System

Abstract

To provide a system for selecting a proper hospital when a user's body condition is bad, and quickly performing reservation and emergency response.SOLUTION: A system comprises: a reception part; an identification part; a determination part; a proposal part; a reservation part; and a request part. The reception part receives input of a body condition. The identification part identifies a disease name on the basis of the information input on the reception part. The determination part determines a symptom on the basis of the disease name identified by the identification part. The proposal part proposes a hospital on the basis of a determination result of the determination part. The reservation part performs reservation to the hospital proposed by the proposal part. The request part requests an ambulance car when it is determined that urgency is high by the determination part.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, the process of selecting an appropriate hospital and making an appointment when you are feeling unwell is complicated, making it difficult to respond quickly in an emergency.

[0005] The system according to the embodiment aims to select an appropriate hospital when a person feels unwell, and to quickly make a reservation or respond to an emergency. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an identification unit, a judgment unit, a proposal unit, a reservation unit, and a request unit. The reception unit inputs the physical condition. The identification unit identifies the name of the disease based on the information input by the reception unit. The judgment unit judges the condition based on the name of the disease identified by the identification unit. The proposal unit suggests a hospital based on the result of the judgment by the judgment unit. The reservation unit makes a reservation at the hospital suggested by the proposal unit. The request unit requests an ambulance if the judgment unit judges that the urgency is high. Effect of the Invention

[0007] The system according to the embodiment can select an appropriate hospital when a user feels unwell, and can quickly make an appointment or respond to an emergency. [Brief description of the drawings]

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

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

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

[0028] (Example 1) The medical support system according to the embodiment of the present invention is a system that, when a user inputs his / her physical condition, identifies the name of the disease and determines whether to recuperate at home or go to a hospital depending on the condition. When the user goes to the hospital, the medical support system suggests an appropriate hospital, transmits the user's condition, past medical history, and medication prescription status to the hospital, and automatically makes a reservation. When an emergency is required, the system automatically requests an ambulance and communicates the location and symptoms. When the user can go to the hospital by himself / herself, the system guides the user to a nearby hospital that suits the user's condition, and automatically transmits the user's symptoms, prescription history, and other information online in advance. For example, a user inputs his / her physical condition. For example, the user inputs symptoms such as fever, cough, and headache. This information is input into AI. Next, AI analyzes the input information and identifies the name of the disease. AI estimates the name of the disease based on data such as past medical history and medication prescription status. For example, if the user has symptoms of fever and cough, AI identifies the possibility of influenza. After the name of the disease is identified, AI determines whether to recuperate at home or go to a hospital depending on the user's condition. For example, if the user has a mild cold, the system suggests recuperating at home, and if the user has severe symptoms, the system suggests going to a hospital. When going to the hospital, the AI ​​will suggest an appropriate hospital. For example, it will suggest a hospital with a specialist or a nearby hospital. In addition, the medical condition, past medical history, medication prescription status, etc. are sent to the hospital, and the reservation is automatically made. In the case of an emergency, the AI ​​will automatically request an ambulance and communicate the location and symptoms. For example, if there are symptoms of a heart attack, the AI ​​will immediately request an ambulance and communicate the user's current location and symptoms to the ambulance team. If the user can go to the hospital by themselves, the AI ​​will guide them to a nearby hospital that suits their medical condition and automatically send their symptoms and prescription history online in advance. For example, if the symptoms are mild, it will guide them to a nearby clinic and send their symptoms and prescription history in advance. This allows the user to receive prompt and appropriate medical care. For example, if the symptoms are highly urgent, an ambulance will be requested quickly and the user will be transported to an appropriate medical institution. Even if the symptoms are mild, the user will be guided to an appropriate medical institution and information will be sent in advance, allowing the user to receive a smooth examination. This allows the medical support system to input the user's physical condition, automatically identify the disease name, determine the medical condition, suggest hospitals, make reservations, and request an ambulance.

[0029] The medical support system according to the embodiment includes a reception unit, a specification unit, a determination unit, a proposal unit, a reservation unit, and a request unit. The reception unit is a unit for a user to input his / her physical condition. To input his / her physical condition, the user inputs symptoms such as fever, cough, and headache. The reception unit provides, for example, an interface for the user to input his / her physical condition. The interface can input the physical condition by, for example, a touch screen, voice input, keyboard input, or other methods. The specification unit specifies the name of a disease based on the information input by the reception unit. The specification unit, for example, uses AI to analyze the input information and specifies the name of a disease. The AI ​​estimates the name of a disease based on data such as past medical history and prescription status of medicine. For example, if there are symptoms of fever and cough, the AI ​​specifies the possibility of influenza. The determination unit judges the condition based on the name of a disease specified by the specification unit. The determination unit judges the condition using, for example, AI. The AI ​​judges the severity and progress of the condition based on the specified name of a disease. For example, in the case of a mild cold, the suggestion unit suggests home treatment, and in the case of severe symptoms, suggests going to a hospital. The suggestion unit suggests an appropriate hospital based on the result of the judgment by the judgment unit. The suggestion unit suggests an appropriate hospital, for example, using AI. The AI ​​suggests hospitals with specialists, nearby hospitals, etc. For example, the reservation unit suggests hospitals with specialists, nearby hospitals, etc. The reservation unit makes a reservation at the hospital suggested by the suggestion unit. The reservation unit makes a reservation, for example, using AI. The AI ​​transmits the medical condition, past medical history, medication prescription status, etc. to the hospital, and automatically makes a reservation. The request unit requests an ambulance when the judgment unit judges that the urgency is high. The request unit requests an ambulance, for example, using AI. The AI ​​conveys the location, symptoms, etc. For example, when there are symptoms of a heart attack, the AI ​​immediately requests an ambulance and conveys the user's current location and symptoms to the ambulance team. As a result, the medical support system according to the embodiment can input the user's physical condition, automatically identify the disease name, judge the medical condition, suggest a hospital, make a reservation, and request an ambulance.

[0030] The reception unit is a part for the user to input his / her physical condition. To input his / her physical condition, the user inputs symptoms such as fever, cough, and headache. The reception unit provides, for example, an interface for the user to input his / her physical condition. The interface can input his / her physical condition by, for example, a touch screen, voice input, keyboard input, or the like. Specifically, when using a touch screen, the user can tap an icon or button on the screen to select symptoms. In the case of voice input, the user speaks his / her symptoms into a microphone, and the system converts the input contents into text using voice recognition technology. In the case of keyboard input, the user can directly input symptoms using a keyboard. These interfaces are designed with the user's convenience in mind, and are intuitive and easy to use. Furthermore, the reception unit also has a function of temporarily storing the information input by the user, allowing corrections and additions as necessary. For example, if a new symptom occurs after the user has completed input, the user can open the interface again to input additional information. This allows the reception unit to accurately and quickly collect the user's physical condition information and pass it on to the next processing step. In addition, to protect the privacy of users, the reception unit encrypts and stores the entered information, and also has a security function to prevent unauthorized access from outside. This allows users to enter their own physical condition information into the system with peace of mind.

[0031] The identification unit identifies the name of the disease based on the information input by the reception unit. The identification unit, for example, uses AI to analyze the input information and identify the name of the disease. The AI ​​estimates the name of the disease based on data such as past medical history and prescription status of medicine. Specifically, the AI ​​uses natural language processing technology to analyze the symptoms input by the user and evaluates the combination and frequency of symptoms. For example, if the user has a fever and cough, the AI ​​identifies the possibility of influenza. Furthermore, the AI ​​performs more accurate disease name identification by taking into account the past medical history and prescription status of medicine. For example, if a user who has had influenza in the past shows similar symptoms again, the AI ​​highly evaluates the possibility of influenza recurrence based on that information. In addition, the identification unit improves the accuracy of disease name identification by comparing the information input by the user with other databases. For example, the possibility of a specific disease can be increased by referring to local infectious disease information and seasonal disease epidemic status. As a result, the identification unit can quickly and accurately identify the name of the disease based on the user's symptoms and pass it on to the next processing step. Furthermore, the identification unit will accumulate data obtained during the disease identification process and use it as learning data for the AI, thereby enabling the accuracy and reliability of the entire system to be continuously improved.

[0032] The judgment unit judges the condition based on the disease name identified by the identification unit. The judgment unit judges the condition using, for example, AI. The AI ​​judges the severity and progression of the condition based on the identified disease name. Specifically, the AI ​​compares the identified disease name with the details of the user's symptoms to evaluate the severity of the condition. For example, in the case of a mild cold, it suggests home treatment, and in the case of severe symptoms, it suggests going to the hospital. The AI ​​uses past data and statistical information to predict the progression pattern of the condition and proposes appropriate responses. For example, if the initial symptoms of influenza are observed, the AI ​​evaluates the possibility of the symptoms worsening based on past data and recommends early medical examination. In addition, the judgment unit can make more personalized judgments by taking into account individual information such as the user's age, gender, and medical history. For example, for elderly users and users with chronic diseases, the risk of the condition becoming severe is high even if the symptoms are the same, so early medical examination is strongly recommended. This allows the judgment unit to propose appropriate responses according to the individual situation of the user and prevent the condition from worsening. In addition, the judgment unit can also perform follow-up after the user executes the proposed response. For example, for a user who has been recommended to recuperate at home, the system can periodically check changes in the user's physical condition and review the response as necessary. This allows the determination unit to continuously monitor the user's health condition and provide appropriate medical support.

[0033] The suggestion unit suggests an appropriate hospital based on the result of the judgment by the judgment unit. The suggestion unit suggests an appropriate hospital using, for example, AI. The AI ​​suggests hospitals with specialists, nearby hospitals, etc. Specifically, the AI ​​searches for the nearest hospital based on the user's current location information, and suggests the most suitable hospital by considering information such as the presence or absence of specialists, medical departments, and consultation hours. For example, it suggests a hospital with a respiratory specialist or a 24-hour emergency hospital. In addition, the suggestion unit takes into consideration information on the congestion status and waiting time of the hospital, so that the user can receive medical treatment quickly. For example, it can preferentially suggest a hospital with a short waiting time from among multiple hospitals. Furthermore, the suggestion unit can suggest a reliable hospital based on the user's past medical history and evaluation information of the medical institution. As a result, the suggestion unit can quickly and accurately suggest the most suitable medical institution to the user and provide support for receiving appropriate medical care. Furthermore, the suggestion unit also provides information for the user to make a reservation at the proposed hospital. For example, by displaying the hospital's contact information and a link to the online reservation system, the user can smoothly complete the reservation procedure. This enables the suggestion unit to support a series of processes for the user to select an appropriate medical institution and receive medical treatment promptly.

[0034] The reservation unit makes a reservation at the hospital proposed by the suggestion unit. The reservation unit makes a reservation using, for example, AI. The AI ​​sends the medical condition, past medical history, medication prescription status, etc. to the hospital and automatically makes the reservation. Specifically, the AI ​​accesses the reservation system of the hospital proposed by the user and enters the user's information to complete the reservation procedure. For example, the user's name, contact information, details of symptoms, desired consultation date and time, etc. are entered to confirm the reservation. In addition, the AI ​​can check the hospital's reservation status in real time and propose the optimal reservation date and time. For example, from multiple consultation time slots, it selects the time slot that is closest to the user's preference. Furthermore, the reservation unit sends a confirmation notification to the user after the reservation is completed. For example, it notifies the user of the reservation details by email or SMS and provides detailed information such as the reservation date and time, the location of the hospital, and the medical department. This allows the user to check the reservation details and prepare to receive treatment with peace of mind. The reservation unit can also handle changes and cancellations of reservations. For example, if the user wants to change the plan, the AI ​​accesses the reservation system again and sets a new reservation date and time. This allows the reservation unit to flexibly respond to the user's needs and support smooth reservation procedures. Furthermore, the reservation unit centrally manages reservation information and allows users to refer to their past reservation history and medical history. This allows users to easily check their past medical history and use it to make their next reservation.

