressful consultation experience. In addition, the consultation unit can use AI to verify and correct data to maintain data integrity and consistency. This allows the consultation unit to conduct consultations based on accurate and reliable data, providing a foundation for the AI ​​agent to perform appropriate examinations and treatments.

[0035] The service provider provides health management and medical advice to users who have received consultations from the consultation service provider. For example, the service provider uses AI to monitor the user's health status and provide appropriate advice. Specifically, the AI ​​continuously collects the user's health data and monitors changes in their health status in real time. This allows for a quick response if an abnormality occurs in the user's health status. The service provider can also estimate the user's emotions and adjust the way advice is expressed based on those emotions. For example, if the user is feeling anxious, the AI ​​can provide advice in a reassuring way, and if the user is relaxed, it can provide advice that includes detailed information. Also, if the user is in a hurry, the AI ​​can provide advice in a concise and quick way, saving time. This allows the service provider to respond flexibly to the user's emotions and provide advice that is less stressful for the user. Furthermore, the service provider can use AI to verify and correct data to maintain data integrity and consistency. This allows the service provider to provide advice based on accurate and reliable data and support the user's health management.

[0036] The dialogue unit interacts with the user based on advice provided by the service provider. The dialogue unit, for example, uses AI to build trust with the user through dialogue. Specifically, the AI ​​provides appropriate answers to the user's questions and concerns, alleviating the user's anxieties and doubts. The dialogue unit can also estimate the user's emotions and adjust the dialogue method based on the estimated emotions. For example, if the user is feeling anxious, the AI ​​will engage in dialogue in a reassuring way, and if the user is relaxed, it can engage in dialogue that includes detailed information. Also, if the user is in a hurry, the AI ​​can engage in dialogue in a concise and quick manner, saving time. This allows the dialogue unit to respond flexibly to the user's emotions, resulting in a less stressful dialogue for the user. Furthermore, the dialogue unit can use AI to verify and correct data to maintain data integrity and consistency. This allows the dialogue unit to engage in dialogue based on accurate and reliable data, building trust with the user. As a result, the smartphone application system using the AI ​​agent according to this embodiment can address the shortage of medical resources and alleviate the burden on medical professionals, while providing user-centric support.

[0037] The reception desk can adjust the level of detail in the initial consultation by referring to the user's past medical history. For example, the reception desk can refer to the user's past medical history and focus on relevant questions. For example, the reception desk can conduct the consultation efficiently by omitting known information based on the user's past medical records. For example, the reception desk can consider the user's past treatment history and collect any necessary additional information. This enables an efficient consultation based on the user's past medical history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past medical data into a generating AI and have the generating AI adjust the level of detail in the consultation.

[0038] The reception desk can collect information about the user's lifestyle and environment during their initial consultation and reflect it in their medical record. For example, the reception desk can ask questions about the user's diet and exercise habits and record them in the medical record. For example, the reception desk can collect information about the user's living and working environment and assess their health risks. For example, the reception desk can ask questions about the user's stress level and sleep patterns and reflect them in the medical record. This makes it possible to create a medical record that takes into account the user's lifestyle and environment information. Some or all of the above processing at the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's lifestyle data into a generating AI and have the generating AI perform the task of reflecting it in the medical record.

[0039] The reception desk can suggest the most suitable medical institution at the time of the user's first visit, taking into account the user's geographical location. For example, the reception desk can suggest the nearest medical institution based on the user's current location. For example, the reception desk can prioritize suggesting medical institutions close to the user's residence or workplace. For example, the reception desk can suggest easily accessible medical institutions, taking into account the user's means of transportation and traffic conditions. This makes it possible to suggest the most suitable medical institution based on the user's geographical location. 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 user's geographical location data into a generating AI and have the generating AI suggest the most suitable medical institution.

[0040] The reception desk can analyze a user's social media activity during their initial consultation and collect relevant health information. For example, the reception desk can extract health-related interests and concerns from the user's social media posts. For example, the reception desk can assess a user's lifestyle and stress levels from their online activities. For example, the reception desk can understand the user's social support situation from their social media friendships and communities. This makes it possible to collect health information based on the user's social media activity. 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 user's social media data into a generating AI and have the generating AI collect health information.

[0041] The registration unit can optimize registration content by referring to the user's past medical data when registering a medical record. For example, the registration unit can prioritize the registration of relevant information based on the user's past medical records. For example, the registration unit can consider the user's past treatment history and register any necessary additional information. For example, the registration unit can refer to the user's past medical history and efficiently register information by omitting duplicate information. This enables efficient medical record registration based on the user's past medical data. Some or all of the above processes in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input the user's past medical data into a generating AI and have the generating AI perform the optimization of the registered medical record content.

[0042] The registration unit can reflect the user's lifestyle and environmental information when registering a medical record. For example, the registration unit can record information about the user's diet and exercise habits in the medical record. For example, the registration unit can reflect information about the user's living environment and work environment in the medical record. For example, the registration unit can record information about the user's stress level and sleep patterns in the medical record. This makes it possible to register a medical record that takes into account the user's lifestyle and environmental information. Some or all of the above processing in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input the user's lifestyle data into a generating AI and have the generating AI perform the task of reflecting it in the medical record.

[0043] The registration unit can prioritize the registration of relevant medical data when registering a medical record, taking into account the user's geographical location information. For example, the registration unit can prioritize the registration of medical data related to the user's place of residence or workplace. For example, the registration unit can prioritize the registration of data for easily accessible medical institutions, taking into account the user's means of transportation and traffic conditions. For example, the registration unit can register data on region-specific health risks based on the user's geographical location information. This enables the registration of optimal medical data based on the user's geographical location information. Some or all of the above processing in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input the user's geographical location data into a generating AI and have the generating AI perform the priority registration of medical data.

[0044] The registration unit can analyze a user's social media activity when registering a medical record and register relevant health information. For example, the registration unit can extract health-related concerns and worries from a user's social media posts and record them in the medical record. For example, the registration unit can evaluate a user's lifestyle and stress levels from their online activities and reflect this in the medical record. For example, the registration unit can understand the status of social support from a user's social media friendships and communities and record this in the medical record. This makes it possible to register health information based on the user's social media activity. Some or all of the above processing in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input the user's social media data into a generating AI and have the generating AI perform the registration of health information.

[0045] The handover unit can optimize the handover content by referring to the user's past medical data when handing over medical records. For example, the handover unit can prioritize the handover of relevant information based on the user's past medical records. For example, the handover unit can consider the user's past treatment history and hand over any necessary additional information. For example, the handover unit can refer to the user's past medical history and efficiently hand over information by omitting duplicate information. This enables efficient medical record handover based on the user's past medical data. Some or all of the above processing in the handover unit may be performed using AI, for example, or without using AI. For example, the handover unit can input the user's past medical data into a generating AI and have the generating AI perform the optimization of the handover content of the medical record.

[0046] The handover unit can reflect the user's lifestyle and environmental information when handing over medical records. For example, the handover unit can record information about the user's diet and exercise habits in the medical record. For example, the handover unit can reflect information about the user's living environment and work environment in the medical record. For example, the handover unit can record information about the user's stress level and sleep patterns in the medical record. This makes it possible to hand over medical records while taking into account the user's lifestyle and environmental information. Some or all of the above processing in the handover unit may be performed using AI, for example, or without using AI. For example, the handover unit can input the user's lifestyle data into a generating AI and have the generating AI perform the reflection of this data in the medical record.

[0047] The data transfer unit can prioritize the transfer of relevant medical data when transferring medical records, taking into account the user's geographical location information. For example, the data transfer unit can prioritize the transfer of medical data related to the user's place of residence or workplace. For example, the data transfer unit can prioritize the transfer of data from easily accessible medical institutions, taking into account the user's means of transportation and traffic conditions. For example, the data transfer unit can transfer data related to region-specific health risks based on the user's geographical location information. This enables the transfer of optimal medical data based on the user's geographical location information. Some or all of the above processing in the data transfer unit may be performed using AI, for example, or without AI. For example, the data transfer unit can input the user's geographical location data into a generating AI and have the generating AI perform the preferential transfer of medical data.

[0048] The handover unit can analyze the user's social media activity during the handover of medical records and transfer relevant health information. For example, the handover unit can extract health-related interests and concerns from the user's social media posts and record them in the medical record. For example, the handover unit can evaluate the user's lifestyle and stress levels from their online activities and reflect them in the medical record. For example, the handover unit can understand the status of social support from the user's social media friendships and communities and record it in the medical record. This makes it possible to transfer health information based on the user's social media activity. Some or all of the above processing in the handover unit may be performed using AI, for example, or without AI. For example, the handover unit can input the user's social media data into a generating AI and have the generating AI perform the transfer of health information.

