system

Abstract

We provide the system. [Solution] A means of receiving health-related information entered into a terminal, A means of identifying potential medical conditions using artificial intelligence based on received health-related information, A means of making an appointment at an appropriate medical institution based on the identified potential medical condition, A means of recording the user's daily health status within the home and detecting abnormalities, A means of analyzing health information entered into a terminal and suggesting recommended medical actions, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, many people lead busy lives and have no time for health management, making it difficult to detect diseases at an early stage. As a result, the condition may progress, making treatment difficult. Additionally, in hospital reservations that require many choices, it is also a problem that an appropriate medical institution cannot be quickly found.

Means for Solving the Problems

[0005] This invention provides a system in which a server receives health-related information entered into a terminal, analyzes that information using artificial intelligence, and identifies potential medical conditions. Based on the identified medical conditions, the system presents the user with a list of suitable medical departments, enabling them to make an appointment at the most appropriate medical institution. Furthermore, it has a function to enhance the security of information by encrypting it during the data transmission stage, and by simultaneously evaluating the severity and urgency of the analyzed medical conditions, it enables efficient and rapid medical response.

[0006] A "terminal" is a computer device used by users to input information and send and receive data with a server via communication.

[0007] "Health-related information" refers to data necessary for evaluating a user's health status, such as symptoms, age, gender, and medical history.

[0008] "Means of receiving" refers to a function or device that allows a server to receive health-related information transmitted from a terminal.

[0009] Artificial intelligence is a technology in which computer systems analyze vast amounts of data, learn, and make predictions, and is particularly used to identify medical conditions.

[0010] "Potential medical conditions" refer to diseases or conditions that can be predicted based on the input information.

[0011] "Means of making reservations" refers to a function or device that automatically makes reservations for medical institutions selected by the user.

[0012] "Encryption" is a technology that transforms the content of data to protect it from third parties and maintain its confidentiality.

[0013] "Means for assessing severity and urgency" refers to a function or device that evaluates the severity of a identified medical condition and the urgency of its timely response using numerical values ​​or ranks. [Brief explanation of the drawing]

[0014] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Modes for Carrying Out the Invention

[0015] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like.

[0018] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention is a system that diagnoses a user's health condition using health-related information and assists with making appointments at medical institutions. This system is implemented using a terminal, a server, and an artificial intelligence model.

[0036] Terminal operation

[0037] The system is configured for users to input their symptoms (e.g., cough or fever) and attribute information (age, gender, medical history, etc.) via the terminal. The terminal has the functionality to properly format this information, encrypt the data, and send it to the server.

[0038] Server operation

[0039] The server receives encrypted data sent from the terminal and performs decryption. It then activates an artificial intelligence model using the received health-related information to analyze the data. The AI ​​refers to a vast amount of medical data and identifies potential medical conditions based on the entered symptoms. It also considers other symptoms related to the condition and the user's medical history to assess the severity and urgency.

[0040] Medical appointment

[0041] After obtaining the diagnosis, the server sends the results back to the terminal. This includes information such as the estimated medical condition, recommended medical department, and the urgency of the visit. Based on the information displayed on the terminal, the user can select from the presented list of medical institutions and proceed with the reservation process. The server works in conjunction with the reservation system to check the availability of the selected medical institution and secure the optimal reservation slot.

[0042] Specific example

[0043] For example, if a user enters "I have a fever and a cough" into their device, the server might use artificial intelligence to analyze the information and identify "influenza" as a possible symptom. Based on this, it might determine that the symptoms are moderately severe and recommend making an appointment at a nearby internal medicine clinic. The user can then select the recommended clinic and make an appointment at a convenient time.

[0044] In this way, the system is configured to provide quick and efficient health management support to people who are busy with their daily lives.

[0045] The following describes the processing flow.

[0046] Step 1:

[0047] The user launches the application on their device and enters their symptoms and personal information into an input form for the medical questionnaire. This includes current symptoms (e.g., headache, fever), past medical history, age, and gender.

[0048] Step 2:

[0049] The terminal encrypts the entered information. For security reasons, the encrypted data is sent to the server using a secure communication protocol.

[0050] Step 3:

[0051] The server receives encrypted data sent from the terminal and decrypts it to make it analyzable. Health-related data and user attribute information are stored on the server in an integrated state.

[0052] Step 4:

[0053] The server activates an artificial intelligence model based on the decrypted health-related information. The AI ​​analyzes the entered symptoms and patient profile, and identifies the most likely medical condition based on past medical data.

[0054] Step 5:

[0055] The server assesses the severity and urgency of the identified medical condition. This assessment is calculated based on the progression of the condition and the combination of symptoms, and determines whether a visit to a medical institution is necessary.

[0056] Step 6:

[0057] The server sends a list of estimated symptoms and recommended medical specialties to the terminal. The list also includes suggested medical facilities that are appropriate for the symptoms.

[0058] Step 7:

[0059] The terminal displays the received diagnosis results and a list of medical institutions on the user interface. The user reviews the displayed information and selects a medical department and medical institution.

[0060] Step 8:

[0061] The user selects their preferred medical institution and date / time via their device and proceeds with the reservation process by pressing a button on the screen.

[0062] Step 9:

[0063] The terminal sends the selected reservation information to the server. The server queries the reservation system, checks the medical institution's schedule, and confirms availability.

[0064] Step 10:

[0065] The server sends confirmation information to the terminal, and a reservation confirmation message is displayed to the user. This allows the user to confirm the reservation details.

[0066] Step 11:

[0067] The device integrates appointment information with a calendar function and reminders, notifying the user of the scheduled date and time of their appointment. This feature helps users remember to visit their medical facilities.

[0068] This system allows users to efficiently assess their health status and quickly book appointments at appropriate medical facilities.

[0069] (Example 1)

[0070] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0071] In modern society, there is a lack of support systems that enable individuals to effectively and quickly understand their own health status and seek appropriate medical care. In particular, it is difficult for users to securely manage their health information, accurately identify their medical condition, and make appointments at medical institutions. This invention aims to improve the quality of medical services and enhance user convenience by solving these problems.

[0072] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0073] In this invention, the server includes means for receiving health-related information entered by the user into an information processing device, means for formatting the received information into a unified format and converting the data, and means for analyzing the data with an AI model to identify potential medical conditions. This enables the user to efficiently and safely understand their own health status and book an appointment at an appropriate medical institution.

[0074] A "user" refers to an individual who uses the system to input health-related information and make appointments at medical institutions.

[0075] An "information processing device" refers to a terminal used by users to input health-related information and transmit that information to a server.

[0076] "Health-related information" refers to information entered by the user, such as symptoms, age, gender, and medical history.

[0077] An "AI model" refers to an artificial intelligence model used to analyze received health-related information and identify potential medical conditions.

[0078] "Potential medical conditions" refer to medical conditions that may be identified using AI models.

[0079] "Data conversion means" refers to the process of formatting received health-related information into a unified format that can be analyzed by the server.

[0080] A "medical department" refers to a specialized department within a medical institution that deals with the identified medical condition.

[0081] "Encryption" refers to the process of transforming data to ensure the secure transmission of health-related information.

[0082] This system aims to diagnose the user's health condition and assist in making appointments at appropriate medical facilities. The system is primarily implemented using terminals, servers, and generative AI models.

[0083] The user first uses an information processing device (for example, a smartphone or computer application) to input their symptoms and personal information. Specifically, this involves inputting information such as symptoms like "cough and fever" and "age, gender, and medical history."

[0084] The terminal formats the input information into an appropriate data format and encrypts the data using an encryption algorithm (e.g., AES encryption). It then has the functionality to send this encrypted data to the server via a communication device.

[0085] The server decrypts the received encrypted data to extract the original health-related information. Based on this information, it activates a generative AI model on the server. The AI ​​model has learned from a large amount of medical data and identifies potential medical conditions based on the received information. For example, if the symptoms "fever and cough" are entered, the AI ​​analyzes this information and determines that it may be influenza.

[0086] Furthermore, the AI ​​model identifies the relevant medical department for the patient's condition and assesses the urgency of seeking treatment at a recommended medical institution. The server generates these results and sends the diagnosis to the user's terminal.

[0087] Users can review the diagnostic results displayed on their device, select a medical institution they wish to visit from the suggested options, and make a reservation. The server then connects with the reservation system of the selected medical institution to check availability and secure the most suitable reservation slot.

[0088] For example, if a user enters "I have a fever and a cough," the server might use AI to analyze this and suggest the possibility of influenza. Internal medicine might be selected as the recommended medical department, and after assessing the need for consultation as moderate, the user might be prompted to make an appointment at a nearby clinic.

[0089] An example of a prompt for the generating AI model would be: "If the user enters 'I have a fever and a cough,' please show the recommended medical department and the procedure for making an appointment."

[0090] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0091] Step 1:

[0092] Users input their health-related information using an information processing device. This input includes symptoms such as cough and fever, and attribute information such as age, gender, and medical history. This constitutes the input data.

[0093] Step 2:

[0094] The device formats the health-related information entered by the user into an appropriate data format. Specifically, it standardizes the format of text data and numerical data. The formatted data is then encrypted using a data encryption algorithm (such as AES). This results in the output of encrypted data that protects privacy.

[0095] Step 3:

[0096] The server receives encrypted data sent from the terminal. By decrypting this data, the original health-related information is reconstructed. The decrypted data then becomes the input data for the AI ​​model.

[0097] Step 4:

[0098] The server activates a generative AI model to analyze the user's health-related information. Based on a large amount of medical data, the AI ​​model identifies potential medical conditions from the input symptom information. For example, if the input information is "fever and cough," the AI ​​will output a diagnosis indicating the possibility of influenza.

[0099] Step 5:

[0100] The server uses the analysis results to assess the severity and urgency of the patient's condition and identify the relevant medical departments. The AI ​​model's analysis results provide the information needed for the next processing step.

[0101] Step 6:

[0102] The server returns the obtained diagnostic results to the user's terminal. The results include the estimated medical condition, the appropriate medical department, and the urgency of the need for medical attention. This information becomes the data displayed on the user's terminal.

[0103] Step 7:

[0104] The user reviews the diagnostic results displayed on the device and selects the medical institution they wish to visit. The user's selection becomes the input data for the medical institution's reservation information.

[0105] Step 8:

[0106] The server connects with the selected medical institution's reservation system to check availability and make a reservation. After checking availability and confirming the reservation, the reservation confirmation information is sent to the user's terminal.

[0107] This series of processes allows users to efficiently check their health status and book necessary medical services.

[0108] (Application Example 1)

[0109] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0110] In modern society, there is a lack of effective means to manage the health anxieties that people experience on a daily basis. In particular, it is difficult to record health status within families and to detect abnormalities early. Furthermore, existing systems are not efficient in selecting appropriate medical institutions based on entered symptoms. As a result, users are unable to receive prompt and appropriate medical services, leading to problems such as worsening health and wasted medical resources.

[0111] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0112] In this invention, the server includes means for receiving health-related information entered into a terminal, means for identifying potential medical conditions using artificial intelligence based on the received health-related information, and means for recording the user's daily health status within the home and detecting abnormalities. This enables the user to monitor their health status on a daily basis, quickly book an appointment at an appropriate medical institution when an abnormality is detected, and receive prompt medical services.

[0113] A "terminal" is a device that a user can directly operate for the purpose of inputting information.

[0114] "Health-related information" refers to information that includes symptoms, age, gender, medical history, and other information related to the user's health status.

[0115] "Artificial intelligence" is a technology that references large amounts of medical data and makes estimations and judgments based on the input information.

[0116] "Potential medical conditions" refer to possible disease states identified by artificial intelligence based on the entered health-related information.

[0117] "Medical appointment booking" refers to the procedure for users to secure a date and time for an appointment at a hospital, clinic, or other medical facility in order to receive medical treatment.

[0118] "Within the home" refers to the place where the user and their family live on a daily basis.

[0119] "Recording your health status" refers to saving health-related information over a certain period of time so that it can be referenced later.

[0120] "Detecting anomalies" means automatically recognizing changes or deviations from the normal state.

[0121] To implement this invention, a terminal such as a mobile device or residential equipment is required. This terminal has an interface for users to input health-related information and has a function to securely transmit the input data to a server. Encryption technology is used for data transmission, specifically the Python cryptography.fernet library.

[0122] The server decodes the received health-related information and analyzes the data in a software environment equipped with an artificial intelligence model. This AI model refers to a vast medical database based on the symptoms entered by the user to identify potential medical conditions. This allows for the estimation of the condition and the selection of appropriate medical facilities. For example, if a user enters "I have a cough and a fever," the AI ​​model can indicate the possibility of influenza.

[0123] The analysis results are sent back from the server to the terminal, making it easy to book appointments at appropriate medical facilities. In particular, users can receive daily health data recording and abnormality detection via a health management robot installed in their home. When this robot detects a change in health status, it alerts the user and recommends specific actions.

[0124] This allows users to select the appropriate medical institution and receive prompt treatment, even in health situations requiring rapid attention. This system, utilizing a generative AI model, supports flexible health management tailored to time and circumstances.

[0125] For example, if you ask the robot, "I've had a cough since yesterday, and my temperature is high. What should I do?", the robot will work with an AI model to analyze the situation and suggest making an appointment at a nearby internal medicine clinic.

