System

Abstract

To provide a system that proposes appropriate hospitals based on users' symptoms and unifies formats of medical charts.SOLUTION: A system according to an embodiment includes a reception unit, an identification unit, a proposal unit, a unification unit, and an input unit. The reception unit inputs a user's symptoms. The identification unit identifies an illness based on the information input by the reception unit. The proposal unit proposes a hospital based on the illness identified by the identification unit. The unification unit unifies formats of medical charts. The input unit inputs a diagnosis result.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] Conventional technologies do not adequately suggest appropriate hospitals based on a user's symptoms or standardize the format of medical records, and there is room for improvement.

[0005] The system according to the embodiment aims to suggest appropriate hospitals based on the user's symptoms and to standardize the format of medical records. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an identification unit, a suggestion unit, a unification unit, and an input unit. The reception unit inputs a user's symptoms. The identification unit identifies an illness based on the information input by the reception unit. The suggestion unit suggests a hospital based on the illness identified by the identification unit. The unification unit unifies the format of medical records. The input unit inputs a diagnosis result. Effect of the Invention

[0007] The system according to the embodiment can suggest appropriate hospitals based on the user's symptoms and standardize the format of medical records. [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 interview system according to the embodiment of the present invention is a system that inputs the user's symptoms, identifies the disease using a generation AI, and suggests a hospital. This system includes a reception unit that inputs the user's symptoms, an identification unit that identifies the disease, a suggestion unit that suggests a hospital, a unification unit that standardizes the format of the medical record, and an input unit that inputs the diagnosis result. For example, a user inputs symptoms such as "I have a headache" or "I have a fever" using a medical interview app. This information is sent to the generation AI, which narrows down possible diseases based on the symptoms. The generation AI identifies the most likely disease based on past data and medical knowledge. Next, based on the disease identified by the generation AI, it suggests an appropriate hospital to the user. Based on information about affiliated hospitals, it selects the hospital that is best suited to the user's current location and symptoms, and notifies the user. For example, a notification such as "We recommend that you go to a nearby internal medicine clinic" is displayed. In addition, the generation AI standardizes the medical records. The generation AI analyzes the medical records, which have different formats for each medical institution, and converts them into a unified format. This makes it easier to share information between different medical institutions, improving the efficiency of medical treatment. In addition, after the user is examined at a hospital, the doctor enters the diagnosis result into the app. This information is sent to the generating AI and used to update the app's ability to identify illnesses. For example, if a new diagnosis is obtained for a particular symptom, that information is reflected in the generating AI, enabling more accurate disease identification in the next and subsequent interviews. This system allows users to receive prompt and appropriate medical services, promotes information sharing between medical institutions, and improves the efficiency of medical treatment. In addition, by using the generating AI, the app's ability to identify illnesses is constantly updated, enabling more accurate diagnoses. This allows the medical interview system to input the user's symptoms, identify illnesses using the generating AI, and suggest hospitals.

[0029] The medical interview system according to the embodiment includes a reception unit, a specification unit, a proposal unit, a unification unit, and an input unit. The reception unit inputs the symptoms of the user. The symptoms of the user include, for example, headache, fever, cough, etc., but are not limited to such examples. The reception unit provides, for example, an interface for the user to input the symptoms to the application. The specification unit specifies the disease based on the information input by the specification unit using a generation AI. The specification unit specifies the disease based on, for example, past data and medical knowledge. The generation AI specifies the disease based on the symptoms using a text generation AI (for example, LLM). The specification unit can also specify the disease based on a combination of symptoms and test results using the generation AI. The proposal unit suggests a hospital based on the disease specified by the specification unit. The proposal unit selects an optimal hospital based on, for example, the user's current location and symptoms. The proposal unit can also suggest a hospital based on criteria such as the hospital's specialty, distance, and rating using the generation AI. The unification unit unifies the format of the medical record. For example, the unification unit analyzes medical records in formats that differ for each medical institution and converts them into a unified format. The generation AI analyzes medical records in different formats and standardizes data formats and items. The input unit is used by a doctor to input a diagnosis result. The input unit provides, for example, an interface for inputting a diagnosis result. The input unit can also input a diagnosis result using the generation AI and transmit the information to the generation AI. As a result, the medical interview system according to the embodiment can input a user's symptoms, identify a disease using the generation AI, and suggest a hospital. 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, the generation AI can input a user's symptoms, identify a disease using the generation AI, and suggest a hospital. The generation AI can identify a disease based on past data and medical knowledge and suggest a hospital. The generation AI can unify the format of the medical record, input a diagnosis result, and transmit the information to the generation AI. As a result, the medical interview system can input a user's symptoms, identify a disease using the generation AI, and suggest a hospital.

[0030] The reception unit inputs the symptoms of the user. The symptoms of the user include, but are not limited to, headache, fever, cough, and the like. The reception unit, for example, provides an interface for the user to input the symptoms into the app. Specifically, the reception unit has a user interface that the user can intuitively operate, and allows the user to input the symptoms in detail through a text input field or multiple choice questions. For example, when the user inputs "headache", more detailed questions such as "location of headache", "intensity of pain", and "frequency of pain" are displayed. In addition, by providing a voice input function, the user can also explain the symptoms by voice. The voice input is converted into text using voice recognition technology and is taken into the system. In addition, the reception unit also has a function of automatically generating related questions by referring to the user's past medical history and health data. This allows the user to efficiently input symptoms, and the system to collect more accurate information.

[0031] The identification unit uses the generation AI to identify the disease based on the information input by the reception unit. The identification unit identifies the disease based on, for example, past data and medical knowledge. The generation AI identifies the disease based on the symptoms using a text generation AI (for example, LLM). Specifically, the generation AI analyzes the symptoms input by the user and identifies the most likely disease by referring to a past case database and medical literature. For example, if the user inputs "fever" and "cough", the generation AI evaluates the possibility of influenza, cold, pneumonia, etc. from the combination of these symptoms. In addition, the generation AI takes into account additional information such as the severity of symptoms, the duration of onset, and concurrent symptoms to make a more accurate diagnosis. Furthermore, the identification unit can improve the accuracy of the diagnosis by incorporating additional information such as test results and image data. For example, it analyzes blood test results and X-ray images to evaluate the relevance to the symptoms. This allows the identification unit to quickly and accurately identify diseases based on the user's symptoms and propose appropriate treatment.

[0032] The suggestion unit suggests a hospital based on the illness identified by the identification unit. The suggestion unit selects the most suitable hospital based on, for example, the user's current location and symptoms. Specifically, the suggestion unit acquires the user's location information and generates a list of nearby medical institutions. The generation AI can also suggest hospitals based on criteria such as the hospital's specialty, distance, and rating. For example, if the user is diagnosed with "pneumonia," the suggestion unit preferentially suggests hospitals and clinics specializing in respiratory diseases. It also takes into account the hospital's congestion status and reservation availability, allowing the user to receive a medical examination quickly. In addition, the suggestion unit also has a function to select an appropriate medical institution by referring to the user's insurance information and past medical history. This allows the user to easily find the medical institution that is best suited to them and receive appropriate treatment quickly.

[0033] The unification department will unify the format of medical records. For example, the unification department will analyze medical records in different formats for each medical institution and convert them into a unified format. Specifically, the generation AI will analyze medical records in different formats and standardize data formats and items. For example, if an item listed as "diagnosis result" at one hospital is listed as "findings" at another hospital, the generation AI will recognize these as the same item and convert them into a unified format. It also has the function of automatically classifying the contents of the medical records and extracting the necessary information. This makes it easier to share information between medical institutions, improving the efficiency and accuracy of medical treatment. In addition, the unification department will promote the digitalization of medical records and has the function of converting paper-based medical records into electronic medical records. This makes it easier for medical institutions to manage medical records and centrally manage patients' medical history.

[0034] The input unit is where the doctor inputs the diagnosis results. The input unit provides, for example, an interface for inputting the diagnosis results. Specifically, the input unit has a user interface that the doctor can easily operate, and provides a dedicated form for inputting the diagnosis results, prescriptions, test results, etc. Furthermore, the generation AI can be used to input the diagnosis results and send the information to the generation AI. For example, when the doctor inputs the diagnosis results by voice, the voice recognition technology is used to convert the voice into text and the text is imported into the system. The input unit also has a function that allows the doctor to input the diagnosis results while referring to the past medical history and related medical information. This allows the doctor to input the diagnosis results efficiently, improving the quality of medical care. Furthermore, the input unit also has a function that automatically analyzes the input diagnosis results, extracts necessary information, and reflects it in the medical record. This reduces the burden on the doctor and improves the efficiency of medical care.

[0035] The identification unit can identify a disease based on past data or medical knowledge. The identification unit identifies a disease based on past data such as past diagnosis results and a patient's medical history. The identification unit can analyze past data and identify a disease using a generation AI. The identification unit can also identify a disease based on medical knowledge such as medical books, papers, and guidelines. The generation AI executes an algorithm for identifying a disease based on medical knowledge. For example, the identification unit identifies a related disease based on past diagnosis results. The identification unit can analyze past diagnosis results and identify a disease using a generation AI. The identification unit can also execute an algorithm for identifying a disease based on medical books and papers. This improves the accuracy of disease identification by utilizing past data and medical knowledge. 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 identify a disease using an AI model that uses past data and medical knowledge as input and identifies a disease.

[0036] The suggestion unit can select an appropriate hospital based on the user's current location or symptoms. The suggestion unit, for example, selects the nearest hospital based on the user's current location. The suggestion unit can acquire the user's current location and select the nearest hospital using the generation AI. The suggestion unit can also select an appropriate hospital based on the user's symptoms. The suggestion unit can analyze the user's symptoms and select an appropriate hospital using the generation AI. For example, the suggestion unit selects the nearest hospital based on GPS data of the user's current location. The suggestion unit can acquire the user's current location and select the nearest hospital using the generation AI. The suggestion unit can also select an appropriate hospital based on the user's symptoms. The suggestion unit can analyze the user's symptoms and select an appropriate hospital using the generation AI. This makes it possible to provide prompt and appropriate medical services by suggesting the optimal hospital based on the user's current location and symptoms. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can suggest hospitals using an AI model that inputs the user's current location and symptoms and selects appropriate hospitals.