[0035] The request unit requests an ambulance when the judgment unit judges that the urgency is high. The request unit requests an ambulance using, for example, AI. The AI ​​conveys the location, symptoms, etc. Specifically, the AI ​​arranges for the nearest ambulance based on the user's current location information and conveys the user's symptoms and urgency to the ambulance team. For example, if there are symptoms of a heart attack, the AI ​​immediately requests an ambulance and conveys the user's current location and symptoms to the ambulance team. In addition, the AI ​​supports a quick and appropriate response by providing the ambulance team with the user's past medical history and allergy information. This allows the ambulance team to quickly perform the necessary medical treatment after arriving at the scene. Furthermore, the request unit can provide the user with first aid instructions until the ambulance arrives. For example, the request unit provides voice and text guidance on the procedures for first aid required in an emergency, such as cardiopulmonary resuscitation and methods of stopping bleeding. This allows the user to perform appropriate first aid until the ambulance arrives. In addition, the request unit notifies the user of the estimated arrival time of the ambulance, allowing the user to wait with peace of mind. For example, the time until the arrival of an ambulance is displayed in real time, and the user is notified of the situation on a regular basis. This allows the request unit to support a prompt and appropriate response in an emergency and ensure the safety of the user. Furthermore, the request unit can monitor the user's condition even after arranging an ambulance, and provide additional instructions and support as necessary. This allows the request unit to provide comprehensive medical support in an emergency and protect the safety and health of the user.

[0036] The suggestion unit can suggest a nearby hospital suitable for the medical condition. For example, when the user can visit the hospital by himself, the suggestion unit introduces a nearby appropriate hospital. The suggestion unit, for example, uses AI to suggest a nearby hospital suitable for the medical condition. The AI ​​suggests a nearby hospital based on the medical condition, past medical history, drug prescription status, etc. For example, if the symptoms are mild, a nearby clinic is introduced and the symptoms and prescription history are transmitted in advance. This makes it possible to introduce an appropriate nearby hospital when the user can visit the hospital by himself. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input information about nearby hospitals to the generation AI and cause the generation AI to execute a suggestion of a hospital suitable for the medical condition.

[0037] The suggestion unit can automatically transmit symptoms or prescription history online in advance. The suggestion unit, for example, transmits information to the hospital in advance to allow the examination to proceed smoothly. The suggestion unit, for example, uses AI to automatically transmit symptoms, prescription history, etc. online in advance. The AI ​​transmits necessary information to the hospital based on the medical condition, past medical history, drug prescription status, etc. For example, if the symptoms are mild, the system guides the patient to a nearby clinic and transmits symptoms and prescription history in advance. This allows the examination to proceed smoothly by transmitting information to the hospital in advance. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input information to be transmitted to the hospital to the generation AI and cause the generation AI to transmit the information.

[0038] The reception unit can analyze the user's past physical condition input history and select an input method. The reception unit, for example, analyzes the user's past physical condition input history and selects the optimal input method. The reception unit, for example, uses AI to analyze the past physical condition input history. The AI ​​selects the optimal input method based on the past input history. For example, the physical condition information frequently input by the user in the past is automatically displayed as a candidate. In addition, the input method (voice, text, etc.) that the user used in the past is preferentially proposed. Furthermore, the physical condition information to be used in a specific time period is predicted and proposed from the user's past input history. In this way, the optimal method is selected based on the past input history, thereby reducing the burden on the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the past physical condition input history to the generation AI and cause the generation AI to select the optimal input method.

[0039] The reception unit can filter based on the user's current living situation or field of interest when inputting the physical condition. For example, the reception unit filters based on the user's current living situation or field of interest when inputting the physical condition. The reception unit, for example, uses AI to analyze the user's living situation and field of interest, and preferentially displays related physical condition information. For example, when the user inputs the current living situation, the reception unit preferentially displays related physical condition information based on the input. In addition, the reception unit filters related physical condition information based on the user's field of interest (for example, health management or fitness). Furthermore, an appropriate physical condition input method is proposed based on the user's living situation (for example, work stress or home situation). This makes it possible to preferentially display related physical condition information based on the user's living situation and field of interest. Part or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input data on the user's living situation and field of interest to the generation AI, and cause the generation AI to execute filtering of related physical condition information.

[0040] The reception unit can preferentially input highly relevant information based on the geographical location information of the user when inputting the physical condition. For example, the reception unit preferentially inputs highly relevant information taking into account the geographical location information of the user when inputting the physical condition. The reception unit, for example, uses AI to analyze the geographical location information of the user and preferentially displays related physical condition information. For example, when the user is in a specific area, the reception unit preferentially inputs physical condition information related to a disease that is prevalent in the area. In addition, based on the user's current location, information on local medical institutions is provided and related physical condition information is input. Furthermore, based on the user's geographical location information, physical condition information related to local environmental factors (for example, temperature and humidity) is preferentially input. As a result, by taking into account the geographical location information, it is possible to preferentially obtain physical condition information related to the area. Part or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to execute the input of highly relevant information.

[0041] The reception unit can analyze the social media activity of the user and input related information when the physical condition is input. The reception unit, for example, analyzes the social media activity of the user and inputs related information when the physical condition is input. The reception unit, for example, uses AI to analyze the social media activity of the user and inputs related physical condition information. For example, information related to recent physical condition is extracted from the user's social media posts and inputs it. In addition, stress and emotional changes are analyzed from the user's social media activity, and related physical condition information is input. Furthermore, related physical condition information is input based on the user's health concerns on social media. In this way, information related to the user's physical condition can be efficiently obtained by analyzing the social media activity. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data to the generation AI and cause the generation AI to execute the input of related information.

[0042] The identification unit can improve the accuracy of identification by referring to past medical history data when identifying a disease name. The identification unit, for example, improves the accuracy of identification by referring to past medical history data when identifying a disease name. The identification unit, for example, uses AI to analyze past medical history data and improves the accuracy of disease name identification. The AI ​​identifies a disease name related to a current symptom based on the past medical history data. For example, the AI ​​identifies a disease name related to a current symptom based on the user's past medical history data. In addition, the AI ​​identifies a disease name that may recur from the user's past medical history data. Furthermore, the AI ​​analyzes the user's past medical history data and provides auxiliary information for improving the accuracy of identification. As a result, the accuracy of disease name identification is improved by referring to the past medical history data. Part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit inputs past medical history data to the generation AI and causes the generation AI to execute improvement of the accuracy of disease name identification.

[0043] The identification unit can identify the disease name based on the user's lifestyle or environmental information when identifying the disease name. For example, the identification unit identifies the disease name by considering the user's lifestyle and environmental information when identifying the disease name. The identification unit, for example, uses AI to analyze the user's lifestyle and environmental information to improve the accuracy of disease name identification. For example, the identification unit identifies the disease name by considering the user's lifestyle (e.g., smoking and drinking). The identification unit also identifies the disease name based on the user's environmental information (e.g., workplace stress and home situation). Furthermore, the identification unit provides auxiliary information for analyzing the user's lifestyle and environmental information and improving the accuracy of identification. As a result, the accuracy of disease name identification is improved by considering the lifestyle and environmental information. Part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit can input the user's lifestyle and environmental information to the generation AI and cause the generation AI to execute the improvement of the accuracy of disease name identification.

[0044] The identification unit can identify the disease name based on the geographical location information of the user when identifying the disease name. For example, the identification unit identifies the disease name by considering the geographical location information of the user when identifying the disease name. The identification unit, for example, uses AI to analyze the geographical location information of the user to improve the accuracy of disease name identification. For example, when the user is in a specific area, the disease name is identified by considering a disease that is prevalent in the area. In addition, based on the user's current location, information on local medical institutions is provided to identify the disease name. Furthermore, the disease name is identified by considering environmental factors (e.g., temperature and humidity) of the area based on the geographical location information of the user. As a result, the disease name related to the area can be identified by considering the geographical location information. Part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute the improvement of the accuracy of disease name identification.

[0045] The identification unit can improve the accuracy of identification by referring to the user's related literature when identifying the disease name. The identification unit, for example, improves the accuracy of identification by referring to the user's related literature when identifying the disease name. The identification unit, for example, uses AI to analyze related literature and improves the accuracy of disease name identification. The AI ​​identifies the disease name related to the current symptoms based on the related literature. For example, it refers to the latest medical literature related to the user's symptoms and identifies the disease name. In addition, it improves the accuracy of identification based on literature related to the user's past medical history. Furthermore, it provides auxiliary information for analyzing literature related to the user's symptoms and improving the accuracy of identification. As a result, the accuracy of disease name identification is improved by referring to the related literature. Part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit can input related literature to the generation AI and cause the generation AI to execute the improvement of the accuracy of disease name identification.

[0046] The judgment unit can improve the accuracy of judgment by referring to past medical condition data when judging the medical condition. The judgment unit, for example, improves the accuracy of judgment by referring to past medical condition data when judging the medical condition. The judgment unit, for example, uses AI to analyze past medical condition data and improves the accuracy of the medical condition judgment. The AI ​​judges a medical condition related to a current symptom based on past medical condition data. For example, based on the user's past medical condition data, judges a medical condition related to a current symptom. In addition, a medical condition that may recur is judged from the user's past medical condition data. Furthermore, the user's past medical condition data is analyzed, and auxiliary information for improving the accuracy of the judgment is provided. As a result, the accuracy of the medical condition judgment is improved by referring to the past medical condition data. Part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input past medical condition data to the generation AI and cause the generation AI to execute the improvement of the accuracy of the medical condition judgment.

[0047] The judgment unit can make a judgment based on the user's lifestyle or environmental information when judging the condition. For example, the judgment unit makes a judgment by considering the user's lifestyle and environmental information when judging the condition. The judgment unit, for example, uses AI to analyze the user's lifestyle and environmental information to improve the accuracy of the condition judgment. For example, the judgment unit judges the condition by considering the user's lifestyle (e.g., smoking and drinking). In addition, the judgment unit judges the condition based on the user's environmental information (e.g., workplace stress and home situation). Furthermore, the judgment unit analyzes the user's lifestyle and environmental information and provides auxiliary information for improving the accuracy of the judgment. As a result, the accuracy of the condition judgment is improved by considering the lifestyle and environmental information. Part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input the user's lifestyle and environmental information to the generation AI and cause the generation AI to execute the improvement of the accuracy of the condition judgment.

[0048] The determination unit can make a determination based on the geographical location information of the user when determining the condition. For example, the determination unit makes a determination by considering the geographical location information of the user when determining the condition. The determination unit, for example, uses AI to analyze the geographical location information of the user to improve the accuracy of the condition determination. For example, when the user is in a specific area, the condition is determined by considering a disease prevalent in that area. In addition, based on the user's current location, information on local medical institutions is provided and the condition is determined. Furthermore, based on the user's geographical location information, the condition is determined by considering environmental factors of the area (for example, temperature and humidity). As a result, the condition related to the area can be determined by considering the geographical location information. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input the user's geographical location information to the generation AI and cause the generation AI to execute the improvement of the accuracy of the condition determination.

[0049] The judgment unit can improve the accuracy of judgment by referring to the user's related literature when judging the condition. The judgment unit, for example, improves the accuracy of judgment by referring to the user's related literature when judging the condition. The judgment unit, for example, uses AI to analyze related literature and improves the accuracy of the condition judgment. The AI ​​judges the current symptom and related condition based on the related literature. For example, it judges the condition by referring to the latest medical literature related to the user's symptoms. In addition, it improves the accuracy of judgment based on the user's past medical history and related literature. Furthermore, it analyzes literature related to the user's symptoms and provides auxiliary information for improving the accuracy of judgment. As a result, the accuracy of the condition judgment is improved by referring to the related literature. Part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input related literature to the generation AI and cause the generation AI to execute the improvement of the accuracy of the condition judgment.

