system

Abstract

We provide the system. [Solution] A means for receiving information about the user's physical condition via an input device, An artificial intelligence means that determines possible symptoms by comparing the received information with a database, A means for making a reservation at a medical institution based on symptoms determined by the aforementioned artificial intelligence means, A system that includes this.

Description

Technical Field

[0005] ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] <管理することが難しいという問題も存在する。これらの課題を解決し、ユーザが効率的かつ迅速に健康管理を行うことができるシステムの提供が求められている。 In modern society, many people lead busy daily lives, and even if they feel something abnormal with their physical condition, they often postpone visiting a medical institution. In such a situation, there is a concern that the early detection of diseases may be delayed. Also, since it is not easy to manage one's own health condition, there is also a problem that it is difficult to select an appropriate medical institution. There is a demand for providing a system that solves these problems and enables users to efficiently and quickly manage their health.

Means for Solving the Problems

[0005] This invention provides a means for receiving information about a user's physical condition by inputting that information via an input device. Furthermore, it provides a means for efficiently determining possible symptoms by comparing the received information with an existing database using artificial intelligence. This artificial intelligence means performs a detailed analysis of symptoms based on past case data and medical information. It also has a means for making a reservation at the most suitable medical institution, taking into account the user's location information and the availability of medical institutions, based on the determined symptoms. As a result, users can quickly understand their health status and receive diagnosis and treatment at an appropriate medical institution.

[0006] A "user" is an individual who uses the system to input information about their health condition.

[0007] An "input device" is a device used by a user to input health information into the system.

[0008] "Information regarding physical condition" refers to data about symptoms and health status experienced by the user.

[0009] A "database" is a collection of information, including past cases and medical knowledge, that is used for matching by artificial intelligence.

[0010] "Artificial intelligence" is a computer program that compares data with a database to determine and analyze possible symptoms.

[0011] "Symptoms" are signs indicating changes or abnormalities in one's physical condition, and serve as the basis for judging one's health status.

[0012] A "medical institution" refers to facilities such as hospitals and clinics that provide diagnosis and treatment.

[0013] An "appointment" is the process of securing a time or date to receive medical treatment or consultation at a medical institution.

[0014] "Location information" refers to data indicating the physical location of a user and is used for the selection of medical institutions.

[0015] "Availability" refers to information indicating the availability of the schedules of examinations and treatments provided by medical institutions.

Brief Explanation of Drawings

[0016] [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 the 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 the emotion engine is combined.

Embodiments for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, the numbered 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.

[0020] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention is a system that enables users to easily manage their own health status and, when necessary, quickly arrange for medical consultations at healthcare facilities. Embodiments of this system are described below.

[0038] Users first access the system using their own devices. These devices include a variety of devices such as smartphones, tablets, and personal computers. Users enter their authentication information on the login screen to access the system.

[0039] When a user enters information about their health condition, that information is sent from the device to the server. This information includes specific symptoms and related health data. The server receives this information and uses an artificial intelligence agent to compare it with a database and diagnose possible illnesses.

[0040] Artificial intelligence efficiently analyzes diseases related to the entered symptoms by referencing a vast medical database. The analysis results are sent to the user's terminal via a server, and the user can use this information to decide on future actions.

[0041] Based on the diagnostic results, the system assists the user in selecting an appropriate medical institution. The server considers the user's location and the availability of each medical institution to present the best booking options. Once the user selects their preferred medical institution and date / time, that information is sent to the institution via the server, and the booking is completed.

[0042] For example, if a user enters into the terminal that they frequently experience headaches, the server receives this information, and an artificial intelligence agent analyzes it. After cross-referencing it with the database, a diagnosis is made that stress-related headaches are likely. The server informs the user of this result and presents them with options to book an appointment at a nearby neurology clinic. The user can then choose a suitable clinic from the suggestions and complete the appointment immediately.