[0037] The unification unit can analyze medical records in different formats of each medical institution and convert them into a unified format. The unification unit analyzes medical records in different formats, such as paper medical records and electronic medical records. The unification unit can analyze medical records in different formats and convert them into a unified format using a generation AI. The generation AI standardizes the data format and items of the medical records. For example, the unification unit scans paper medical records and converts them into digital data. The unification unit can scan paper medical records and convert them into digital data using a generation AI. The unification unit can also analyze electronic medical records and convert them into a unified format. The unification unit can analyze electronic medical records and convert them into a unified format using a generation AI. This makes it easier to share information between different medical institutions by unifying the format of the medical records. Some or all of the above-mentioned processing in the unification unit may be performed, for example, using AI, or may be performed without using AI. For example, the unification unit can unify the medical records using an AI model that inputs medical records in different formats and converts them into a unified format.

[0038] The input unit allows a doctor to input a diagnosis result and transmit the information to the artificial intelligence. The input unit provides, for example, an interface through which a doctor inputs a diagnosis result. The input unit can input a diagnosis result using a generation AI and transmit the information to the generation AI. The generation AI updates the disease identification power based on the diagnosis result. For example, the input unit allows a doctor to input a diagnosis result and transmit the information to the generation AI. The input unit can input a diagnosis result using a generation AI and transmit the information to the generation AI. Thereby, by transmitting the diagnosis result to the generation AI, the disease identification power of the app can be constantly updated. Some or all of the above-mentioned processing in the input unit may be performed, for example, using AI, or may be performed without using AI. For example, the input unit can transmit a diagnosis result to the generation AI using an AI model that takes the diagnosis result as an input and updates the disease identification power.

[0039] The reception unit can analyze the user's past symptom input history and suggest the optimal input method. The reception unit, for example, automatically displays symptoms that the user frequently input in the past as candidates. The reception unit can analyze the user's past symptom input history and display the frequently input symptoms as candidates using the generation AI. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. The reception unit can analyze the user's past input methods and suggest the optimal input method using the generation AI. Furthermore, the reception unit can predict and suggest symptoms to be input in a specific time period from the user's past input history. The reception unit can analyze the user's past input history and predict and suggest symptoms to be input in a specific time period using the generation AI. As a result, the optimal input method can be suggested to the user by analyzing the past symptom input history. 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 past symptom input history to the generation AI and cause the generation AI to execute a suggestion of the optimal input method.

[0040] The reception unit can filter the input contents based on the user's current health condition and lifestyle habits when inputting symptoms. The reception unit, for example, preferentially displays related symptoms based on the user's current health condition. The reception unit can analyze the user's health condition using the generation AI and preferentially display related symptoms. The reception unit can also filter the input contents taking into account the user's lifestyle habits (smoking, drinking, etc.). The reception unit can analyze the user's lifestyle habits using the generation AI and filter the input contents. Furthermore, the reception unit can also preferentially display related symptoms based on the user's medical history. The reception unit can analyze the user's medical history using the generation AI and preferentially display related symptoms. As a result, by filtering the input contents based on the user's health condition and lifestyle habits, more relevant symptoms can be input. 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 data on the user's health condition and lifestyle habits to the generation AI and cause the generation AI to filter the input contents.

[0041] The reception unit can preferentially input highly relevant symptoms in consideration of the geographical location information of the user when inputting symptoms. The reception unit preferentially displays related symptoms based on, for example, the climate and environment of the user's current location. The reception unit can acquire the geographical location information of the user using the generation AI and preferentially display related symptoms. The reception unit can also preferentially display region-specific diseases and symptoms based on the geographical location information of the user. The reception unit can acquire the geographical location information of the user using the generation AI and preferentially display region-specific diseases and symptoms. Furthermore, the reception unit can also preferentially display diseases and symptoms that are prevalent in the vicinity based on the geographical location information of the user. The reception unit can acquire the geographical location information of the user using the generation AI and preferentially display diseases and symptoms that are prevalent in the vicinity. Thereby, region-specific symptoms can be preferentially input by considering the geographical location information of the user. 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 geographical location information to the generation AI and cause the generation AI to prioritize the display of highly relevant symptoms.

[0042] The reception unit can analyze the user's social media activity and input the related symptoms when inputting the symptoms. The reception unit, for example, analyzes the user's social media posts and automatically inputs the related symptoms. The reception unit can use the generation AI to analyze the user's social media activity and automatically input the related symptoms. The reception unit can also extract health-related keywords from the user's social media activity and input the related symptoms. The reception unit can use the generation AI to analyze the user's social media activity, extract health-related keywords, and input the related symptoms. The reception unit can also analyze the user's friends' posts on social media and input the related symptoms. The reception unit can use the generation AI to analyze the user's social media activity and input the related symptoms from the friends' posts. In this way, the symptoms related to the user can be automatically input by analyzing the social media activity. 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 user's social media activity to the generation AI and cause the generation AI to execute the input of the related symptoms.

[0043] The identification unit can improve the accuracy of identification by referring to the user's past diagnostic results when identifying a disease. The identification unit, for example, preferentially identifies related diseases based on the user's past diagnostic results. The identification unit can use the generation AI to analyze the user's past diagnostic results and preferentially identify related diseases. The identification unit can also analyze the user's past diagnostic results and improve the accuracy of identification. The identification unit can use the generation AI to analyze the user's past diagnostic results and improve the accuracy of identification. Furthermore, the identification unit can also adjust the specific algorithm by referring to the user's past diagnostic results. The identification unit can use the generation AI to analyze the user's past diagnostic results and adjust the specific algorithm. As a result, the accuracy of disease identification is improved by referring to the past diagnostic results. A 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 past diagnostic results to the generation AI and cause the generation AI to execute the improvement of the accuracy of identification.

[0044] The identification unit can customize the specific technique based on the user's living environment and occupation when identifying a disease. The identification unit customizes the specific technique, for example, taking into account the user's living environment (urban area, rural area, etc.). The identification unit can analyze the user's living environment and customize the specific technique using the generation AI. The identification unit can also customize the specific technique taking into account the user's occupation (desk work, physical labor, etc.). The identification unit can analyze the user's occupation and customize the specific technique using the generation AI. Furthermore, the identification unit can also customize the specific technique taking into account the user's lifestyle (smoking, drinking, etc.). The identification unit can analyze the user's lifestyle and customize the specific technique using the generation AI. As a result, by customizing the specific technique based on the user's living environment and occupation, more appropriate disease identification is possible. 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 data on the user's living environment and occupation to the generation AI and cause the generation AI to execute customization of the specific technique.

[0045] The identification unit can improve the accuracy of identification by considering the geographical location information of the user when identifying a disease. The identification unit improves the accuracy of identification by considering, for example, the climate and environment of the user's current location. The identification unit can acquire the geographical location information of the user using the generation AI and improve the accuracy of identification. The identification unit can also preferentially identify region-specific diseases based on the geographical location information of the user. The identification unit can acquire the geographical location information of the user using the generation AI and preferentially identify region-specific diseases. The identification unit can also preferentially identify diseases that are prevalent in the neighborhood based on the geographical location information of the user. The identification unit can acquire the geographical location information of the user using the generation AI and preferentially identify diseases that are prevalent in the neighborhood. As a result, region-specific diseases can be preferentially identified by considering the geographical location information of the user. A 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 identification.

[0046] The identification unit can adjust the specific technique by analyzing the social media activity of the user when identifying the illness. The identification unit, for example, analyzes the social media posts of the user to identify the relevant illness. The identification unit can use the generation AI to analyze the social media activity of the user to identify the relevant illness. The identification unit can also extract health-related keywords from the user's social media activity and adjust the specific technique. The identification unit can use the generation AI to analyze the user's social media activity, extract health-related keywords, and adjust the specific technique. The identification unit can also analyze the posts of the user's friends on social media to identify the relevant illness. The identification unit can use the generation AI to analyze the user's social media activity and identify the relevant illness from the posts of the friends. In this way, the illness related to the user can be identified by analyzing the social media activity. A 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 social media activity to the generation AI and cause the generation AI to execute the adjustment of the specific technique.

[0047] When proposing a hospital, the suggestion unit can make an optimal suggestion by referring to the user's past hospital use history. The suggestion unit suggests an optimal hospital, for example, based on a hospital used by the user in the past. The suggestion unit can use the generation AI to analyze the user's past hospital use history and suggest an optimal hospital. The suggestion unit can also analyze the user's past hospital use history and suggest an optimal hospital. The suggestion unit can use the generation AI to analyze the user's past hospital use history and suggest an optimal hospital. Furthermore, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past hospital use history. The suggestion unit can use the generation AI to analyze the user's past hospital use history and improve the accuracy of the suggestion. As a result, the optimal hospital can be suggested to the user by referring to the past hospital use history. 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 past hospital use history to the generation AI and cause the generation AI to execute the suggestion of the optimal hospital.

[0048] When proposing a hospital, the suggestion unit can customize the proposed content based on the user's current health condition and living environment. The suggestion unit, for example, proposes an optimal hospital based on the user's current health condition. The suggestion unit can use the generation AI to analyze the user's health condition and propose the optimal hospital. The suggestion unit can also propose the optimal hospital taking into account the user's living environment (urban area, rural area, etc.). The suggestion unit can use the generation AI to analyze the user's living environment and propose the optimal hospital. The suggestion unit can also propose the optimal hospital taking into account the user's lifestyle (smoking, drinking, etc.). The suggestion unit can use the generation AI to analyze the user's lifestyle and propose the optimal hospital. This enables more appropriate hospital proposals by customizing the proposed content based on the user's health condition and living environment. 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 data on the user's health condition and living environment to the generation AI and cause the generation AI to customize the proposed content.

[0049] When proposing a hospital, the suggestion unit can propose an optimal hospital by taking into consideration the geographical location information of the user. For example, the suggestion unit proposes the nearest hospital based on the user's current location. The suggestion unit can acquire the geographical location information of the user using the generation AI and propose the nearest hospital. The suggestion unit can also propose a hospital that can treat a disease specific to the region based on the geographical location information of the user. The suggestion unit can acquire the geographical location information of the user using the generation AI and propose a hospital that can treat a disease specific to the region. Furthermore, the suggestion unit can also propose a hospital with good transportation access based on the geographical location information of the user. The suggestion unit can acquire the geographical location information of the user using the generation AI and propose a hospital with good transportation access. This makes it possible to propose an optimal hospital by taking into consideration the geographical location information of the user. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute the proposal of an optimal hospital.