[0050] The suggestion unit can improve the accuracy of the suggestion by referring to past hospital usage data when suggesting a hospital. For example, the suggestion unit improves the accuracy of the suggestion by referring to past hospital usage data when suggesting a hospital. The suggestion unit, for example, uses AI to analyze past hospital usage data and improves the accuracy of the hospital suggestion. The AI ​​suggests an optimal hospital based on the past hospital usage data. For example, based on the user's past hospital usage data, an optimal hospital is suggested. Also, from the user's past hospital usage data, a hospital that may be revisited is suggested. Furthermore, the user's past hospital usage data is analyzed and auxiliary information for improving the accuracy of the suggestion is provided. As a result, the accuracy of the hospital suggestion is improved by referring to the past hospital usage data. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input past hospital usage data to the generation AI and cause the generation AI to execute the improvement of the accuracy of the hospital suggestion.

[0051] The suggestion unit can make a suggestion based on the user's lifestyle or environmental information when suggesting a hospital. For example, when suggesting a hospital, the suggestion unit makes a suggestion taking into account the user's lifestyle and environmental information. The suggestion unit, for example, uses AI to analyze the user's lifestyle and environmental information to improve the accuracy of the hospital suggestion. For example, the suggestion unit suggests a hospital taking into account the user's lifestyle (e.g., smoking and drinking). Also, the suggestion unit suggests a hospital based on the user's environmental information (e.g., workplace stress and home situation). Furthermore, the suggestion unit provides auxiliary information for analyzing the user's lifestyle and environmental information and improving the accuracy of the suggestion. As a result, the accuracy of the hospital suggestion is improved by taking into account the lifestyle and environmental information. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's lifestyle and environmental information to the generation AI and cause the generation AI to execute the improvement of the accuracy of the hospital suggestion.

[0052] The suggestion unit can make a suggestion based on the geographical location information of the user when proposing a hospital. For example, the suggestion unit makes a suggestion taking into account the geographical location information of the user when proposing a hospital. The suggestion unit, for example, uses AI to analyze the geographical location information of the user and improves the accuracy of the hospital suggestion. For example, when the user is in a specific area, the suggestion unit proposes the optimal hospital in the area. In addition, based on the user's current location, a nearby hospital is proposed. Furthermore, based on the user's geographical location information, information on local medical institutions is provided and the optimal hospital is proposed. In this way, by taking the geographical location information into account, a hospital related to the area can be proposed. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's geographical location information to the generation AI and cause the generation AI to execute the improvement of the accuracy of the hospital suggestion.

[0053] The suggestion unit can improve the accuracy of the suggestion by referring to the user's related literature when suggesting a hospital. The suggestion unit, for example, improves the accuracy of the suggestion by referring to the user's related literature when suggesting a hospital. The suggestion unit, for example, uses AI to analyze the related literature and improves the accuracy of the hospital suggestion. The AI ​​proposes a hospital related to the current symptoms based on the related literature. For example, the AI ​​refers to the latest medical literature related to the user's symptoms and suggests an optimal hospital. In addition, the accuracy of the suggestion is improved based on the literature related to the user's past medical history. Furthermore, auxiliary information for analyzing literature related to the user's symptoms and improving the accuracy of the suggestion is provided. As a result, the accuracy of the hospital suggestion is improved by referring to the related literature. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the related literature to the generation AI and cause the generation AI to execute the improvement of the accuracy of the hospital suggestion.

[0054] The reservation unit can improve the accuracy of reservations by referring to past reservation data at the time of making a reservation. The reservation unit, for example, improves the accuracy of reservations by referring to past reservation data at the time of making a reservation. The reservation unit, for example, uses AI to analyze past reservation data and improves the accuracy of reservations. The AI ​​proposes an optimal reservation method based on past reservation data. For example, based on the user's past reservation data, an optimal reservation method is proposed. In addition, from the user's past reservation data, hospitals that may be revisited are proposed. Furthermore, the user's past reservation data is analyzed and auxiliary information for improving the accuracy of reservations is provided. As a result, the accuracy of reservations is improved by referring to the past reservation data. Part or all of the above-mentioned processing in the reservation unit may be performed, for example, using AI, or may be performed without using AI. For example, the reservation unit can input past reservation data to the generation AI and cause the generation AI to execute improvement of reservation accuracy.

[0055] The reservation unit can make a reservation based on the user's lifestyle or environmental information at the time of reservation. The reservation unit, for example, makes a reservation by considering the user's lifestyle and environmental information at the time of reservation. The reservation unit, for example, uses AI to analyze the user's lifestyle and environmental information to improve the accuracy of the reservation. For example, the reservation is made by considering the user's lifestyle (e.g., smoking and drinking). Also, the reservation is made based on the user's environmental information (e.g., workplace stress and home situation). Furthermore, the reservation unit analyzes the user's lifestyle and environmental information and provides auxiliary information for improving the accuracy of the reservation. As a result, the accuracy of the reservation is improved by considering the lifestyle and environmental information. Part or all of the above-mentioned processing in the reservation unit may be performed, for example, using AI, or may be performed without using AI. For example, the reservation unit can input the user's lifestyle and environmental information to the generation AI and cause the generation AI to execute the improvement of the accuracy of the reservation.

[0056] The reservation unit can select a reservation method based on the geographical location information of the user at the time of reservation. The reservation unit, for example, selects the optimal reservation method by considering the geographical location information of the user at the time of reservation. The reservation unit, for example, uses AI to analyze the geographical location information of the user and selects the optimal reservation method. For example, when the user is in a specific area, the reservation unit proposes the optimal reservation method for that area. In addition, based on the user's current location, reservations are made preferentially at nearby hospitals. Furthermore, based on the user's geographical location information, information on local medical institutions is provided and the optimal reservation method is selected. As a result, the optimal reservation method related to the area can be selected by considering the geographical location information. Part or all of the above-mentioned processing in the reservation unit may be performed, for example, using AI, or may be performed without using AI. For example, the reservation unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal reservation method.

[0057] The reservation unit can analyze the user's social media activity at the time of reservation and propose the optimal reservation method. The reservation unit, for example, analyzes the user's social media activity at the time of reservation and proposes the optimal reservation method. The reservation unit, for example, uses AI to analyze the user's social media activity and proposes the optimal reservation method. For example, information about the user's recent physical condition is extracted from the user's social media posts, and the optimal reservation method is proposed. In addition, stress and emotional changes are analyzed from the user's social media activity, and the optimal reservation method is proposed. Furthermore, the optimal reservation method is proposed based on the user's health concerns on social media. In this way, the optimal reservation method can be proposed to the user by analyzing social media activity. Part or all of the above-mentioned processing in the reservation unit may be performed, for example, using AI, or may be performed without using AI. For example, the reservation unit can input the user's social media activity data to the generation AI and cause the generation AI to execute a proposal of the optimal reservation method.

[0058] The request unit can improve the accuracy of the request by referring to past request data when requesting an ambulance. For example, when requesting an ambulance, the request unit improves the accuracy of the request by referring to past request data. The request unit, for example, uses AI to analyze the past request data and improves the accuracy of the request. The AI ​​proposes an optimal method of requesting an ambulance based on the past request data. For example, based on the user's past request data, an optimal method of requesting an ambulance is proposed. In addition, an ambulance is requested based on symptoms that may recur from the user's past request data. Furthermore, the user's past request data is analyzed and auxiliary information for improving the accuracy of the request is provided. As a result, the accuracy of the ambulance request is improved by referring to the past request data. Part or all of the above-mentioned processing in the request unit may be performed, for example, using AI, or may be performed without using AI. For example, the request unit can input past request data to the generation AI and cause the generation AI to execute improvement of the accuracy of the request.

[0059] The request unit can make a request based on the user's lifestyle or environmental information when requesting an ambulance. For example, when requesting an ambulance, the request unit makes a request taking into consideration the user's lifestyle and environmental information. The request unit, for example, uses AI to analyze the user's lifestyle and environmental information to improve the accuracy of the request. For example, an ambulance is requested taking into consideration the user's lifestyle (e.g., smoking and drinking). In addition, an ambulance is requested based on the user's environmental information (e.g., workplace stress and home situation). Furthermore, auxiliary information is provided to analyze the user's lifestyle and environmental information and improve the accuracy of the request. As a result, the accuracy of the ambulance request is improved by taking into consideration the lifestyle and environmental information. A part or all of the above-mentioned processing in the request unit may be performed using AI, for example, or may be performed without using AI. For example, the request unit can input the user's lifestyle and environmental information into the generation AI and cause the generation AI to execute the improvement of the accuracy of the request.

[0060] The request unit can select a request method based on the geographical location information of the user when requesting an ambulance. For example, when requesting an ambulance, the request unit selects the optimal request method by considering the geographical location information of the user. The request unit, for example, uses AI to analyze the geographical location information of the user and selects the optimal request method. For example, when the user is in a specific area, the request unit proposes the optimal ambulance request method for that area. In addition, based on the user's current location, a request for a nearby ambulance is prioritized. Furthermore, based on the user's geographical location information, information on local medical institutions is provided and the optimal ambulance request method is selected. As a result, the optimal ambulance request method related to the area can be selected by considering the geographical location information. Part or all of the above-mentioned processing in the request unit may be performed, for example, using AI, or may be performed without using AI. For example, the request unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal request method.

[0061] The request unit can analyze the user's social media activity and propose the optimal request method when requesting an ambulance. For example, when requesting an ambulance, the request unit analyzes the user's social media activity and proposes the optimal request method. The request unit, for example, uses AI to analyze the user's social media activity and proposes the optimal request method. For example, information about the user's recent physical condition is extracted from the user's social media posts, and the optimal request method is proposed. In addition, changes in stress and emotions are analyzed from the user's social media activity, and the optimal request method is proposed. Furthermore, the optimal request method for an ambulance is proposed based on the user's health concerns on social media. As a result, the optimal request method for an ambulance can be proposed to the user by analyzing social media activity. Part or all of the above-mentioned processing in the request unit may be performed, for example, using AI, or may be performed without using AI. For example, the request unit can input the user's social media activity data to the generation AI and cause the generation AI to execute the proposal of the optimal request method.

[0062] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0063] When the user inputs his / her physical condition, the reception unit can refer to the user's past physical condition data and automatically complete the input contents. For example, symptoms that the user has frequently input in the past can be automatically displayed as candidates, reducing the effort of inputting. In addition, the reception unit can predict related symptoms based on the past physical condition data and suggest them to the user. This allows the user to input his / her physical condition quickly and accurately. Furthermore, the reception unit can analyze the user's past physical condition data, predict symptoms related to specific times of day or seasons, and customize the input contents.

[0064] The suggestion unit can suggest hospitals taking into consideration the user's current activity status when inputting the user's physical condition. For example, when the user is at work, the suggestion unit can preferentially suggest nearby hospitals and clinics to allow the user to receive medical treatment quickly. Also, when the user is traveling, the suggestion unit can suggest the most suitable medical institution based on the user's current location. Furthermore, when the user is exercising, the suggestion unit can also suggest a medical institution that specializes in sports medicine. This makes it possible to suggest appropriate medical institutions according to the user's activity status.