[0049] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal algorithm based on past learning data and perform learning. For example, the learning unit can analyze past learning results and adjust the algorithm parameters. For example, the learning unit can extract effective learning methods from past learning data and optimize the learning algorithm. This enables efficient optimization of the learning algorithm based on past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0050] The learning unit can incorporate the user's lifestyle and environmental information during the learning process. For example, the learning unit can incorporate information about the user's eating habits and exercise habits into the learning data. For example, the learning unit can incorporate information about the user's living environment and work environment into the learning data. For example, the learning unit can incorporate information about the user's stress level and sleep patterns into the learning data. This enables learning that takes into account the user's lifestyle and environmental information. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's lifestyle data into a generating AI and have the generating AI perform the process of incorporating it into the learning data.

[0051] The learning unit can weight the learning data based on the registration date of the medical record during the learning process. For example, the learning unit can prioritize learning and weighting data from recently registered medical records. For example, the learning unit can refer to past medical record data and perform appropriate weighting. For example, the learning unit can adjust the importance of the learning data based on the registration date of the medical record. This enables appropriate weighting of the learning data based on the registration date of the medical record. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input medical record registration date data into a generating AI and have the generating AI perform the weighting of the learning data.

[0052] The learning unit can analyze the user's social media activity during learning and learn relevant health information. For example, the learning unit can extract health-related concerns and worries from the user's social media posts and reflect them in the learning data. For example, the learning unit can evaluate the user's lifestyle and stress levels from their online activities and reflect them in the learning data. For example, the learning unit can understand the status of social support from the user's social media friendships and communities and reflect it in the learning data. This makes it possible to learn health information based on the user's social media activity. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's social media data into a generating AI and have the generating AI perform the learning of health information.

[0053] The consultation unit can optimize the consultation content by referring to the user's past medical data at the time of the consultation. For example, the consultation unit can prioritize relevant information based on the user's past medical records. For example, the consultation unit can collect necessary additional information by considering the user's past treatment history. For example, the consultation unit can efficiently conduct a consultation by referring to the user's past medical history and omitting redundant information. This enables efficient consultations based on the user's past medical data. Some or all of the above processing in the consultation unit may be performed using AI, for example, or without AI. For example, the consultation unit can input the user's past medical data into a generating AI and have the generating AI perform the optimization of the consultation content.

[0054] The consultation unit can reflect the user's lifestyle and environmental information during the consultation. For example, the consultation unit can reflect information about the user's diet and exercise habits in the consultation content. For example, the consultation unit can reflect information about the user's living environment and work environment in the consultation content. For example, the consultation unit can reflect information about the user's stress level and sleep patterns in the consultation content. This makes it possible to conduct consultations that take into account the user's lifestyle and environmental information. Some or all of the above processing in the consultation unit may be performed using AI, for example, or without using AI. For example, the consultation unit can input the user's lifestyle data into a generating AI and have the generating AI perform the task of reflecting it in the consultation content.

[0055] The medical consultation unit can suggest the most suitable medical institution by considering the user's geographical location information at the time of consultation. For example, the medical consultation unit can suggest the nearest medical institution based on the user's current location. For example, the medical consultation unit can prioritize suggesting medical institutions close to the user's residence or workplace. For example, the medical consultation unit can suggest easily accessible medical institutions by considering the user's means of transportation and traffic conditions. This makes it possible to suggest the most suitable medical institution based on the user's geographical location information. Some or all of the above processing in the medical consultation unit may be performed using AI, for example, or without AI. For example, the medical consultation unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of suggesting the most suitable medical institution.

[0056] The consultation unit can analyze the user's social media activity during a consultation and collect relevant health information. For example, the consultation unit can extract health-related concerns and worries from the user's social media posts. For example, the consultation unit can assess the user's lifestyle and stress levels from their online activities. For example, the consultation unit can understand the user's social support situation from their social media friendships and communities. This makes it possible to collect health information based on the user's social media activity. Some or all of the above processing in the consultation unit may be performed using AI, for example, or without AI. For example, the consultation unit can input the user's social media data into a generating AI and have the generating AI collect health information.

[0057] The service provider can optimize the advice content by referring to the user's past medical data when providing advice. For example, the service provider can prioritize relevant information when providing advice based on the user's past medical records. For example, the service provider can consider the user's past treatment history and provide necessary additional information. For example, the service provider can refer to the user's past medical history and provide advice efficiently by omitting redundant information. This enables efficient advice based on the user's past medical data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past medical data into a generating AI and have the generating AI perform the optimization of the advice content.

[0058] The service provider can incorporate the user's lifestyle and environmental information when providing advice. For example, the service provider can incorporate information about the user's diet and exercise habits into the advice. For example, the service provider can incorporate information about the user's living environment and work environment into the advice. For example, the service provider can incorporate information about the user's stress level and sleep patterns into the advice. This makes it possible to provide advice that takes into account the user's lifestyle and environmental information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's lifestyle data into a generating AI and have the generating AI perform the task of incorporating it into the advice.

[0059] The service provider can prioritize providing relevant advice by considering the user's geographical location when providing advice. For example, the service provider can prioritize providing advice related to the user's place of residence or workplace. For example, the service provider can prioritize providing advice on easily accessible medical facilities by considering the user's means of transportation and traffic conditions. For example, the service provider can provide advice on region-specific health risks based on the user's geographical location. This makes it possible to provide optimal advice based on the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location data into a generating AI and have the generating AI perform the priority provision of advice.

[0060] The service provider can analyze the user's social media activity when providing advice and provide relevant health information. For example, the service provider can extract health-related concerns and worries from the user's social media posts and reflect them in the advice. For example, the service provider can evaluate the user's lifestyle and stress levels from their online activities and reflect them in the advice. For example, the service provider can understand the user's social support situation from their social media friendships and communities and reflect it in the advice. This makes it possible to provide health information based on the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media data into a generating AI and have the generating AI provide health information.

[0061] The dialogue unit can optimize the dialogue content by referring to the user's past medical data during the dialogue. For example, the dialogue unit can prioritize relevant information based on the user's past medical records. For example, the dialogue unit can consider the user's past treatment history and provide necessary additional information. For example, the dialogue unit can refer to the user's past medical history and conduct the dialogue efficiently by omitting redundant information. This enables efficient dialogue based on the user's past medical data. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's past medical data into a generating AI and have the generating AI perform the optimization of the dialogue content.

[0062] The dialogue unit can reflect the user's lifestyle and environmental information during the conversation. For example, the dialogue unit can reflect information about the user's eating habits and exercise habits in the conversation. For example, the dialogue unit can reflect information about the user's living environment and work environment in the conversation. For example, the dialogue unit can reflect information about the user's stress level and sleep patterns in the conversation. This makes it possible to have a conversation that takes into account the user's lifestyle and environmental information. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's lifestyle data into a generating AI and have the generating AI perform the task of reflecting it in the conversation.

[0063] The dialogue unit can provide optimal dialogue content by considering the user's geographical location information during the conversation. For example, the dialogue unit can prioritize information related to the user's place of residence or workplace. For example, the dialogue unit can prioritize information about easily accessible medical facilities by considering the user's mode of transportation and traffic conditions. For example, the dialogue unit can provide information about region-specific health risks based on the user's geographical location information. This makes it possible to provide optimal dialogue content based on the user's geographical location information. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's geographical location data into a generating AI and have the generating AI provide the dialogue content.

[0064] The dialogue unit can analyze the user's social media activity during a conversation and reflect relevant health information in the dialogue. For example, the dialogue unit can extract health-related concerns and worries from the user's social media posts and reflect them in the dialogue. For example, the dialogue unit can evaluate the user's lifestyle and stress levels from their online activities and reflect them in the dialogue. For example, the dialogue unit can understand the user's social support situation from their social media friendships and communities and reflect it in the dialogue. This enables a health information dialogue based on the user's social media activity. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's social media data into a generating AI and have the generating AI execute a health information dialogue.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] A smartphone application system using an AI agent can suggest the most suitable medical institution considering the user's geographical location. For example, it can suggest the nearest medical institution based on the user's current location. It can also prioritize suggesting medical institutions close to the user's residence or workplace. Furthermore, it can suggest easily accessible medical institutions considering the user's mode of transportation and traffic conditions. This makes it possible to suggest the most suitable medical institution based on the user's geographical location. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of suggesting the most suitable medical institution.

[0067] A smartphone application system using an AI agent can analyze a user's social media activity and collect relevant health information. For example, it can extract health-related interests and concerns from a user's social media posts. It can also assess lifestyle habits and stress levels from a user's online activity. Furthermore, it can understand the user's social support situation from their social media friendships and communities. This enables the collection of health information based on the user's social media activity. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's social media data into a generating AI and have the generating AI collect health information.