[0050] When proposing a hospital, the suggestion unit can adjust the proposed content by analyzing the user's social media activity. For example, the suggestion unit can analyze the user's social media post and propose a related hospital. The suggestion unit can use the generation AI to analyze the user's social media activity and propose a related hospital. The suggestion unit can also extract health-related keywords from the user's social media activity and propose a related hospital. The suggestion unit can use the generation AI to analyze the user's social media activity, extract health-related keywords, and propose a related hospital. The suggestion unit can also analyze the user's friends' posts on social media and propose a related hospital. The suggestion unit can use the generation AI to analyze the user's social media activity and propose a related hospital from the friends' posts. In this way, a hospital related to the user can be proposed by analyzing the social media activity. 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 social media activity to the generation AI and cause the generation AI to adjust the proposed content.

[0051] The unification unit can improve the accuracy of unification by referring to past medical record data when unifying the medical records. The unification unit, for example, preferentially unifies related information based on past medical record data. The unification unit can use the generation AI to analyze past medical record data and preferentially unify related information. The unification unit can also analyze past medical record data and improve the accuracy of unification. The unification unit can use the generation AI to analyze past medical record data and improve the accuracy of unification. Furthermore, the unification unit can also adjust the unification algorithm by referring to the past medical record data. The unification unit can use the generation AI to analyze past medical record data and adjust the unification algorithm. As a result, the accuracy of the medical record unification is improved by referring to the past medical record data. Part or all of the above-mentioned processing in the unification unit may be performed, for example, using AI, or may be performed without using AI. For example, the unification unit can input past medical record data to the generation AI and cause the generation AI to execute the improvement of the accuracy of unification.

[0052] The unification unit can customize the unified format based on the characteristics of each medical institution when unifying the medical records. The unification unit customizes the unified format, for example, taking into account the characteristics of each medical institution (medical specialty, size, etc.). The unification unit can analyze the characteristics of each medical institution using the generation AI and customize the unified format. The unification unit can also customize the unified format taking into account the medical treatment style of each medical institution. The unification unit can analyze the medical treatment style of each medical institution using the generation AI and customize the unified format. The unification unit can also customize the unified format taking into account the medical equipment and systems used by each medical institution. The unification unit can analyze the medical equipment and systems used by each medical institution using the generation AI and customize the unified format. This enables more appropriate unification of medical records by customizing the unified format based on the characteristics of each medical institution. Some or all of the above-mentioned processing in the unification unit may be performed, for example, using AI, or may be performed without using AI. For example, the unification unit can input characteristic data of each medical institution into the generation AI and cause the generation AI to execute customization of the unified format.

[0053] The unification unit can improve the accuracy of unification by considering the geographical location information of each medical institution when unifying the medical records. The unification unit unifies the region-specific medical information based on the geographical location information of each medical institution, for example. The unification unit can use the generation AI to analyze the geographical location information of each medical institution and unify the region-specific medical information. The unification unit can also improve the accuracy of unification based on the geographical location information of each medical institution. The unification unit can use the generation AI to analyze the geographical location information of each medical institution and improve the accuracy of unification. Furthermore, the unification unit can adjust the unification algorithm based on the geographical location information of each medical institution. The unification unit can use the generation AI to analyze the geographical location information of each medical institution and adjust the unification algorithm. As a result, the accuracy of the medical record unification is improved by considering the geographical location information of each medical institution. Part or all of the above-mentioned processing in the unification unit may be performed, for example, using AI, or may be performed without using AI. For example, the unification unit can input the geographical location information of each medical institution to the generation AI and cause the generation AI to execute the improvement of the accuracy of unification.

[0054] The unification department can analyze the social media activities of each medical institution and adjust the unified format when unifying the medical records. The unification department, for example, analyzes the social media posts of each medical institution and reflects related information in the unified format. The unification department can use the generation AI to analyze the social media activities of each medical institution and reflect related information in the unified format. The unification department can also extract keywords related to medical treatment from the social media activities of each medical institution and adjust the unified format. The unification department can use the generation AI to analyze the social media activities of each medical institution, extract keywords related to medical treatment, and adjust the unified format. The unification department can also analyze the feedback of patients on social media of each medical institution and adjust the unified format. The unification department can use the generation AI to analyze the social media activities of each medical institution and adjust the unified format based on the feedback of patients. In this way, a unified format suitable for each medical institution can be provided by analyzing social media activities. Some or all of the above-mentioned processing in the unification department may be performed, for example, using AI, or may be performed without using AI. For example, the unification department can input the social media activities of each medical institution into the generation AI and cause the generation AI to adjust the unified format.

[0055] The input unit can improve the accuracy of the input by referring to past diagnostic results when inputting a diagnostic result. For example, the input unit preferentially inputs related information based on past diagnostic results. The input unit can analyze past diagnostic results using a generation AI and preferentially input related information. The input unit can also analyze past diagnostic results and improve the accuracy of the input. The input unit can analyze past diagnostic results using a generation AI and improve the accuracy of the input. Furthermore, the input unit can also adjust the input algorithm by referring to past diagnostic results. The input unit can analyze past diagnostic results using a generation AI and adjust the input algorithm. As a result, the accuracy of the diagnostic result input is improved by referring to the past diagnostic results. A part or all of the above-mentioned processing in the input unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the input unit can input past diagnostic results to the generation AI and cause the generation AI to execute the improvement of the accuracy of the input.

[0056] The input unit can customize the input contents based on the doctor's specialty and experience when inputting a diagnosis result. The input unit customizes the input contents, for example, taking into account the doctor's specialty (internal medicine, surgery, etc.). The input unit can analyze the doctor's specialty using a generation AI and customize the input contents. The input unit can also customize the input contents taking into account the doctor's experience (years, number of cases, etc.). The input unit can analyze the doctor's experience using a generation AI and customize the input contents. Furthermore, the input unit can also customize the input contents taking into account the doctor's medical practice style. The input unit can analyze the doctor's medical practice style using a generation AI and customize the input contents. This enables more appropriate diagnosis result input by customizing the input contents based on the doctor's specialty and experience. Part or all of the above-mentioned processing in the input unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the input unit can input data on the doctor's specialty and experience to the generation AI and cause the generation AI to customize the input contents.

[0057] The input unit can improve the accuracy of the input by taking into account the geographical location information of the medical institution when inputting the diagnosis result. For example, the input unit preferentially inputs a diagnosis result specific to a region based on the geographical location information of the medical institution. The input unit can use the generation AI to analyze the geographical location information of the medical institution and preferentially input a diagnosis result specific to a region. The input unit can also improve the accuracy of the input based on the geographical location information of the medical institution. The input unit can use the generation AI to analyze the geographical location information of the medical institution and improve the accuracy of the input. Furthermore, the input unit can also adjust the input algorithm based on the geographical location information of the medical institution. The input unit can use the generation AI to analyze the geographical location information of the medical institution and adjust the input algorithm. As a result, the accuracy of the diagnosis result input is improved by taking into account the geographical location information of the medical institution. A part or all of the above-mentioned processing in the input unit may be performed, for example, using AI, or may be performed without using AI. For example, the input unit can input the geographical location information of the medical institution to the generation AI and cause the generation AI to execute the improvement of the accuracy of the input.

[0058] The input unit can adjust the input content by analyzing the social media activity of the medical institution when inputting the diagnosis result. The input unit can, for example, analyze the social media posts of the medical institution and input the related diagnosis result. The input unit can use the generation AI to analyze the social media activity of the medical institution and input the related diagnosis result. The input unit can also extract keywords related to medical treatment from the social media activity of the medical institution and adjust the input content. The input unit can use the generation AI to analyze the social media activity of the medical institution, extract keywords related to medical treatment, and adjust the input content. Furthermore, the input unit can analyze the patient's feedback on the social media of the medical institution and adjust the input content. The input unit can use the generation AI to analyze the social media activity of the medical institution and adjust the input content based on the patient's feedback. In this way, by analyzing the social media activity, it is possible to input a diagnosis result related to the medical institution. Part or all of the above-mentioned processing in the input unit may be performed, for example, using AI, or may be performed without using AI. For example, the input unit can input the social media activity of the medical institution to the generation AI and cause the generation AI to adjust the input content.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] When the user inputs symptoms, the reception unit can customize the input contents by referring to the user's past medical history. For example, if the user has suffered from a specific disease in the past, symptoms related to that disease can be preferentially displayed. The reception unit can also adjust the input contents taking into consideration the treatments and prescribed medicines the user has received in the past. Furthermore, specific tests and diagnoses can be recommended based on the user's past medical history. This makes it possible to input more appropriate symptoms by utilizing the user's past medical history.

[0061] The identification unit can analyze the lifestyle habit data of the user to improve the accuracy of disease identification. For example, if the user is a smoker, diseases related to smoking can be preferentially identified. In addition, the identification unit can evaluate the risk of lifestyle-related diseases and narrow down specific diseases by taking into account the user's diet and exercise habits. Furthermore, the identification unit can analyze the user's sleep patterns and stress levels to identify diseases influenced by these factors. As a result, by utilizing the user's lifestyle habit data, more accurate disease identification is possible.

[0062] The suggestion unit can monitor the user's current health condition in real time and suggest the most suitable hospital. For example, if the user is using a wearable device, it can suggest hospitals with high urgency based on data such as heart rate and blood pressure obtained from the device. It can also analyze data such as the user's body temperature and oxygen saturation and suggest appropriate specialists. Furthermore, if the user's health condition suddenly changes, it can quickly suggest the nearest emergency hospital. This makes it possible to suggest more appropriate hospitals by utilizing real-time health data.

[0063] When standardizing medical records, the Unification Department can customize the unified format by taking into account the clinical guidelines of each medical institution. For example, it can analyze the clinical guidelines adopted by each medical institution and adjust the items in the medical record based on that. It can also customize the format of the medical record by taking into account guidelines specialized for a specific medical department. It can also provide a unified format that reflects the clinical protocols of each medical institution. This makes it possible to unify medical records more appropriately by taking into account the clinical guidelines.

[0064] The input unit can customize the input interface taking into account the doctor's clinical style when inputting diagnosis results. For example, if the doctor prefers text input, detailed text input options can be provided. Also, if the doctor prefers voice input, an interface that prioritizes voice input can be provided. Furthermore, if the doctor makes extensive use of images and graphs, an interface that allows them to be easily inserted can be provided. In this way, by providing an interface that suits the doctor's clinical style, more comfortable input of diagnosis results is possible.