[0065] When the user inputs his / her physical condition, the suggestion unit can suggest a hospital taking into consideration the user's dietary history. For example, if the user has specific dietary restrictions, the suggestion unit can suggest a medical institution that can accommodate those restrictions. In addition, the suggestion unit can predict related symptoms based on the contents of meals recently consumed by the user and suggest an appropriate medical institution. Furthermore, the suggestion unit can analyze the user's dietary history and suggest a hospital based on the nutritional state. In this way, it is possible to suggest an appropriate medical institution based on the user's dietary history.

[0066] The reception unit can analyze the user's past physical condition input history and improve the accuracy of the input contents. For example, the current input contents are complemented based on the physical condition information previously input by the user. Also, based on the past input history, related symptoms can be predicted and suggested to the user. Furthermore, the reception unit can analyze the user's past input history, find specific patterns, and provide feedback to improve the accuracy of the input contents. In this way, the accuracy of the input contents can be improved by utilizing the user's past input history.

[0067] The reception unit can perform filtering based on the user's current living situation or field of interest when inputting the physical condition. For example, when the user inputs the current living situation, related physical condition information is preferentially displayed based on the input. Also, related physical condition information is filtered based on the user's field of interest (e.g., health management or fitness). Furthermore, an appropriate physical condition input method is suggested based on the user's living situation (e.g., work stress or home situation). This makes it possible to preferentially display related physical condition information based on the user's living situation or field of interest.

[0068] When inputting physical condition information, the reception unit can preferentially input information that is highly relevant based on the user's geographical location information. For example, when the user is in a specific area, the reception unit preferentially inputs physical condition information related to diseases that are prevalent in that area. Also, based on the user's current location, information on local medical institutions is provided and related physical condition information is input. Furthermore, based on the user's geographical location information, physical condition information related to local environmental factors (e.g., temperature and humidity) is preferentially input. In this way, by taking into account the geographical location information, it is possible to preferentially obtain physical condition information related to the area.

[0069] The reception unit can analyze the user's social media activity and input related information when inputting the user's physical condition. For example, information related to recent physical condition is extracted from the user's social media posts and input. In addition, changes in stress and emotions are analyzed from the user's social media activity, and related physical condition information is input. Furthermore, related physical condition information is input based on the user's health concerns on social media. In this way, information related to the user's physical condition can be efficiently obtained by analyzing social media activity.

[0070] The process flow of the first embodiment will be briefly described below.

[0071] Step 1: The reception unit is a section where the user inputs their physical condition. The user inputs symptoms such as fever, cough, and headache. The reception unit provides an interface such as a touch screen, voice input, and keyboard input. Step 2: The identification unit identifies the name of the disease based on the information input by the reception unit. The identification unit uses AI to analyze the input information and identify the name of the disease. For example, if the patient has a fever and cough, the AI ​​will identify the possibility of influenza. Step 3: The judgment unit judges the condition based on the disease name identified by the identification unit. The judgment unit uses AI to judge the severity and progression of the condition. For example, if it is a mild cold, it suggests staying at home and if the symptoms are severe, it suggests going to the hospital. Step 4: The suggestion unit suggests appropriate hospitals based on the results of the judgment by the judgment unit. The suggestion unit uses AI to suggest hospitals with specialists or nearby hospitals. Step 5: The reservation department makes a reservation at the hospital suggested by the proposal department. The reservation department uses AI to send the patient's condition, past medical history, medication prescription status, etc. to the hospital and automatically makes the reservation. Step 6: The request unit requests an ambulance if the judgment unit judges the emergency to be high. The request unit uses AI to communicate the location, symptoms, etc. For example, if there are symptoms of a heart attack, the AI ​​immediately requests an ambulance and communicates the user's current location and symptoms to the emergency team.

[0072] (Example 2) The medical support system according to the embodiment of the present invention is a system that, when a user inputs his / her physical condition, identifies the name of the disease and determines whether to recuperate at home or go to a hospital depending on the condition. When the user goes to the hospital, the medical support system suggests an appropriate hospital, transmits the user's condition, past medical history, and medication prescription status to the hospital, and automatically makes a reservation. When an emergency is required, the system automatically requests an ambulance and communicates the location and symptoms. When the user can go to the hospital by himself / herself, the system guides the user to a nearby hospital that suits the user's condition, and automatically transmits the user's symptoms, prescription history, and other information online in advance. For example, a user inputs his / her physical condition. For example, the user inputs symptoms such as fever, cough, and headache. This information is input into AI. Next, AI analyzes the input information and identifies the name of the disease. AI estimates the name of the disease based on data such as past medical history and medication prescription status. For example, if the user has symptoms of fever and cough, AI identifies the possibility of influenza. After the name of the disease is identified, AI determines whether to recuperate at home or go to a hospital depending on the user's condition. For example, if the user has a mild cold, the system suggests recuperating at home, and if the user has severe symptoms, the system suggests going to a hospital. When going to the hospital, the AI ​​will suggest an appropriate hospital. For example, it will suggest a hospital with a specialist or a nearby hospital. In addition, the medical condition, past medical history, medication prescription status, etc. are sent to the hospital, and the reservation is automatically made. In the case of an emergency, the AI ​​will automatically request an ambulance and communicate the location and symptoms. For example, if there are symptoms of a heart attack, the AI ​​will immediately request an ambulance and communicate the user's current location and symptoms to the ambulance team. If the user can go to the hospital by themselves, the AI ​​will guide them to a nearby hospital that suits their medical condition and automatically send their symptoms and prescription history online in advance. For example, if the symptoms are mild, it will guide them to a nearby clinic and send their symptoms and prescription history in advance. This allows the user to receive prompt and appropriate medical care. For example, if the symptoms are highly urgent, an ambulance will be requested quickly and the user will be transported to an appropriate medical institution. Even if the symptoms are mild, the user will be guided to an appropriate medical institution and information will be sent in advance, allowing the user to receive a smooth examination. This allows the medical support system to input the user's physical condition, automatically identify the disease name, determine the medical condition, suggest hospitals, make reservations, and request an ambulance.

[0073] The medical support system according to the embodiment includes a reception unit, a specification unit, a determination unit, a proposal unit, a reservation unit, and a request unit. The reception unit is a unit for a user to input his / her physical condition. To input his / her physical condition, the user inputs symptoms such as fever, cough, and headache. The reception unit provides, for example, an interface for the user to input his / her physical condition. The interface can input the physical condition by, for example, a touch screen, voice input, keyboard input, or other methods. The specification unit specifies the name of a disease based on the information input by the reception unit. The specification unit, for example, uses AI to analyze the input information and specifies the name of a disease. The AI ​​estimates the name of a disease based on data such as past medical history and prescription status of medicine. For example, if there are symptoms of fever and cough, the AI ​​specifies the possibility of influenza. The determination unit judges the condition based on the name of a disease specified by the specification unit. The determination unit judges the condition using, for example, AI. The AI ​​judges the severity and progress of the condition based on the specified name of a disease. For example, in the case of a mild cold, the suggestion unit suggests home treatment, and in the case of severe symptoms, suggests going to a hospital. The suggestion unit suggests an appropriate hospital based on the result of the judgment by the judgment unit. The suggestion unit suggests an appropriate hospital, for example, using AI. The AI ​​suggests hospitals with specialists, nearby hospitals, etc. For example, the reservation unit suggests hospitals with specialists, nearby hospitals, etc. The reservation unit makes a reservation at the hospital suggested by the suggestion unit. The reservation unit makes a reservation, for example, using AI. The AI ​​transmits the medical condition, past medical history, medication prescription status, etc. to the hospital, and automatically makes a reservation. The request unit requests an ambulance when the judgment unit judges that the urgency is high. The request unit requests an ambulance, for example, using AI. The AI ​​conveys the location, symptoms, etc. For example, when there are symptoms of a heart attack, the AI ​​immediately requests an ambulance and conveys the user's current location and symptoms to the ambulance team. As a result, the medical support system according to the embodiment can input the user's physical condition, automatically identify the disease name, judge the medical condition, suggest a hospital, make a reservation, and request an ambulance.

[0074] The reception unit is a part for the user to input his / her physical condition. To input his / her physical condition, the user inputs symptoms such as fever, cough, and headache. The reception unit provides, for example, an interface for the user to input his / her physical condition. The interface can input his / her physical condition by, for example, a touch screen, voice input, keyboard input, or the like. Specifically, when using a touch screen, the user can tap an icon or button on the screen to select symptoms. In the case of voice input, the user speaks his / her symptoms into a microphone, and the system converts the input contents into text using voice recognition technology. In the case of keyboard input, the user can directly input symptoms using a keyboard. These interfaces are designed with the user's convenience in mind, and are intuitive and easy to use. Furthermore, the reception unit also has a function of temporarily storing the information input by the user, allowing corrections and additions as necessary. For example, if a new symptom occurs after the user has completed input, the user can open the interface again to input additional information. This allows the reception unit to accurately and quickly collect the user's physical condition information and pass it on to the next processing step. In addition, to protect the privacy of users, the reception unit encrypts and stores the entered information, and also has a security function to prevent unauthorized access from outside. This allows users to enter their own physical condition information into the system with peace of mind.

[0075] The identification unit identifies the name of the disease based on the information input by the reception unit. The identification unit, for example, uses AI to analyze the input information and identify the name of the disease. The AI ​​estimates the name of the disease based on data such as past medical history and prescription status of medicine. Specifically, the AI ​​uses natural language processing technology to analyze the symptoms input by the user and evaluates the combination and frequency of symptoms. For example, if the user has a fever and cough, the AI ​​identifies the possibility of influenza. Furthermore, the AI ​​performs more accurate disease name identification by taking into account the past medical history and prescription status of medicine. For example, if a user who has had influenza in the past shows similar symptoms again, the AI ​​highly evaluates the possibility of influenza recurrence based on that information. In addition, the identification unit improves the accuracy of disease name identification by comparing the information input by the user with other databases. For example, the possibility of a specific disease can be increased by referring to local infectious disease information and seasonal disease epidemic status. As a result, the identification unit can quickly and accurately identify the name of the disease based on the user's symptoms and pass it on to the next processing step. Furthermore, the identification unit will accumulate data obtained during the disease identification process and use it as learning data for the AI, thereby enabling the accuracy and reliability of the entire system to be continuously improved.

[0076] The judgment unit judges the condition based on the disease name identified by the identification unit. The judgment unit judges the condition using, for example, AI. The AI ​​judges the severity and progression of the condition based on the identified disease name. Specifically, the AI ​​compares the identified disease name with the details of the user's symptoms to evaluate the severity of the condition. For example, in the case of a mild cold, it suggests home treatment, and in the case of severe symptoms, it suggests going to the hospital. The AI ​​uses past data and statistical information to predict the progression pattern of the condition and proposes appropriate responses. For example, if the initial symptoms of influenza are observed, the AI ​​evaluates the possibility of the symptoms worsening based on past data and recommends early medical examination. In addition, the judgment unit can make more personalized judgments by taking into account individual information such as the user's age, gender, and medical history. For example, for elderly users and users with chronic diseases, the risk of the condition becoming severe is high even if the symptoms are the same, so early medical examination is strongly recommended. This allows the judgment unit to propose appropriate responses according to the individual situation of the user and prevent the condition from worsening. In addition, the judgment unit can also perform follow-up after the user executes the proposed response. For example, for a user who has been recommended to recuperate at home, the system can periodically check changes in the user's physical condition and review the response as necessary. This allows the determination unit to continuously monitor the user's health condition and provide appropriate medical support.