[0068] A smartphone application system using an AI agent can optimize the content of a medical examination by referring to the user's past medical data. For example, it can prioritize relevant information based on the user's past medical records during the examination. It can also collect necessary additional information considering the user's past treatment history. Furthermore, it can efficiently conduct the examination by referring to the user's past medical history and omitting redundant information. This enables efficient examinations based on the user's past medical data. Some or all of the above processing in the examination unit may be performed using AI or not. For example, the examination unit can input the user's past medical data into a generating AI and have the generating AI perform the optimization of the examination content.

[0069] A smartphone application system using an AI agent can perform medical consultations that reflect the user's lifestyle and environmental information. For example, information about the user's diet and exercise habits can be reflected in the consultation. Information about the user's living and working environment can also be reflected in the consultation. Furthermore, information about the user's stress level and sleep patterns can be reflected in the consultation. This makes it possible to perform consultations that take into account the user's lifestyle and environmental information. Some or all of the above processing in the consultation unit may be performed using AI or not. For example, the consultation unit can input the user's lifestyle data into a generating AI and have the generating AI perform the task of reflecting it in the consultation.

[0070] A smartphone application system using an AI agent can optimize the contents of a medical record by referring to the user's past medical data. For example, it can prioritize the registration of relevant information based on the user's past medical records. It can also register necessary additional information considering the user's past treatment history. Furthermore, it can efficiently register information by referring to the user's past medical history and omitting duplicate information. This enables efficient medical record registration based on the user's past medical data. Some or all of the above processing in the medical record registration unit may be performed using AI or not. For example, the medical record registration unit can input the user's past medical data into a generating AI and have the generating AI perform the optimization of the medical record contents.

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

[0072] Step 1: The reception desk conducts an initial consultation. The reception desk verifies the user's basic information and symptoms and creates a medical record. Furthermore, they can ask about the user's past medical history and lifestyle habits to gather detailed information. Using AI, they can also estimate the user's emotions and adjust the consultation questions based on those emotions. Step 2: The registration department registers the medical records created by the reception department into the database. The contents of the medical records are saved as digital data using the electronic medical record system. AI can also be used to optimize the contents of the medical records. Step 3: The handover unit hands over the medical records registered by the registration unit to the AI ​​agent. The contents of the medical records are transferred to the AI ​​agent and used as data for the AI ​​agent to learn about the user. The method of handing over medical records can also be optimized using AI. Step 4: The learning unit learns from medical records and similar cases that were handed over by the handover unit. Using AI, it analyzes a large amount of medical data and learns examination methods suitable for the user. It can also estimate the user's emotions and select training data based on those estimated emotions. Step 5: The consultation unit consults the user based on the information learned by the learning unit. Using an AI agent, it checks the user's symptoms and provides appropriate medical advice. It can also estimate the user's emotions and adjust the consultation method based on those emotions. Step 6: The service provider provides health management and medical advice to users who have been referred by the consultation service provider. Using AI, the service provider monitors the user's health status and provides appropriate advice. It can also estimate the user's emotions and adjust the way the advice is expressed based on those emotions. Step 7: The dialogue unit interacts with the user based on the advice provided by the service unit. Using AI, it builds trust with the user through the dialogue. It can also estimate the user's emotions and adjust the dialogue method based on the estimated emotions.

[0073] (Example of form 2) The smartphone application system using an AI agent according to an embodiment of the present invention is a system aimed at addressing the shortage of medical resources and reducing the burden on medical professionals in a super-aging society. In this system, the actual primary care physician conducts an interview during the initial consultation and creates a patient's medical record. This medical record is registered in a database and handed over to the AI ​​agent. The AI ​​agent learns from the registered medical record and similar cases, enabling personalized consultations. Users can consult with the AI ​​agent through the smartphone app and receive health management and medical advice 24 hours a day. Furthermore, the AI ​​agent distributes resources by acting as a substitute for the primary care physician, handling elderly patients and non-urgent matters, while actual doctors handle emergencies. The AI ​​agent also acts as a conversational partner, alleviating the user's loneliness. This mechanism helps to reduce rising medical costs, doctor shortages, and the burden on medical professionals, and can prevent hospital waiting rooms from becoming mere social spaces. By creating a database of users' hobbies and preferences and providing personalized service, it can offer a source of emotional support. For example, an AI agent can provide appropriate topics of conversation based on the user's hobbies and interests, and build trust through dialogue with the user. Furthermore, the AI ​​agent can constantly monitor the user's health and respond quickly if any abnormalities are detected. This allows users to live their daily lives with peace of mind. In addition, the AI ​​agent can provide advice that takes into account the user's lifestyle and environmental information. For example, it can provide specific advice for a healthy lifestyle based on the user's diet and exercise habits. The AI ​​agent can also monitor the user's stress levels and sleep patterns, and provide advice on relaxation methods and sleep improvement as needed. This allows for comprehensive support of the user's health. As a result, smartphone application systems using AI agents can address the shortage of medical resources and alleviate the burden on healthcare professionals, while providing user-centric support.

[0074] The smartphone application system using an AI agent according to this embodiment comprises a reception unit, a registration unit, a handover unit, a learning unit, a consultation unit, a provision unit, and a dialogue unit. The reception unit conducts an initial medical interview. The reception unit, for example, confirms the user's basic information and symptoms and creates a medical record. The reception unit can also ask questions about the user's past medical history and lifestyle habits to collect detailed information. The reception unit can also use AI to estimate the user's emotions and adjust the content of the medical interview questions based on the estimated emotions. For example, if the user is feeling anxious, the reception unit can ask questions in a gentle tone to provide reassurance. Also, if the user is relaxed, the reception unit can ask detailed questions to collect deeper information. Furthermore, if the user is in a hurry, the reception unit can ask concise and quick questions to save time. The registration unit registers the medical record created by the reception unit into a database. The registration unit saves the contents of the medical record as digital data, for example, using an electronic medical record system. The registration unit can also use AI to optimize the contents of the medical record. For example, the registration unit can refer to the user's past medical data and prioritize the registration of relevant information. The registration unit can also register medical records while reflecting the user's lifestyle and environmental information. The handover unit hands over the medical records registered by the registration unit to an AI agent. For example, the handover unit transfers the contents of the medical records to the AI ​​agent, which then uses this data to learn about the user. The handover unit can also use AI to optimize the method of handing over medical records. For example, the handover unit can estimate the user's emotions and adjust the method of handing over the medical records based on the estimated emotions. The learning unit learns cases similar to the medical records handed over by the handover unit. For example, the learning unit uses AI to analyze large amounts of medical data and learn appropriate examination methods for the user. The learning unit can also estimate the user's emotions and select learning data based on the estimated emotions. The consultation unit visits the user based on the information learned by the learning unit. For example, the consultation unit uses an AI agent to confirm the user's symptoms and provide appropriate medical advice.The consultation unit can estimate the user's emotions and adjust the consultation method based on the estimated emotions. The provision unit provides health management and medical advice to users who have been consulted by the consultation unit. The provision unit can, for example, use AI to monitor the user's health status and provide appropriate advice. The provision unit can also estimate the user's emotions and adjust the way the advice is expressed based on the estimated emotions. The dialogue unit interacts with the user based on the advice provided by the provision unit. The dialogue unit can, for example, use AI to build a relationship of trust with the user through dialogue. The dialogue unit can also estimate the user's emotions and adjust the method of dialogue based on the estimated emotions. As a result, the smartphone application system using the AI ​​agent according to this embodiment can address the shortage of medical resources and reduce the burden on medical professionals, while realizing a user-centric approach.

[0075] The reception desk conducts the initial consultation. For example, the reception desk verifies the user's basic information and symptoms and creates a medical record. Specifically, it inputs basic information such as the user's name, age, gender, and contact information, and records details such as the current symptoms, their history, onset date, and changes in symptoms. Furthermore, the reception desk can gather detailed information by asking questions about the user's past medical history and lifestyle. For example, it can inquire about past illnesses and treatments, current medications, allergies, smoking and drinking habits, exercise habits, and diet. This allows the reception desk to understand the user's overall health status and collect basic data for appropriate examination and treatment. The reception desk can also use AI to estimate the user's emotions and adjust the consultation questions based on the estimated emotions. For example, the AI ​​analyzes the user's emotions from their facial expressions, tone of voice, and word choice to estimate states such as anxiety, tension, and relaxation. If the user is feeling anxious, the AI ​​will ask reassuring questions in a gentle tone; if the user is relaxed, it will ask detailed questions to gather deeper information. Furthermore, if the user is in a hurry, the AI ​​can ask concise and quick questions, saving time. This allows the reception staff to respond flexibly to the user's emotions, resulting in a less stressful consultation experience for the user.