[0043] In this way, this system enables users to efficiently and quickly manage their health status and supports them in receiving appropriate medical treatment at a healthcare facility.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The user accesses the system using a device and enters their authentication information on the login screen.

[0047] Step 2:

[0048] The terminal sends the entered authentication information to the server. The server performs authentication by comparing it with the information in the database.

[0049] Step 3:

[0050] If the server successfully authenticates the user, it sends a login success response to the terminal, and the user accesses the main screen.

[0051] Step 4:

[0052] The user enters information about their health condition from the main screen. Specific symptoms and related health information are entered in the symptom input form.

[0053] Step 5:

[0054] The terminal sends the entered symptom information back to the server.

[0055] Step 6:

[0056] The server receives the information, calls an artificial intelligence agent, and processes the information.

[0057] Step 7:

[0058] An artificial intelligence agent compares the symptoms against a database, analyzes possible diseases that match the symptoms, and makes a determination.

[0059] Step 8:

[0060] The server prepares the diagnostic results returned by the artificial intelligence agent and generates a list of medical institutions as needed.

[0061] Step 9:

[0062] The server sends the diagnostic results and appointment options for medical institutions to the terminal.

[0063] Step 10:

[0064] The user reviews the received diagnosis results and selects a medical facility and time they wish to book from the displayed list of healthcare providers.

[0065] Step 11:

[0066] The device sends the selected reservation information to the server.

[0067] Step 12:

[0068] The server connects with the medical institution's reservation system and completes the reservation process. The server then sends a reservation confirmation to the terminal.

[0069] Step 13:

[0070] The user receives a reservation confirmation notification on their device, and their health management is completed.

[0071] (Example 1)

[0072] 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."

[0073] In modern society, health management is a crucial issue, but for many people, it is difficult to properly understand their own health status and access medical facilities promptly when necessary amidst their busy daily lives. Furthermore, a lack of available information when deciding which medical facility to visit can result in missing opportunities to receive optimal medical services. To address this challenge, a system is needed that efficiently diagnoses the user's health status and assists in making appointments at appropriate medical facilities.

[0074] 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.

[0075] In this invention, the server includes means for receiving data on the user's health status via the user's information terminal, artificial intelligence means for identifying possible diseases by comparing the received data with an information storage medium, and means for securing reservations at medical service facilities based on the diseases identified by the artificial intelligence means. This enables the user to accurately understand their own health status and quickly make reservations at appropriate medical institutions.

[0076] A "user information terminal" is an electronic device used by a user to input data about their health status and access the system, and specifically includes devices such as smartphones, tablets, and personal computers.

[0077] "Health status data" refers to information about a user's physical condition and symptoms, which forms the basis for analysis by the system. This includes vital data such as body temperature and pulse rate, as well as specific symptoms.

[0078] An "information storage medium" refers to a database that stores past case data and medical knowledge used for analyzing data related to health conditions.

[0079] "Artificial intelligence means" refers to technology that performs an automated intelligent process to analyze received health status data and identify possible diseases by referring to information storage media.

[0080] "Means of securing reservations at medical service facilities" refers to a process that uses artificial intelligence to identify diseases, selects the most suitable medical institution for the user, and ensures that the reservation is made.

[0081] "Reservation confirmation information" refers to detailed information used to notify the user that their reservation at a medical service provider has been successfully completed, allowing the user to confirm the details of their reservation.

[0082] "Location information" refers to data used to identify a user's current location and is considered when providing users with appropriate medical facilities.

[0083] This invention provides a system that allows users to properly manage their health status and efficiently make appointments at the most suitable medical institutions as needed. A specific example of this system is shown below.

[0084] Users access this system using information terminals, specifically smartphones, tablets, or personal computers. First, the terminal displays a login screen, where the user enters their authentication information to log in to the system. After that, the terminal provides an interface for the user to input specific data about their health status. For example, the user can input information such as body temperature and symptoms.