[0077] The suggestion unit suggests an appropriate hospital based on the result of the judgment by the judgment unit. The suggestion unit suggests an appropriate hospital using, for example, AI. The AI ​​suggests hospitals with specialists, nearby hospitals, etc. Specifically, the AI ​​searches for the nearest hospital based on the user's current location information, and suggests the most suitable hospital by considering information such as the presence or absence of specialists, medical departments, and consultation hours. For example, it suggests a hospital with a respiratory specialist or a 24-hour emergency hospital. In addition, the suggestion unit takes into consideration information on the congestion status and waiting time of the hospital, so that the user can receive medical treatment quickly. For example, it can preferentially suggest a hospital with a short waiting time from among multiple hospitals. Furthermore, the suggestion unit can suggest a reliable hospital based on the user's past medical history and evaluation information of the medical institution. As a result, the suggestion unit can quickly and accurately suggest the most suitable medical institution to the user and provide support for receiving appropriate medical care. Furthermore, the suggestion unit also provides information for the user to make a reservation at the proposed hospital. For example, by displaying the hospital's contact information and a link to the online reservation system, the user can smoothly complete the reservation procedure. This enables the suggestion unit to support a series of processes for the user to select an appropriate medical institution and receive medical treatment promptly.

[0078] The reservation unit makes a reservation at the hospital proposed by the suggestion unit. The reservation unit makes a reservation using, for example, AI. The AI ​​sends the medical condition, past medical history, medication prescription status, etc. to the hospital and automatically makes the reservation. Specifically, the AI ​​accesses the reservation system of the hospital proposed by the user and enters the user's information to complete the reservation procedure. For example, the user's name, contact information, details of symptoms, desired consultation date and time, etc. are entered to confirm the reservation. In addition, the AI ​​can check the hospital's reservation status in real time and propose the optimal reservation date and time. For example, from multiple consultation time slots, it selects the time slot that is closest to the user's preference. Furthermore, the reservation unit sends a confirmation notification to the user after the reservation is completed. For example, it notifies the user of the reservation details by email or SMS and provides detailed information such as the reservation date and time, the location of the hospital, and the medical department. This allows the user to check the reservation details and prepare to receive treatment with peace of mind. The reservation unit can also handle changes and cancellations of reservations. For example, if the user wants to change the plan, the AI ​​accesses the reservation system again and sets a new reservation date and time. This allows the reservation unit to flexibly respond to the user's needs and support smooth reservation procedures. Furthermore, the reservation unit centrally manages reservation information and allows users to refer to their past reservation history and medical history. This allows users to easily check their past medical history and use it to make their next reservation.

[0079] The request unit requests an ambulance when the judgment unit judges that the urgency is high. The request unit requests an ambulance using, for example, AI. The AI ​​conveys the location, symptoms, etc. Specifically, the AI ​​arranges for the nearest ambulance based on the user's current location information and conveys the user's symptoms and urgency to the ambulance team. For example, if there are symptoms of a heart attack, the AI ​​immediately requests an ambulance and conveys the user's current location and symptoms to the ambulance team. In addition, the AI ​​supports a quick and appropriate response by providing the ambulance team with the user's past medical history and allergy information. This allows the ambulance team to quickly perform the necessary medical treatment after arriving at the scene. Furthermore, the request unit can provide the user with first aid instructions until the ambulance arrives. For example, the request unit provides voice and text guidance on the procedures for first aid required in an emergency, such as cardiopulmonary resuscitation and methods of stopping bleeding. This allows the user to perform appropriate first aid until the ambulance arrives. In addition, the request unit notifies the user of the estimated arrival time of the ambulance, allowing the user to wait with peace of mind. For example, the time until the arrival of an ambulance is displayed in real time, and the user is notified of the situation on a regular basis. This allows the request unit to support a prompt and appropriate response in an emergency and ensure the safety of the user. Furthermore, the request unit can monitor the user's condition even after arranging an ambulance, and provide additional instructions and support as necessary. This allows the request unit to provide comprehensive medical support in an emergency and protect the safety and health of the user.

[0080] The suggestion unit can suggest a nearby hospital suitable for the medical condition. For example, when the user can visit the hospital by himself, the suggestion unit introduces a nearby appropriate hospital. The suggestion unit, for example, uses AI to suggest a nearby hospital suitable for the medical condition. The AI ​​suggests a nearby hospital based on the medical condition, past medical history, drug prescription status, etc. For example, if the symptoms are mild, a nearby clinic is introduced and the symptoms and prescription history are transmitted in advance. This makes it possible to introduce an appropriate nearby hospital when the user can visit the hospital by himself. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input information about nearby hospitals to the generation AI and cause the generation AI to execute a suggestion of a hospital suitable for the medical condition.

[0081] The suggestion unit can automatically transmit symptoms or prescription history online in advance. The suggestion unit, for example, transmits information to the hospital in advance to allow the examination to proceed smoothly. The suggestion unit, for example, uses AI to automatically transmit symptoms, prescription history, etc. online in advance. The AI ​​transmits necessary information to the hospital based on the medical condition, past medical history, drug prescription status, etc. For example, if the symptoms are mild, the system guides the patient to a nearby clinic and transmits symptoms and prescription history in advance. This allows the examination to proceed smoothly by transmitting information to the hospital in advance. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input information to be transmitted to the hospital to the generation AI and cause the generation AI to transmit the information.

[0082] The reception unit can estimate the user's emotion and adjust the input method of the physical condition based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and adjusts the input method of the physical condition based on the estimated user's emotion. The reception unit is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is stressed, the reception unit provides a simple interface and minimizes the input procedure. Also, when the user is relaxed, the reception unit provides detailed input options and suggests a customizable input method. Furthermore, when the user is in a hurry, the reception unit prioritizes voice input and allows the user to input the physical condition quickly. This allows more appropriate physical condition information to be obtained by adjusting the input method according to the user's emotion. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the input method based on the emotion.

[0083] The reception unit can analyze the user's past physical condition input history and select an input method. The reception unit, for example, analyzes the user's past physical condition input history and selects the optimal input method. The reception unit, for example, uses AI to analyze the past physical condition input history. The AI ​​selects the optimal input method based on the past input history. For example, the physical condition information frequently input by the user in the past is automatically displayed as a candidate. In addition, the input method (voice, text, etc.) that the user used in the past is preferentially proposed. Furthermore, the physical condition information to be used in a specific time period is predicted and proposed from the user's past input history. In this way, the optimal method is selected based on the past input history, thereby reducing the burden on the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the past physical condition input history to the generation AI and cause the generation AI to select the optimal input method.

[0084] The reception unit can filter based on the user's current living situation or field of interest when inputting the physical condition. For example, the reception unit filters based on the user's current living situation or field of interest when inputting the physical condition. The reception unit, for example, uses AI to analyze the user's living situation and field of interest, and preferentially displays related physical condition information. For example, when the user inputs the current living situation, the reception unit preferentially displays related physical condition information based on the input. In addition, the reception unit filters related physical condition information based on the user's field of interest (for example, health management or fitness). Furthermore, an appropriate physical condition input method is proposed based on the user's living situation (for example, work stress or home situation). This makes it possible to preferentially display related physical condition information based on the user's living situation and field of interest. Part or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input data on the user's living situation and field of interest to the generation AI, and cause the generation AI to execute filtering of related physical condition information.

[0085] The reception unit can estimate the user's emotion and determine the priority of the physical condition information to be input based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and determines the priority of the physical condition information to be input based on the estimated user's emotion. The reception unit is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is nervous, the reception unit encourages the user to input important physical condition information with priority. Also, when the user is relaxed, the reception unit suggests that detailed physical condition information be input. Furthermore, when the user is in a hurry, the reception unit prioritizes input of the most important physical condition information. In this way, by determining the priority of the physical condition information according to the user's emotion, important information can be acquired with priority. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of the physical condition information based on the emotion.

[0086] The reception unit can preferentially input highly relevant information based on the geographical location information of the user when inputting the physical condition. For example, the reception unit preferentially inputs highly relevant information taking into account the geographical location information of the user when inputting the physical condition. The reception unit, for example, uses AI to analyze the geographical location information of the user and preferentially displays related physical condition information. For example, when the user is in a specific area, the reception unit preferentially inputs physical condition information related to a disease that is prevalent in the area. In addition, based on the user's current location, information on local medical institutions is provided and related physical condition information is input. Furthermore, based on the user's geographical location information, physical condition information related to local environmental factors (for example, temperature and humidity) is preferentially input. As a result, by taking into account the geographical location information, it is possible to preferentially obtain physical condition information related to the area. Part or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to execute the input of highly relevant information.

[0087] The reception unit can analyze the social media activity of the user and input related information when the physical condition is input. The reception unit, for example, analyzes the social media activity of the user and inputs related information when the physical condition is input. The reception unit, for example, uses AI to analyze the social media activity of the user and inputs related physical condition information. For example, information related to recent physical condition is extracted from the user's social media posts and inputs it. In addition, stress and emotional changes are analyzed from the user's social media activity, and related physical condition information is input. Furthermore, related physical condition information is input based on the user's health concerns on social media. In this way, information related to the user's physical condition can be efficiently obtained by analyzing the social media activity. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data to the generation AI and cause the generation AI to execute the input of related information.

[0088] The identification unit can estimate the user's emotion and adjust the disease name identification method based on the estimated user's emotion. The identification unit, for example, estimates the user's emotion and adjusts the disease name identification method based on the estimated user's emotion. The identification unit is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user feels anxious, the identification unit provides a quick and concise disease name identification method. Also, when the user is relaxed, the identification unit identifies the disease name through detailed questions. Furthermore, when the user is in a hurry, the identification unit quickly identifies the disease name based on the past medical history and current symptoms. In this way, a more appropriate disease name can be identified by adjusting the disease name identification method according to the user's emotion. Part or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the disease name identification method based on emotion.

[0089] The identification unit can improve the accuracy of identification by referring to past medical history data when identifying a disease name. The identification unit, for example, improves the accuracy of identification by referring to past medical history data when identifying a disease name. The identification unit, for example, uses AI to analyze past medical history data and improves the accuracy of disease name identification. The AI ​​identifies a disease name related to a current symptom based on the past medical history data. For example, the AI ​​identifies a disease name related to a current symptom based on the user's past medical history data. In addition, the AI ​​identifies a disease name that may recur from the user's past medical history data. Furthermore, the AI ​​analyzes the user's past medical history data and provides auxiliary information for improving the accuracy of identification. As a result, the accuracy of disease name identification is improved by referring to the past medical history data. Part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit inputs past medical history data to the generation AI and causes the generation AI to execute improvement of the accuracy of disease name identification.

[0090] The identification unit can identify the disease name based on the user's lifestyle or environmental information when identifying the disease name. For example, the identification unit identifies the disease name by considering the user's lifestyle and environmental information when identifying the disease name. The identification unit, for example, uses AI to analyze the user's lifestyle and environmental information to improve the accuracy of disease name identification. For example, the identification unit identifies the disease name by considering the user's lifestyle (e.g., smoking and drinking). The identification unit also identifies the disease name based on the user's environmental information (e.g., workplace stress and home situation). Furthermore, the identification unit provides auxiliary information for analyzing the user's lifestyle and environmental information and improving the accuracy of identification. As a result, the accuracy of disease name identification is improved by considering the lifestyle and environmental information. Part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit can input the user's lifestyle and environmental information to the generation AI and cause the generation AI to execute the improvement of the accuracy of disease name identification.

[0091] The identification unit can estimate the user's emotion and adjust the display method of the identified disease name based on the estimated user's emotion. The identification unit, for example, estimates the user's emotion and adjusts the display method of the identified disease name based on the estimated user's emotion. The identification unit is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user feels anxious, the identification unit provides a display method that is simple and gives a sense of security. Furthermore, when the user is relaxed, the identification unit provides a display method that includes detailed information. Furthermore, when the user is in a hurry, the identification unit provides a concise display method that focuses on the main points. This makes it possible to provide more appropriate information by adjusting the display method of the disease name according to the user's emotion. Part or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method of the disease name based on the emotion.