[0076] The registration department registers medical records created by the reception department into a database. The registration department uses, for example, an electronic medical record system to save the contents of the medical records as digital data. Specifically, it inputs data such as the user's basic information, symptoms, medical history, and lifestyle collected by the reception department into the electronic medical record system and saves it to the database. This allows for centralized management of user information and quick access when needed. The registration department can also use AI to optimize the content of medical records. For example, AI can refer to the user's past medical data and prioritize the registration of relevant information. This ensures that important information is not overlooked and allows for efficient medical record creation. Furthermore, the registration department can incorporate the user's lifestyle and environmental information into the medical records. For example, if a user lives in a specific region, the medical record can be created considering the environmental factors of that region (e.g., climate and air pollution). This allows for a comprehensive understanding of factors that may affect the user's health. In addition, the registration department can use AI to verify and correct data to maintain data integrity and consistency. This allows the registration department to create accurate and reliable medical records, providing a foundation for healthcare professionals to provide appropriate examinations and treatments.

[0077] The handover unit transfers medical records registered by the registration unit to the AI ​​agent. For example, the handover unit transfers the contents of the medical record to the AI ​​agent, which then uses this data to learn about the user. Specifically, it extracts the necessary data from the electronic medical record system and provides it to the AI ​​agent in an appropriate format. This allows the AI ​​agent to learn about the user's health status and risk factors based on data such as the user's basic information, symptoms, medical history, and lifestyle. The handover unit can also use AI to optimize the method of handing over medical records. For example, the AI ​​can estimate the user's emotions and adjust the method of handing over the medical record based on the estimated emotions. If the user is feeling anxious, the AI ​​can hand over the medical record in a way that provides reassurance; if the user is relaxed, it can hand over a medical record containing detailed information. Also, if the user is in a hurry, the AI ​​can hand over the medical record in a concise and quick way, saving time. This allows the handover unit to respond flexibly to the user's emotions, resulting in a less stressful handover for the user. Furthermore, the data transfer unit can use AI to verify and correct data in order to maintain data integrity and consistency. This allows the data transfer unit to provide accurate and reliable data to the AI ​​agent, creating a foundation for the AI ​​agent to perform appropriate diagnoses and treatments.

[0078] The learning unit learns from medical records and similar cases handed over by the handover unit. For example, the learning unit uses AI to analyze large amounts of medical data and learn appropriate examination methods for the user. Specifically, the AI ​​searches for cases similar to the user's symptoms and medical history based on past case data, and analyzes the examination methods and treatment results for those cases. This allows the AI ​​to propose the most suitable examination method and treatment plan for the user. The learning unit can also estimate the user's emotions and select learning data based on those estimated emotions. For example, if the user is feeling anxious, the AI ​​prioritizes learning examination methods and treatment plans that provide reassurance; if the user is relaxed, it can learn examination methods and treatment plans that include detailed information. Furthermore, if the user is in a hurry, the AI ​​can learn concise and rapid examination methods and treatment plans, saving time. This allows the learning unit to respond flexibly to the user's emotions, resulting in less stressful consultations for the user. In addition, the learning unit can use AI to verify and correct data to maintain data integrity and consistency. This allows the learning unit to learn based on accurate and reliable data, providing a foundation for AI agents to perform appropriate diagnoses and treatments.

[0079] The consultation unit provides consultations to users based on information learned by the learning unit. For example, the consultation unit uses an AI agent to confirm the user's symptoms and provide appropriate medical advice. Specifically, the AI ​​agent evaluates the user's health status based on data such as symptoms, medical history, and lifestyle, and proposes appropriate examination methods and treatment plans. The consultation unit can also estimate the user's emotions and adjust the consultation method based on those emotions. For example, if the user is feeling anxious, the AI ​​agent can conduct the consultation in a reassuring way; if the user is relaxed, it can conduct a consultation that includes detailed information. Furthermore, if the user is in a hurry, the AI ​​agent can conduct the consultation in a concise and rapid manner, saving time. This allows the consultation unit to respond flexibly to the user's emotions, resulting in a less stressful consultation experience. In addition, the consultation unit can use AI to verify and correct data to maintain data integrity and consistency. This allows the consultation unit to conduct consultations based on accurate and reliable data, providing a foundation for the AI ​​agent to perform appropriate examinations and treatments.

[0080] The service provider provides health management and medical advice to users who have received consultations from the consultation service provider. For example, the service provider uses AI to monitor the user's health status and provide appropriate advice. Specifically, the AI ​​continuously collects the user's health data and monitors changes in their health status in real time. This allows for a quick response if an abnormality occurs in the user's health status. The service provider can also estimate the user's emotions and adjust the way advice is expressed based on those emotions. For example, if the user is feeling anxious, the AI ​​can provide advice in a reassuring way, and if the user is relaxed, it can provide advice that includes detailed information. Also, if the user is in a hurry, the AI ​​can provide advice in a concise and quick way, saving time. This allows the service provider to respond flexibly to the user's emotions and provide advice that is less stressful for the user. Furthermore, the service provider can use AI to verify and correct data to maintain data integrity and consistency. This allows the service provider to provide advice based on accurate and reliable data and support the user's health management.

[0081] The dialogue unit interacts with the user based on advice provided by the service provider. The dialogue unit, for example, uses AI to build trust with the user through dialogue. Specifically, the AI ​​provides appropriate answers to the user's questions and concerns, alleviating the user's anxieties and doubts. The dialogue unit can also estimate the user's emotions and adjust the dialogue method based on the estimated emotions. For example, if the user is feeling anxious, the AI ​​will engage in dialogue in a reassuring way, and if the user is relaxed, it can engage in dialogue that includes detailed information. Also, if the user is in a hurry, the AI ​​can engage in dialogue in a concise and quick manner, saving time. This allows the dialogue unit to respond flexibly to the user's emotions, resulting in a less stressful dialogue for the user. Furthermore, the dialogue unit can use AI to verify and correct data to maintain data integrity and consistency. This allows the dialogue unit to engage in dialogue based on accurate and reliable data, building trust with the user. As a result, the smartphone application system using the AI ​​agent according to this embodiment can address the shortage of medical resources and alleviate the burden on medical professionals, while providing user-centric support.

[0082] The reception desk can estimate the user's emotions and adjust the questions asked during the consultation based on the estimated emotions. For example, if the user is feeling anxious, the reception desk can ask reassuring questions in a gentle tone. If the user is relaxed, the reception desk can ask detailed questions to gather deeper information. If the user is in a hurry, the reception desk can ask concise and quick questions to save time. This enables an appropriate consultation tailored to the user'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 can input the user's facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0083] The reception desk can adjust the level of detail in the initial consultation by referring to the user's past medical history. For example, the reception desk can refer to the user's past medical history and focus on relevant questions. For example, the reception desk can conduct the consultation efficiently by omitting known information based on the user's past medical records. For example, the reception desk can consider the user's past treatment history and collect any necessary additional information. This enables an efficient consultation based on the user's past medical history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past medical data into a generating AI and have the generating AI adjust the level of detail in the consultation.

[0084] The reception desk can collect information about the user's lifestyle and environment during their initial consultation and reflect it in their medical record. For example, the reception desk can ask questions about the user's diet and exercise habits and record them in the medical record. For example, the reception desk can collect information about the user's living and working environment and assess their health risks. For example, the reception desk can ask questions about the user's stress level and sleep patterns and reflect them in the medical record. This makes it possible to create a medical record that takes into account the user's lifestyle and environment information. Some or all of the above processing at the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's lifestyle data into a generating AI and have the generating AI perform the task of reflecting it in the medical record.

[0085] The reception desk can estimate the user's emotions and adjust the order of the consultation based on the estimated emotions. For example, if the user is nervous, the reception desk can start with simple questions to help them relax. If the user is relaxed, the reception desk can ask important questions first to efficiently gather information. If the user is in a hurry, the reception desk can prioritize important questions to save time. This enables an appropriate order of consultations according to the user'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 user's facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0086] The reception desk can suggest the most suitable medical institution at the time of the user's first visit, taking into account the user's geographical location. For example, the reception desk can suggest the nearest medical institution based on the user's current location. For example, the reception desk can prioritize suggesting medical institutions close to the user's residence or workplace. For example, the reception desk can suggest easily accessible medical institutions, taking into account the user's means of transportation and traffic conditions. This makes it possible to suggest the most suitable medical institution based on the user's geographical location. 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 user's geographical location data into a generating AI and have the generating AI suggest the most suitable medical institution.