[0126] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0127] Step 1:

[0128] The user inputs health-related information, such as cough or fever, via the terminal. The terminal encrypts the input information and prepares it for data transmission. The input at this time is information indicating the user's health status, and the output is encrypted data. The cryptography.fernet library is used for encryption.

[0129] Step 2:

[0130] The device sends encrypted health-related information to the server. The server decrypts the received data using the same encryption library. The input is encrypted data, and the output is the original decrypted health information. This process ensures the secure handling of the data.

[0131] Step 3:

[0132] The server activates a generative AI model to analyze the decrypted information. The AI ​​model compares the input symptoms and related information with an accumulated medical database to identify potential medical conditions. The input for this analysis is the decrypted health information, and the output is the estimated medical condition. Specifically, it performs symptom pattern matching and correlation analysis.

[0133] Step 4:

[0134] The server identifies appropriate medical facilities and generates booking information based on the estimated medical condition. The input is the estimated medical condition, which is the output of an AI model; the output is a list of recommended medical facilities and available time slots. The process includes searching a database of medical facilities to narrow down the choices to the most suitable candidates.

[0135] Step 5:

[0136] The user views a list of medical institutions provided by the server via their terminal and selects their desired facility and date / time. Based on the selection, the server confirms the reservation at the chosen medical institution and sends the information back to the user. The output includes reservation confirmation details. Reservation confirmation is performed through the interface with the reservation system.

[0137] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0138] This invention combines a system that analyzes a user's health status and makes reservations at appropriate medical institutions with an emotion engine that recognizes the user's emotional state. In implementing this system, the terminal, server, emotion engine, and artificial intelligence model work in coordination.

[0139] Terminal operation

[0140] The user inputs their symptoms and attribute information into the device. The emotion engine then recognizes the user's emotional state in real time based on their voice, facial expressions, and input speed. This emotion is considered a bias factor in the assessment of their health status, and the device transmits this information to the server.

[0141] Server operation

[0142] The server decrypts encrypted data sent from the terminal and analyzes the integrated symptom information, user attribute information, and emotional state. The artificial intelligence model identifies the medical condition based on this data, but corrects the diagnosis based on information provided by the emotion engine. For example, if the user is feeling anxious, this emotion may affect the diagnosis, so a careful estimation of the medical condition is performed.

[0143] Medical appointment

[0144] Based on the identified medical condition, the system recommends and presents a suitable medical department to the user. Furthermore, based on the user's emotional state, a medical facility with a relaxing environment may be recommended. The user can select from the presented options and make a reservation via their device.

[0145] Specific example

[0146] For example, if a user inputs "stomach pain accompanied by chronic anxiety" into the terminal, the emotion engine evaluates the calmness of the user's voice and detects an increase in anxiety. Based on this information, the server may identify a stress-related digestive disorder such as "irritable bowel syndrome." Based on this result, it may recommend an internal medicine specialist who can help reduce stress and suggest making an appointment immediately.

[0147] This system is expected to contribute to maintaining users' physical and psychological health by enabling health management that takes users' emotions into consideration and facilitating smooth access to medical services.

[0148] The following describes the processing flow.

[0149] Step 1:

[0150] The user launches the application on their device and enters their symptoms and attribute information into the input fields for the medical questionnaire. At this time, the emotion engine recognizes emotions from the user's facial expressions and voice through the user's camera and microphone.

[0151] Step 2:

[0152] The device encrypts the entered health-related information and emotional state and transmits it to the server using a secure communication channel.

[0153] Step 3:

[0154] The server decrypts the received encrypted data, making health-related information and emotional data available for analysis. It then integrates the dataset, taking into account the user's current emotional state.

[0155] Step 4:

[0156] The AI ​​model on the server analyzes the received health-related information and compares it with past medical data to identify potential medical conditions. It also adjusts the impact of the user's emotions on the diagnostic results, taking into account information from the emotion engine.

[0157] Step 5:

[0158] The server sends a list of identified potential medical conditions, along with the relevant medical departments and recommended healthcare facilities, to the terminal. In some cases, healthcare facilities that offer a relaxing environment for the user may be selected.

[0159] Step 6:

[0160] The terminal displays information received from the server on the user interface. Users can review and select the suggested medical departments and medical institutions.

[0161] Step 7:

[0162] Users select their preferred medical institution and date / time via their device and submit a reservation request.

[0163] Step 8:

[0164] The terminal sends the selected reservation information to the server, which interfaces with the reservation system to check the medical institution's schedule and confirm the optimal reservation.

[0165] Step 9:

[0166] The server sends confirmation information to the terminal, and the reservation details are displayed to the user on the terminal. This allows the user to check their schedule and manage their visit plans using the terminal's reminder function as needed.

[0167] (Example 2)

[0168] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0169] In modern society, health management and the selection of medical institutions have become complex, placing a significant burden on users to receive appropriate medical care. Furthermore, the lack of diagnoses and medical institution selection that take into account users' emotional states makes it difficult for them to receive optimal medical services. The secure handling of personal information is also a crucial requirement.

[0170] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0171] In this invention, the server includes means for performing information analysis based on health-related information, attribute information, and emotional state information; means for identifying potential medical conditions using generative artificial intelligence; and means for correcting diagnostic results based on emotional state and booking appropriate medical facilities. This enables safe and efficient health management and medical access that takes the user's emotions into consideration.

[0172] A "terminal" is a device used by users to input health-related information and personal information.

[0173] "Health-related information" refers to information about a user's own health status or symptoms.

[0174] "Attribute information" refers to information that includes the user's basic characteristics, such as age, gender, and medical history.

[0175] "Emotional state information" refers to information about a user's emotions obtained from their voice, facial expressions, input speed, etc.

[0176] "Generative artificial intelligence" refers to artificial intelligence used to identify potential medical conditions based on collected information.

[0177] "Potential medical conditions" refer to illnesses or health conditions that are predicted based on the information entered by the user.

[0178] "Medical institutions" refer to hospitals and clinics that users visit to receive medical treatment.

[0179] "Reservation" means securing a date and time in advance to receive medical treatment at a chosen medical institution.

[0180] "Encryption" is a technology that transforms data in order to transmit information securely.

[0181] "Correction of diagnostic results" is the process of adjusting the diagnostic results while taking into account the user's emotional state.

[0182] This invention is a system that analyzes a user's health and emotional state and assists in selecting and booking appropriate medical facilities. This system primarily consists of a terminal, a server, an emotion engine, and generative artificial intelligence.

[0183] The terminal is a device for receiving health-related and attribute information entered by the user. Users input their health status and symptoms in text format. They also input basic attribute information such as age, gender, and medical history. The terminal has an emotion engine built in that analyzes voice, facial expressions, and input speed to recognize the user's emotional state in real time.

[0184] The device encrypts this information and sends it to the server. The server decrypts the encrypted data and integrates and analyzes health-related information, attribute information, and emotional state information. Generative artificial intelligence is used in the analysis to identify potential medical conditions. In this process, the diagnostic results are corrected based on information provided by the emotion engine. For example, if the user is experiencing anxiety, the diagnosis is made more carefully based on that information.

[0185] Once the medical condition is identified, the server recommends a medical department and suggests options for a relaxing healthcare facility. The user can then select a healthcare facility from the presented options and make an appointment through their terminal.

[0186] For example, if a user complains of "stomach pain accompanied by chronic anxiety," the device's emotion engine detects the user's emotional state, and the server's generated artificial intelligence takes this into account to identify a medical condition such as "irritable bowel syndrome." Based on this result, the server recommends an internal medicine specialist who can help reduce stress and immediately suggests making an appointment.

[0187] A concrete example of a prompt for a generative AI model is, "Please diagnose the chronic anxiety and stomach ache reported by the user, combining them with the emotion engine data." This prompt instructs the system to consider the user's emotions when making a diagnosis.

[0188] This system aims to enable comprehensive health management that takes users' emotions into consideration, as well as smooth access to medical facilities.

[0189] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0190] Step 1:

[0191] Users enter their health-related and personal information into the device. This information includes specific symptoms (e.g., "chronic anxiety," "stomach ache") and basic attributes (e.g., age, gender). The entered information is stored on the device as digital data.

[0192] Step 2:

[0193] The device uses an emotion engine to analyze the user's voice tone, facial expressions, and input speed in real time. This identifies the user's emotional state and generates emotional state information as numerical data. This information is recorded as bias, which is necessary when evaluating health status.

[0194] Step 3:

[0195] The terminal encrypts the entered health-related information, attribute information, and emotional state information, and sends it to the server in a single batch. This data, as input, is transmitted to the server in a secure format.

[0196] Step 4:

[0197] The server decrypts the received encrypted data and converts it into an analyzable format. The decrypted data includes symptom information, user attribute information, and emotional state information.

[0198] Step 5:

[0199] The server uses generative artificial intelligence to identify potential medical conditions based on this data. During this process, emotional states are treated as a corrective factor, as they can influence the diagnosis. The identified medical conditions are returned as disease names as output.

[0200] Step 6:

[0201] The server suggests appropriate medical departments and healthcare facilities based on the diagnosed condition. Furthermore, it includes healthcare facilities with a relaxing environment suitable for the user's emotional state as options. This presents the user with a list of potential healthcare facilities.

[0202] Step 7:

[0203] The user selects their preferred facility from the presented list of medical institutions and makes a reservation via their terminal. Upon confirmation of the reservation, the user is provided with confirmation information regarding the reservation details.

[0204] (Application Example 2)

[0205] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0206] Currently, many health management systems rely on user self-reporting, and emotional biases can influence diagnoses. Furthermore, the selection and booking of medical facilities are based solely on the patient's symptoms, lacking booking services that consider the user's psychological well-being. As a result, there is a challenge in that it is difficult for users to receive the most appropriate medical services.

[0207] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0208] In this invention, the server includes means for receiving health-related information and detected emotional states entered into a terminal; means for using artificial intelligence to identify potential health problems and correct emotional biases based on the received health-related information and emotional states; and means for suggesting and booking the most suitable medical institution based on the user's health and emotional states. This enables personalized selection of medical institutions that take the user's emotional state into consideration and a smooth booking process.

[0209] A "terminal" is a device used by users to input health-related information and emotional states.

[0210] "Health-related information" refers to data about the user's physical condition and symptoms.

[0211] "Emotional state" refers to the psychological state detected from the user's voice and facial expressions.

[0212] "Artificial intelligence" refers to computational models or technologies used to analyze health-related information and emotional states.

[0213] "Potential health problems" refer to medical conditions or health disorders that may be inferred from the user's health-related information and emotional state.

[0214] "Correcting emotional bias" means adjusting the diagnostic results to take into account the detected emotional state.

[0215] A "medical institution" refers to a facility that provides medical services.

[0216] "Methods for making reservations" refers to a function that allows users to select a date and time and complete procedures in advance so that they can receive medical services at a medical institution.

[0217] This invention realizes a system that takes user health-related information and emotional state as input, and then suggests and makes reservations for appropriate medical facilities based on that information.

[0218] The server receives health-related information entered by the user via the terminal, as well as emotional states detected from voice and facial expressions, and integrates and processes this information. This includes calculations that use advanced artificial intelligence technology to identify potential health problems and correct emotional biases. Software libraries such as "OpenCV," "TENSORFLOW®," and "PyTorch" are used for analysis. The server also plays a role in suggesting appropriate medical institutions and managing appointment procedures.

[0219] As a concrete example of its operation, if a user says to the robot assistant in the morning, "Good morning, I feel a little unwell," the device analyzes the user's tone of voice and facial expressions through voice and camera to assess their emotional state. Based on this information, the device estimates the user's health status, taking emotional bias into account, and presents the user with a list of different medical departments. In this process, it particularly recommends medical institutions that can help the user relax, depending on their mental state, and makes an appointment if requested.

[0220] The generative AI model provides diagnostic support and processes input data using prompts such as: "Based on the symptoms and emotional state data reported by the user, please suggest the most appropriate health management method for this person."

[0221] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0222] Step 1:

[0223] The user speaks into the device, inputting health-related information and emotions via voice. The device uses a microphone to collect voice data and sends it to an emotion analysis engine. The input is voice data, and the output is the result of converting the voice data directly into a file.

[0224] Step 2:

[0225] The device uses its built-in camera and emotion analysis engine to capture the user's facial expressions and analyze their emotional state. Input is still images or video data, and output is the result of real-time emotion classification. Feature extraction and emotion recognition are performed using technologies such as OpenCV and TensorFlow.

[0226] Step 3:

[0227] The terminal sends the collected audio and facial expression analysis results to the server. The server receives this data and integrates it with the data necessary for estimating the health status. The input is emotional state data, and the output is the integrated dataset.

[0228] Step 4:

[0229] The server uses an artificial intelligence model to identify potential health problems from an integrated dataset. Here, a PyTorch-based anomaly detection model is used to estimate potential health problems. The input is the integrated dataset, and the output is the estimated health problem.

[0230] Step 5:

[0231] The server further corrects for emotional bias and adjusts the diagnosis of health problems. In this process, the influence of emotions such as stress and anxiety on the results is mitigated using a "generative AI model." The input is the estimated health problem and emotional state, and the output is the estimated result of the corrected health state.

[0232] Step 6:

[0233] The server suggests suitable medical facilities to the user based on the diagnostic results and generates appointment information. A generative AI model is used to select hospitals that are likely to provide the user with relaxation. The input is the corrected diagnostic results, and the output is a list of medical facilities.