[0065] The process flow of the first embodiment will be briefly described below.

[0066] Step 1: The reception unit inputs the user's symptoms. The user's symptoms include, but are not limited to, headache, fever, cough, etc. The reception unit, for example, provides an interface through which the user inputs the symptoms into the app. Step 2: The identification unit uses the generation AI to identify the illness based on the information input by the reception unit. The identification unit identifies the illness based on, for example, past data or medical knowledge. The generation AI uses a text generation AI (e.g., LLM) to identify the illness based on symptoms. The identification unit can also use the generation AI to identify the illness based on a combination of symptoms or test results. Step 3: The suggestion unit suggests hospitals based on the illness identified by the identification unit. The suggestion unit selects the most suitable hospital based on, for example, the user's current location and symptoms. The suggestion unit can also use the generative AI to suggest hospitals based on criteria such as the hospital's specialty, distance, and rating. Step 4: The unification department unifies the format of medical records. The unification department analyzes medical records in different formats for each medical institution, for example, and converts them into a unified format. The generation AI analyzes medical records in different formats and standardizes data formats and items. Step 5: The input unit allows the doctor to input the diagnosis result. The input unit provides, for example, an interface for inputting the diagnosis result. The input unit can also input the diagnosis result using the generation AI and transmit the information to the generation AI.

[0067] (Example 2) The medical interview system according to the embodiment of the present invention is a system that inputs the user's symptoms, identifies the disease using a generation AI, and suggests a hospital. This system includes a reception unit that inputs the user's symptoms, an identification unit that identifies the disease, a suggestion unit that suggests a hospital, a unification unit that standardizes the format of the medical record, and an input unit that inputs the diagnosis result. For example, a user inputs symptoms such as "I have a headache" or "I have a fever" using a medical interview app. This information is sent to the generation AI, which narrows down possible diseases based on the symptoms. The generation AI identifies the most likely disease based on past data and medical knowledge. Next, based on the disease identified by the generation AI, it suggests an appropriate hospital to the user. Based on information about affiliated hospitals, it selects the hospital that is best suited to the user's current location and symptoms, and notifies the user. For example, a notification such as "We recommend that you go to a nearby internal medicine clinic" is displayed. In addition, the generation AI standardizes the medical records. The generation AI analyzes the medical records, which have different formats for each medical institution, and converts them into a unified format. This makes it easier to share information between different medical institutions, improving the efficiency of medical treatment. In addition, after the user is examined at a hospital, the doctor enters the diagnosis result into the app. This information is sent to the generating AI and used to update the app's ability to identify illnesses. For example, if a new diagnosis is obtained for a particular symptom, that information is reflected in the generating AI, enabling more accurate disease identification in the next and subsequent interviews. This system allows users to receive prompt and appropriate medical services, promotes information sharing between medical institutions, and improves the efficiency of medical treatment. In addition, by using the generating AI, the app's ability to identify illnesses is constantly updated, enabling more accurate diagnoses. This allows the medical interview system to input the user's symptoms, identify illnesses using the generating AI, and suggest hospitals.

[0068] The medical interview system according to the embodiment includes a reception unit, a specification unit, a proposal unit, a unification unit, and an input unit. The reception unit inputs the symptoms of the user. The symptoms of the user include, for example, headache, fever, cough, etc., but are not limited to such examples. The reception unit provides, for example, an interface for the user to input the symptoms to the application. The specification unit specifies the disease based on the information input by the specification unit using a generation AI. The specification unit specifies the disease based on, for example, past data and medical knowledge. The generation AI specifies the disease based on the symptoms using a text generation AI (for example, LLM). The specification unit can also specify the disease based on a combination of symptoms and test results using the generation AI. The proposal unit suggests a hospital based on the disease specified by the specification unit. The proposal unit selects an optimal hospital based on, for example, the user's current location and symptoms. The proposal unit can also suggest a hospital based on criteria such as the hospital's specialty, distance, and rating using the generation AI. The unification unit unifies the format of the medical record. For example, the unification unit analyzes medical records in formats that differ for each medical institution and converts them into a unified format. The generation AI analyzes medical records in different formats and standardizes data formats and items. The input unit is used by a doctor to input a diagnosis result. The input unit provides, for example, an interface for inputting a diagnosis result. The input unit can also input a diagnosis result using the generation AI and transmit the information to the generation AI. As a result, the medical interview system according to the embodiment can input a user's symptoms, identify a disease using the generation AI, and suggest a hospital. 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, the generation AI can input a user's symptoms, identify a disease using the generation AI, and suggest a hospital. The generation AI can identify a disease based on past data and medical knowledge and suggest a hospital. The generation AI can unify the format of the medical record, input a diagnosis result, and transmit the information to the generation AI. As a result, the medical interview system can input a user's symptoms, identify a disease using the generation AI, and suggest a hospital.

[0069] The reception unit inputs the symptoms of the user. The symptoms of the user include, but are not limited to, headache, fever, cough, and the like. The reception unit, for example, provides an interface for the user to input the symptoms into the app. Specifically, the reception unit has a user interface that the user can intuitively operate, and allows the user to input the symptoms in detail through a text input field or multiple choice questions. For example, when the user inputs "headache", more detailed questions such as "location of headache", "intensity of pain", and "frequency of pain" are displayed. In addition, by providing a voice input function, the user can also explain the symptoms by voice. The voice input is converted into text using voice recognition technology and is taken into the system. In addition, the reception unit also has a function of automatically generating related questions by referring to the user's past medical history and health data. This allows the user to efficiently input symptoms, and the system to collect more accurate information.

[0070] The identification unit uses the generation AI to identify the disease based on the information input by the reception unit. The identification unit identifies the disease based on, for example, past data and medical knowledge. The generation AI identifies the disease based on the symptoms using a text generation AI (for example, LLM). Specifically, the generation AI analyzes the symptoms input by the user and identifies the most likely disease by referring to a past case database and medical literature. For example, if the user inputs "fever" and "cough", the generation AI evaluates the possibility of influenza, cold, pneumonia, etc. from the combination of these symptoms. In addition, the generation AI takes into account additional information such as the severity of symptoms, the duration of onset, and concurrent symptoms to make a more accurate diagnosis. Furthermore, the identification unit can improve the accuracy of the diagnosis by incorporating additional information such as test results and image data. For example, it analyzes blood test results and X-ray images to evaluate the relevance to the symptoms. This allows the identification unit to quickly and accurately identify diseases based on the user's symptoms and propose appropriate treatment.

[0071] The suggestion unit suggests a hospital based on the illness identified by the identification unit. The suggestion unit selects the most suitable hospital based on, for example, the user's current location and symptoms. Specifically, the suggestion unit acquires the user's location information and generates a list of nearby medical institutions. The generation AI can also suggest hospitals based on criteria such as the hospital's specialty, distance, and rating. For example, if the user is diagnosed with "pneumonia," the suggestion unit preferentially suggests hospitals and clinics specializing in respiratory diseases. It also takes into account the hospital's congestion status and reservation availability, allowing the user to receive a medical examination quickly. In addition, the suggestion unit also has a function to select an appropriate medical institution by referring to the user's insurance information and past medical history. This allows the user to easily find the medical institution that is best suited to them and receive appropriate treatment quickly.

[0072] The unification department will unify the format of medical records. For example, the unification department will analyze medical records in different formats for each medical institution and convert them into a unified format. Specifically, the generation AI will analyze medical records in different formats and standardize data formats and items. For example, if an item listed as "diagnosis result" at one hospital is listed as "findings" at another hospital, the generation AI will recognize these as the same item and convert them into a unified format. It also has the function of automatically classifying the contents of the medical records and extracting the necessary information. This makes it easier to share information between medical institutions, improving the efficiency and accuracy of medical treatment. In addition, the unification department will promote the digitalization of medical records and has the function of converting paper-based medical records into electronic medical records. This makes it easier for medical institutions to manage medical records and centrally manage patients' medical history.

[0073] The input unit is where the doctor inputs the diagnosis results. The input unit provides, for example, an interface for inputting the diagnosis results. Specifically, the input unit has a user interface that the doctor can easily operate, and provides a dedicated form for inputting the diagnosis results, prescriptions, test results, etc. Furthermore, the generation AI can be used to input the diagnosis results and send the information to the generation AI. For example, when the doctor inputs the diagnosis results by voice, the voice recognition technology is used to convert the voice into text and the text is imported into the system. The input unit also has a function that allows the doctor to input the diagnosis results while referring to the past medical history and related medical information. This allows the doctor to input the diagnosis results efficiently, improving the quality of medical care. Furthermore, the input unit also has a function that automatically analyzes the input diagnosis results, extracts necessary information, and reflects it in the medical record. This reduces the burden on the doctor and improves the efficiency of medical care.

[0074] The identification unit can identify a disease based on past data or medical knowledge. The identification unit identifies a disease based on past data such as past diagnosis results and a patient's medical history. The identification unit can analyze past data and identify a disease using a generation AI. The identification unit can also identify a disease based on medical knowledge such as medical books, papers, and guidelines. The generation AI executes an algorithm for identifying a disease based on medical knowledge. For example, the identification unit identifies a related disease based on past diagnosis results. The identification unit can analyze past diagnosis results and identify a disease using a generation AI. The identification unit can also execute an algorithm for identifying a disease based on medical books and papers. This improves the accuracy of disease identification by utilizing past data and medical knowledge. 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 identify a disease using an AI model that uses past data and medical knowledge as input and identifies a disease.

[0075] The suggestion unit can select an appropriate hospital based on the user's current location or symptoms. The suggestion unit, for example, selects the nearest hospital based on the user's current location. The suggestion unit can acquire the user's current location and select the nearest hospital using the generation AI. The suggestion unit can also select an appropriate hospital based on the user's symptoms. The suggestion unit can analyze the user's symptoms and select an appropriate hospital using the generation AI. For example, the suggestion unit selects the nearest hospital based on GPS data of the user's current location. The suggestion unit can acquire the user's current location and select the nearest hospital using the generation AI. The suggestion unit can also select an appropriate hospital based on the user's symptoms. The suggestion unit can analyze the user's symptoms and select an appropriate hospital using the generation AI. This makes it possible to provide prompt and appropriate medical services by suggesting the optimal hospital based on the user's current location and symptoms. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can suggest hospitals using an AI model that inputs the user's current location and symptoms and selects appropriate hospitals.