[0087] The reception desk can analyze a user's social media activity during their initial consultation and collect relevant health information. For example, the reception desk can extract health-related interests and concerns from the user's social media posts. For example, the reception desk can assess a user's lifestyle and stress levels from their online activities. For example, the reception desk can understand the user's social support situation from their social media friendships and communities. This makes it possible to collect health information based on the user's social media activity. 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 user's social media data into a generating AI and have the generating AI collect health information.

[0088] The registration unit can estimate the user's emotions and adjust the content of the medical record based on the estimated emotions. For example, if the user is feeling anxious, the registration unit can provide reassuring information. For example, if the user is relaxed, the registration unit can provide detailed information. For example, if the user is in a hurry, the registration unit can provide concise and to-the-point information. This enables appropriate medical record entries according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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 registration unit may be performed using AI, or not using AI. For example, the registration unit can input the user's facial expression data into the generative AI and have the generative AI adjust the content of the medical record.

[0089] The registration unit can optimize registration content by referring to the user's past medical data when registering a medical record. For example, the registration unit can prioritize the registration of relevant information based on the user's past medical records. For example, the registration unit can consider the user's past treatment history and register any necessary additional information. For example, the registration unit can refer to the user's past medical history and efficiently register information by omitting duplicate information. This enables efficient medical record registration based on the user's past medical data. Some or all of the above processes in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input the user's past medical data into a generating AI and have the generating AI perform the optimization of the registered medical record content.

[0090] The registration unit can reflect the user's lifestyle and environmental information when registering a medical record. For example, the registration unit can record information about the user's diet and exercise habits in the medical record. For example, the registration unit can reflect information about the user's living environment and work environment in the medical record. For example, the registration unit can record information about the user's stress level and sleep patterns in the medical record. This makes it possible to register a medical record that takes into account the user's lifestyle and environmental information. Some or all of the above processing in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input the user's lifestyle data into a generating AI and have the generating AI perform the task of reflecting it in the medical record.

[0091] The registration unit can estimate the user's emotions and determine the priority of medical records based on the estimated emotions. For example, if the user is feeling anxious, the registration unit can prioritize registering the medical record and provide a quick response. For example, if the user is relaxed, the registration unit can register the medical record with the normal priority. For example, if the user is in a hurry, the registration unit can quickly register the medical record and provide an urgent response. This makes it possible to determine appropriate medical record priorities according to the user'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 registration unit may be performed using AI, or not using AI. For example, the registration unit can input user facial expression data into the generative AI and have the generative AI determine the priority of medical records.

[0092] The registration unit can prioritize the registration of relevant medical data when registering a medical record, taking into account the user's geographical location information. For example, the registration unit can prioritize the registration of medical data related to the user's place of residence or workplace. For example, the registration unit can prioritize the registration of data for easily accessible medical institutions, taking into account the user's means of transportation and traffic conditions. For example, the registration unit can register data on region-specific health risks based on the user's geographical location information. This enables the registration of optimal medical data based on the user's geographical location information. Some or all of the above processing in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input the user's geographical location data into a generating AI and have the generating AI perform the priority registration of medical data.

[0093] The registration unit can analyze a user's social media activity when registering a medical record and register relevant health information. For example, the registration unit can extract health-related concerns and worries from a user's social media posts and record them in the medical record. For example, the registration unit can evaluate a user's lifestyle and stress levels from their online activities and reflect this in the medical record. For example, the registration unit can understand the status of social support from a user's social media friendships and communities and record this in the medical record. This makes it possible to register health information based on the user's social media activity. Some or all of the above processing in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input the user's social media data into a generating AI and have the generating AI perform the registration of health information.

[0094] The handover unit can estimate the user's emotions and adjust the method of handing over the medical record based on the estimated emotions. For example, if the user is feeling anxious, the handover unit can carefully hand over the medical record in a way that provides reassurance. For example, if the user is relaxed, the handover unit can efficiently hand over the medical record. For example, if the user is in a hurry, the handover unit can quickly hand over the medical record and take immediate action. This enables appropriate medical record handover according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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-described processes in the handover unit may be performed using AI, for example, or without AI. For example, the handover unit can input the user's facial expression data into the generative AI and have the generative AI adjust the method of handing over the medical record.

[0095] The handover unit can optimize the handover content by referring to the user's past medical data when handing over medical records. For example, the handover unit can prioritize the handover of relevant information based on the user's past medical records. For example, the handover unit can consider the user's past treatment history and hand over any necessary additional information. For example, the handover unit can refer to the user's past medical history and efficiently hand over information by omitting duplicate information. This enables efficient medical record handover based on the user's past medical data. Some or all of the above processing in the handover unit may be performed using AI, for example, or without using AI. For example, the handover unit can input the user's past medical data into a generating AI and have the generating AI perform the optimization of the handover content of the medical record.

[0096] The handover unit can reflect the user's lifestyle and environmental information when handing over medical records. For example, the handover unit can record information about the user's diet and exercise habits in the medical record. For example, the handover unit can reflect information about the user's living environment and work environment in the medical record. For example, the handover unit can record information about the user's stress level and sleep patterns in the medical record. This makes it possible to hand over medical records while taking into account the user's lifestyle and environmental information. Some or all of the above processing in the handover unit may be performed using AI, for example, or without using AI. For example, the handover unit can input the user's lifestyle data into a generating AI and have the generating AI perform the reflection of this data in the medical record.

[0097] The handover unit can estimate the user's emotions and adjust the order in which medical records are handed over based on the estimated emotions. For example, if the user is feeling anxious, the handover unit may hand over important information first to provide reassurance. For example, if the user is relaxed, the handover unit may efficiently hand over information. For example, if the user is in a hurry, the handover unit may quickly hand over important information and take immediate action. This enables an appropriate order for handing over medical records according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 handover unit may be performed using AI, for example, or without AI. For example, the handover unit may input the user's facial expression data into the generative AI and have the generative AI adjust the order in which medical records are handed over.

[0098] The data transfer unit can prioritize the transfer of relevant medical data when transferring medical records, taking into account the user's geographical location information. For example, the data transfer unit can prioritize the transfer of medical data related to the user's place of residence or workplace. For example, the data transfer unit can prioritize the transfer of data from easily accessible medical institutions, taking into account the user's means of transportation and traffic conditions. For example, the data transfer unit can transfer data related to region-specific health risks based on the user's geographical location information. This enables the transfer of optimal medical data based on the user's geographical location information. Some or all of the above processing in the data transfer unit may be performed using AI, for example, or without AI. For example, the data transfer unit can input the user's geographical location data into a generating AI and have the generating AI perform the preferential transfer of medical data.

[0099] The handover unit can analyze the user's social media activity during the handover of medical records and transfer relevant health information. For example, the handover unit can extract health-related interests and concerns from the user's social media posts and record them in the medical record. For example, the handover unit can evaluate the user's lifestyle and stress levels from their online activities and reflect them in the medical record. For example, the handover unit can understand the status of social support from the user's social media friendships and communities and record it in the medical record. This makes it possible to transfer health information based on the user's social media activity. Some or all of the above processing in the handover unit may be performed using AI, for example, or without AI. For example, the handover unit can input the user's social media data into a generating AI and have the generating AI perform the transfer of health information.

[0100] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is feeling anxious, the learning unit will prioritize learning data that provides a sense of security. For example, if the user is relaxed, the learning unit can learn detailed data to aim for a deeper understanding. For example, if the user is in a hurry, the learning unit can learn concise and to-the-point data. This makes it possible to select appropriate training data according to the user'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 learning unit may be performed using AI, for example, or not using AI. For example, the learning unit can input user facial expression data into a generative AI and have the generative AI perform the selection of training data.

[0101] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal algorithm based on past learning data and perform learning. For example, the learning unit can analyze past learning results and adjust the algorithm parameters. For example, the learning unit can extract effective learning methods from past learning data and optimize the learning algorithm. This enables efficient optimization of the learning algorithm based on past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0102] The learning unit can incorporate the user's lifestyle and environmental information during the learning process. For example, the learning unit can incorporate information about the user's eating habits and exercise habits into the learning data. For example, the learning unit can incorporate information about the user's living environment and work environment into the learning data. For example, the learning unit can incorporate information about the user's stress level and sleep patterns into the learning data. This enables learning that takes into account the user's lifestyle and environmental information. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's lifestyle data into a generating AI and have the generating AI perform the process of incorporating it into the learning data.

[0103] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, if the user is feeling anxious, the learning unit can learn frequently to provide reassurance. If the user is relaxed, the learning unit can learn at a normal frequency. If the user is in a hurry, the learning unit can learn quickly to provide an immediate response. This allows for appropriate adjustment of the learning frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 learning unit may be performed using AI, or not using AI. For example, the learning unit can input user facial expression data into the generative AI and have the generative AI adjust the learning frequency.