[0234] Step 7:

[0235] The user selects a medical institution from the provided list and confirms the reservation. The terminal sends the reservation online to the selected medical institution; the input is the selected medical institution information, and the output is the reservation confirmation result.

[0236] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0237] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0238] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0239] [Second Embodiment]

[0240] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0241] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0242] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0243] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0244] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0245] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0246] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0247] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0248] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0249] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0250] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0251] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0252] This invention is a system that diagnoses a user's health condition using health-related information and assists with making appointments at medical institutions. This system is implemented using a terminal, a server, and an artificial intelligence model.

[0253] Terminal operation

[0254] The system is configured for users to input their symptoms (e.g., cough or fever) and attribute information (age, gender, medical history, etc.) via the terminal. The terminal has the functionality to properly format this information, encrypt the data, and send it to the server.

[0255] Server operation

[0256] The server receives encrypted data sent from the terminal and performs decryption. It then activates an artificial intelligence model using the received health-related information to analyze the data. The AI ​​refers to a vast amount of medical data and identifies potential medical conditions based on the entered symptoms. It also considers other symptoms related to the condition and the user's medical history to assess the severity and urgency.

[0257] Medical appointment

[0258] After obtaining the diagnosis, the server sends the results back to the terminal. This includes information such as the estimated medical condition, recommended medical department, and the urgency of the visit. Based on the information displayed on the terminal, the user can select from the presented list of medical institutions and proceed with the reservation process. The server works in conjunction with the reservation system to check the availability of the selected medical institution and secure the optimal reservation slot.

[0259] Specific example

[0260] For example, if a user enters "I have a fever and a cough" into their device, the server might use artificial intelligence to analyze the information and identify "influenza" as a possible symptom. Based on this, it might determine that the symptoms are moderately severe and recommend making an appointment at a nearby internal medicine clinic. The user can then select the recommended clinic and make an appointment at a convenient time.

[0261] In this way, the system is configured to provide quick and efficient health management support to people who are busy with their daily lives.

[0262] The following describes the processing flow.

[0263] Step 1:

[0264] The user launches the application on their device and enters their symptoms and personal information into an input form for the medical questionnaire. This includes current symptoms (e.g., headache, fever), past medical history, age, and gender.

[0265] Step 2:

[0266] The terminal encrypts the entered information. For security reasons, the encrypted data is sent to the server using a secure communication protocol.

[0267] Step 3:

[0268] The server receives encrypted data sent from the terminal and decrypts it to make it analyzable. Health-related data and user attribute information are stored on the server in an integrated state.

[0269] Step 4:

[0270] The server activates an artificial intelligence model based on the decrypted health-related information. The AI ​​analyzes the entered symptoms and patient profile, and identifies the most likely medical condition based on past medical data.

[0271] Step 5:

[0272] The server assesses the severity and urgency of the identified medical condition. This assessment is calculated based on the progression of the condition and the combination of symptoms, and determines whether a visit to a medical institution is necessary.

[0273] Step 6:

[0274] The server sends a list of estimated symptoms and recommended medical specialties to the terminal. The list also includes suggested medical facilities that are appropriate for the symptoms.

[0275] Step 7:

[0276] The terminal displays the received diagnosis results and a list of medical institutions on the user interface. The user checks the displayed information and selects a department and a medical institution.

[0277] Step 8:

[0278] The user selects a desired medical institution and date / time via the terminal, and presses a button on the screen to proceed with the reservation process.

[0279] Step 9:

[0280] The terminal sends the selected reservation information to the server. The server queries the reservation system, checks the schedule of the medical institution, and confirms the availability.

[0281] Step 10:

[0282] <用 The server sends the information that the reservation has been confirmed to the terminal, and a reservation confirmation message is displayed to the user. Thus, the user can check the reservation details.

[0283] Step 11:

[0284] The terminal coordinates the reservation information with the calendar function and reminder, and notifies the user of the scheduled appointment date / time. With this function, the user can visit the medical institution without forgetting.

[0285] Through the steps of this system, the user can efficiently evaluate their own health condition and quickly reserve an appropriate medical institution. <用

[0286] (Example 1)

[0287] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0288] It should be noted that there seems to be a misspelling in the original text where "<用 " and "<用 " are likely incorrect notations. I've translated them as they are but they might need to be corrected in the original source.In modern society, there is a lack of support systems that enable individuals to effectively and quickly understand their own health status and seek appropriate medical care. In particular, it is difficult for users to securely manage their health information, accurately identify their medical condition, and make appointments at medical institutions. This invention aims to improve the quality of medical services and enhance user convenience by solving these problems.

[0289] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0290] In this invention, the server includes means for receiving health-related information entered by the user into an information processing device, means for formatting the received information into a unified format and converting the data, and means for analyzing the data with an AI model to identify potential medical conditions. This enables the user to efficiently and safely understand their own health status and book an appointment at an appropriate medical institution.

[0291] A "user" refers to an individual who uses the system to input health-related information and make appointments at medical institutions.

[0292] An "information processing device" refers to a terminal used by users to input health-related information and transmit that information to a server.

[0293] "Health-related information" refers to information entered by the user, such as symptoms, age, gender, and medical history.

[0294] An "AI model" refers to an artificial intelligence model used to analyze received health-related information and identify potential medical conditions.

[0295] "Potential medical conditions" refer to medical conditions that may be identified using AI models.

[0296] "Data conversion means" refers to the process of formatting received health-related information into a unified format that can be analyzed by the server.

[0297] A "medical department" refers to a specialized department within a medical institution that deals with the identified medical condition.

[0298] "Encryption" refers to the process of transforming data to ensure the secure transmission of health-related information.

[0299] This system aims to diagnose the user's health condition and assist in making appointments at appropriate medical facilities. The system is primarily implemented using terminals, servers, and generative AI models.

[0300] The user first uses an information processing device (for example, a smartphone or computer application) to input their symptoms and personal information. Specifically, this involves inputting information such as symptoms like "cough and fever" and "age, gender, and medical history."

[0301] The terminal formats the input information into an appropriate data format and encrypts the data using an encryption algorithm (e.g., AES encryption). It then has the functionality to send this encrypted data to the server via a communication device.

[0302] The server decrypts the received encrypted data to extract the original health-related information. Based on this information, it activates a generative AI model on the server. The AI ​​model has learned from a large amount of medical data and identifies potential medical conditions based on the received information. For example, if the symptoms "fever and cough" are entered, the AI ​​analyzes this information and determines that it may be influenza.

[0303] Furthermore, the AI ​​model identifies the relevant medical department for the patient's condition and assesses the urgency of seeking treatment at a recommended medical institution. The server generates these results and sends the diagnosis to the user's terminal.

[0304] Users can review the diagnostic results displayed on their device, select a medical institution they wish to visit from the suggested options, and make a reservation. The server then connects with the reservation system of the selected medical institution to check availability and secure the most suitable reservation slot.

[0305] As a specific example, when a user inputs "having fever and cough", the server may analyze this with AI and suggest the possibility of influenza. Internal medicine is selected as the recommended medical department, and after evaluating that the need for medical treatment is moderate, reservation at a nearby clinic is promoted.

[0306] An example of a prompt sentence for the generative AI model is "Please show the recommended medical department and the process of reservation procedures when the user inputs 'having fever and cough'."

[0307] The flow of the specific process in Example 1 will be described with reference to FIG. 11.

[0308] Step 1:

[0309] The user uses an information processing device to input their health-related information. The input information includes "cough and fever" as symptoms and "age, gender, medical history" as attribute information, etc. This becomes the input data.

[0310] Step 2:

[0311] The terminal formats the health-related information input by the user into an appropriate data format. Specifically, it performs format unification of text data and standardization of numerical data. The formatted data is then encrypted using a data encryption algorithm (such as AES). As a result, encrypted data with protected privacy is output.

[0312] Step 3:

[0313] The server receives the encrypted data sent from the terminal. By decrypting this data, the original health-related information is reconstructed. The decrypted data becomes the input data for the AI model.

[0314] Step 4:

[0315] The server activates a generative AI model to analyze the user's health-related information. Based on a large amount of medical data, the AI ​​model identifies potential medical conditions from the input symptom information. For example, if the input information is "fever and cough," the AI ​​will output a diagnosis indicating the possibility of influenza.

[0316] Step 5:

[0317] The server uses the analysis results to assess the severity and urgency of the patient's condition and identify the relevant medical departments. The AI ​​model's analysis results provide the information needed for the next processing step.

[0318] Step 6:

[0319] The server returns the obtained diagnostic results to the user's terminal. The results include the estimated medical condition, the appropriate medical department, and the urgency of the need for medical attention. This information becomes the data displayed on the user's terminal.

[0320] Step 7:

[0321] The user reviews the diagnostic results displayed on the device and selects the medical institution they wish to visit. The user's selection becomes the input data for the medical institution's reservation information.

[0322] Step 8:

[0323] The server connects with the selected medical institution's reservation system to check availability and make a reservation. After checking availability and confirming the reservation, the reservation confirmation information is sent to the user's terminal.

[0324] This series of processes allows users to efficiently check their health status and book necessary medical services.

[0325] (Application Example 1)

[0326] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0327] In modern society, there is a lack of effective means to manage the health anxieties that people experience on a daily basis. In particular, it is difficult to record health status within families and to detect abnormalities early. Furthermore, existing systems are not efficient in selecting appropriate medical institutions based on entered symptoms. As a result, users are unable to receive prompt and appropriate medical services, leading to problems such as worsening health and wasted medical resources.

[0328] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0329] In this invention, the server includes means for receiving health-related information entered into a terminal, means for identifying potential medical conditions using artificial intelligence based on the received health-related information, and means for recording the user's daily health status within the home and detecting abnormalities. This enables the user to monitor their health status on a daily basis, quickly book an appointment at an appropriate medical institution when an abnormality is detected, and receive prompt medical services.

[0330] A "terminal" is a device that a user can directly operate for the purpose of inputting information.

[0331] "Health-related information" refers to information that includes symptoms, age, gender, medical history, and other information related to the user's health status.

[0332] "Artificial intelligence" is a technology that references large amounts of medical data and makes estimations and judgments based on the input information.

[0333] "Potential medical conditions" refer to possible disease states identified by artificial intelligence based on the entered health-related information.

[0334] "Medical appointment booking" refers to the procedure for users to secure a date and time for an appointment at a hospital, clinic, or other medical facility in order to receive medical treatment.

[0335] "Within the home" refers to the place where the user and their family live on a daily basis.

[0336] "Recording your health status" refers to saving health-related information over a certain period of time so that it can be referenced later.

[0337] "Detecting anomalies" means automatically recognizing changes or deviations from the normal state.

[0338] To implement this invention, a terminal such as a mobile device or residential equipment is required. This terminal has an interface for users to input health-related information and has a function to securely transmit the input data to a server. Encryption technology is used for data transmission, specifically the Python cryptography.fernet library.

[0339] The server decodes the received health-related information and analyzes the data in a software environment equipped with an artificial intelligence model. This AI model refers to a vast medical database based on the symptoms entered by the user to identify potential medical conditions. This allows for the estimation of the condition and the selection of appropriate medical facilities. For example, if a user enters "I have a cough and a fever," the AI ​​model can indicate the possibility of influenza.

[0340] The analysis results are sent back from the server to the terminal, making it easy to book appointments at appropriate medical facilities. In particular, users can receive daily health data recording and abnormality detection via a health management robot installed in their home. When this robot detects a change in health status, it alerts the user and recommends specific actions.

[0341] This allows users to select the appropriate medical institution and receive prompt treatment, even in health situations requiring rapid attention. This system, utilizing a generative AI model, supports flexible health management tailored to time and circumstances.

[0342] For example, if you ask the robot, "I've had a cough since yesterday, and my temperature is high. What should I do?", the robot will work with an AI model to analyze the situation and suggest making an appointment at a nearby internal medicine clinic.

[0343] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0344] Step 1:

[0345] The user inputs health-related information, such as cough or fever, via the terminal. The terminal encrypts the input information and prepares it for data transmission. The input at this time is information indicating the user's health status, and the output is encrypted data. The cryptography.fernet library is used for encryption.

[0346] Step 2:

[0347] The device sends encrypted health-related information to the server. The server decrypts the received data using the same encryption library. The input is encrypted data, and the output is the original decrypted health information. This process ensures the secure handling of the data.

[0348] Step 3:

[0349] The server activates a generative AI model to analyze the decrypted information. The AI ​​model compares the input symptoms and related information with an accumulated medical database to identify potential medical conditions. The input for this analysis is the decrypted health information, and the output is the estimated medical condition. Specifically, it performs symptom pattern matching and correlation analysis.

[0350] Step 4:

[0351] The server identifies appropriate medical facilities and generates booking information based on the estimated medical condition. The input is the estimated medical condition, which is the output of an AI model; the output is a list of recommended medical facilities and available time slots. The process includes searching a database of medical facilities to narrow down the choices to the most suitable candidates.

[0352] Step 5:

[0353] The user views a list of medical institutions provided by the server via their terminal and selects their desired facility and date / time. Based on the selection, the server confirms the reservation at the chosen medical institution and sends the information back to the user. The output includes reservation confirmation details. Reservation confirmation is performed through the interface with the reservation system.