[0076] The unification unit can analyze medical records in different formats of each medical institution and convert them into a unified format. The unification unit analyzes medical records in different formats, such as paper medical records and electronic medical records. The unification unit can analyze medical records in different formats and convert them into a unified format using a generation AI. The generation AI standardizes the data format and items of the medical records. For example, the unification unit scans paper medical records and converts them into digital data. The unification unit can scan paper medical records and convert them into digital data using a generation AI. The unification unit can also analyze electronic medical records and convert them into a unified format. The unification unit can analyze electronic medical records and convert them into a unified format using a generation AI. This makes it easier to share information between different medical institutions by unifying the format of the medical records. Some or all of the above-mentioned processing in the unification unit may be performed, for example, using AI, or may be performed without using AI. For example, the unification unit can unify the medical records using an AI model that inputs medical records in different formats and converts them into a unified format.

[0077] The input unit allows a doctor to input a diagnosis result and transmit the information to the artificial intelligence. The input unit provides, for example, an interface through which a doctor inputs a diagnosis result. The input unit can input a diagnosis result using a generation AI and transmit the information to the generation AI. The generation AI updates the disease identification power based on the diagnosis result. For example, the input unit allows a doctor to input a diagnosis result and transmit the information to the generation AI. The input unit can input a diagnosis result using a generation AI and transmit the information to the generation AI. Thereby, by transmitting the diagnosis result to the generation AI, the disease identification power of the app can be constantly updated. Some or all of the above-mentioned processing in the input unit may be performed, for example, using AI, or may be performed without using AI. For example, the input unit can transmit a diagnosis result to the generation AI using an AI model that takes the diagnosis result as an input and updates the disease identification power.

[0078] The reception unit can estimate the user's emotion and adjust the symptom input interface based on the estimated user's emotion. For example, when the user is feeling stressed, the reception unit provides a simple interface and minimizes the input procedure. The reception unit can estimate the user's emotion and provide a simple interface using a generation AI. In addition, the reception unit can provide detailed input options and suggest a customizable input method when the user is relaxed. The reception unit can estimate the user's emotion and provide detailed input options using a generation AI. Furthermore, the reception unit can prioritize voice input and enable the user to input symptoms quickly when the user is in a hurry. The reception unit can estimate the user's emotion using a generation AI and provide an interface that prioritizes voice input. This allows for more comfortable symptom input by adjusting the interface according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit may input the user's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0079] The reception unit can analyze the user's past symptom input history and suggest the optimal input method. The reception unit, for example, automatically displays symptoms that the user frequently input in the past as candidates. The reception unit can analyze the user's past symptom input history and display the frequently input symptoms as candidates using the generation AI. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. The reception unit can analyze the user's past input methods and suggest the optimal input method using the generation AI. Furthermore, the reception unit can predict and suggest symptoms to be input in a specific time period from the user's past input history. The reception unit can analyze the user's past input history and predict and suggest symptoms to be input in a specific time period using the generation AI. As a result, the optimal input method can be suggested to the user by analyzing the past symptom input history. 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 past symptom input history to the generation AI and cause the generation AI to execute a suggestion of the optimal input method.

[0080] The reception unit can filter the input contents based on the user's current health condition and lifestyle habits when inputting symptoms. The reception unit, for example, preferentially displays related symptoms based on the user's current health condition. The reception unit can analyze the user's health condition using the generation AI and preferentially display related symptoms. The reception unit can also filter the input contents taking into account the user's lifestyle habits (smoking, drinking, etc.). The reception unit can analyze the user's lifestyle habits using the generation AI and filter the input contents. Furthermore, the reception unit can also preferentially display related symptoms based on the user's medical history. The reception unit can analyze the user's medical history using the generation AI and preferentially display related symptoms. As a result, by filtering the input contents based on the user's health condition and lifestyle habits, more relevant symptoms can be input. 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 data on the user's health condition and lifestyle habits to the generation AI and cause the generation AI to filter the input contents.

[0081] The reception unit can estimate the user's emotion and determine the priority of symptoms to be input based on the estimated user's emotion. For example, when the user feels anxious, the reception unit urges the user to input serious symptoms with priority. The reception unit can estimate the user's emotion using the generation AI and urge the user to input serious symptoms with priority. In addition, the reception unit can also urge the user to input detailed symptoms when the user is relaxed. The reception unit can estimate the user's emotion using the generation AI and urge the user to input detailed symptoms. In addition, the reception unit can also urge the user to input major symptoms with priority when the user is in a hurry. The reception unit can estimate the user's emotion using the generation AI and urge the user to input major symptoms with priority. In this way, by determining the priority of symptoms according to the user's emotion, more important symptoms can be input with priority. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. 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 may input the user's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0082] The reception unit can preferentially input highly relevant symptoms in consideration of the geographical location information of the user when inputting symptoms. The reception unit preferentially displays related symptoms based on, for example, the climate and environment of the user's current location. The reception unit can acquire the geographical location information of the user using the generation AI and preferentially display related symptoms. The reception unit can also preferentially display region-specific diseases and symptoms based on the geographical location information of the user. The reception unit can acquire the geographical location information of the user using the generation AI and preferentially display region-specific diseases and symptoms. Furthermore, the reception unit can also preferentially display diseases and symptoms that are prevalent in the vicinity based on the geographical location information of the user. The reception unit can acquire the geographical location information of the user using the generation AI and preferentially display diseases and symptoms that are prevalent in the vicinity. Thereby, region-specific symptoms can be preferentially input by considering the geographical location information of the user. 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 geographical location information to the generation AI and cause the generation AI to prioritize the display of highly relevant symptoms.

[0083] The reception unit can analyze the user's social media activity and input the related symptoms when inputting the symptoms. The reception unit, for example, analyzes the user's social media posts and automatically inputs the related symptoms. The reception unit can use the generation AI to analyze the user's social media activity and automatically input the related symptoms. The reception unit can also extract health-related keywords from the user's social media activity and input the related symptoms. The reception unit can use the generation AI to analyze the user's social media activity, extract health-related keywords, and input the related symptoms. The reception unit can also analyze the user's friends' posts on social media and input the related symptoms. The reception unit can use the generation AI to analyze the user's social media activity and input the related symptoms from the friends' posts. In this way, the symptoms related to the user can be automatically input by analyzing the social media activity. 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 user's social media activity to the generation AI and cause the generation AI to execute the input of the related symptoms.

[0084] The identification unit can estimate the user's emotion and adjust the disease identification algorithm based on the estimated user's emotion. For example, when the user is feeling anxious, the identification unit uses an algorithm that preferentially identifies serious illnesses. The identification unit can use an algorithm that uses a generation AI to estimate the user's emotion and preferentially identifies serious illnesses. In addition, the identification unit can also use an algorithm that performs detailed disease identification when the user is relaxed. The identification unit can use an algorithm that uses a generation AI to estimate the user's emotion and perform detailed disease identification. In addition, the identification unit can also use an algorithm that quickly identifies major illnesses when the user is in a hurry. The identification unit can use an algorithm that uses a generation AI to estimate the user's emotion and quickly identify major illnesses. This allows for more appropriate disease identification by adjusting the disease identification algorithm according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit may input the user's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0085] The identification unit can improve the accuracy of identification by referring to the user's past diagnostic results when identifying a disease. The identification unit, for example, preferentially identifies related diseases based on the user's past diagnostic results. The identification unit can use the generation AI to analyze the user's past diagnostic results and preferentially identify related diseases. The identification unit can also analyze the user's past diagnostic results and improve the accuracy of identification. The identification unit can use the generation AI to analyze the user's past diagnostic results and improve the accuracy of identification. Furthermore, the identification unit can also adjust the specific algorithm by referring to the user's past diagnostic results. The identification unit can use the generation AI to analyze the user's past diagnostic results and adjust the specific algorithm. As a result, the accuracy of disease identification is improved by referring to the past diagnostic results. A 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 past diagnostic results to the generation AI and cause the generation AI to execute the improvement of the accuracy of identification.

[0086] The identification unit can customize the specific technique based on the user's living environment and occupation when identifying a disease. The identification unit customizes the specific technique, for example, taking into account the user's living environment (urban area, rural area, etc.). The identification unit can analyze the user's living environment and customize the specific technique using the generation AI. The identification unit can also customize the specific technique taking into account the user's occupation (desk work, physical labor, etc.). The identification unit can analyze the user's occupation and customize the specific technique using the generation AI. Furthermore, the identification unit can also customize the specific technique taking into account the user's lifestyle (smoking, drinking, etc.). The identification unit can analyze the user's lifestyle and customize the specific technique using the generation AI. As a result, by customizing the specific technique based on the user's living environment and occupation, more appropriate disease identification is possible. 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 data on the user's living environment and occupation to the generation AI and cause the generation AI to execute customization of the specific technique.

[0087] The identification unit can estimate the user's emotion and determine the priority of illnesses to be identified based on the estimated user's emotion. For example, when the user is feeling anxious, the identification unit preferentially identifies serious illnesses. The identification unit can estimate the user's emotion and preferentially identify serious illnesses using the generation AI. Moreover, the identification unit can perform detailed illness identification when the user is relaxed. The identification unit can estimate the user's emotion and perform detailed illness identification using the generation AI. Furthermore, the identification unit can quickly identify major illnesses when the user is in a hurry. The identification unit can estimate the user's emotion and quickly identify major illnesses using the generation AI. Thereby, by determining the priority of illnesses according to the user's emotion, more important illnesses can be preferentially identified. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the identification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the identification unit can input a user's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0088] The identification unit can improve the accuracy of identification by considering the geographical location information of the user when identifying a disease. The identification unit improves the accuracy of identification by considering, for example, the climate and environment of the user's current location. The identification unit can acquire the geographical location information of the user using the generation AI and improve the accuracy of identification. The identification unit can also preferentially identify region-specific diseases based on the geographical location information of the user. The identification unit can acquire the geographical location information of the user using the generation AI and preferentially identify region-specific diseases. The identification unit can also preferentially identify diseases that are prevalent in the neighborhood based on the geographical location information of the user. The identification unit can acquire the geographical location information of the user using the generation AI and preferentially identify diseases that are prevalent in the neighborhood. As a result, region-specific diseases can be preferentially identified by considering the geographical location information of the user. A 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 identification.