[0104] The learning unit can weight the learning data based on the registration date of the medical record during the learning process. For example, the learning unit can prioritize learning and weighting data from recently registered medical records. For example, the learning unit can refer to past medical record data and perform appropriate weighting. For example, the learning unit can adjust the importance of the learning data based on the registration date of the medical record. This enables appropriate weighting of the learning data based on the registration date of the medical record. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input medical record registration date data into a generating AI and have the generating AI perform the weighting of the learning data.

[0105] The learning unit can analyze the user's social media activity during learning and learn relevant health information. For example, the learning unit can extract health-related concerns and worries from the user's social media posts and reflect them in the learning data. For example, the learning unit can evaluate the user's lifestyle and stress levels from their online activities and reflect them in the learning data. For example, the learning unit can understand the status of social support from the user's social media friendships and communities and reflect it in the learning data. This makes it possible to learn health information based on the user's social media activity. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's social media data into a generating AI and have the generating AI perform the learning of health information.

[0106] The consultation unit can estimate the user's emotions and adjust the consultation method based on the estimated emotions. For example, if the user is feeling anxious, the consultation unit will conduct the consultation carefully in a way that provides reassurance. For example, if the user is relaxed, the consultation unit can conduct the consultation efficiently. For example, if the user is in a hurry, the consultation unit can conduct the consultation quickly and provide prompt assistance. This makes it possible to adjust the consultation method appropriately according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 consultation unit may be performed using AI, for example, or without AI. For example, the consultation unit can input the user's facial expression data into the generative AI and have the generative AI perform the adjustment of the consultation method.

[0107] The consultation unit can optimize the consultation content by referring to the user's past medical data at the time of the consultation. For example, the consultation unit can prioritize relevant information based on the user's past medical records. For example, the consultation unit can collect necessary additional information by considering the user's past treatment history. For example, the consultation unit can efficiently conduct a consultation by referring to the user's past medical history and omitting redundant information. This enables efficient consultations based on the user's past medical data. Some or all of the above processing in the consultation unit may be performed using AI, for example, or without AI. For example, the consultation unit can input the user's past medical data into a generating AI and have the generating AI perform the optimization of the consultation content.

[0108] The consultation unit can reflect the user's lifestyle and environmental information during the consultation. For example, the consultation unit can reflect information about the user's diet and exercise habits in the consultation content. For example, the consultation unit can reflect information about the user's living environment and work environment in the consultation content. For example, the consultation unit can reflect information about the user's stress level and sleep patterns in the consultation content. This makes it possible to conduct consultations that take into account the user's lifestyle and environmental information. Some or all of the above processing in the consultation unit may be performed using AI, for example, or without using AI. For example, the consultation unit can input the user's lifestyle data into a generating AI and have the generating AI perform the task of reflecting it in the consultation content.

[0109] The receiving unit can estimate the user's emotions and adjust the order of information delivery based on the estimated emotions. For example, if the user is feeling anxious, the receiving unit will deliver important information first to provide reassurance. For example, if the user is relaxed, the receiving unit can deliver information efficiently. For example, if the user is in a hurry, the receiving unit can quickly deliver important information and take immediate action. This makes it possible to adjust the order of information delivery appropriately according to the user'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 receiving unit may be performed using AI, for example, or without AI. For example, the receiving unit can input the user's facial expression data into the generative AI and have the generative AI perform the adjustment of the order of information delivery.

[0110] The medical consultation unit can suggest the most suitable medical institution by considering the user's geographical location information at the time of consultation. For example, the medical consultation unit can suggest the nearest medical institution based on the user's current location. For example, the medical consultation unit can prioritize suggesting medical institutions close to the user's residence or workplace. For example, the medical consultation unit can suggest easily accessible medical institutions by considering the user's means of transportation and traffic conditions. This makes it possible to suggest the most suitable medical institution based on the user's geographical location information. Some or all of the above processing in the medical consultation unit may be performed using AI, for example, or without AI. For example, the medical consultation unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of suggesting the most suitable medical institution.

[0111] The consultation unit can analyze the user's social media activity during a consultation and collect relevant health information. For example, the consultation unit can extract health-related concerns and worries from the user's social media posts. For example, the consultation unit can assess the user's lifestyle and stress levels from their online activities. For example, the consultation unit can understand the user's social support situation from their social media friendships and communities. This makes it possible to collect health information based on the user's social media activity. Some or all of the above processing in the consultation unit may be performed using AI, for example, or without AI. For example, the consultation unit can input the user's social media data into a generating AI and have the generating AI collect health information.

[0112] The service provider can estimate the user's emotions and adjust the way advice is expressed based on the estimated emotions. For example, if the user is feeling anxious, the service provider can give advice in a gentle tone to provide reassurance. For example, if the user is relaxed, the service provider can give detailed advice to promote deeper understanding. For example, if the user is in a hurry, the service provider can give concise and to-the-point advice. This enables the expression of advice to be appropriate to the user'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 service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user facial expression data into the generative AI and have the generative AI adjust the way advice is expressed.

[0113] The service provider can optimize the advice content by referring to the user's past medical data when providing advice. For example, the service provider can prioritize relevant information when providing advice based on the user's past medical records. For example, the service provider can consider the user's past treatment history and provide necessary additional information. For example, the service provider can refer to the user's past medical history and provide advice efficiently by omitting redundant information. This enables efficient advice based on the user's past medical data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past medical data into a generating AI and have the generating AI perform the optimization of the advice content.

[0114] The service provider can incorporate the user's lifestyle and environmental information when providing advice. For example, the service provider can incorporate information about the user's diet and exercise habits into the advice. For example, the service provider can incorporate information about the user's living environment and work environment into the advice. For example, the service provider can incorporate information about the user's stress level and sleep patterns into the advice. This makes it possible to provide advice that takes into account the user's lifestyle and environmental information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's lifestyle data into a generating AI and have the generating AI perform the task of incorporating it into the advice.

[0115] The service provider can estimate the user's emotions and determine the priority of advice based on the estimated emotions. For example, if the user is feeling anxious, the service provider will prioritize providing reassuring advice. For example, if the user is relaxed, the service provider can provide advice with normal priority. For example, if the user is in a hurry, the service provider can provide important advice quickly and take immediate action. This makes it possible to determine the appropriate priority of advice according to the user'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 service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user facial expression data into the generative AI and have the generative AI determine the priority of advice.

[0116] The service provider can prioritize providing relevant advice by considering the user's geographical location when providing advice. For example, the service provider can prioritize providing advice related to the user's place of residence or workplace. For example, the service provider can prioritize providing advice on easily accessible medical facilities by considering the user's means of transportation and traffic conditions. For example, the service provider can provide advice on region-specific health risks based on the user's geographical location. This makes it possible to provide optimal advice based on the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location data into a generating AI and have the generating AI perform the priority provision of advice.

[0117] The service provider can analyze the user's social media activity when providing advice and provide relevant health information. For example, the service provider can extract health-related concerns and worries from the user's social media posts and reflect them in the advice. For example, the service provider can evaluate the user's lifestyle and stress levels from their online activities and reflect them in the advice. For example, the service provider can understand the user's social support situation from their social media friendships and communities and reflect it in the advice. This makes it possible to provide health information based on the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media data into a generating AI and have the generating AI provide health information.

[0118] The dialogue unit can estimate the user's emotions and adjust the dialogue method based on the estimated emotions. For example, if the user is feeling anxious, the dialogue unit will use a gentle tone to provide reassurance. If the user is relaxed, the dialogue unit can provide detailed information and engage in a deeper conversation. If the user is in a hurry, the dialogue unit can engage in a concise and to-the-point conversation. This allows for the adjustment of the dialogue method to be appropriate according to the user'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 dialogue unit may be performed using AI, or not using AI. For example, the dialogue unit can input user facial expression data into the generative AI and have the generative AI adjust the dialogue method.

[0119] The dialogue unit can optimize the dialogue content by referring to the user's past medical data during the dialogue. For example, the dialogue unit can prioritize relevant information based on the user's past medical records. For example, the dialogue unit can consider the user's past treatment history and provide necessary additional information. For example, the dialogue unit can refer to the user's past medical history and conduct the dialogue efficiently by omitting redundant information. This enables efficient dialogue based on the user's past medical data. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's past medical data into a generating AI and have the generating AI perform the optimization of the dialogue content.

[0120] The dialogue unit can reflect the user's lifestyle and environmental information during the conversation. For example, the dialogue unit can reflect information about the user's eating habits and exercise habits in the conversation. For example, the dialogue unit can reflect information about the user's living environment and work environment in the conversation. For example, the dialogue unit can reflect information about the user's stress level and sleep patterns in the conversation. This makes it possible to have a conversation that takes into account the user's lifestyle and environmental information. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's lifestyle data into a generating AI and have the generating AI perform the task of reflecting it in the conversation.