[0092] The identification unit can identify the disease name based on the geographical location information of the user when identifying the disease name. For example, the identification unit identifies the disease name by considering the geographical location information of the user when identifying the disease name. The identification unit, for example, uses AI to analyze the geographical location information of the user to improve the accuracy of disease name identification. For example, when the user is in a specific area, the disease name is identified by considering a disease that is prevalent in the area. In addition, based on the user's current location, information on local medical institutions is provided to identify the disease name. Furthermore, the disease name is identified by considering environmental factors (e.g., temperature and humidity) of the area based on the geographical location information of the user. As a result, the disease name related to the area can be identified by considering the geographical location information. Part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute the improvement of the accuracy of disease name identification.

[0093] The identification unit can improve the accuracy of identification by referring to the user's related literature when identifying the disease name. The identification unit, for example, improves the accuracy of identification by referring to the user's related literature when identifying the disease name. The identification unit, for example, uses AI to analyze related literature and improves the accuracy of disease name identification. The AI ​​identifies the disease name related to the current symptoms based on the related literature. For example, it refers to the latest medical literature related to the user's symptoms and identifies the disease name. In addition, it improves the accuracy of identification based on literature related to the user's past medical history. Furthermore, it provides auxiliary information for analyzing literature related to the user's symptoms and improving the accuracy of identification. As a result, the accuracy of disease name identification is improved by referring to the related literature. Part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit can input related literature to the generation AI and cause the generation AI to execute the improvement of the accuracy of disease name identification.

[0094] The judgment unit can estimate the user's emotion and adjust the disease state judgment method based on the estimated user's emotion. The judgment unit, for example, estimates the user's emotion and adjusts the disease state judgment method based on the estimated user's emotion. The judgment unit is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user feels anxious, the judgment unit provides a quick and concise disease state judgment method. Also, when the user is relaxed, the judgment unit judges the disease state through detailed questions. Furthermore, when the user is in a hurry, the judgment unit judges the disease state quickly based on the past medical history and current symptoms. This allows for a more appropriate disease state judgment by adjusting the disease state judgment method according to the user's emotion. A part or all of the above-mentioned processing in the judgment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the judgment unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the disease state judgment method based on the emotion.

[0095] The judgment unit can improve the accuracy of judgment by referring to past medical condition data when judging the medical condition. The judgment unit, for example, improves the accuracy of judgment by referring to past medical condition data when judging the medical condition. The judgment unit, for example, uses AI to analyze past medical condition data and improves the accuracy of the medical condition judgment. The AI ​​judges a medical condition related to a current symptom based on past medical condition data. For example, based on the user's past medical condition data, judges a medical condition related to a current symptom. In addition, a medical condition that may recur is judged from the user's past medical condition data. Furthermore, the user's past medical condition data is analyzed, and auxiliary information for improving the accuracy of the judgment is provided. As a result, the accuracy of the medical condition judgment is improved by referring to the past medical condition data. Part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input past medical condition data to the generation AI and cause the generation AI to execute the improvement of the accuracy of the medical condition judgment.

[0096] The judgment unit can make a judgment based on the user's lifestyle or environmental information when judging the condition. For example, the judgment unit makes a judgment by considering the user's lifestyle and environmental information when judging the condition. The judgment unit, for example, uses AI to analyze the user's lifestyle and environmental information to improve the accuracy of the condition judgment. For example, the judgment unit judges the condition by considering the user's lifestyle (e.g., smoking and drinking). In addition, the judgment unit judges the condition based on the user's environmental information (e.g., workplace stress and home situation). Furthermore, the judgment unit analyzes the user's lifestyle and environmental information and provides auxiliary information for improving the accuracy of the judgment. As a result, the accuracy of the condition judgment is improved by considering the lifestyle and environmental information. Part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input the user's lifestyle and environmental information to the generation AI and cause the generation AI to execute the improvement of the accuracy of the condition judgment.

[0097] The judgment unit can estimate the user's emotion and adjust the display method of the medical condition determined based on the estimated user's emotion. The judgment unit, for example, estimates the user's emotion and adjusts the display method of the medical condition determined based on the estimated user's emotion. The judgment unit is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, if the user feels anxious, the judgment unit provides a display method that is simple and gives a sense of security. Also, if the user is relaxed, the judgment unit provides a display method that includes detailed information. Furthermore, if the user is in a hurry, the judgment unit provides a concise display method that covers the main points. This makes it possible to provide more appropriate information by adjusting the display method of the medical condition according to the user's emotion. Part or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method of the medical condition based on the emotion.

[0098] The determination unit can make a determination based on the geographical location information of the user when determining the condition. For example, the determination unit makes a determination by considering the geographical location information of the user when determining the condition. The determination unit, for example, uses AI to analyze the geographical location information of the user to improve the accuracy of the condition determination. For example, when the user is in a specific area, the condition is determined by considering a disease prevalent in that area. In addition, based on the user's current location, information on local medical institutions is provided and the condition is determined. Furthermore, based on the user's geographical location information, the condition is determined by considering environmental factors of the area (for example, temperature and humidity). As a result, the condition related to the area can be determined by considering the geographical location information. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input the user's geographical location information to the generation AI and cause the generation AI to execute the improvement of the accuracy of the condition determination.

[0099] The judgment unit can improve the accuracy of judgment by referring to the user's related literature when judging the condition. The judgment unit, for example, improves the accuracy of judgment by referring to the user's related literature when judging the condition. The judgment unit, for example, uses AI to analyze related literature and improves the accuracy of the condition judgment. The AI ​​judges the current symptom and related condition based on the related literature. For example, it judges the condition by referring to the latest medical literature related to the user's symptoms. In addition, it improves the accuracy of judgment based on the user's past medical history and related literature. Furthermore, it analyzes literature related to the user's symptoms and provides auxiliary information for improving the accuracy of judgment. As a result, the accuracy of the condition judgment is improved by referring to the related literature. Part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input related literature to the generation AI and cause the generation AI to execute the improvement of the accuracy of the condition judgment.

[0100] The suggestion unit can estimate the user's emotion and adjust the hospital suggestion method based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion and adjusts the hospital suggestion method based on the estimated user's emotion. The suggestion unit is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user feels anxious, the suggestion unit provides a hospital suggestion method that gives a sense of security. Also, when the user is relaxed, the suggestion unit provides a hospital suggestion method that includes detailed information. Furthermore, when the user is in a hurry, the suggestion unit provides a quick and concise hospital suggestion method. This allows a more appropriate hospital to be suggested by adjusting the hospital suggestion method according to the user's emotion. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's emotion data to the generation AI and cause the generation AI to execute adjustment of the hospital suggestion method based on the emotion.

[0101] The suggestion unit can improve the accuracy of the suggestion by referring to past hospital usage data when suggesting a hospital. For example, the suggestion unit improves the accuracy of the suggestion by referring to past hospital usage data when suggesting a hospital. The suggestion unit, for example, uses AI to analyze past hospital usage data and improves the accuracy of the hospital suggestion. The AI ​​suggests an optimal hospital based on the past hospital usage data. For example, based on the user's past hospital usage data, an optimal hospital is suggested. Also, from the user's past hospital usage data, a hospital that may be revisited is suggested. Furthermore, the user's past hospital usage data is analyzed and auxiliary information for improving the accuracy of the suggestion is provided. As a result, the accuracy of the hospital suggestion is improved by referring to the past hospital usage data. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input past hospital usage data to the generation AI and cause the generation AI to execute the improvement of the accuracy of the hospital suggestion.

[0102] The suggestion unit can make a suggestion based on the user's lifestyle or environmental information when suggesting a hospital. For example, when suggesting a hospital, the suggestion unit makes a suggestion taking into account the user's lifestyle and environmental information. The suggestion unit, for example, uses AI to analyze the user's lifestyle and environmental information to improve the accuracy of the hospital suggestion. For example, the suggestion unit suggests a hospital taking into account the user's lifestyle (e.g., smoking and drinking). Also, the suggestion unit suggests a hospital based on the user's environmental information (e.g., workplace stress and home situation). Furthermore, the suggestion unit provides auxiliary information for analyzing the user's lifestyle and environmental information and improving the accuracy of the suggestion. As a result, the accuracy of the hospital suggestion is improved by taking into account the lifestyle and environmental information. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's lifestyle and environmental information to the generation AI and cause the generation AI to execute the improvement of the accuracy of the hospital suggestion.

[0103] The suggestion unit can estimate the user's emotion and adjust the display method of the proposed hospital based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion and adjusts the display method of the proposed hospital based on the estimated user's emotion. The suggestion unit is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user feels anxious, the suggestion unit provides a display method that is simple and gives a sense of security. Furthermore, when the user is relaxed, the suggestion unit provides a display method that includes detailed information. Furthermore, when the user is in a hurry, the suggestion unit provides a concise display method that covers the main points. This makes it possible to provide more appropriate information by adjusting the display method of the hospital according to the user's emotion. Part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method of the hospital based on the emotion.

[0104] The suggestion unit can make a suggestion based on the geographical location information of the user when proposing a hospital. For example, the suggestion unit makes a suggestion taking into account the geographical location information of the user when proposing a hospital. The suggestion unit, for example, uses AI to analyze the geographical location information of the user and improves the accuracy of the hospital suggestion. For example, when the user is in a specific area, the suggestion unit proposes the optimal hospital in the area. In addition, based on the user's current location, a nearby hospital is proposed. Furthermore, based on the user's geographical location information, information on local medical institutions is provided and the optimal hospital is proposed. In this way, by taking the geographical location information into account, a hospital related to the area can be proposed. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's geographical location information to the generation AI and cause the generation AI to execute the improvement of the accuracy of the hospital suggestion.

[0105] The suggestion unit can improve the accuracy of the suggestion by referring to the user's related literature when suggesting a hospital. The suggestion unit, for example, improves the accuracy of the suggestion by referring to the user's related literature when suggesting a hospital. The suggestion unit, for example, uses AI to analyze the related literature and improves the accuracy of the hospital suggestion. The AI ​​proposes a hospital related to the current symptoms based on the related literature. For example, the AI ​​refers to the latest medical literature related to the user's symptoms and suggests an optimal hospital. In addition, the accuracy of the suggestion is improved based on the literature related to the user's past medical history. Furthermore, auxiliary information for analyzing literature related to the user's symptoms and improving the accuracy of the suggestion is provided. As a result, the accuracy of the hospital suggestion is improved by referring to the related literature. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the related literature to the generation AI and cause the generation AI to execute the improvement of the accuracy of the hospital suggestion.

[0106] The reservation unit can estimate the user's emotion and adjust the reservation method based on the estimated user's emotion. The reservation unit, for example, estimates the user's emotion and adjusts the reservation method based on the estimated user's emotion. The reservation unit is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, if the user feels anxious, the reservation unit provides a reservation method that is simple and reassuring. Also, if the user is relaxed, the reservation unit provides a reservation method that includes detailed information. Furthermore, if the user is in a hurry, the reservation unit provides a quick and concise reservation method. This allows for more appropriate reservations by adjusting the reservation method according to the user's emotion. Part or all of the above-mentioned processing in the reservation unit may be performed, for example, using AI, or may be performed without using AI. For example, the reservation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the reservation method based on the emotion.

[0107] The reservation unit can improve the accuracy of reservations by referring to past reservation data at the time of making a reservation. The reservation unit, for example, improves the accuracy of reservations by referring to past reservation data at the time of making a reservation. The reservation unit, for example, uses AI to analyze past reservation data and improves the accuracy of reservations. The AI ​​proposes an optimal reservation method based on past reservation data. For example, based on the user's past reservation data, an optimal reservation method is proposed. In addition, from the user's past reservation data, hospitals that may be revisited are proposed. Furthermore, the user's past reservation data is analyzed and auxiliary information for improving the accuracy of reservations is provided. As a result, the accuracy of reservations is improved by referring to the past reservation data. Part or all of the above-mentioned processing in the reservation unit may be performed, for example, using AI, or may be performed without using AI. For example, the reservation unit can input past reservation data to the generation AI and cause the generation AI to execute improvement of reservation accuracy.