[0354] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0355] This invention combines a system that analyzes a user's health status and makes reservations at appropriate medical institutions with an emotion engine that recognizes the user's emotional state. In implementing this system, the terminal, server, emotion engine, and artificial intelligence model work in coordination.

[0356] Terminal operation

[0357] The user inputs their symptoms and attribute information into the device. The emotion engine then recognizes the user's emotional state in real time based on their voice, facial expressions, and input speed. This emotion is considered a bias factor in the assessment of their health status, and the device transmits this information to the server.

[0358] Server operation

[0359] The server decrypts encrypted data sent from the terminal and analyzes the integrated symptom information, user attribute information, and emotional state. The artificial intelligence model identifies the medical condition based on this data, but corrects the diagnosis based on information provided by the emotion engine. For example, if the user is feeling anxious, this emotion may affect the diagnosis, so a careful estimation of the medical condition is performed.

[0360] Medical appointment

[0361] Based on the identified medical condition, the system recommends and presents a suitable medical department to the user. Furthermore, based on the user's emotional state, a medical facility with a relaxing environment may be recommended. The user can select from the presented options and make a reservation via their device.

[0362] Specific example

[0363] For example, if a user inputs "stomach pain accompanied by chronic anxiety" into the terminal, the emotion engine evaluates the calmness of the user's voice and detects an increase in anxiety. Based on this information, the server may identify a stress-related digestive disorder such as "irritable bowel syndrome." Based on this result, it may recommend an internal medicine specialist who can help reduce stress and suggest making an appointment immediately.

[0364] This system is expected to contribute to maintaining users' physical and psychological health by enabling health management that takes users' emotions into consideration and facilitating smooth access to medical services.

[0365] The following describes the processing flow.

[0366] Step 1:

[0367] The user launches the application on their device and enters their symptoms and attribute information into the input fields for the medical questionnaire. At this time, the emotion engine recognizes emotions from the user's facial expressions and voice through the user's camera and microphone.

[0368] Step 2:

[0369] The device encrypts the entered health-related information and emotional state and transmits it to the server using a secure communication channel.

[0370] Step 3:

[0371] The server decrypts the received encrypted data, making health-related information and emotional data available for analysis. It then integrates the dataset, taking into account the user's current emotional state.

[0372] Step 4:

[0373] The AI ​​model on the server analyzes the received health-related information and compares it with past medical data to identify potential medical conditions. It also adjusts the impact of the user's emotions on the diagnostic results, taking into account information from the emotion engine.

[0374] Step 5:

[0375] The server sends a list of identified potential medical conditions, along with the relevant medical departments and recommended healthcare facilities, to the terminal. In some cases, healthcare facilities that offer a relaxing environment for the user may be selected.

[0376] Step 6:

[0377] The terminal displays information received from the server on the user interface. Users can review and select the suggested medical departments and medical institutions.

[0378] Step 7:

[0379] Users select their preferred medical institution and date / time via their device and submit a reservation request.

[0380] Step 8:

[0381] The terminal sends the selected reservation information to the server, which interfaces with the reservation system to check the medical institution's schedule and confirm the optimal reservation.

[0382] Step 9:

[0383] The server sends confirmation information to the terminal, and the reservation details are displayed to the user on the terminal. This allows the user to check their schedule and manage their visit plans using the terminal's reminder function as needed.

[0384] (Example 2)

[0385] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0386] In modern society, health management and the selection of medical institutions have become complex, placing a significant burden on users to receive appropriate medical care. Furthermore, the lack of diagnoses and medical institution selection that take into account users' emotional states makes it difficult for them to receive optimal medical services. The secure handling of personal information is also a crucial requirement.

[0387] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0388] In this invention, the server includes means for performing information analysis based on health-related information, attribute information, and emotional state information; means for identifying potential medical conditions using generative artificial intelligence; and means for correcting diagnostic results based on emotional state and booking appropriate medical facilities. This enables safe and efficient health management and medical access that takes the user's emotions into consideration.

[0389] A "terminal" is a device used by users to input health-related information and personal information.

[0390] "Health-related information" refers to information about a user's own health status or symptoms.

[0391] "Attribute information" refers to information that includes the user's basic characteristics, such as age, gender, and medical history.

[0392] "Emotional state information" refers to information about a user's emotions obtained from their voice, facial expressions, input speed, etc.

[0393] "Generative artificial intelligence" refers to artificial intelligence used to identify potential medical conditions based on collected information.

[0394] "Potential medical conditions" refer to illnesses or health conditions that are predicted based on the information entered by the user.

[0395] "Medical institutions" refer to hospitals and clinics that users visit to receive medical treatment.

[0396] "Reservation" means securing a date and time in advance to receive medical treatment at a chosen medical institution.

[0397] "Encryption" is a technology that transforms data in order to transmit information securely.

[0398] "Correction of diagnostic results" is the process of adjusting the diagnostic results while taking into account the user's emotional state.

[0399] This invention is a system that analyzes a user's health and emotional state and assists in selecting and booking appropriate medical facilities. This system primarily consists of a terminal, a server, an emotion engine, and generative artificial intelligence.

[0400] The terminal is a device for receiving health-related and attribute information entered by the user. Users input their health status and symptoms in text format. They also input basic attribute information such as age, gender, and medical history. The terminal has an emotion engine built in that analyzes voice, facial expressions, and input speed to recognize the user's emotional state in real time.

[0401] The device encrypts this information and sends it to the server. The server decrypts the encrypted data and integrates and analyzes health-related information, attribute information, and emotional state information. Generative artificial intelligence is used in the analysis to identify potential medical conditions. In this process, the diagnostic results are corrected based on information provided by the emotion engine. For example, if the user is experiencing anxiety, the diagnosis is made more carefully based on that information.

[0402] Once the medical condition is identified, the server recommends a medical department and suggests options for a relaxing healthcare facility. The user can then select a healthcare facility from the presented options and make an appointment through their terminal.

[0403] For example, if a user complains of "stomach pain accompanied by chronic anxiety," the device's emotion engine detects the user's emotional state, and the server's generated artificial intelligence takes this into account to identify a medical condition such as "irritable bowel syndrome." Based on this result, the server recommends an internal medicine specialist who can help reduce stress and immediately suggests making an appointment.

[0404] A concrete example of a prompt for a generative AI model is, "Please diagnose the chronic anxiety and stomach ache reported by the user, combining them with the emotion engine data." This prompt instructs the system to consider the user's emotions when making a diagnosis.

[0405] This system aims to enable comprehensive health management that takes users' emotions into consideration, as well as smooth access to medical facilities.

[0406] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0407] Step 1:

[0408] Users enter their health-related and personal information into the device. This information includes specific symptoms (e.g., "chronic anxiety," "stomach ache") and basic attributes (e.g., age, gender). The entered information is stored on the device as digital data.

[0409] Step 2:

[0410] The device uses an emotion engine to analyze the user's voice tone, facial expressions, and input speed in real time. This identifies the user's emotional state and generates emotional state information as numerical data. This information is recorded as bias, which is necessary when evaluating health status.

[0411] Step 3:

[0412] The terminal encrypts the entered health-related information, attribute information, and emotional state information, and sends it to the server in a single batch. This data, as input, is transmitted to the server in a secure format.

[0413] Step 4:

[0414] The server decrypts the received encrypted data and converts it into an analyzable format. The decrypted data includes symptom information, user attribute information, and emotional state information.

[0415] Step 5:

[0416] The server uses generative artificial intelligence to identify potential medical conditions based on this data. During this process, emotional states are treated as a corrective factor, as they can influence the diagnosis. The identified medical conditions are returned as disease names as output.

[0417] Step 6:

[0418] The server suggests appropriate medical departments and healthcare facilities based on the diagnosed condition. Furthermore, it includes healthcare facilities with a relaxing environment suitable for the user's emotional state as options. This presents the user with a list of potential healthcare facilities.

[0419] Step 7:

[0420] The user selects their preferred facility from the presented list of medical institutions and makes a reservation via their terminal. Upon confirmation of the reservation, the user is provided with confirmation information regarding the reservation details.

[0421] (Application Example 2)

[0422] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0423] Currently, many health management systems rely on user self-reporting, and emotional biases can influence diagnoses. Furthermore, the selection and booking of medical facilities are based solely on the patient's symptoms, lacking booking services that consider the user's psychological well-being. As a result, there is a challenge in that it is difficult for users to receive the most appropriate medical services.

[0424] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0425] In this invention, the server includes means for receiving health-related information and detected emotional states entered into a terminal; means for using artificial intelligence to identify potential health problems and correct emotional biases based on the received health-related information and emotional states; and means for suggesting and booking the most suitable medical institution based on the user's health and emotional states. This enables personalized selection of medical institutions that take the user's emotional state into consideration and a smooth booking process.

[0426] A "terminal" is a device used by users to input health-related information and emotional states.

[0427] "Health-related information" refers to data about the user's physical condition and symptoms.

[0428] "Emotional state" refers to the psychological state detected from the user's voice and facial expressions.

[0429] "Artificial intelligence" refers to computational models or technologies used to analyze health-related information and emotional states.

[0430] "Potential health problems" refer to medical conditions or health disorders that may be inferred from the user's health-related information and emotional state.

[0431] "Correcting emotional bias" means adjusting the diagnostic results to take into account the detected emotional state.

[0432] A "medical institution" refers to a facility that provides medical services.

[0433] "Methods for making reservations" refers to a function that allows users to select a date and time and complete procedures in advance so that they can receive medical services at a medical institution.

[0434] This invention realizes a system that takes user health-related information and emotional state as input, and then suggests and makes reservations for appropriate medical facilities based on that information.

[0435] The server receives health-related information entered by the user via the terminal, along with emotional states detected from voice and facial expressions, and integrates and processes this information. This includes calculations that use advanced artificial intelligence techniques to identify potential health problems and correct for emotional bias. Software libraries such as "OpenCV," "TensorFlow," and "PyTorch" are used for analysis. The server also plays a role in suggesting appropriate medical institutions and managing appointment procedures.

[0436] As a concrete example of its operation, if a user says to the robot assistant in the morning, "Good morning, I feel a little unwell," the device analyzes the user's tone of voice and facial expressions through voice and camera to assess their emotional state. Based on this information, the device estimates the user's health status, taking emotional bias into account, and presents the user with a list of different medical departments. In this process, it particularly recommends medical institutions that can help the user relax, depending on their mental state, and makes an appointment if requested.

[0437] The generative AI model provides diagnostic support and processes input data using prompts such as: "Based on the symptoms and emotional state data reported by the user, please suggest the most appropriate health management method for this person."

[0438] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0439] Step 1:

[0440] The user speaks into the device, inputting health-related information and emotions via voice. The device uses a microphone to collect voice data and sends it to an emotion analysis engine. The input is voice data, and the output is the result of converting the voice data directly into a file.

[0441] Step 2:

[0442] The device uses its built-in camera and emotion analysis engine to capture the user's facial expressions and analyze their emotional state. Input is still images or video data, and output is the result of real-time emotion classification. Feature extraction and emotion recognition are performed using technologies such as OpenCV and TensorFlow.

[0443] Step 3:

[0444] The terminal sends the collected audio and facial expression analysis results to the server. The server receives this data and integrates it with the data necessary for estimating the health status. The input is emotional state data, and the output is the integrated dataset.

[0445] Step 4:

[0446] The server uses an artificial intelligence model to identify potential health problems from an integrated dataset. Here, a PyTorch-based anomaly detection model is used to estimate potential health problems. The input is the integrated dataset, and the output is the estimated health problem.

[0447] Step 5:

[0448] The server further corrects for emotional bias and adjusts the diagnosis of health problems. In this process, the influence of emotions such as stress and anxiety on the results is mitigated using a "generative AI model." The input is the estimated health problem and emotional state, and the output is the estimated result of the corrected health state.

[0449] Step 6:

[0450] The server suggests suitable medical facilities to the user based on the diagnostic results and generates appointment information. A generative AI model is used to select hospitals that are likely to provide the user with relaxation. The input is the corrected diagnostic results, and the output is a list of medical facilities.

[0451] Step 7:

[0452] The user selects a medical institution from the provided list and confirms the reservation. The terminal sends the reservation online to the selected medical institution; the input is the selected medical institution information, and the output is the reservation confirmation result.

[0453] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0454] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0455] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0456] [Third Embodiment]

[0457] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0458] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0459] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0460] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0461] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0462] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0463] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0464] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0465] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0466] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0467] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0468] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0469] This invention is a system that diagnoses a user's health condition using health-related information and assists with making appointments at medical institutions. This system is implemented using a terminal, a server, and an artificial intelligence model.

[0470] Terminal operation

[0471] The system is configured for users to input their symptoms (e.g., cough or fever) and attribute information (age, gender, medical history, etc.) via the terminal. The terminal has the functionality to properly format this information, encrypt the data, and send it to the server.

[0472] Server operation

[0473] The server receives encrypted data sent from the terminal and performs decryption. It then activates an artificial intelligence model using the received health-related information to analyze the data. The AI ​​refers to a vast amount of medical data and identifies potential medical conditions based on the entered symptoms. It also considers other symptoms related to the condition and the user's medical history to assess the severity and urgency.