[0121] The dialogue unit can estimate the user's emotions and adjust the order of the dialogue based on the estimated emotions. For example, if the user is feeling anxious, the dialogue unit will deliver important information first to provide reassurance. If the user is relaxed, the dialogue unit can deliver information efficiently. If the user is in a hurry, the dialogue unit can quickly deliver important information and provide prompt assistance. This makes it possible to adjust the order of the dialogue appropriately according to the user'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 dialogue unit may be performed using AI, or not using AI. For example, the dialogue unit can input user facial expression data into the generative AI and have the generative AI adjust the order of the dialogue.

[0122] The dialogue unit can provide optimal dialogue content by considering the user's geographical location information during the conversation. For example, the dialogue unit can prioritize information related to the user's place of residence or workplace. For example, the dialogue unit can prioritize information about easily accessible medical facilities by considering the user's mode of transportation and traffic conditions. For example, the dialogue unit can provide information about region-specific health risks based on the user's geographical location information. This makes it possible to provide optimal dialogue content based on the user's geographical location information. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's geographical location data into a generating AI and have the generating AI provide the dialogue content.

[0123] The dialogue unit can analyze the user's social media activity during a conversation and reflect relevant health information in the dialogue. For example, the dialogue unit can extract health-related concerns and worries from the user's social media posts and reflect them in the dialogue. For example, the dialogue unit can evaluate the user's lifestyle and stress levels from their online activities and reflect them in the dialogue. For example, the dialogue unit can understand the user's social support situation from their social media friendships and communities and reflect it in the dialogue. This enables a health information dialogue based on the user's social media activity. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's social media data into a generating AI and have the generating AI execute a health information dialogue.

[0124] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0125] A smartphone application system using an AI agent can estimate the user's emotions and adjust the order of the consultation based on the estimated emotions. For example, if the user is feeling anxious, the system can start with simple questions to provide reassurance. If the user is relaxed, more detailed questions can be asked first to gather deeper information. Furthermore, if the user is in a hurry, important questions can be prioritized to save time. This enables an appropriate consultation order tailored to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above-described processes in the consultation unit may be performed using AI or not. For example, the consultation unit can input the user's facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0126] A smartphone application system using an AI agent can suggest the most suitable medical institution considering the user's geographical location. For example, it can suggest the nearest medical institution based on the user's current location. It can also prioritize suggesting medical institutions close to the user's residence or workplace. Furthermore, it can suggest easily accessible medical institutions considering the user's mode of transportation and traffic conditions. This makes it possible to suggest the most suitable medical institution based on the user's geographical location. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of suggesting the most suitable medical institution.

[0127] A smartphone application system using an AI agent can analyze a user's social media activity and collect relevant health information. For example, it can extract health-related interests and concerns from a user's social media posts. It can also assess lifestyle habits and stress levels from a user's online activity. Furthermore, it can understand the user's social support situation from their social media friendships and communities. This enables the collection of health information based on the user's social media activity. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's social media data into a generating AI and have the generating AI collect health information.

[0128] A smartphone application system using an AI agent can estimate the user's emotions and adjust the way advice is expressed based on those emotions. For example, if the user is feeling anxious, the system can provide advice in a gentle tone to reassure them. If the user is relaxed, the system can provide detailed advice to promote deeper understanding. Furthermore, if the user is in a hurry, the system can provide concise and to-the-point advice. This enables the expression of advice to be appropriate to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may be, but is not limited to, text-generating AI or multimodal-generating AI. Some or all of the above-described processing in the advice unit may be performed using AI or not. For example, the advice unit can input the user's facial expression data into the generative AI and have the generative AI adjust the way the advice is expressed.

[0129] A smartphone application system using an AI agent can optimize the content of a medical examination by referring to the user's past medical data. For example, it can prioritize relevant information based on the user's past medical records during the examination. It can also collect necessary additional information considering the user's past treatment history. Furthermore, it can efficiently conduct the examination by referring to the user's past medical history and omitting redundant information. This enables efficient examinations based on the user's past medical data. Some or all of the above processing in the examination unit may be performed using AI or not. For example, the examination unit can input the user's past medical data into a generating AI and have the generating AI perform the optimization of the examination content.

[0130] A smartphone application system using an AI agent can estimate the user's emotions and adjust the conversation method based on the estimated emotions. For example, if the user is feeling anxious, the system can speak in a gentle tone to provide reassurance. If the user is relaxed, the system can provide detailed information and engage in a deeper conversation. Furthermore, if the user is in a hurry, the system can engage in a concise and to-the-point conversation. This allows for the adjustment of the conversation method to suit the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may be, but is not limited to, text-generating AI or multimodal-generating AI. Some or all of the above processing in the conversation unit may be performed using AI or not. For example, the conversation unit can input the user's facial expression data into the generative AI and have the generative AI adjust the conversation method.

[0131] A smartphone application system using an AI agent can perform medical consultations that reflect the user's lifestyle and environmental information. For example, information about the user's diet and exercise habits can be reflected in the consultation. Information about the user's living and working environment can also be reflected in the consultation. Furthermore, information about the user's stress level and sleep patterns can be reflected in the consultation. This makes it possible to perform consultations that take into account the user's lifestyle and environmental information. Some or all of the above processing in the consultation unit may be performed using AI or not. For example, the consultation unit can input the user's lifestyle data into a generating AI and have the generating AI perform the task of reflecting it in the consultation.

[0132] A smartphone application system using an AI agent can estimate the user's emotions and adjust the content of the medical record based on those emotions. For example, if the user is feeling anxious, the system can provide reassuring information. If the user is relaxed, the system can provide detailed information. Furthermore, if the user is in a hurry, the system can provide concise and to-the-point information. This enables appropriate medical record entries tailored to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may include, but is not limited to, text-generating AI or multimodal-generating AI. Some or all of the above-described processes in the medical record entry section may be performed using AI or not. For example, the medical record entry section can input user facial expression data into the generative AI and have the generative AI adjust the content of the medical record.

[0133] A smartphone application system using an AI agent can optimize the contents of a medical record by referring to the user's past medical data. For example, it can prioritize the registration of relevant information based on the user's past medical records. It can also register necessary additional information considering the user's past treatment history. Furthermore, it can efficiently register information by referring to the user's past medical history and omitting duplicate information. This enables efficient medical record registration based on the user's past medical data. Some or all of the above processing in the medical record registration unit may be performed using AI or not. For example, the medical record registration unit can input the user's past medical data into a generating AI and have the generating AI perform the optimization of the medical record contents.

[0134] A smartphone application system using an AI agent can estimate the user's emotions and determine the priority of medical records based on those emotions. For example, if the user is feeling anxious, the medical record can be registered with priority and a quick response can be provided. If the user is relaxed, the medical record can be registered with the normal priority. Furthermore, if the user is in a hurry, the medical record can be registered quickly and a prompt response can be provided. This makes it possible to determine appropriate medical record priorities according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the medical record priority determination unit may be performed using AI or not. For example, the medical record priority determination unit can input the user's facial expression data into the generative AI and have the generative AI perform the determination of medical record priorities.

[0135] The following briefly describes the processing flow for example form 2.