[0089] The identification unit can adjust the specific technique by analyzing the social media activity of the user when identifying the illness. The identification unit, for example, analyzes the social media posts of the user to identify the relevant illness. The identification unit can use the generation AI to analyze the social media activity of the user to identify the relevant illness. The identification unit can also extract health-related keywords from the user's social media activity and adjust the specific technique. The identification unit can use the generation AI to analyze the user's social media activity, extract health-related keywords, and adjust the specific technique. The identification unit can also analyze the posts of the user's friends on social media to identify the relevant illness. The identification unit can use the generation AI to analyze the user's social media activity and identify the relevant illness from the posts of the friends. In this way, the illness related to the user can be identified by analyzing the social media activity. A 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 social media activity to the generation AI and cause the generation AI to execute the adjustment of the specific technique.

[0090] The suggestion unit can estimate the user's emotion and adjust the hospital suggestion expression method based on the estimated user's emotion. For example, when the user feels anxious, the suggestion unit uses an expression method that gives a sense of security. The suggestion unit can estimate the user's emotion and use an expression method that gives a sense of security using the generation AI. In addition, the suggestion unit can also use an expression method including detailed information when the user is relaxed. The suggestion unit can estimate the user's emotion and use an expression method including detailed information using the generation AI. In addition, the suggestion unit can also use a concise and quick expression method when the user is in a hurry. The suggestion unit can estimate the user's emotion and use a concise and quick expression method using the generation AI. This enables more appropriate hospital suggestions by adjusting the hospital suggestion expression method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit may input the user's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0091] When proposing a hospital, the suggestion unit can make an optimal suggestion by referring to the user's past hospital use history. The suggestion unit suggests an optimal hospital, for example, based on a hospital used by the user in the past. The suggestion unit can use the generation AI to analyze the user's past hospital use history and suggest an optimal hospital. The suggestion unit can also analyze the user's past hospital use history and suggest an optimal hospital. The suggestion unit can use the generation AI to analyze the user's past hospital use history and suggest an optimal hospital. Furthermore, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past hospital use history. The suggestion unit can use the generation AI to analyze the user's past hospital use history and improve the accuracy of the suggestion. As a result, the optimal hospital can be suggested to the user by referring to the past hospital use history. 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 past hospital use history to the generation AI and cause the generation AI to execute the suggestion of the optimal hospital.

[0092] When proposing a hospital, the suggestion unit can customize the proposed content based on the user's current health condition and living environment. The suggestion unit, for example, proposes an optimal hospital based on the user's current health condition. The suggestion unit can use the generation AI to analyze the user's health condition and propose the optimal hospital. The suggestion unit can also propose the optimal hospital taking into account the user's living environment (urban area, rural area, etc.). The suggestion unit can use the generation AI to analyze the user's living environment and propose the optimal hospital. The suggestion unit can also propose the optimal hospital taking into account the user's lifestyle (smoking, drinking, etc.). The suggestion unit can use the generation AI to analyze the user's lifestyle and propose the optimal hospital. This enables more appropriate hospital proposals by customizing the proposed content based on the user's health condition and living environment. 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 data on the user's health condition and living environment to the generation AI and cause the generation AI to customize the proposed content.

[0093] The suggestion unit can estimate the user's emotion and determine the priority order of the hospitals to be proposed based on the estimated user's emotion. For example, when the user feels anxious, the suggestion unit preferentially suggests hospitals that give a sense of security. The suggestion unit can use the generation AI to estimate the user's emotion and preferentially suggest hospitals that give a sense of security. In addition, when the user is relaxed, the suggestion unit can also preferentially suggest hospitals that include detailed information. The suggestion unit can use the generation AI to estimate the user's emotion and preferentially suggest hospitals that include detailed information. Furthermore, when the user is in a hurry, the suggestion unit can also preferentially suggest hospitals that can respond quickly. The suggestion unit can use the generation AI to estimate the user's emotion and preferentially suggest hospitals that can respond quickly. This enables more appropriate hospital proposals by determining the priority order of hospitals according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit may input the user's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0094] When proposing a hospital, the suggestion unit can propose an optimal hospital by taking into consideration the geographical location information of the user. For example, the suggestion unit proposes the nearest hospital based on the user's current location. The suggestion unit can acquire the geographical location information of the user using the generation AI and propose the nearest hospital. The suggestion unit can also propose a hospital that can treat a disease specific to the region based on the geographical location information of the user. The suggestion unit can acquire the geographical location information of the user using the generation AI and propose a hospital that can treat a disease specific to the region. Furthermore, the suggestion unit can also propose a hospital with good transportation access based on the geographical location information of the user. The suggestion unit can acquire the geographical location information of the user using the generation AI and propose a hospital with good transportation access. This makes it possible to propose an optimal hospital by taking into consideration the geographical location information of the user. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute the proposal of an optimal hospital.

[0095] When proposing a hospital, the suggestion unit can adjust the proposed content by analyzing the user's social media activity. For example, the suggestion unit can analyze the user's social media post and propose a related hospital. The suggestion unit can use the generation AI to analyze the user's social media activity and propose a related hospital. The suggestion unit can also extract health-related keywords from the user's social media activity and propose a related hospital. The suggestion unit can use the generation AI to analyze the user's social media activity, extract health-related keywords, and propose a related hospital. The suggestion unit can also analyze the user's friends' posts on social media and propose a related hospital. The suggestion unit can use the generation AI to analyze the user's social media activity and propose a related hospital from the friends' posts. In this way, a hospital related to the user can be proposed by analyzing the social media activity. 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 social media activity to the generation AI and cause the generation AI to adjust the proposed content.

[0096] The unification unit can estimate the user's emotion and adjust the algorithm of the medical record unification based on the estimated emotion of the user. For example, when the user feels anxious, the unification unit uses an algorithm of the medical record unification that gives a sense of security. The unification unit can use an algorithm of the medical record unification that uses the generation AI to estimate the emotion of the user and gives a sense of security. In addition, the unification unit can use an algorithm for detailed medical record unification when the user is relaxed. The unification unit can use an algorithm for estimating the emotion of the user and performing detailed medical record unification using the generation AI. Furthermore, the unification unit can use an algorithm for rapid medical record unification when the user is in a hurry. The unification unit can use an algorithm for estimating the emotion of the user and performing rapid medical record unification using the generation AI. This allows for more appropriate medical record unification by adjusting the algorithm of the medical record unification according to the emotion of the user. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. Some or all of the above-mentioned processes in the unification unit may be performed using, for example, AI, or may be performed without using AI. For example, the unification unit may input the user's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0097] The unification unit can improve the accuracy of unification by referring to past medical record data when unifying the medical records. The unification unit, for example, preferentially unifies related information based on past medical record data. The unification unit can use the generation AI to analyze past medical record data and preferentially unify related information. The unification unit can also analyze past medical record data and improve the accuracy of unification. The unification unit can use the generation AI to analyze past medical record data and improve the accuracy of unification. Furthermore, the unification unit can also adjust the unification algorithm by referring to the past medical record data. The unification unit can use the generation AI to analyze past medical record data and adjust the unification algorithm. As a result, the accuracy of the medical record unification is improved by referring to the past medical record data. Part or all of the above-mentioned processing in the unification unit may be performed, for example, using AI, or may be performed without using AI. For example, the unification unit can input past medical record data to the generation AI and cause the generation AI to execute the improvement of the accuracy of unification.

[0098] The unification unit can customize the unified format based on the characteristics of each medical institution when unifying the medical records. The unification unit customizes the unified format, for example, taking into account the characteristics of each medical institution (medical specialty, size, etc.). The unification unit can analyze the characteristics of each medical institution using the generation AI and customize the unified format. The unification unit can also customize the unified format taking into account the medical treatment style of each medical institution. The unification unit can analyze the medical treatment style of each medical institution using the generation AI and customize the unified format. The unification unit can also customize the unified format taking into account the medical equipment and systems used by each medical institution. The unification unit can analyze the medical equipment and systems used by each medical institution using the generation AI and customize the unified format. This enables more appropriate unification of medical records by customizing the unified format based on the characteristics of each medical institution. Some or all of the above-mentioned processing in the unification unit may be performed, for example, using AI, or may be performed without using AI. For example, the unification unit can input characteristic data of each medical institution into the generation AI and cause the generation AI to execute customization of the unified format.

[0099] The unification unit can estimate the user's emotion and determine the priority of the medical record unification based on the estimated user's emotion. For example, when the user feels anxious, the unification unit prioritizes the medical record unification that gives a sense of security. The unification unit can estimate the user's emotion and prioritize the medical record unification that gives a sense of security using the generation AI. In addition, the unification unit can prioritize detailed medical record unification when the user is relaxed. The unification unit can estimate the user's emotion and prioritize detailed medical record unification using the generation AI. Furthermore, the unification unit can quickly unify the medical record when the user is in a hurry. The unification unit can estimate the user's emotion and quickly unify the medical record using the generation AI. This enables more appropriate medical record unification by determining the priority of the medical record unification according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processes in the unification unit may be performed using, for example, AI, or may be performed without using AI. For example, the unification unit may input the user's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0100] The unification unit can improve the accuracy of unification by considering the geographical location information of each medical institution when unifying the medical records. The unification unit unifies the region-specific medical information based on the geographical location information of each medical institution, for example. The unification unit can use the generation AI to analyze the geographical location information of each medical institution and unify the region-specific medical information. The unification unit can also improve the accuracy of unification based on the geographical location information of each medical institution. The unification unit can use the generation AI to analyze the geographical location information of each medical institution and improve the accuracy of unification. Furthermore, the unification unit can adjust the unification algorithm based on the geographical location information of each medical institution. The unification unit can use the generation AI to analyze the geographical location information of each medical institution and adjust the unification algorithm. As a result, the accuracy of the medical record unification is improved by considering the geographical location information of each medical institution. Part or all of the above-mentioned processing in the unification unit may be performed, for example, using AI, or may be performed without using AI. For example, the unification unit can input the geographical location information of each medical institution to the generation AI and cause the generation AI to execute the improvement of the accuracy of unification.