[0108] The reservation unit can make a reservation based on the user's lifestyle or environmental information at the time of reservation. The reservation unit, for example, makes a reservation by considering the user's lifestyle and environmental information at the time of reservation. The reservation unit, for example, uses AI to analyze the user's lifestyle and environmental information to improve the accuracy of the reservation. For example, the reservation is made by considering the user's lifestyle (e.g., smoking and drinking). Also, the reservation is made based on the user's environmental information (e.g., workplace stress and home situation). Furthermore, the reservation unit analyzes the user's lifestyle and environmental information and provides auxiliary information for improving the accuracy of the reservation. As a result, the accuracy of the reservation is improved by considering the lifestyle and environmental information. Part or all of the above-mentioned processing in the reservation unit may be performed, for example, using AI, or may be performed without using AI. For example, the reservation unit can input the user's lifestyle and environmental information to the generation AI and cause the generation AI to execute the improvement of the accuracy of the reservation.

[0109] The reservation unit can estimate the user's emotion and determine the priority of reservations based on the estimated user's emotion. The reservation unit, for example, estimates the user's emotion and determines the priority of reservations based on the estimated user's emotion. The reservation unit is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user feels anxious, the reservation unit prioritizes important reservations. Also, when the user is relaxed, the reservation unit prioritizes reservations including detailed information. Furthermore, when the user is in a hurry, the reservation unit prioritizes the most important reservations. In this way, important reservations can be prioritized by determining the priority of reservations according to the user's emotion. Part or all of the above-mentioned processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input the user's emotion data into the generation AI and cause the generation AI to execute the determination of reservation priority based on emotion.

[0110] The reservation unit can select a reservation method based on the geographical location information of the user at the time of reservation. The reservation unit, for example, selects the optimal reservation method by considering the geographical location information of the user at the time of reservation. The reservation unit, for example, uses AI to analyze the geographical location information of the user and selects the optimal reservation method. For example, when the user is in a specific area, the reservation unit proposes the optimal reservation method for that area. In addition, based on the user's current location, reservations are made preferentially at nearby hospitals. Furthermore, based on the user's geographical location information, information on local medical institutions is provided and the optimal reservation method is selected. As a result, the optimal reservation method related to the area can be selected by considering the geographical location information. Part or all of the above-mentioned processing in the reservation unit may be performed, for example, using AI, or may be performed without using AI. For example, the reservation unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal reservation method.

[0111] The reservation unit can analyze the user's social media activity at the time of reservation and propose the optimal reservation method. The reservation unit, for example, analyzes the user's social media activity at the time of reservation and proposes the optimal reservation method. The reservation unit, for example, uses AI to analyze the user's social media activity and proposes the optimal reservation method. For example, information about the user's recent physical condition is extracted from the user's social media posts, and the optimal reservation method is proposed. In addition, stress and emotional changes are analyzed from the user's social media activity, and the optimal reservation method is proposed. Furthermore, the optimal reservation method is proposed based on the user's health concerns on social media. In this way, the optimal reservation method can be proposed to the user by analyzing social media activity. Part or all of the above-mentioned processing in the reservation unit may be performed, for example, using AI, or may be performed without using AI. For example, the reservation unit can input the user's social media activity data to the generation AI and cause the generation AI to execute a proposal of the optimal reservation method.

[0112] The request unit can estimate the user's emotion and adjust the method of requesting an ambulance based on the estimated user's emotion. The request unit, for example, estimates the user's emotion and adjusts the method of requesting an ambulance based on the estimated user's emotion. The request unit is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user feels anxious, the request unit provides a quick and concise method of requesting an ambulance. Also, when the user is relaxed, the request unit provides a method of requesting an ambulance including detailed information. Furthermore, when the user is in a hurry, the request unit promptly requests an ambulance based on the past medical history and current symptoms. As a result, a quick and appropriate request can be made by adjusting the method of requesting an ambulance according to the user's emotion. A part or all of the above-mentioned processing in the request unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the request unit can input the user's emotion data into the generation AI and cause the generation AI to execute adjustment of the method of requesting an ambulance based on the emotion.

[0113] The request unit can improve the accuracy of the request by referring to past request data when requesting an ambulance. For example, when requesting an ambulance, the request unit improves the accuracy of the request by referring to past request data. The request unit, for example, uses AI to analyze the past request data and improves the accuracy of the request. The AI ​​proposes an optimal method of requesting an ambulance based on the past request data. For example, based on the user's past request data, an optimal method of requesting an ambulance is proposed. In addition, an ambulance is requested based on symptoms that may recur from the user's past request data. Furthermore, the user's past request data is analyzed and auxiliary information for improving the accuracy of the request is provided. As a result, the accuracy of the ambulance request is improved by referring to the past request data. Part or all of the above-mentioned processing in the request unit may be performed, for example, using AI, or may be performed without using AI. For example, the request unit can input past request data to the generation AI and cause the generation AI to execute improvement of the accuracy of the request.

[0114] The request unit can make a request based on the user's lifestyle or environmental information when requesting an ambulance. For example, when requesting an ambulance, the request unit makes a request taking into consideration the user's lifestyle and environmental information. The request unit, for example, uses AI to analyze the user's lifestyle and environmental information to improve the accuracy of the request. For example, an ambulance is requested taking into consideration the user's lifestyle (e.g., smoking and drinking). In addition, an ambulance is requested based on the user's environmental information (e.g., workplace stress and home situation). Furthermore, auxiliary information is provided to analyze the user's lifestyle and environmental information and improve the accuracy of the request. As a result, the accuracy of the ambulance request is improved by taking into consideration the lifestyle and environmental information. A part or all of the above-mentioned processing in the request unit may be performed using AI, for example, or may be performed without using AI. For example, the request unit can input the user's lifestyle and environmental information into the generation AI and cause the generation AI to execute the improvement of the accuracy of the request.

[0115] The request unit can estimate the user's emotion and determine the priority of requests based on the estimated user's emotion. The request unit, for example, estimates the user's emotion and determines the priority of requests based on the estimated user's emotion. The request unit is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user feels anxious, the request unit prioritizes important requests. Also, when the user is relaxed, the request unit prioritizes requests including detailed information. Furthermore, when the user is in a hurry, the request unit prioritizes the most important request. In this way, by determining the priority of requests according to the user's emotion, important requests can be prioritized. A part or all of the above-mentioned processing in the request unit may be performed using, for example, AI, or may be performed without using AI. For example, the request unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of requests based on emotion.

[0116] The request unit can select a request method based on the geographical location information of the user when requesting an ambulance. For example, when requesting an ambulance, the request unit selects the optimal request method by considering the geographical location information of the user. The request unit, for example, uses AI to analyze the geographical location information of the user and selects the optimal request method. For example, when the user is in a specific area, the request unit proposes the optimal ambulance request method for that area. In addition, based on the user's current location, a request for a nearby ambulance is prioritized. Furthermore, based on the user's geographical location information, information on local medical institutions is provided and the optimal ambulance request method is selected. As a result, the optimal ambulance request method related to the area can be selected by considering the geographical location information. Part or all of the above-mentioned processing in the request unit may be performed, for example, using AI, or may be performed without using AI. For example, the request unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal request method.

[0117] The request unit can analyze the user's social media activity and propose the optimal request method when requesting an ambulance. For example, when requesting an ambulance, the request unit analyzes the user's social media activity and proposes the optimal request method. The request unit, for example, uses AI to analyze the user's social media activity and proposes the optimal request method. For example, information about the user's recent physical condition is extracted from the user's social media posts, and the optimal request method is proposed. In addition, changes in stress and emotions are analyzed from the user's social media activity, and the optimal request method is proposed. Furthermore, the optimal request method for an ambulance is proposed based on the user's health concerns on social media. As a result, the optimal request method for an ambulance can be proposed to the user by analyzing social media activity. Part or all of the above-mentioned processing in the request unit may be performed, for example, using AI, or may be performed without using AI. For example, the request unit can input the user's social media activity data to the generation AI and cause the generation AI to execute the proposal of the optimal request method.

[0118] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0119] When the user inputs his / her physical condition, the reception unit can refer to the user's past physical condition data and automatically complete the input contents. For example, symptoms that the user has frequently input in the past can be automatically displayed as candidates, reducing the effort of inputting. In addition, the reception unit can predict related symptoms based on the past physical condition data and suggest them to the user. This allows the user to input his / her physical condition quickly and accurately. Furthermore, the reception unit can analyze the user's past physical condition data, predict symptoms related to specific times of day or seasons, and customize the input contents.

[0120] The suggestion unit can suggest hospitals taking into consideration the user's current activity status when inputting the user's physical condition. For example, when the user is at work, the suggestion unit can preferentially suggest nearby hospitals and clinics to allow the user to receive medical treatment quickly. Also, when the user is traveling, the suggestion unit can suggest the most suitable medical institution based on the user's current location. Furthermore, when the user is exercising, the suggestion unit can also suggest a medical institution that specializes in sports medicine. This makes it possible to suggest appropriate medical institutions according to the user's activity status.

[0121] When the user inputs his / her physical condition, the suggestion unit can suggest a hospital taking into consideration the user's dietary history. For example, if the user has specific dietary restrictions, the suggestion unit can suggest a medical institution that can accommodate those restrictions. In addition, the suggestion unit can predict related symptoms based on the contents of meals recently consumed by the user and suggest an appropriate medical institution. Furthermore, the suggestion unit can analyze the user's dietary history and suggest a hospital based on the nutritional state. In this way, it is possible to suggest an appropriate medical institution based on the user's dietary history.

[0122] The reception unit can estimate the user's emotions and provide feedback of the physical condition input based on the estimated user's emotions. For example, if the user feels anxious, the reception unit displays an encouraging message to provide a sense of security. If the user feels relaxed, the reception unit provides detailed feedback to deepen the user's understanding. Furthermore, if the user is in a hurry, the reception unit provides concise feedback to allow the user to proceed to the next step quickly. This makes it possible to provide appropriate feedback according to the user's emotions.

[0123] The reception unit can analyze the user's past physical condition input history and improve the accuracy of the input contents. For example, the current input contents are complemented based on the physical condition information previously input by the user. Also, based on the past input history, related symptoms can be predicted and suggested to the user. Furthermore, the reception unit can analyze the user's past input history, find specific patterns, and provide feedback to improve the accuracy of the input contents. In this way, the accuracy of the input contents can be improved by utilizing the user's past input history.

[0124] The reception unit can perform filtering based on the user's current living situation or field of interest when inputting the physical condition. For example, when the user inputs the current living situation, related physical condition information is preferentially displayed based on the input. Also, related physical condition information is filtered based on the user's field of interest (e.g., health management or fitness). Furthermore, an appropriate physical condition input method is suggested based on the user's living situation (e.g., work stress or home situation). This makes it possible to preferentially display related physical condition information based on the user's living situation or field of interest.

[0125] The reception unit can estimate the user's emotions and determine the priority of the physical condition information to be input based on the estimated user's emotions. For example, if the user is nervous, the reception unit encourages the user to input important physical condition information with priority. If the user is relaxed, the reception unit suggests that the user input detailed physical condition information. Furthermore, if the user is in a hurry, the reception unit prioritizes input of the most important physical condition information. In this way, important information can be acquired with priority by determining the priority of physical condition information according to the user's emotions.