[0136] Step 1: The reception desk conducts an initial consultation. The reception desk verifies the user's basic information and symptoms and creates a medical record. Furthermore, they can ask about the user's past medical history and lifestyle habits to gather detailed information. Using AI, they can also estimate the user's emotions and adjust the consultation questions based on those emotions. Step 2: The registration department registers the medical records created by the reception department into the database. The contents of the medical records are saved as digital data using the electronic medical record system. AI can also be used to optimize the contents of the medical records. Step 3: The handover unit hands over the medical records registered by the registration unit to the AI ​​agent. The contents of the medical records are transferred to the AI ​​agent and used as data for the AI ​​agent to learn about the user. The method of handing over medical records can also be optimized using AI. Step 4: The learning unit learns from medical records and similar cases that were handed over by the handover unit. Using AI, it analyzes a large amount of medical data and learns examination methods suitable for the user. It can also estimate the user's emotions and select training data based on those estimated emotions. Step 5: The consultation unit consults the user based on the information learned by the learning unit. Using an AI agent, it checks the user's symptoms and provides appropriate medical advice. It can also estimate the user's emotions and adjust the consultation method based on those emotions. Step 6: The service provider provides health management and medical advice to users who have been referred by the consultation service provider. Using AI, the service provider monitors the user's health status and provides appropriate advice. It can also estimate the user's emotions and adjust the way the advice is expressed based on those emotions. Step 7: The dialogue unit interacts with the user based on the advice provided by the service unit. Using AI, it builds trust with the user through the dialogue. It can also estimate the user's emotions and adjust the dialogue method based on the estimated emotions.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the reception unit, registration unit, handover unit, learning unit, consultation unit, provision unit, and dialogue 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, which confirms the user's basic information and symptoms and creates a medical record. The registration unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which stores the contents of the medical record as digital data. The handover unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which transfers the contents of the medical record to the AI ​​agent. The learning unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which learns cases similar to the medical record. The consultation unit is implemented by, for example, the control unit 46A of the smart device 14, which confirms the user's symptoms and provides appropriate medical advice. The provision unit is implemented by, for example, the control unit 46A of the smart device 14, which monitors the user's health status and provides appropriate advice. The dialogue unit is implemented, for example, by the control unit 46A of the smart device 14, and builds a relationship of trust with the user through dialogue. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0141] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the multiple elements described above, including the reception unit, registration unit, handover unit, learning unit, consultation unit, provision unit, and dialogue unit, is implemented by, for example, 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, which confirms the user's basic information and symptoms and creates a medical record. The registration unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which stores the contents of the medical record as digital data. The handover unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which transfers the contents of the medical record to the AI ​​agent. The learning unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which learns cases similar to the medical record. The consultation unit is implemented by, for example, the control unit 46A of the smart glasses 214, which confirms the user's symptoms and provides appropriate medical advice. The provision unit is implemented by, for example, the control unit 46A of the smart glasses 214, which monitors the user's health status and provides appropriate advice. The dialogue unit is implemented, for example, by the control unit 46A of the smart glasses 214, and builds a relationship of trust with the user through dialogue. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0157] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] Each of the multiple elements described above, including the reception unit, registration unit, handover unit, learning unit, consultation unit, provision unit, and dialogue 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, which confirms the user's basic information and symptoms and creates a medical record. The registration unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which stores the contents of the medical record as digital data. The handover unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which transfers the contents of the medical record to the AI ​​agent. The learning unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which learns cases similar to the medical record. The consultation unit is implemented by, for example, the control unit 46A of the headset terminal 314, which confirms the user's symptoms and provides appropriate medical advice. The provision unit is implemented by, for example, the control unit 46A of the headset terminal 314, which monitors the user's health status and provides appropriate advice. The dialogue unit is implemented, for example, by the control unit 46A of the headset-type terminal 314, and builds a relationship of trust with the user through dialogue. The correspondence between each part and the device or control unit is not limited to the example described above and can be modified in various ways.

[0173] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.).

[0186] 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.

[0187] 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.

[0188] 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.

[0189] Each of the multiple elements described above, including the reception unit, registration unit, handover unit, learning unit, consultation unit, provision unit, and dialogue 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, which confirms the user's basic information and symptoms and creates a medical record. The registration unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which stores the contents of the medical record as digital data. The handover unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which transfers the contents of the medical record to the AI ​​agent. The learning unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which learns cases similar to the medical record. The consultation unit is implemented by, for example, the control unit 46A of the robot 414, which confirms the user's symptoms and provides appropriate medical advice. The provision unit is implemented by, for example, the control unit 46A of the robot 414, which monitors the user's health status and provides appropriate advice. The dialogue unit is implemented, for example, by the control unit 46A of the robot 414, and builds a relationship of trust with the user through dialogue. The correspondence between each part and the device or control unit is not limited to the example described above and can be modified in various ways.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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."

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] (Note 1) The reception area where the initial consultation is conducted, A registration unit that registers the medical record created by the reception unit into a database, A transfer unit that transfers the medical records registered by the registration unit to the AI ​​agent, A learning unit that learns cases similar to the medical records taken over by the aforementioned handover unit, A receiving unit that receives a user based on the information learned by the learning unit, A provision unit that provides health management and medical advice to users who have received medical treatment through the aforementioned consultation unit, The system includes a dialogue unit that interacts with the user based on the advice provided by the aforementioned provision unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is The system estimates the user's emotions and adjusts the questions in the questionnaire based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is During the initial consultation, the level of detail in the medical history is adjusted by referring to the user's past medical records. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is During the initial consultation, collect information about the user's lifestyle and environment, and reflect it in their medical record. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is The system estimates the user's emotions and adjusts the order of the questions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is During the initial consultation, we will suggest the most suitable medical institution, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is During the initial consultation, we analyze the user's social media activity and collect relevant health information. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned registration unit is The system estimates the user's emotions and adjusts the content of the medical record based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned registration unit is When registering a medical record, the system optimizes the registration content by referencing the user's past medical data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned registration unit is When registering a medical record, the system will reflect the user's lifestyle and environmental information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned registration unit is The system estimates the user's emotions and prioritizes medical records based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned registration unit is When registering medical records, the system prioritizes the registration of relevant medical data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned registration unit is When registering a medical record, the system analyzes the user's social media activity and registers relevant health information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned handover section is, The system estimates the user's emotions and adjusts the method of handing over medical records based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned handover section is, When transferring medical records, the system optimizes the transfer process by referencing the user's past medical data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned handover section is, When transferring medical records, reflect the user's lifestyle and environmental information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned handover section is, The system estimates the user's emotions and adjusts the order in which medical records are handed over based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned handover section is, When transferring medical records, the system prioritizes the transfer of relevant medical data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned handover section is, When transferring medical records, analyze the user's social media activity and transfer relevant health information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned learning unit, During training, the learning algorithm optimizes by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned learning unit, The learning process incorporates the user's lifestyle and environmental information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned learning unit, During training, the training data is weighted based on when the medical record was registered. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned learning unit, During training, the system analyzes users' social media activity and learns relevant health information. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned examination unit is, The system estimates the user's emotions and adjusts the consultation method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned examination unit is, The system optimizes the consultation process by referencing the user's past medical data during the visit. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned examination unit is, The system incorporates the user's lifestyle and environmental information during medical consultations. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned examination unit is, The system estimates the user's emotions and adjusts the order of consultations based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned examination unit is, When you visit a medical facility, we will suggest the most suitable medical institution considering your geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned examination unit is, During a medical consultation, we analyze the user's social media activity and collect relevant health information. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When providing advice, the system optimizes the advice content by referring to the user's past medical data. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned supply unit is, When providing advice, reflect the user's lifestyle and environmental information. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned supply unit is, It estimates the user's emotions and prioritizes advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned supply unit is, When providing advice, we take the user's geographical location into consideration and prioritize providing relevant advice. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned supply unit is, When providing advice, we analyze the user's social media activity and provide relevant health information. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned dialogue unit, It estimates the user's emotions and adjusts the way it interacts based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned dialogue unit, The system optimizes the conversation by referencing the user's past medical data during the interaction. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned dialogue unit, Reflect the user's lifestyle and environmental information during conversations. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned dialogue unit, It estimates the user's emotions and adjusts the order of the conversation based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned dialogue unit, During conversations, the system takes the user's geographical location into consideration to provide the most appropriate dialogue. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned dialogue unit, Analyze the user's social media activity during conversations and incorporate relevant health information into the dialogue. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0209] 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 where the initial consultation is conducted, A registration unit that registers the medical record created by the reception unit into a database, A transfer unit that transfers the medical records registered by the registration unit to the AI ​​agent, A learning unit that learns cases similar to the medical records taken over by the aforementioned handover unit, A receiving unit that receives a user based on the information learned by the learning unit, A provision unit that provides health management and medical advice to users who have received medical treatment through the aforementioned consultation unit, The system includes a dialogue unit that interacts with the user based on the advice provided by the aforementioned provision unit. A system characterized by the following features.

2. The aforementioned reception unit is The system estimates the user's emotions and adjusts the questions in the questionnaire based on those emotions. The system according to feature 1.

3. The aforementioned reception unit is During the initial consultation, the level of detail in the medical history is adjusted by referring to the user's past medical records. The system according to feature 1.

4. The aforementioned reception unit is During the initial consultation, collect information about the user's lifestyle and environment, and reflect it in their medical record. The system according to feature 1.

5. The aforementioned reception unit is The system estimates the user's emotions and adjusts the order of the questions based on those emotions. The system according to feature 1.

6. The aforementioned reception unit is During the initial consultation, we will suggest the most suitable medical institution, taking into account the user's geographical location. The system according to feature 1.

7. The aforementioned reception unit is During the initial consultation, we analyze the user's social media activity and collect relevant health information. The system according to feature 1.

8. The aforementioned registration unit is The system estimates the user's emotions and adjusts the content of the medical record based on those estimated emotions. The system according to feature 1.

Images