[0474] Medical appointment

[0475] After obtaining the diagnosis, the server sends the results back to the terminal. This includes information such as the estimated medical condition, recommended medical department, and the urgency of the visit. Based on the information displayed on the terminal, the user can select from the presented list of medical institutions and proceed with the reservation process. The server works in conjunction with the reservation system to check the availability of the selected medical institution and secure the optimal reservation slot.

[0476] Specific example

[0477] For example, if a user enters "I have a fever and a cough" into their device, the server might use artificial intelligence to analyze the information and identify "influenza" as a possible symptom. Based on this, it might determine that the symptoms are moderately severe and recommend making an appointment at a nearby internal medicine clinic. The user can then select the recommended clinic and make an appointment at a convenient time.

[0478] In this way, the system is configured to provide quick and efficient health management support to people who are busy with their daily lives.

[0479] The following describes the processing flow.

[0480] Step 1:

[0481] The user launches the application on their device and enters their symptoms and personal information into an input form for the medical questionnaire. This includes current symptoms (e.g., headache, fever), past medical history, age, and gender.

[0482] Step 2:

[0483] The terminal encrypts the entered information. For security reasons, the encrypted data is sent to the server using a secure communication protocol.

[0484] Step 3:

[0485] The server receives encrypted data sent from the terminal and decrypts it to make it analyzable. Health-related data and user attribute information are stored on the server in an integrated state.

[0486] Step 4:

[0487] The server activates an artificial intelligence model based on the decrypted health-related information. The AI ​​analyzes the entered symptoms and patient profile, and identifies the most likely medical condition based on past medical data.

[0488] Step 5:

[0489] The server assesses the severity and urgency of the identified medical condition. This assessment is calculated based on the progression of the condition and the combination of symptoms, and determines whether a visit to a medical institution is necessary.

[0490] Step 6:

[0491] The server sends a list of estimated symptoms and recommended medical specialties to the terminal. The list also includes suggested medical facilities that are appropriate for the symptoms.

[0492] Step 7:

[0493] The terminal displays the received diagnosis results and a list of medical institutions on the user interface. The user reviews the displayed information and selects a medical department and medical institution.

[0494] Step 8:

[0495] The user selects their preferred medical institution and date / time via their device and proceeds with the reservation process by pressing a button on the screen.

[0496] Step 9:

[0497] The terminal sends the selected reservation information to the server. The server queries the reservation system, checks the medical institution's schedule, and confirms availability.

[0498] Step 10:

[0499] The server sends confirmation information to the terminal, and a reservation confirmation message is displayed to the user. This allows the user to confirm the reservation details.

[0500] Step 11:

[0501] The device integrates appointment information with a calendar function and reminders, notifying the user of the scheduled date and time of their appointment. This feature helps users remember to visit their medical facilities.

[0502] This system allows users to efficiently assess their health status and quickly book appointments at appropriate medical facilities.

[0503] (Example 1)

[0504] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0505] In modern society, there is a lack of support systems that enable individuals to effectively and quickly understand their own health status and seek appropriate medical care. In particular, it is difficult for users to securely manage their health information, accurately identify their medical condition, and make appointments at medical institutions. This invention aims to improve the quality of medical services and enhance user convenience by solving these problems.

[0506] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0507] In this invention, the server includes means for receiving health-related information entered by the user into an information processing device, means for formatting the received information into a unified format and converting the data, and means for analyzing the data with an AI model to identify potential medical conditions. This enables the user to efficiently and safely understand their own health status and book an appointment at an appropriate medical institution.

[0508] A "user" refers to an individual who uses the system to input health-related information and make appointments at medical institutions.

[0509] An "information processing device" refers to a terminal used by users to input health-related information and transmit that information to a server.

[0510] "Health-related information" refers to information entered by the user, such as symptoms, age, gender, and medical history.

[0511] An "AI model" refers to an artificial intelligence model used to analyze received health-related information and identify potential medical conditions.

[0512] "Potential medical conditions" refer to medical conditions that may be identified using AI models.

[0513] "Data conversion means" refers to the process of formatting received health-related information into a unified format that can be analyzed by the server.

[0514] A "medical department" refers to a specialized department within a medical institution that deals with the identified medical condition.

[0515] "Encryption" refers to the process of transforming data to ensure the secure transmission of health-related information.

[0516] This system aims to diagnose the user's health condition and assist in making appointments at appropriate medical facilities. The system is primarily implemented using terminals, servers, and generative AI models.

[0517] The user first uses an information processing device (for example, a smartphone or computer application) to input their symptoms and personal information. Specifically, this involves inputting information such as symptoms like "cough and fever" and "age, gender, and medical history."

[0518] The terminal formats the input information into an appropriate data format and encrypts the data using an encryption algorithm (e.g., AES encryption). It then has the functionality to send this encrypted data to the server via a communication device.

[0519] The server decrypts the received encrypted data to extract the original health-related information. Based on this information, it activates a generative AI model on the server. The AI ​​model has learned from a large amount of medical data and identifies potential medical conditions based on the received information. For example, if the symptoms "fever and cough" are entered, the AI ​​analyzes this information and determines that it may be influenza.

[0520] Furthermore, the AI ​​model identifies the relevant medical department for the patient's condition and assesses the urgency of seeking treatment at a recommended medical institution. The server generates these results and sends the diagnosis to the user's terminal.

[0521] Users can review the diagnostic results displayed on their device, select a medical institution they wish to visit from the suggested options, and make a reservation. The server then connects with the reservation system of the selected medical institution to check availability and secure the most suitable reservation slot.

[0522] For example, if a user enters "I have a fever and a cough," the server might use AI to analyze this and suggest the possibility of influenza. Internal medicine might be selected as the recommended medical department, and after assessing the need for consultation as moderate, the user might be prompted to make an appointment at a nearby clinic.

[0523] An example of a prompt for the generating AI model would be: "If the user enters 'I have a fever and a cough,' please show the recommended medical department and the procedure for making an appointment."

[0524] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0525] Step 1:

[0526] Users input their health-related information using an information processing device. This input includes symptoms such as cough and fever, and attribute information such as age, gender, and medical history. This constitutes the input data.

[0527] Step 2:

[0528] The device formats the health-related information entered by the user into an appropriate data format. Specifically, it standardizes the format of text data and numerical data. The formatted data is then encrypted using a data encryption algorithm (such as AES). This results in the output of encrypted data that protects privacy.

[0529] Step 3:

[0530] The server receives encrypted data sent from the terminal. By decrypting this data, the original health-related information is reconstructed. The decrypted data then becomes the input data for the AI ​​model.

[0531] Step 4:

[0532] The server activates a generative AI model to analyze the user's health-related information. Based on a large amount of medical data, the AI ​​model identifies potential medical conditions from the input symptom information. For example, if the input information is "fever and cough," the AI ​​will output a diagnosis indicating the possibility of influenza.

[0533] Step 5:

[0534] The server uses the analysis results to assess the severity and urgency of the patient's condition and identify the relevant medical departments. The AI ​​model's analysis results provide the information needed for the next processing step.

[0535] Step 6:

[0536] The server returns the obtained diagnostic results to the user's terminal. The results include the estimated medical condition, the appropriate medical department, and the urgency of the need for medical attention. This information becomes the data displayed on the user's terminal.

[0537] Step 7:

[0538] The user reviews the diagnostic results displayed on the device and selects the medical institution they wish to visit. The user's selection becomes the input data for the medical institution's reservation information.

[0539] Step 8:

[0540] The server connects with the selected medical institution's reservation system to check availability and make a reservation. After checking availability and confirming the reservation, the reservation confirmation information is sent to the user's terminal.

[0541] This series of processes allows users to efficiently check their health status and book necessary medical services.

[0542] (Application Example 1)

[0543] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0544] In modern society, there is a lack of effective means to manage the health anxieties that people experience on a daily basis. In particular, it is difficult to record health status within families and to detect abnormalities early. Furthermore, existing systems are not efficient in selecting appropriate medical institutions based on entered symptoms. As a result, users are unable to receive prompt and appropriate medical services, leading to problems such as worsening health and wasted medical resources.

[0545] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0546] In this invention, the server includes means for receiving health-related information entered into a terminal, means for identifying potential medical conditions using artificial intelligence based on the received health-related information, and means for recording the user's daily health status within the home and detecting abnormalities. This enables the user to monitor their health status on a daily basis, quickly book an appointment at an appropriate medical institution when an abnormality is detected, and receive prompt medical services.

[0547] A "terminal" is a device that a user can directly operate for the purpose of inputting information.

[0548] "Health-related information" refers to information that includes symptoms, age, gender, medical history, and other information related to the user's health status.

[0549] "Artificial intelligence" is a technology that references large amounts of medical data and makes estimations and judgments based on the input information.

[0550] "Potential medical conditions" refer to possible disease states identified by artificial intelligence based on the entered health-related information.

[0551] "Medical appointment booking" refers to the procedure for users to secure a date and time for an appointment at a hospital, clinic, or other medical facility in order to receive medical treatment.

[0552] "Within the home" refers to the place where the user and their family live on a daily basis.

[0553] "Recording your health status" refers to saving health-related information over a certain period of time so that it can be referenced later.

[0554] "Detecting anomalies" means automatically recognizing changes or deviations from the normal state.

[0555] To implement this invention, a terminal such as a mobile device or residential equipment is required. This terminal has an interface for users to input health-related information and has a function to securely transmit the input data to a server. Encryption technology is used for data transmission, specifically the Python cryptography.fernet library.

[0556] The server decodes the received health-related information and analyzes the data in a software environment equipped with an artificial intelligence model. This AI model refers to a vast medical database based on the symptoms entered by the user to identify potential medical conditions. This allows for the estimation of the condition and the selection of appropriate medical facilities. For example, if a user enters "I have a cough and a fever," the AI ​​model can indicate the possibility of influenza.

[0557] The analysis results are sent back from the server to the terminal, making it easy to book appointments at appropriate medical facilities. In particular, users can receive daily health data recording and abnormality detection via a health management robot installed in their home. When this robot detects a change in health status, it alerts the user and recommends specific actions.

[0558] This allows users to select the appropriate medical institution and receive prompt treatment, even in health situations requiring rapid attention. This system, utilizing a generative AI model, supports flexible health management tailored to time and circumstances.

[0559] For example, if you ask the robot, "I've had a cough since yesterday, and my temperature is high. What should I do?", the robot will work with an AI model to analyze the situation and suggest making an appointment at a nearby internal medicine clinic.

[0560] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0561] Step 1:

[0562] The user inputs health-related information, such as cough or fever, via the terminal. The terminal encrypts the input information and prepares it for data transmission. The input at this time is information indicating the user's health status, and the output is encrypted data. The cryptography.fernet library is used for encryption.

[0563] Step 2:

[0564] The device sends encrypted health-related information to the server. The server decrypts the received data using the same encryption library. The input is encrypted data, and the output is the original decrypted health information. This process ensures the secure handling of the data.

[0565] Step 3:

[0566] The server activates a generative AI model to analyze the decrypted information. The AI ​​model compares the input symptoms and related information with an accumulated medical database to identify potential medical conditions. The input for this analysis is the decrypted health information, and the output is the estimated medical condition. Specifically, it performs symptom pattern matching and correlation analysis.

[0567] Step 4:

[0568] The server identifies appropriate medical facilities and generates booking information based on the estimated medical condition. The input is the estimated medical condition, which is the output of an AI model; the output is a list of recommended medical facilities and available time slots. The process includes searching a database of medical facilities to narrow down the choices to the most suitable candidates.

[0569] Step 5:

[0570] The user views a list of medical institutions provided by the server via their terminal and selects their desired facility and date / time. Based on the selection, the server confirms the reservation at the chosen medical institution and sends the information back to the user. The output includes reservation confirmation details. Reservation confirmation is performed through the interface with the reservation system.

[0571] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0572] This invention combines a system that analyzes a user's health status and makes reservations at appropriate medical institutions with an emotion engine that recognizes the user's emotional state. In implementing this system, the terminal, server, emotion engine, and artificial intelligence model work in coordination.

[0573] Terminal operation

[0574] The user inputs their symptoms and attribute information into the device. The emotion engine then recognizes the user's emotional state in real time based on their voice, facial expressions, and input speed. This emotion is considered a bias factor in the assessment of their health status, and the device transmits this information to the server.

[0575] Server operation

[0576] The server decrypts encrypted data sent from the terminal and analyzes the integrated symptom information, user attribute information, and emotional state. The artificial intelligence model identifies the medical condition based on this data, but corrects the diagnosis based on information provided by the emotion engine. For example, if the user is feeling anxious, this emotion may affect the diagnosis, so a careful estimation of the medical condition is performed.

[0577] Medical appointment

[0578] Based on the identified medical condition, the system recommends and presents a suitable medical department to the user. Furthermore, based on the user's emotional state, a medical facility with a relaxing environment may be recommended. The user can select from the presented options and make a reservation via their device.

[0579] Specific example

[0580] For example, if a user inputs "stomach pain accompanied by chronic anxiety" into the terminal, the emotion engine evaluates the calmness of the user's voice and detects an increase in anxiety. Based on this information, the server may identify a stress-related digestive disorder such as "irritable bowel syndrome." Based on this result, it may recommend an internal medicine specialist who can help reduce stress and suggest making an appointment immediately.