[0126] When inputting physical condition information, the reception unit can preferentially input information that is highly relevant based on the user's geographical location information. For example, when the user is in a specific area, the reception unit preferentially inputs physical condition information related to diseases that are prevalent in that area. Also, based on the user's current location, information on local medical institutions is provided and related physical condition information is input. Furthermore, based on the user's geographical location information, physical condition information related to local environmental factors (e.g., temperature and humidity) is preferentially input. In this way, by taking into account the geographical location information, it is possible to preferentially obtain physical condition information related to the area.

[0127] The reception unit can analyze the user's social media activity and input related information when inputting the user's physical condition. For example, information related to recent physical condition is extracted from the user's social media posts and input. In addition, changes in stress and emotions are analyzed from the user's social media activity, and related physical condition information is input. Furthermore, related physical condition information is input based on the user's health concerns on social media. In this way, information related to the user's physical condition can be efficiently obtained by analyzing social media activity.

[0128] The identification unit can estimate the user's emotions and adjust the disease name identification method based on the estimated user's emotions. For example, when the user feels anxious, the identification unit provides a quick and concise disease name identification method. When the user feels relaxed, the identification unit identifies the disease name through detailed questions. When the user is in a hurry, the identification unit quickly identifies the disease name based on the user's past medical history and current symptoms. In this way, a more appropriate disease name can be identified by adjusting the disease name identification method according to the user's emotions.

[0129] The process flow of the second embodiment will be briefly described below.

[0130] Step 1: The reception unit is a section where the user inputs their physical condition. The user inputs symptoms such as fever, cough, and headache. The reception unit provides an interface such as a touch screen, voice input, and keyboard input. Step 2: The identification unit identifies the name of the disease based on the information input by the reception unit. The identification unit uses AI to analyze the input information and identify the name of the disease. For example, if the patient has a fever and cough, the AI ​​will identify the possibility of influenza. Step 3: The judgment unit judges the condition based on the disease name identified by the identification unit. The judgment unit uses AI to judge the severity and progression of the condition. For example, if it is a mild cold, it suggests staying at home and if the symptoms are severe, it suggests going to the hospital. Step 4: The suggestion unit suggests appropriate hospitals based on the results of the judgment by the judgment unit. The suggestion unit uses AI to suggest hospitals with specialists or nearby hospitals. Step 5: The reservation department makes a reservation at the hospital suggested by the proposal department. The reservation department uses AI to send the patient's condition, past medical history, medication prescription status, etc. to the hospital and automatically makes the reservation. Step 6: The request unit requests an ambulance if the judgment unit determines that the emergency is high. The request unit uses AI to communicate the location, symptoms, etc. For example, if there are symptoms of a heart attack, the AI ​​immediately requests an ambulance and communicates the user's current location and symptoms to the emergency team.

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0132] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0133] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements including the reception unit, the identification unit, the judgment unit, the proposal unit, the reservation unit, and the request unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit inputs the user's physical condition using a touch screen or a voice input device of the smart device 14. The identification unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and identifies the name of the disease using AI. The judgment unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and judges the condition of the disease. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests an appropriate hospital. The reservation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and makes a reservation at the hospital. The request unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and requests an ambulance in an emergency. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0135] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0136] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0142] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0143] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0145] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0146] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0147] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0149] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements including the reception unit, the identification unit, the judgment unit, the proposal unit, the reservation unit, and the request unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit inputs the user's physical condition using a voice input device of the smart glasses 214. The identification unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and identifies the name of the disease using AI. The judgment unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and judges the condition of the disease. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests an appropriate hospital. The reservation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and makes a reservation at the hospital. The request unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and requests an ambulance in an emergency. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0151] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0152] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0153] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0154] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0155] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0157] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0158] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0159] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0160] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0161] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0162] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0163] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0164] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0165] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0166] Each of the multiple elements including the above-mentioned reception unit, identification unit, judgment unit, suggestion unit, reservation unit, and request unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit inputs the user's physical condition using a voice input device of the headset type terminal 314. The identification unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and identifies the name of the disease using AI. The judgment unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and judges the condition of the disease. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests an appropriate hospital. The reservation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and makes a reservation at the hospital. The request unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and requests an ambulance in an emergency. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0167] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0168] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0169] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0170] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0171] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0172] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0173] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0174] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0175] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0176] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0177] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0178] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0179] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0180] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0181] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0182] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0183] Each of the multiple elements including the reception unit, the identification unit, the judgment unit, the proposal unit, the reservation unit, and the request unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit inputs the user's physical condition using a voice input device of the robot 414. The identification unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and identifies the name of the disease using AI. The judgment unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and judges the condition of the disease. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests an appropriate hospital. The reservation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and makes a reservation at the hospital. The request unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and requests an ambulance in an emergency. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0184] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0185] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0186] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0187] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0188] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0189] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0190] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0191] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0192] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0193] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0194] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0195] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0196] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0197] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0198] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0199] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0200] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0201] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0202] (Appendix 1) A reception desk where you can input your physical condition, An identification unit that identifies a disease name based on the information input by the reception unit; A determination unit for determining a disease state based on the disease name identified by the identification unit; a suggestion unit that suggests hospitals based on the result of the determination by the determination unit; a reservation unit for making a reservation at the hospital recommended by the recommendation unit; A request unit that requests an ambulance when the determination unit determines that the emergency is high. A system characterized by: (Appendix 2) The suggestion unit, Suggest nearby hospitals that suit your medical condition 2. The system of claim 1, (Appendix 3) The suggestion unit, Automatically submit your symptom or prescription history online in advance 2. The system of claim 1, (Appendix 4) The reception unit is Estimate the user's emotions and adjust the input method of physical condition based on the estimated user emotions. 2. The system of claim 1, (Appendix 5) The reception unit is Analyze the user's past health input history and select the input method 2. The system of claim 1, (Appendix 6) The reception unit is When inputting physical condition, filtering is performed based on the user's current life situation or areas of interest. 2. The system of claim 1, (Appendix 7) The reception unit is Estimate the user's emotions and determine the priority of input physical condition information based on the estimated user emotions. 2. The system of claim 1, (Appendix 8) The reception unit is When inputting physical condition information, the system prioritizes input of relevant information based on the user's geographic location. 2. The system of claim 1, (Appendix 9) The reception unit is When inputting a user's physical condition, the system analyzes the user's social media activity and inputs related information. 2. The system of claim 1, (Appendix 10) The identification unit is Inferring user emotions and adjusting the disease name identification method based on the estimated user emotions 2. The system of claim 1, (Appendix 11) The identification unit is When identifying a disease, improve accuracy by referring to past medical history data 2. The system of claim 1, (Appendix 12) The identification unit is When identifying a disease, the name is determined based on the user's lifestyle or environmental information. 2. The system of claim 1, (Appendix 13) The identification unit is Inferring a user's emotions and adjusting the display method of the identified disease name based on the estimated user's emotions 2. The system of claim 1, (Appendix 14) The identification unit is When identifying a disease, the name is determined based on the user's geographic location. 2. The system of claim 1, (Appendix 15) The identification unit is When identifying a disease, the accuracy of the identification is improved by referring to the user's related literature. 2. The system of claim 1, (Appendix 16) The determination unit is To estimate a user's emotion and adjust a method of judging a medical condition based on the estimated user's emotion 2. The system of claim 1, (Appendix 17) The determination unit is When assessing a patient's condition, past medical condition data can be referenced to improve the accuracy of the assessment. 2. The system of claim 1, (Appendix 18) The determination unit is When assessing a patient's condition, the decision is made based on the user's lifestyle or environmental information. 2. The system of claim 1, (Appendix 19) The determination unit is To estimate a user's emotion and adjust a display method of a determined medical condition based on the estimated user's emotion. 2. The system of claim 1, (Appendix 20) The determination unit is When assessing a patient's condition, the decision is based on the user's geographic location. 2. The system of claim 1, (Appendix 21) The determination unit is When assessing a patient's condition, the system can improve the accuracy of the assessment by referring to related literature. 2. The system of claim 1, (Appendix 22) The suggestion unit, Estimate the user's emotions and adjust the hospital suggestion method based on the estimated user's emotions 2. The system of claim 1, (Appendix 23) The suggestion unit, When suggesting hospitals, improve the accuracy of the suggestions by referring to past hospital usage data 2. The system of claim 1, (Appendix 24) The suggestion unit, When suggesting hospitals, suggestions are made based on the user's lifestyle or environmental information. 2. The system of claim 1, (Appendix 25) The suggestion unit, Estimate user sentiment and adjust the display of suggested hospitals based on the estimated user sentiment 2. The system of claim 1, (Appendix 26) The suggestion unit, When suggesting hospitals, suggestions are made based on the user's geographic location information. 2. The system of claim 1, (Appendix 27) The suggestion unit, When suggesting hospitals, the system improves the accuracy of the suggestions by referring to the user's related literature. 2. The system of claim 1, (Appendix 28) The reservation unit: To estimate a user's emotion and adjust a reservation method based on the estimated user's emotion. 2. The system of claim 1, (Appendix 29) The reservation unit: When making a reservation, refer to past reservation data to improve reservation accuracy 2. The system of claim 1, (Appendix 30) The reservation unit: When making a reservation, make the reservation based on the user's lifestyle or environmental information 2. The system of claim 1, (Appendix 31) The reservation unit: To estimate a user's emotion and prioritize reservations based on the estimated user's emotion. 2. The system of claim 1, (Appendix 32) The reservation unit: When making a reservation, select the reservation method based on the user's geographic location 2. The system of claim 1, (Appendix 33) The reservation unit: When making a reservation, analyze the user's social media activity to suggest the best way to make a reservation 2. The system of claim 1, (Appendix 34) The request unit: Estimate the user's emotions and adjust the ambulance request method based on the estimated user's emotions 2. The system of claim 1, (Appendix 35) The request unit: When requesting an ambulance, improve the accuracy of the request by referring to past request data 2. The system of claim 1, (Appendix 36) The request unit: When requesting an ambulance, the request is made based on the user's lifestyle or environmental information. 2. The system of claim 1, (Appendix 37) The request unit: Estimate user emotions and prioritize requests based on the estimated user emotions 2. The system of claim 1, (Appendix 38) The request unit: When requesting an ambulance, the method of request is selected based on the user's geographic location. 2. The system of claim 1, (Appendix 39) The request unit: When requesting an ambulance, the system analyzes the user's social media activity to suggest the best way to request an ambulance 2. The system of claim 1, [Explanation of symbols]

[0203] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception desk where you can input your physical condition, An identification unit that identifies a disease name based on the information input by the reception unit; A determination unit for determining a disease state based on the disease name identified by the identification unit; a suggestion unit that suggests hospitals based on the result of the determination by the determination unit; a reservation unit for making a reservation at the hospital recommended by the recommendation unit; A request unit that requests an ambulance when the determination unit determines that the emergency is high. A system characterized by:

2. The suggestion unit, Suggest nearby hospitals that suit your medical condition 2. The system of claim 1 .

3. The suggestion unit, Automatically submit your symptom or prescription history online in advance 2. The system of claim 1 .

4. The reception unit is Estimate the user's emotions and adjust the input method of physical condition based on the estimated user emotions.

2. The system of claim 1 .

5. The reception unit is Analyze the user's past health input history and select the input method 2. The system of claim 1 .

6. The reception unit is When inputting physical condition, filtering is performed based on the user's current life situation or areas of interest.

2. The system of claim 1 .

7. The reception unit is Estimate the user's emotions and determine the priority of input physical condition information based on the estimated user emotions.

2. The system of claim 1 .

8. The reception unit is When inputting physical condition information, the system prioritizes input of relevant information based on the user's geographic location.

2. The system of claim 1 .

Images