[0101] The unification department can analyze the social media activities of each medical institution and adjust the unified format when unifying the medical records. The unification department, for example, analyzes the social media posts of each medical institution and reflects related information in the unified format. The unification department can use the generation AI to analyze the social media activities of each medical institution and reflect related information in the unified format. The unification department can also extract keywords related to medical treatment from the social media activities of each medical institution and adjust the unified format. The unification department can use the generation AI to analyze the social media activities of each medical institution, extract keywords related to medical treatment, and adjust the unified format. The unification department can also analyze the feedback of patients on social media of each medical institution and adjust the unified format. The unification department can use the generation AI to analyze the social media activities of each medical institution and adjust the unified format based on the feedback of patients. In this way, a unified format suitable for each medical institution can be provided by analyzing social media activities. Some or all of the above-mentioned processing in the unification department may be performed, for example, using AI, or may be performed without using AI. For example, the unification department can input the social media activities of each medical institution into the generation AI and cause the generation AI to adjust the unified format.

[0102] The input unit can estimate the user's emotion and adjust the interface for inputting the diagnosis result based on the estimated emotion of the user. For example, when the user feels anxious, the input unit provides a simple and highly visible interface. The input unit can estimate the user's emotion using a generation AI and provide a simple and highly visible interface. In addition, the input unit can also provide detailed input options when the user is relaxed. The input unit can estimate the user's emotion using a generation AI and provide detailed input options. Furthermore, when the user is in a hurry, the input unit can prioritize voice input and enable the user to quickly input the diagnosis result. The input unit can estimate the user's emotion using a generation AI and provide an interface that prioritizes voice input. This allows for a more comfortable input of the diagnosis result by adjusting the interface according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the input unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the input unit can input a user's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0103] The input unit can improve the accuracy of the input by referring to past diagnostic results when inputting a diagnostic result. For example, the input unit preferentially inputs related information based on past diagnostic results. The input unit can analyze past diagnostic results using a generation AI and preferentially input related information. The input unit can also analyze past diagnostic results and improve the accuracy of the input. The input unit can analyze past diagnostic results using a generation AI and improve the accuracy of the input. Furthermore, the input unit can also adjust the input algorithm by referring to past diagnostic results. The input unit can analyze past diagnostic results using a generation AI and adjust the input algorithm. As a result, the accuracy of the diagnostic result input is improved by referring to the past diagnostic results. A part or all of the above-mentioned processing in the input unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the input unit can input past diagnostic results to the generation AI and cause the generation AI to execute the improvement of the accuracy of the input.

[0104] The input unit can customize the input contents based on the doctor's specialty and experience when inputting a diagnosis result. The input unit customizes the input contents, for example, taking into account the doctor's specialty (internal medicine, surgery, etc.). The input unit can analyze the doctor's specialty using a generation AI and customize the input contents. The input unit can also customize the input contents taking into account the doctor's experience (years, number of cases, etc.). The input unit can analyze the doctor's experience using a generation AI and customize the input contents. Furthermore, the input unit can also customize the input contents taking into account the doctor's medical practice style. The input unit can analyze the doctor's medical practice style using a generation AI and customize the input contents. This enables more appropriate diagnosis result input by customizing the input contents based on the doctor's specialty and experience. Part or all of the above-mentioned processing in the input unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the input unit can input data on the doctor's specialty and experience to the generation AI and cause the generation AI to customize the input contents.

[0105] The input unit can estimate the user's emotion and determine the priority of the diagnostic results to be input based on the estimated user's emotion. For example, when the user feels anxious, the input unit preferentially inputs a serious diagnostic result. The input unit can estimate the user's emotion using the generation AI and preferentially input a serious diagnostic result. Moreover, when the user is relaxed, the input unit can also preferentially input a detailed diagnostic result. The input unit can estimate the user's emotion using the generation AI and preferentially input a detailed diagnostic result. Furthermore, when the user is in a hurry, the input unit can also quickly input a major diagnostic result. The input unit can estimate the user's emotion using the generation AI and quickly input a major diagnostic result. Thereby, by determining the priority of the diagnostic results according to the user's emotion, it is possible to preferentially input a more important diagnostic result. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the input unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the input unit can input a user's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0106] The input unit can improve the accuracy of the input by taking into account the geographical location information of the medical institution when inputting the diagnosis result. For example, the input unit preferentially inputs a diagnosis result specific to a region based on the geographical location information of the medical institution. The input unit can use the generation AI to analyze the geographical location information of the medical institution and preferentially input a diagnosis result specific to a region. The input unit can also improve the accuracy of the input based on the geographical location information of the medical institution. The input unit can use the generation AI to analyze the geographical location information of the medical institution and improve the accuracy of the input. Furthermore, the input unit can also adjust the input algorithm based on the geographical location information of the medical institution. The input unit can use the generation AI to analyze the geographical location information of the medical institution and adjust the input algorithm. As a result, the accuracy of the diagnosis result input is improved by taking into account the geographical location information of the medical institution. A part or all of the above-mentioned processing in the input unit may be performed, for example, using AI, or may be performed without using AI. For example, the input unit can input the geographical location information of the medical institution to the generation AI and cause the generation AI to execute the improvement of the accuracy of the input.

[0107] The input unit can adjust the input content by analyzing the social media activity of the medical institution when inputting the diagnosis result. The input unit can, for example, analyze the social media posts of the medical institution and input the related diagnosis result. The input unit can use the generation AI to analyze the social media activity of the medical institution and input the related diagnosis result. The input unit can also extract keywords related to medical treatment from the social media activity of the medical institution and adjust the input content. The input unit can use the generation AI to analyze the social media activity of the medical institution, extract keywords related to medical treatment, and adjust the input content. Furthermore, the input unit can analyze the patient's feedback on the social media of the medical institution and adjust the input content. The input unit can use the generation AI to analyze the social media activity of the medical institution and adjust the input content based on the patient's feedback. In this way, by analyzing the social media activity, it is possible to input a diagnosis result related to the medical institution. Part or all of the above-mentioned processing in the input unit may be performed, for example, using AI, or may be performed without using AI. For example, the input unit can input the social media activity of the medical institution to the generation AI and cause the generation AI to adjust the input content.

[0108] The input unit can estimate the user's emotion and adjust the interface for inputting the diagnosis result based on the estimated emotion of the user. For example, when the user feels anxious, the input unit provides a simple and highly visible interface. The input unit can estimate the user's emotion using a generation AI and provide a simple and highly visible interface. In addition, the input unit can also provide detailed input options when the user is relaxed. The input unit can estimate the user's emotion using a generation AI and provide detailed input options. Furthermore, when the user is in a hurry, the input unit can prioritize voice input and enable the user to quickly input the diagnosis result. The input unit can estimate the user's emotion using a generation AI and provide an interface that prioritizes voice input. This allows for a more comfortable input of the diagnosis result by adjusting the interface according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the input unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the input unit can input a user's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0109] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0110] When the user inputs symptoms, the reception unit can customize the input contents by referring to the user's past medical history. For example, if the user has suffered from a specific disease in the past, symptoms related to that disease can be preferentially displayed. The reception unit can also adjust the input contents taking into consideration the treatments and prescribed medicines the user has received in the past. Furthermore, specific tests and diagnoses can be recommended based on the user's past medical history. This makes it possible to input more appropriate symptoms by utilizing the user's past medical history.

[0111] The identification unit can analyze the lifestyle habit data of the user to improve the accuracy of disease identification. For example, if the user is a smoker, diseases related to smoking can be preferentially identified. In addition, the identification unit can evaluate the risk of lifestyle-related diseases and narrow down specific diseases by taking into account the user's diet and exercise habits. Furthermore, the identification unit can analyze the user's sleep patterns and stress levels to identify diseases influenced by these factors. As a result, by utilizing the user's lifestyle habit data, more accurate disease identification is possible.

[0112] The suggestion unit can monitor the user's current health condition in real time and suggest the most suitable hospital. For example, if the user is using a wearable device, it can suggest hospitals with high urgency based on data such as heart rate and blood pressure obtained from the device. It can also analyze data such as the user's body temperature and oxygen saturation and suggest appropriate specialists. Furthermore, if the user's health condition suddenly changes, it can quickly suggest the nearest emergency hospital. This makes it possible to suggest more appropriate hospitals by utilizing real-time health data.

[0113] When standardizing medical records, the Unification Department can customize the unified format by taking into account the clinical guidelines of each medical institution. For example, it can analyze the clinical guidelines adopted by each medical institution and adjust the items in the medical record based on that. It can also customize the format of the medical record by taking into account guidelines specialized for a specific medical department. It can also provide a unified format that reflects the clinical protocols of each medical institution. This makes it possible to unify medical records more appropriately by taking into account the clinical guidelines.

[0114] The input unit can customize the input interface taking into account the doctor's clinical style when inputting diagnosis results. For example, if the doctor prefers text input, detailed text input options can be provided. Also, if the doctor prefers voice input, an interface that prioritizes voice input can be provided. Furthermore, if the doctor makes extensive use of images and graphs, an interface that allows them to be easily inserted can be provided. In this way, by providing an interface that suits the doctor's clinical style, more comfortable input of diagnosis results is possible.

[0115] The reception unit can estimate the user's emotion and adjust the symptom input interface based on the estimated user's emotion. For example, when the user is feeling stressed, a simple interface can be provided to minimize input steps. When the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, when the user is in a hurry, voice input can be prioritized to enable the user to input symptoms quickly. In this way, by adjusting the interface according to the user's emotion, a more comfortable symptom input can be achieved.

[0116] The identification unit can estimate the user's emotions and adjust the disease identification algorithm based on the estimated user's emotions. For example, when the user feels anxious, an algorithm that prioritizes identifying serious illnesses is used. When the user is relaxed, an algorithm that performs detailed disease identification can also be used. Furthermore, when the user is in a hurry, an algorithm that quickly identifies major illnesses can also be used. In this way, by adjusting the disease identification algorithm according to the user's emotions, more appropriate disease identification is possible.

[0117] The suggestion unit can estimate the user's emotions and adjust the way in which hospital suggestions are expressed based on the estimated user's emotions. For example, if the user feels anxious, an expression method that gives a sense of security can be used. If the user is relaxed, an expression method including detailed information can also be used. Furthermore, if the user is in a hurry, a concise and quick expression method can also be used. In this way, by adjusting the expression method of hospital suggestions according to the user's emotions, more appropriate hospital suggestions can be made.