[0581] This system is expected to contribute to maintaining users' physical and psychological health by enabling health management that takes users' emotions into consideration and facilitating smooth access to medical services.

[0582] The following describes the processing flow.

[0583] Step 1:

[0584] The user launches the application on their device and enters their symptoms and attribute information into the input fields for the medical questionnaire. At this time, the emotion engine recognizes emotions from the user's facial expressions and voice through the user's camera and microphone.

[0585] Step 2:

[0586] The device encrypts the entered health-related information and emotional state and transmits it to the server using a secure communication channel.

[0587] Step 3:

[0588] The server decrypts the received encrypted data, making health-related information and emotional data available for analysis. It then integrates the dataset, taking into account the user's current emotional state.

[0589] Step 4:

[0590] The AI ​​model on the server analyzes the received health-related information and compares it with past medical data to identify potential medical conditions. It also adjusts the impact of the user's emotions on the diagnostic results, taking into account information from the emotion engine.

[0591] Step 5:

[0592] The server sends a list of identified potential medical conditions, along with the relevant medical departments and recommended healthcare facilities, to the terminal. In some cases, healthcare facilities that offer a relaxing environment for the user may be selected.

[0593] Step 6:

[0594] The terminal displays information received from the server on the user interface. Users can review and select the suggested medical departments and medical institutions.

[0595] Step 7:

[0596] Users select their preferred medical institution and date / time via their device and submit a reservation request.

[0597] Step 8:

[0598] The terminal sends the selected reservation information to the server, which interfaces with the reservation system to check the medical institution's schedule and confirm the optimal reservation.

[0599] Step 9:

[0600] The server sends confirmation information to the terminal, and the reservation details are displayed to the user on the terminal. This allows the user to check their schedule and manage their visit plans using the terminal's reminder function as needed.

[0601] (Example 2)

[0602] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0603] In modern society, health management and the selection of medical institutions have become complex, placing a significant burden on users to receive appropriate medical care. Furthermore, the lack of diagnoses and medical institution selection that take into account users' emotional states makes it difficult for them to receive optimal medical services. The secure handling of personal information is also a crucial requirement.

[0604] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0605] In this invention, the server includes means for performing information analysis based on health-related information, attribute information, and emotional state information; means for identifying potential medical conditions using generative artificial intelligence; and means for correcting diagnostic results based on emotional state and booking appropriate medical facilities. This enables safe and efficient health management and medical access that takes the user's emotions into consideration.

[0606] A "terminal" is a device used by users to input health-related information and personal information.

[0607] "Health-related information" refers to information about a user's own health status or symptoms.

[0608] "Attribute information" refers to information that includes the user's basic characteristics, such as age, gender, and medical history.

[0609] "Emotional state information" refers to information about a user's emotions obtained from their voice, facial expressions, input speed, etc.

[0610] "Generative artificial intelligence" refers to artificial intelligence used to identify potential medical conditions based on collected information.

[0611] "Potential medical conditions" refer to illnesses or health conditions that are predicted based on the information entered by the user.

[0612] "Medical institutions" refer to hospitals and clinics that users visit to receive medical treatment.

[0613] "Reservation" means securing a date and time in advance to receive medical treatment at a chosen medical institution.

[0614] "Encryption" is a technology that transforms data in order to transmit information securely.

[0615] "Correction of diagnostic results" is the process of adjusting the diagnostic results while taking into account the user's emotional state.

[0616] This invention is a system that analyzes a user's health and emotional state and assists in selecting and booking appropriate medical facilities. This system primarily consists of a terminal, a server, an emotion engine, and generative artificial intelligence.

[0617] The terminal is a device for receiving health-related and attribute information entered by the user. Users input their health status and symptoms in text format. They also input basic attribute information such as age, gender, and medical history. The terminal has an emotion engine built in that analyzes voice, facial expressions, and input speed to recognize the user's emotional state in real time.

[0618] The device encrypts this information and sends it to the server. The server decrypts the encrypted data and integrates and analyzes health-related information, attribute information, and emotional state information. Generative artificial intelligence is used in the analysis to identify potential medical conditions. In this process, the diagnostic results are corrected based on information provided by the emotion engine. For example, if the user is experiencing anxiety, the diagnosis is made more carefully based on that information.

[0619] Once the medical condition is identified, the server recommends a medical department and suggests options for a relaxing healthcare facility. The user can then select a healthcare facility from the presented options and make an appointment through their terminal.

[0620] For example, if a user complains of "stomach pain accompanied by chronic anxiety," the device's emotion engine detects the user's emotional state, and the server's generated artificial intelligence takes this into account to identify a medical condition such as "irritable bowel syndrome." Based on this result, the server recommends an internal medicine specialist who can help reduce stress and immediately suggests making an appointment.

[0621] A concrete example of a prompt for a generative AI model is, "Please diagnose the chronic anxiety and stomach ache reported by the user, combining them with the emotion engine data." This prompt instructs the system to consider the user's emotions when making a diagnosis.

[0622] This system aims to enable comprehensive health management that takes users' emotions into consideration, as well as smooth access to medical facilities.

[0623] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0624] Step 1:

[0625] Users enter their health-related and personal information into the device. This information includes specific symptoms (e.g., "chronic anxiety," "stomach ache") and basic attributes (e.g., age, gender). The entered information is stored on the device as digital data.

[0626] Step 2:

[0627] The device uses an emotion engine to analyze the user's voice tone, facial expressions, and input speed in real time. This identifies the user's emotional state and generates emotional state information as numerical data. This information is recorded as bias, which is necessary when evaluating health status.

[0628] Step 3:

[0629] The terminal encrypts the entered health-related information, attribute information, and emotional state information, and sends it to the server in a single batch. This data, as input, is transmitted to the server in a secure format.

[0630] Step 4:

[0631] The server decrypts the received encrypted data and converts it into an analyzable format. The decrypted data includes symptom information, user attribute information, and emotional state information.

[0632] Step 5:

[0633] The server uses generative artificial intelligence to identify potential medical conditions based on this data. During this process, emotional states are treated as a corrective factor, as they can influence the diagnosis. The identified medical conditions are returned as disease names as output.

[0634] Step 6:

[0635] The server suggests appropriate medical departments and healthcare facilities based on the diagnosed condition. Furthermore, it includes healthcare facilities with a relaxing environment suitable for the user's emotional state as options. This presents the user with a list of potential healthcare facilities.

[0636] Step 7:

[0637] The user selects their preferred facility from the presented list of medical institutions and makes a reservation via their terminal. Upon confirmation of the reservation, the user is provided with confirmation information regarding the reservation details.

[0638] (Application Example 2)

[0639] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0640] Currently, many health management systems rely on user self-reporting, and emotional biases can influence diagnoses. Furthermore, the selection and booking of medical facilities are based solely on the patient's symptoms, lacking booking services that consider the user's psychological well-being. As a result, there is a challenge in that it is difficult for users to receive the most appropriate medical services.

[0641] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0642] In this invention, the server includes means for receiving health-related information and detected emotional states entered into a terminal; means for using artificial intelligence to identify potential health problems and correct emotional biases based on the received health-related information and emotional states; and means for suggesting and booking the most suitable medical institution based on the user's health and emotional states. This enables personalized selection of medical institutions that take the user's emotional state into consideration and a smooth booking process.

[0643] A "terminal" is a device used by users to input health-related information and emotional states.

[0644] "Health-related information" refers to data about the user's physical condition and symptoms.

[0645] "Emotional state" refers to the psychological state detected from the user's voice and facial expressions.

[0646] "Artificial intelligence" refers to computational models or technologies used to analyze health-related information and emotional states.

[0647] "Potential health problems" refer to medical conditions or health disorders that may be inferred from the user's health-related information and emotional state.

[0648] "Correcting emotional bias" means adjusting the diagnostic results to take into account the detected emotional state.

[0649] A "medical institution" refers to a facility that provides medical services.

[0650] "Methods for making reservations" refers to a function that allows users to select a date and time and complete procedures in advance so that they can receive medical services at a medical institution.

[0651] This invention realizes a system that takes user health-related information and emotional state as input, and then suggests and makes reservations for appropriate medical facilities based on that information.

[0652] The server receives health-related information entered by the user via the terminal, along with emotional states detected from voice and facial expressions, and integrates and processes this information. This includes calculations that use advanced artificial intelligence techniques to identify potential health problems and correct for emotional bias. Software libraries such as "OpenCV," "TensorFlow," and "PyTorch" are used for analysis. The server also plays a role in suggesting appropriate medical institutions and managing appointment procedures.

[0653] As a concrete example of its operation, if a user says to the robot assistant in the morning, "Good morning, I feel a little unwell," the device analyzes the user's tone of voice and facial expressions through voice and camera to assess their emotional state. Based on this information, the device estimates the user's health status, taking emotional bias into account, and presents the user with a list of different medical departments. In this process, it particularly recommends medical institutions that can help the user relax, depending on their mental state, and makes an appointment if requested.

[0654] The generative AI model provides diagnostic support and processes input data using prompts such as: "Based on the symptoms and emotional state data reported by the user, please suggest the most appropriate health management method for this person."

[0655] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0656] Step 1:

[0657] The user speaks into the device, inputting health-related information and emotions via voice. The device uses a microphone to collect voice data and sends it to an emotion analysis engine. The input is voice data, and the output is the result of converting the voice data directly into a file.

[0658] Step 2:

[0659] The device uses its built-in camera and emotion analysis engine to capture the user's facial expressions and analyze their emotional state. Input is still images or video data, and output is the result of real-time emotion classification. Feature extraction and emotion recognition are performed using technologies such as OpenCV and TensorFlow.

[0660] Step 3:

[0661] The terminal sends the collected audio and facial expression analysis results to the server. The server receives this data and integrates it with the data necessary for estimating the health status. The input is emotional state data, and the output is the integrated dataset.

[0662] Step 4:

[0663] The server uses an artificial intelligence model to identify potential health problems from an integrated dataset. Here, a PyTorch-based anomaly detection model is used to estimate potential health problems. The input is the integrated dataset, and the output is the estimated health problem.

[0664] Step 5:

[0665] The server further corrects for emotional bias and adjusts the diagnosis of health problems. In this process, the influence of emotions such as stress and anxiety on the results is mitigated using a "generative AI model." The input is the estimated health problem and emotional state, and the output is the estimated result of the corrected health state.

[0666] Step 6:

[0667] The server suggests suitable medical facilities to the user based on the diagnostic results and generates appointment information. A generative AI model is used to select hospitals that are likely to provide the user with relaxation. The input is the corrected diagnostic results, and the output is a list of medical facilities.

[0668] Step 7:

[0669] The user selects a medical institution from the provided list and confirms the reservation. The terminal sends the reservation online to the selected medical institution; the input is the selected medical institution information, and the output is the reservation confirmation result.

[0670] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0671] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0672] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0673] [Fourth Embodiment]

[0674] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0675] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0676] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0677] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0678] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0679] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0680] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0681] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0682] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0683] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0684] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0685] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0686] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0687] This invention is a system that diagnoses a user's health condition using health-related information and assists with making appointments at medical institutions. This system is implemented using a terminal, a server, and an artificial intelligence model.

[0688] Terminal operation

[0689] The system is configured for users to input their symptoms (e.g., cough or fever) and attribute information (age, gender, medical history, etc.) via the terminal. The terminal has the functionality to properly format this information, encrypt the data, and send it to the server.

[0690] Server operation

[0691] The server receives encrypted data sent from the terminal and performs decryption. It then activates an artificial intelligence model using the received health-related information to analyze the data. The AI ​​refers to a vast amount of medical data and identifies potential medical conditions based on the entered symptoms. It also considers other symptoms related to the condition and the user's medical history to assess the severity and urgency.

[0692] Medical appointment

[0693] After obtaining the diagnosis, the server sends the results back to the terminal. This includes information such as the estimated medical condition, recommended medical department, and the urgency of the visit. Based on the information displayed on the terminal, the user can select from the presented list of medical institutions and proceed with the reservation process. The server works in conjunction with the reservation system to check the availability of the selected medical institution and secure the optimal reservation slot.

[0694] Specific example

[0695] For example, if a user enters "I have a fever and a cough" into their device, the server might use artificial intelligence to analyze the information and identify "influenza" as a possible symptom. Based on this, it might determine that the symptoms are moderately severe and recommend making an appointment at a nearby internal medicine clinic. The user can then select the recommended clinic and make an appointment at a convenient time.

[0696] In this way, the system is configured to provide quick and efficient health management support to people who are busy with their daily lives.

[0697] The following describes the processing flow.

[0698] Step 1:

[0699] The user launches the application on their device and enters their symptoms and personal information into an input form for the medical questionnaire. This includes current symptoms (e.g., headache, fever), past medical history, age, and gender.

[0700] Step 2:

[0701] The terminal encrypts the entered information. For security reasons, the encrypted data is sent to the server using a secure communication protocol.

[0702] Step 3:

[0703] The server receives encrypted data sent from the terminal and decrypts it to make it analyzable. Health-related data and user attribute information are stored on the server in an integrated state.

[0704] Step 4:

[0705] The server activates an artificial intelligence model based on the decrypted health-related information. The AI ​​analyzes the entered symptoms and patient profile, and identifies the most likely medical condition based on past medical data.

[0706] Step 5:

[0707] The server assesses the severity and urgency of the identified medical condition. This assessment is calculated based on the progression of the condition and the combination of symptoms, and determines whether a visit to a medical institution is necessary.