[0118] The unification unit can estimate the user's emotions and adjust the chart unification algorithm based on the estimated user's emotions. For example, if the user feels anxious, a chart unification algorithm that provides a sense of security is used. If the user feels relaxed, an algorithm that performs detailed chart unification can also be used. Furthermore, if the user is in a hurry, an algorithm that performs quick chart unification can also be used. In this way, by adjusting the chart unification algorithm according to the user's emotions, more appropriate chart unification is possible.

[0119] The input unit can estimate the user's emotions and adjust the interface for inputting a diagnosis result based on the estimated user's emotions. For example, if the user feels anxious, a simple and highly visible interface can be provided. If the user feels relaxed, detailed input options can also be provided. Furthermore, if the user is in a hurry, voice input can be prioritized to allow the user to input a diagnosis result quickly. In this way, by adjusting the interface according to the user's emotions, a more comfortable diagnosis result input can be achieved.

[0120] The process flow of the second embodiment will be briefly described below.

[0121] Step 1: The reception unit inputs the user's symptoms. The user's symptoms include, but are not limited to, headache, fever, cough, etc. The reception unit, for example, provides an interface through which the user inputs the symptoms into the app. Step 2: The identification unit uses the generation AI to identify the illness based on the information input by the reception unit. The identification unit identifies the illness based on, for example, past data or medical knowledge. The generation AI uses a text generation AI (e.g., LLM) to identify the illness based on symptoms. The identification unit can also use the generation AI to identify the illness based on a combination of symptoms or test results. Step 3: The suggestion unit suggests hospitals based on the illness identified by the identification unit. The suggestion unit selects the most suitable hospital based on, for example, the user's current location and symptoms. The suggestion unit can also use the generative AI to suggest hospitals based on criteria such as the hospital's specialty, distance, and rating. Step 4: The unification department unifies the format of medical records. The unification department analyzes medical records in different formats for each medical institution, for example, and converts them into a unified format. The generation AI analyzes medical records in different formats and standardizes data formats and items. Step 5: The input unit allows the doctor to input the diagnosis result. The input unit provides, for example, an interface for inputting the diagnosis result. The input unit can also input the diagnosis result using the generation AI and transmit the information to the generation AI.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] Each of the multiple elements including the above-mentioned reception unit, identification unit, suggestion unit, unification unit, and input unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14, and provides an interface for the user to input symptoms. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies a disease using a generation AI. The suggestion unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and suggests a hospital based on the identified disease. The unification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and unifies the format of the medical record. The input unit is realized, for example, by the reception device 38 of the smart device 14, and provides an interface for the doctor to input the diagnosis result. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0126] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] Each of the multiple elements including the above-mentioned reception unit, identification unit, suggestion unit, unification unit, and input unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214, and provides an interface for the user to input symptoms by voice. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies a disease using a generation AI. The suggestion unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and suggests a hospital based on the identified disease. The unification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and unifies the format of the medical record. The input unit is realized, for example, by the microphone 238 of the smart glasses 214, and provides an interface for the doctor to input the diagnosis result by voice. 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.

[0142] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] Each of the multiple elements including the above-mentioned reception unit, identification unit, suggestion unit, unification unit, and input 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 is realized by the microphone 238 of the headset type terminal 314, and provides an interface for the user to input symptoms by voice. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies a disease using a generation AI. The suggestion unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and suggests a hospital based on the identified disease. The unification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and unifies the format of the medical record. The input unit is realized, for example, by the microphone 238 of the headset type terminal 314, and provides an interface for the doctor to input the diagnosis result by voice. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0158] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] Each of the multiple elements including the reception unit, the identification unit, the suggestion unit, the unification unit, and the input 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 is realized by the microphone 238 of the robot 414, and provides an interface for the user to input symptoms by voice. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies a disease using a generation AI. The suggestion unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and suggests a hospital based on the identified disease. The unification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and unifies the format of the medical record. The input unit is realized, for example, by the microphone 238 of the robot 414, and provides an interface for the doctor to input the diagnosis result by voice. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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."

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] (Appendix 1) a reception unit for inputting a user's symptoms; an identification unit that identifies a disease based on the information input by the reception unit; a suggestion unit that suggests a hospital based on the disease identified by the identification unit; The unification department, which standardizes the format of medical records, An input unit for inputting a diagnosis result. A system characterized by: (Appendix 2) The identification unit is Identifying illnesses based on historical data or medical knowledge 2. The system of claim 1, (Appendix 3) The suggestion unit, Select the right hospital based on the user's location or symptoms 2. The system of claim 1, (Appendix 4) The Ministry of Unification, Analyze medical records in different formats from each medical institution and convert them into a unified format 2. The system of claim 1, (Appendix 5) The input unit is Doctors enter diagnosis results and send the information to artificial intelligence 2. The system of claim 1, (Appendix 6) The identification unit is Update disease identification capabilities based on new diagnostic results 2. The system of claim 1, (Appendix 7) The reception unit is Estimate the user's emotion and change the symptom input interface based on the estimated user emotion. 2. The system of claim 1, (Appendix 8) The reception unit is Analyzes the user's symptom input history and suggests the optimal input method 2. The system of claim 1, (Appendix 9) The reception unit is When entering symptoms, filter the input based on the user's current health condition and lifestyle habits 2. The system of claim 1, (Appendix 10) The reception unit is Estimate the user's emotions and prioritize the symptoms to be input based on the estimated user's emotions. 2. The system of claim 1, (Appendix 11) The reception unit is When entering symptoms, the system takes into account the user's geographic location information and prioritizes the most relevant symptoms. 2. The system of claim 1, (Appendix 12) The reception unit is When a symptom is entered, the system analyzes the user's social media activity and enters related symptoms. 2. The system of claim 1, (Appendix 13) The identification unit is Inferring user emotions and adjusting disease identification algorithms based on the inferred user emotions 2. The system of claim 1, (Appendix 14) The identification unit is When identifying a disease, the accuracy of the identification can be improved by referring to the user's past diagnosis results. 2. The system of claim 1, (Appendix 15) The identification unit is Customize specific methods for disease identification based on the user's living environment and occupation 2. The system of claim 1, (Appendix 16) The identification unit is Estimating a user's emotions and prioritizing diseases to be identified based on the estimated user's emotions 2. The system of claim 1, (Appendix 17) The identification unit is When identifying diseases, the accuracy of the identification can be improved by taking into account the geographic location of the user. 2. The system of claim 1, (Appendix 18) The identification unit is Analyzing users' social media activity to tailor specific techniques when identifying illnesses 2. The system of claim 1, (Appendix 19) The suggestion unit, Estimate the user's emotions and adjust the presentation of hospital suggestions based on the estimated user emotions 2. The system of claim 1, (Appendix 20) The suggestion unit, When suggesting hospitals, the system makes optimal suggestions by referring to the user's past hospital usage history. 2. The system of claim 1, (Appendix 21) The suggestion unit, When suggesting hospitals, the suggestions are customized based on the user's current health condition and living environment. 2. The system of claim 1, (Appendix 22) The suggestion unit, Estimate the user's emotions and prioritize the hospitals to be suggested based on the estimated user's emotions. 2. The system of claim 1, (Appendix 23) The suggestion unit, When suggesting hospitals, the system takes into account the user's geographic location information to suggest the most suitable hospital. 2. The system of claim 1, (Appendix 24) The suggestion unit, Analyze users' social media activity to tailor hospital proposals 2. The system of claim 1, (Appendix 25) The Ministry of Unification, Inferring user emotions and adjusting the chart unification algorithm based on the estimated user emotions 2. The system of claim 1, (Appendix 26) The Ministry of Unification, When standardizing medical records, refer to past medical record data to improve the accuracy of the standardization. 2. The system of claim 1, (Appendix 27) The Ministry of Unification, When standardizing medical records, customize the unified format based on the characteristics of each medical institution. 2. The system of claim 1, (Appendix 28) The Ministry of Unification, Estimate the user's emotions and determine the priority of chart unification based on the estimated user's emotions 2. The system of claim 1, (Appendix 29) The Ministry of Unification, When unifying medical records, the accuracy of unification is improved by taking into account the geographic location information of each medical institution. 2. The system of claim 1, (Appendix 30) The Ministry of Unification, When standardizing medical records, analyze the social media activity of each medical institution and adjust the unified format. 2. The system of claim 1, (Appendix 31) The input unit is To estimate a user's emotion and adjust an interface for inputting a diagnosis result based on the estimated user's emotion. 2. The system of claim 1, (Appendix 32) The input unit is When entering diagnostic results, improve accuracy by referring to past diagnostic results. 2. The system of claim 1, (Appendix 33) The input unit is When entering diagnostic results, customize the input content based on the doctor's specialty and experience 2. The system of claim 1, (Appendix 34) The input unit is A user's emotion is estimated, and a priority order of the diagnosis results to be input is determined based on the estimated user's emotion. 2. The system of claim 1, (Appendix 35) The input unit is When entering diagnostic results, the accuracy of the input is improved by taking into account the geographic location information of the medical institution. 2. The system of claim 1, (Appendix 36) The input unit is When entering diagnostic results, adjust the input by analyzing the medical institution's social media activity 2. The system of claim 1, [Explanation of symbols]

[0194] 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 unit for inputting a user's symptoms; an identification unit that identifies a disease based on the information input by the reception unit; a suggestion unit that suggests a hospital based on the disease identified by the identification unit; The unification department, which standardizes the format of medical records, An input unit for inputting a diagnosis result. A system characterized by:

2. The identification unit is Identifying illnesses based on historical data or medical knowledge 2. The system of claim 1 .

3. The suggestion unit, Select the right hospital based on the user's location or symptoms 2. The system of claim 1 .

4. The Ministry of Unification, Analyze medical records in different formats from each medical institution and convert them into a unified format 2. The system of claim 1 .

5. The input unit is Doctors enter diagnosis results and send the information to artificial intelligence 2. The system of claim 1 .

6. The identification unit is Update disease identification capabilities based on new diagnostic results 2. The system of claim 1 .

7. The reception unit is Estimate the user's emotion and change the symptom input interface based on the estimated user emotion.

2. The system of claim 1 .

8. The reception unit is Analyzes the user's symptom input history and suggests the optimal input method 2. The system of claim 1 .

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