[0708] Step 6:

[0709] The server sends a list of estimated symptoms and recommended medical specialties to the terminal. The list also includes suggested medical facilities that are appropriate for the symptoms.

[0710] Step 7:

[0711] The terminal displays the received diagnosis results and a list of medical institutions on the user interface. The user reviews the displayed information and selects a medical department and medical institution.

[0712] Step 8:

[0713] The user selects their preferred medical institution and date / time via their device and proceeds with the reservation process by pressing a button on the screen.

[0714] Step 9:

[0715] The terminal sends the selected reservation information to the server. The server queries the reservation system, checks the medical institution's schedule, and confirms availability.

[0716] Step 10:

[0717] The server sends confirmation information to the terminal, and a reservation confirmation message is displayed to the user. This allows the user to confirm the reservation details.

[0718] Step 11:

[0719] The device integrates appointment information with a calendar function and reminders, notifying the user of the scheduled date and time of their appointment. This feature helps users remember to visit their medical facilities.

[0720] This system allows users to efficiently assess their health status and quickly book appointments at appropriate medical facilities.

[0721] (Example 1)

[0722] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0723] In modern society, there is a lack of support systems that enable individuals to effectively and quickly understand their own health status and seek appropriate medical care. In particular, it is difficult for users to securely manage their health information, accurately identify their medical condition, and make appointments at medical institutions. This invention aims to improve the quality of medical services and enhance user convenience by solving these problems.

[0724] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0725] In this invention, the server includes means for receiving health-related information entered by the user into an information processing device, means for formatting the received information into a unified format and converting the data, and means for analyzing the data with an AI model to identify potential medical conditions. This enables the user to efficiently and safely understand their own health status and book an appointment at an appropriate medical institution.

[0726] A "user" refers to an individual who uses the system to input health-related information and make appointments at medical institutions.

[0727] An "information processing device" refers to a terminal used by users to input health-related information and transmit that information to a server.

[0728] "Health-related information" refers to information entered by the user, such as symptoms, age, gender, and medical history.

[0729] An "AI model" refers to an artificial intelligence model used to analyze received health-related information and identify potential medical conditions.

[0730] "Potential medical conditions" refer to medical conditions that may be identified using AI models.

[0731] "Data conversion means" refers to the process of formatting received health-related information into a unified format that can be analyzed by the server.

[0732] A "medical department" refers to a specialized department within a medical institution that deals with the identified medical condition.

[0733] "Encryption" refers to the process of transforming data to ensure the secure transmission of health-related information.

[0734] This system aims to diagnose the user's health condition and assist in making appointments at appropriate medical facilities. The system is primarily implemented using terminals, servers, and generative AI models.

[0735] The user first uses an information processing device (for example, a smartphone or computer application) to input their symptoms and personal information. Specifically, this involves inputting information such as symptoms like "cough and fever" and "age, gender, and medical history."

[0736] The terminal formats the input information into an appropriate data format and encrypts the data using an encryption algorithm (e.g., AES encryption). It then has the functionality to send this encrypted data to the server via a communication device.

[0737] The server decrypts the received encrypted data to extract the original health-related information. Based on this information, it activates a generative AI model on the server. The AI ​​model has learned from a large amount of medical data and identifies potential medical conditions based on the received information. For example, if the symptoms "fever and cough" are entered, the AI ​​analyzes this information and determines that it may be influenza.

[0738] Furthermore, the AI ​​model identifies the relevant medical department for the patient's condition and assesses the urgency of seeking treatment at a recommended medical institution. The server generates these results and sends the diagnosis to the user's terminal.

[0739] Users can review the diagnostic results displayed on their device, select a medical institution they wish to visit from the suggested options, and make a reservation. The server then connects with the reservation system of the selected medical institution to check availability and secure the most suitable reservation slot.

[0740] For example, if a user enters "I have a fever and a cough," the server might use AI to analyze this and suggest the possibility of influenza. Internal medicine might be selected as the recommended medical department, and after assessing the need for consultation as moderate, the user might be prompted to make an appointment at a nearby clinic.

[0741] An example of a prompt for the generating AI model would be: "If the user enters 'I have a fever and a cough,' please show the recommended medical department and the procedure for making an appointment."

[0742] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0743] Step 1:

[0744] Users input their health-related information using an information processing device. This input includes symptoms such as cough and fever, and attribute information such as age, gender, and medical history. This constitutes the input data.

[0745] Step 2:

[0746] The device formats the health-related information entered by the user into an appropriate data format. Specifically, it standardizes the format of text data and numerical data. The formatted data is then encrypted using a data encryption algorithm (such as AES). This results in the output of encrypted data that protects privacy.

[0747] Step 3:

[0748] The server receives encrypted data sent from the terminal. By decrypting this data, the original health-related information is reconstructed. The decrypted data then becomes the input data for the AI ​​model.

[0749] Step 4:

[0750] The server activates a generative AI model to analyze the user's health-related information. Based on a large amount of medical data, the AI ​​model identifies potential medical conditions from the input symptom information. For example, if the input information is "fever and cough," the AI ​​will output a diagnosis indicating the possibility of influenza.

[0751] Step 5:

[0752] The server uses the analysis results to assess the severity and urgency of the patient's condition and identify the relevant medical departments. The AI ​​model's analysis results provide the information needed for the next processing step.

[0753] Step 6:

[0754] The server returns the obtained diagnostic results to the user's terminal. The results include the estimated medical condition, the appropriate medical department, and the urgency of the need for medical attention. This information becomes the data displayed on the user's terminal.

[0755] Step 7:

[0756] The user reviews the diagnostic results displayed on the device and selects the medical institution they wish to visit. The user's selection becomes the input data for the medical institution's reservation information.

[0757] Step 8:

[0758] The server connects with the selected medical institution's reservation system to check availability and make a reservation. After checking availability and confirming the reservation, the reservation confirmation information is sent to the user's terminal.

[0759] This series of processes allows users to efficiently check their health status and book necessary medical services.

[0760] (Application Example 1)

[0761] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0762] In modern society, there is a lack of effective means to manage the health anxieties that people experience on a daily basis. In particular, it is difficult to record health status within families and to detect abnormalities early. Furthermore, existing systems are not efficient in selecting appropriate medical institutions based on entered symptoms. As a result, users are unable to receive prompt and appropriate medical services, leading to problems such as worsening health and wasted medical resources.

[0763] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0764] In this invention, the server includes means for receiving health-related information entered into a terminal, means for identifying potential medical conditions using artificial intelligence based on the received health-related information, and means for recording the user's daily health status within the home and detecting abnormalities. This enables the user to monitor their health status on a daily basis, quickly book an appointment at an appropriate medical institution when an abnormality is detected, and receive prompt medical services.

[0765] A "terminal" is a device that a user can directly operate for the purpose of inputting information.

[0766] "Health-related information" refers to information that includes symptoms, age, gender, medical history, and other information related to the user's health status.

[0767] "Artificial intelligence" is a technology that references large amounts of medical data and makes estimations and judgments based on the input information.

[0768] "Potential medical conditions" refer to possible disease states identified by artificial intelligence based on the entered health-related information.

[0769] "Medical appointment booking" refers to the procedure for users to secure a date and time for an appointment at a hospital, clinic, or other medical facility in order to receive medical treatment.

[0770] "Within the home" refers to the place where the user and their family live on a daily basis.

[0771] "Recording your health status" refers to saving health-related information over a certain period of time so that it can be referenced later.

[0772] "Detecting anomalies" means automatically recognizing changes or deviations from the normal state.

[0773] To implement this invention, a terminal such as a mobile device or residential equipment is required. This terminal has an interface for users to input health-related information and has a function to securely transmit the input data to a server. Encryption technology is used for data transmission, specifically the Python cryptography.fernet library.

[0774] The server decodes the received health-related information and analyzes the data in a software environment equipped with an artificial intelligence model. This AI model refers to a vast medical database based on the symptoms entered by the user to identify potential medical conditions. This allows for the estimation of the condition and the selection of appropriate medical facilities. For example, if a user enters "I have a cough and a fever," the AI ​​model can indicate the possibility of influenza.

[0775] The analysis results are sent back from the server to the terminal, making it easy to book appointments at appropriate medical facilities. In particular, users can receive daily health data recording and abnormality detection via a health management robot installed in their home. When this robot detects a change in health status, it alerts the user and recommends specific actions.

[0776] This allows users to select the appropriate medical institution and receive prompt treatment, even in health situations requiring rapid attention. This system, utilizing a generative AI model, supports flexible health management tailored to time and circumstances.

[0777] For example, if you ask the robot, "I've had a cough since yesterday, and my temperature is high. What should I do?", the robot will work with an AI model to analyze the situation and suggest making an appointment at a nearby internal medicine clinic.

[0778] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0779] Step 1:

[0780] The user inputs health-related information, such as cough or fever, via the terminal. The terminal encrypts the input information and prepares it for data transmission. The input at this time is information indicating the user's health status, and the output is encrypted data. The cryptography.fernet library is used for encryption.

[0781] Step 2:

[0782] The device sends encrypted health-related information to the server. The server decrypts the received data using the same encryption library. The input is encrypted data, and the output is the original decrypted health information. This process ensures the secure handling of the data.

[0783] Step 3:

[0784] The server activates a generative AI model to analyze the decrypted information. The AI ​​model compares the input symptoms and related information with an accumulated medical database to identify potential medical conditions. The input for this analysis is the decrypted health information, and the output is the estimated medical condition. Specifically, it performs symptom pattern matching and correlation analysis.

[0785] Step 4:

[0786] The server identifies appropriate medical facilities and generates booking information based on the estimated medical condition. The input is the estimated medical condition, which is the output of an AI model; the output is a list of recommended medical facilities and available time slots. The process includes searching a database of medical facilities to narrow down the choices to the most suitable candidates.

[0787] Step 5:

[0788] The user views a list of medical institutions provided by the server via their terminal and selects their desired facility and date / time. Based on the selection, the server confirms the reservation at the chosen medical institution and sends the information back to the user. The output includes reservation confirmation details. Reservation confirmation is performed through the interface with the reservation system.

[0789] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0790] This invention combines a system that analyzes a user's health status and makes reservations at appropriate medical institutions with an emotion engine that recognizes the user's emotional state. In implementing this system, the terminal, server, emotion engine, and artificial intelligence model work in coordination.

[0791] Terminal operation

[0792] The user inputs their symptoms and attribute information into the device. The emotion engine then recognizes the user's emotional state in real time based on their voice, facial expressions, and input speed. This emotion is considered a bias factor in the assessment of their health status, and the device transmits this information to the server.

[0793] Server operation

[0794] The server decrypts encrypted data sent from the terminal and analyzes the integrated symptom information, user attribute information, and emotional state. The artificial intelligence model identifies the medical condition based on this data, but corrects the diagnosis based on information provided by the emotion engine. For example, if the user is feeling anxious, this emotion may affect the diagnosis, so a careful estimation of the medical condition is performed.

[0795] Medical appointment

[0796] Based on the identified medical condition, the system recommends and presents a suitable medical department to the user. Furthermore, based on the user's emotional state, a medical facility with a relaxing environment may be recommended. The user can select from the presented options and make a reservation via their device.

[0797] Specific example

[0798] For example, if a user inputs "stomach pain accompanied by chronic anxiety" into the terminal, the emotion engine evaluates the calmness of the user's voice and detects an increase in anxiety. Based on this information, the server may identify a stress-related digestive disorder such as "irritable bowel syndrome." Based on this result, it may recommend an internal medicine specialist who can help reduce stress and suggest making an appointment immediately.

[0799] This system is expected to contribute to maintaining users' physical and psychological health by enabling health management that takes users' emotions into consideration and facilitating smooth access to medical services.

[0800] The following describes the processing flow.

[0801] Step 1:

[0802] The user launches the application on their device and enters their symptoms and attribute information into the input fields for the medical questionnaire. At this time, the emotion engine recognizes emotions from the user's facial expressions and voice through the user's camera and microphone.

[0803] Step 2:

[0804] The device encrypts the entered health-related information and emotional state and transmits it to the server using a secure communication channel.

[0805] Step 3:

[0806] The server decrypts the received encrypted data, making health-related information and emotional data available for analysis. It then integrates the dataset, taking into account the user's current emotional state.

[0807] Step 4:

[0808] The AI ​​model on the server analyzes the received health-related information and compares it with past medical data to identify potential medical conditions. It also adjusts the impact of the user's emotions on the diagnostic results, taking into account information from the emotion engine.

[0809] Step 5:

[0810] The server sends a list of identified potential medical conditions, along with the relevant medical departments and recommended healthcare facilities, to the terminal. In some cases, healthcare facilities that offer a relaxing environment for the user may be selected.

[0811] Step 6:

[0812] The terminal displays information received from the server on the user interface. Users can review and select the suggested medical departments and medical institutions.

[0813] Step 7:

[0814] Users select their preferred medical institution and date / time via their device and submit a reservation request.

[0815] Step 8:

[0816] The terminal sends the selected reservation information to the server, which interfaces with the reservation system to check the medical institution's schedule and confirm the optimal reservation.

[0817] Step 9:

[0818] The server sends confirmation information to the terminal, and the reservation details are displayed to the user on the terminal. This allows the user to check their schedule and manage their visit plans using the terminal's reminder function as needed.

[0819] (Example 2)

[0820] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0821] In modern society, health management and the selection of medical institutions have become complex, placing a significant burden on users to receive appropriate medical care. Furthermore, the lack of diagnoses and medical institution selection that take into account users' emotional states makes it difficult for them to receive optimal medical services. The secure handling of personal information is also a crucial requirement.

[0822] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0823] In this invention, the server includes means for performing information analysis based on health-related information, attribute information, and emotional state information; means for identifying potential medical conditions using generative artificial intelligence; and means for correcting diagnostic results based on emotional state and booking appropriate medical facilities. This enables safe and efficient health management and medical access that takes the user's emotions into consideration.

[0824] A "terminal" is a device used by users to input health-related information and personal information.

[0825] "Health-related information" refers to information about a user's own health status or symptoms.

[0826] "Attribute information" refers to information that includes the user's basic characteristics, such as age, gender, and medical history.

[0827] "Emotional state information" refers to information about a user's emotions obtained from their voice, facial expressions, input speed, etc.

[0828] "Generative artificial intelligence" refers to artificial intelligence used to identify potential medical conditions based on collected information.

[0829] "Potential medical conditions" refer to illnesses or health conditions that are predicted based on the information entered by the user.

[0830] "Medical institutions" refer to hospitals and clinics that users visit to receive medical treatment.

[0831] "Reservation" means securing a date and time in advance to receive medical treatment at a chosen medical institution.

[0832] "Encryption" is a technology that transforms data in order to transmit information securely.

[0833] "Correction of diagnostic results" is the process of adjusting the diagnostic results while taking into account the user's emotional state.

[0834] This invention is a system that analyzes a user's health and emotional state and assists in selecting and booking appropriate medical facilities. This system primarily consists of a terminal, a server, an emotion engine, and generative artificial intelligence.

[0835] The terminal is a device for receiving health-related and attribute information entered by the user. Users input their health status and symptoms in text format. They also input basic attribute information such as age, gender, and medical history. The terminal has an emotion engine built in that analyzes voice, facial expressions, and input speed to recognize the user's emotional state in real time.

[0836] The device encrypts this information and sends it to the server. The server decrypts the encrypted data and integrates and analyzes health-related information, attribute information, and emotional state information. Generative artificial intelligence is used in the analysis to identify potential medical conditions. In this process, the diagnostic results are corrected based on information provided by the emotion engine. For example, if the user is experiencing anxiety, the diagnosis is made more carefully based on that information.

[0837] Once the medical condition is identified, the server recommends a medical department and suggests options for a relaxing healthcare facility. The user can then select a healthcare facility from the presented options and make an appointment through their terminal.

[0838] For example, if a user complains of "stomach pain accompanied by chronic anxiety," the device's emotion engine detects the user's emotional state, and the server's generated artificial intelligence takes this into account to identify a medical condition such as "irritable bowel syndrome." Based on this result, the server recommends an internal medicine specialist who can help reduce stress and immediately suggests making an appointment.

[0839] A concrete example of a prompt for a generative AI model is, "Please diagnose the chronic anxiety and stomach ache reported by the user, combining them with the emotion engine data." This prompt instructs the system to consider the user's emotions when making a diagnosis.

[0840] This system aims to enable comprehensive health management that takes users' emotions into consideration, as well as smooth access to medical facilities.

[0841] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0842] Step 1:

[0843] Users enter their health-related and personal information into the device. This information includes specific symptoms (e.g., "chronic anxiety," "stomach ache") and basic attributes (e.g., age, gender). The entered information is stored on the device as digital data.

[0844] Step 2:

[0845] The device uses an emotion engine to analyze the user's voice tone, facial expressions, and input speed in real time. This identifies the user's emotional state and generates emotional state information as numerical data. This information is recorded as bias, which is necessary when evaluating health status.

[0846] Step 3:

[0847] The terminal encrypts the entered health-related information, attribute information, and emotional state information, and sends it to the server in a single batch. This data, as input, is transmitted to the server in a secure format.

[0848] Step 4:

[0849] The server decrypts the received encrypted data and converts it into an analyzable format. The decrypted data includes symptom information, user attribute information, and emotional state information.

[0850] Step 5:

[0851] The server uses generative artificial intelligence to identify potential medical conditions based on this data. During this process, emotional states are treated as a corrective factor, as they can influence the diagnosis. The identified medical conditions are returned as disease names as output.

[0852] Step 6:

[0853] The server suggests appropriate medical departments and healthcare facilities based on the diagnosed condition. Furthermore, it includes healthcare facilities with a relaxing environment suitable for the user's emotional state as options. This presents the user with a list of potential healthcare facilities.

[0854] Step 7:

[0855] The user selects their preferred facility from the presented list of medical institutions and makes a reservation via their terminal. Upon confirmation of the reservation, the user is provided with confirmation information regarding the reservation details.

[0856] (Application Example 2)

[0857] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0858] Currently, many health management systems rely on user self-reporting, and emotional biases can influence diagnoses. Furthermore, the selection and booking of medical facilities are based solely on the patient's symptoms, lacking booking services that consider the user's psychological well-being. As a result, there is a challenge in that it is difficult for users to receive the most appropriate medical services.

[0859] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0860] In this invention, the server includes means for receiving health-related information and detected emotional states entered into a terminal; means for using artificial intelligence to identify potential health problems and correct emotional biases based on the received health-related information and emotional states; and means for suggesting and booking the most suitable medical institution based on the user's health and emotional states. This enables personalized selection of medical institutions that take the user's emotional state into consideration and a smooth booking process.

[0861] A "terminal" is a device used by users to input health-related information and emotional states.

[0862] "Health-related information" refers to data about the user's physical condition and symptoms.

[0863] "Emotional state" refers to the psychological state detected from the user's voice and facial expressions.

[0864] "Artificial intelligence" refers to computational models or technologies used to analyze health-related information and emotional states.

[0865] "Potential health problems" refer to medical conditions or health disorders that may be inferred from the user's health-related information and emotional state.

[0866] "Correcting emotional bias" means adjusting the diagnostic results to take into account the detected emotional state.

[0867] A "medical institution" refers to a facility that provides medical services.

[0868] "Methods for making reservations" refers to a function that allows users to select a date and time and complete procedures in advance so that they can receive medical services at a medical institution.

[0869] This invention realizes a system that takes user health-related information and emotional state as input, and then suggests and makes reservations for appropriate medical facilities based on that information.

[0870] The server receives health-related information entered by the user via the terminal, along with emotional states detected from voice and facial expressions, and integrates and processes this information. This includes calculations that use advanced artificial intelligence techniques to identify potential health problems and correct for emotional bias. Software libraries such as "OpenCV," "TensorFlow," and "PyTorch" are used for analysis. The server also plays a role in suggesting appropriate medical institutions and managing appointment procedures.

[0871] As a concrete example of its operation, if a user says to the robot assistant in the morning, "Good morning, I feel a little unwell," the device analyzes the user's tone of voice and facial expressions through voice and camera to assess their emotional state. Based on this information, the device estimates the user's health status, taking emotional bias into account, and presents the user with a list of different medical departments. In this process, it particularly recommends medical institutions that can help the user relax, depending on their mental state, and makes an appointment if requested.

[0872] The generative AI model provides diagnostic support and processes input data using prompts such as: "Based on the symptoms and emotional state data reported by the user, please suggest the most appropriate health management method for this person."

[0873] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0874] Step 1:

[0875] The user speaks into the device, inputting health-related information and emotions via voice. The device uses a microphone to collect voice data and sends it to an emotion analysis engine. The input is voice data, and the output is the result of converting the voice data directly into a file.

[0876] Step 2:

[0877] The device uses its built-in camera and emotion analysis engine to capture the user's facial expressions and analyze their emotional state. Input is still images or video data, and output is the result of real-time emotion classification. Feature extraction and emotion recognition are performed using technologies such as OpenCV and TensorFlow.

[0878] Step 3:

[0879] The terminal sends the collected audio and facial expression analysis results to the server. The server receives this data and integrates it with the data necessary for estimating the health status. The input is emotional state data, and the output is the integrated dataset.

[0880] Step 4:

[0881] The server uses an artificial intelligence model to identify potential health problems from an integrated dataset. Here, a PyTorch-based anomaly detection model is used to estimate potential health problems. The input is the integrated dataset, and the output is the estimated health problem.

[0882] Step 5:

[0883] The server further corrects for emotional bias and adjusts the diagnosis of health problems. In this process, the influence of emotions such as stress and anxiety on the results is mitigated using a "generative AI model." The input is the estimated health problem and emotional state, and the output is the estimated result of the corrected health state.

[0884] Step 6:

[0885] The server suggests suitable medical facilities to the user based on the diagnostic results and generates appointment information. A generative AI model is used to select hospitals that are likely to provide the user with relaxation. The input is the corrected diagnostic results, and the output is a list of medical facilities.

[0886] Step 7:

[0887] The user selects a medical institution from the provided list and confirms the reservation. The terminal sends the reservation online to the selected medical institution; the input is the selected medical institution information, and the output is the reservation confirmation result.

[0888] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0889] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0890] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0891] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0892] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0893] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0894] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0895] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0896] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0897] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0898] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0899] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0900] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0901] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0902] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0903] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0904] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0905] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0906] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0907] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0908] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0909] The following is further disclosed regarding the embodiments described above.

[0910] (Claim 1)

[0911] A means of receiving health-related information entered into a terminal,

[0912] A means of identifying potential medical conditions using artificial intelligence based on received health-related information,

[0913] A means of making an appointment at an appropriate medical institution based on the identified potential medical condition,

[0914] A system that includes this.

[0915] (Claim 2)

[0916] The system according to claim 1, further comprising means for encrypting and transmitting input health-related information.

[0917] (Claim 3)

[0918] The system according to claim 1, further comprising means for assessing the severity and urgency of a potential medical condition.

[0919] "Example 1"

[0920] (Claim 1)

[0921] A means for receiving health-related information entered by a user into an information processing device,

[0922] The received information is formatted into a unified format, and the data is converted using a means.

[0923] A means of receiving health-related information from an information processing device, analyzing it with an AI model, and identifying potential medical conditions,

[0924] A means to support the selection of the medical department related to the identified medical condition,

[0925] The means of making a reservation at the selected medical institution,

[0926] A system that includes this.

[0927] (Claim 2)

[0928] The system according to claim 1, further comprising means for encrypting health-related information using an information processing device and transmitting it to a communication device.

[0929] (Claim 3)

[0930] The system according to claim 1, further comprising means for evaluating the severity and urgency of a potential medical condition using an AI model.

[0931] "Application Example 1"

[0932] (Claim 1)

[0933] A means of receiving health-related information entered into a terminal,

[0934] A means of identifying potential medical conditions using artificial intelligence based on received health-related information,

[0935] A means of making an appointment at an appropriate medical institution based on the identified potential medical condition,

[0936] A means of recording the user's daily health status within the home and detecting abnormalities,

[0937] A means of analyzing health information entered into a terminal and suggesting recommended medical actions,

[0938] A system that includes this.

[0939] (Claim 2)

[0940] The system according to claim 1, further comprising means for encrypting and transmitting input health-related information.

[0941] (Claim 3)

[0942] The system according to claim 1, further comprising means for assessing the severity and urgency of a potential medical condition.

[0943] "Example 2 of combining an emotion engine"

[0944] (Claim 1)

[0945] A means for receiving health-related information and attribute information entered into a terminal,

[0946] A means of identifying potential medical conditions using generative artificial intelligence based on received health-related information, attribute information, and emotional state information,

[0947] A means of selecting and making an appointment at an appropriate medical institution based on identified potential medical conditions and emotional states.

[0948] A system that includes this.

[0949] (Claim 2)

[0950] The system according to claim 1, further comprising means for encrypting and transmitting input health-related information, attribute information, and emotional state information.

[0951] (Claim 3)

[0952] The system according to claim 1, further comprising means for correcting the diagnostic result in consideration of the severity, urgency, and emotional state of a potential medical condition.

[0953] "Application example 2 when combining with an emotional engine"

[0954] (Claim 1)

[0955] A means for receiving health-related information and detected emotional states entered into a terminal,

[0956] A means of using artificial intelligence to identify potential health problems based on received health-related information and emotional states, and to correct emotional biases accordingly.

[0957] A means of suggesting and booking the most suitable medical facility based on the user's health and emotional state.

[0958] A system that includes this.

[0959] (Claim 2)

[0960] The system according to claim 1, further comprising means for encrypting and transmitting input health-related information and emotional states.

[0961] (Claim 3)

[0962] The system according to claim 1, further comprising means for assessing the severity and urgency of a potential health problem, taking into account emotional state. [Explanation of Symbols]

[0963] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving health-related information entered into a terminal, A means of identifying potential medical conditions using artificial intelligence based on received health-related information, A means of making an appointment at an appropriate medical institution based on the identified potential medical condition, A means of recording the user's daily health status within the home and detecting abnormalities, A means of analyzing health information entered into a terminal and suggesting recommended medical actions, A system that includes this.

2. The system according to claim 1, further comprising means for encrypting and transmitting input health-related information.

3. The system according to claim 1, further comprising means for assessing the severity and urgency of a potential medical condition.

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