[0085] Once information is entered, the terminal sends that data to the server. This data is then compared against a database of information stored on the server, namely, past case data and medical knowledge. The server uses artificial intelligence to analyze this data and identify possible diseases related to the user's symptoms. This artificial intelligence uses machine learning algorithms to analyze vast amounts of medical data.

[0086] The analysis results are sent from the server to the user's terminal. Furthermore, the server considers the user's location information and the availability of medical service providers to select the most suitable medical facility and propose a reservation. Based on this information, the user selects a preferred medical institution from the presented options and confirms the reservation. The reservation information is sent to the medical institution via the server, and reservation confirmation information is returned to the user's terminal, allowing the user to check the reservation details.

[0087] For example, if a user frequently experiences headaches, they can input "mild headache and fatigue" as symptoms into the system. This information is sent to the server, and the artificial intelligence analysis may identify it as a stress-related headache. Based on this result, the server can suggest a nearby neurology clinic to the user, allowing them to make an appointment immediately.

[0088] An example of a prompt message is an input to a generative AI model in the form of, "Create a program that analyzes the health data entered by the user, identifies possible diseases, and provides appointment information for available medical facilities."

[0089] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0090] Step 1:

[0091] The user accesses an information terminal and logs into the system. The terminal displays a screen for the user to enter their authentication information (user ID and password). The entered authentication information is sent from the terminal to the server. The server receives this information, verifies it against the registered information, and performs authentication. If authentication is successful, the user can proceed to the next step. The data entered is the user's authentication information, and the output is the authentication result (success or failure).

[0092] Step 2:

[0093] After successful authentication, the user enters data about their health status through the terminal. The terminal displays an interface for entering symptoms and vital information. Specifically, the user can enter symptoms such as "body temperature 37.5 degrees" and "mild chest pain." The entered health data is sent from the terminal to the server. The information entered here is health-related, and the output of this step is the raw data received by the server.

[0094] Step 3:

[0095] The server receives health status data transmitted from the terminal and compares it with a database stored on an information storage medium. The server analyzes the input data using artificial intelligence and identifies related diseases. Since the database contains past cases and medical knowledge, the server refers to this to process the data. For example, it assesses the risk of heart disease based on data such as "chest pain." The input is raw health status data, and the output is the result of identifying possible diseases.

[0096] Step 4:

[0097] The analysis results are sent from the server to the user's terminal, which then displays the results to the user. Specifically, a list of possible diseases and their urgency levels are displayed. Based on this information, the user then decides on their next course of action. The input is the disease data identified in the previous step, and the output is a visualized version of that data, providing feedback to the user.

[0098] Step 5:

[0099] Based on the analysis results, the server presents a selection of medical facilities, taking into account the user's location and the availability of medical institutions. This process combines the user's current location with data from partner medical institutions. For example, the server lists "internal medicine clinics available within a 2km radius." The input is the user's location and medical institution availability data, and the output is a list of optimized booking options.

[0100] Step 6:

[0101] The user selects a suitable medical institution from those suggested via the terminal and confirms the reservation for the desired date and time. The terminal sends the reservation information to the server, which then transmits it to the medical institution's system. Finally, the user receives reservation confirmation information and reviews the detailed visit plan. The input is the user's selected reservation details, and the output is the reservation confirmation information.

[0102] (Application Example 1)

[0103] 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."

[0104] In modern society, it is crucial for the elderly to regularly monitor their own health and arrange for prompt visits to medical institutions when necessary. However, it is often difficult for the elderly themselves to do this efficiently. Solving this problem and optimizing health management for the elderly in nursing homes and home care settings is essential.

[0105] 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.

[0106] In this invention, the server includes means for receiving information about the user's health status via an input device, machine learning means for determining possible medical conditions by comparing the received information with data resources, means for making reservations at medical facilities based on the medical conditions determined by the machine learning means, and means for coordinating with private medical professionals or caregivers. This enables elderly individuals to easily manage their own health status and, if necessary, to quickly arrange visits to appropriate medical institutions.

[0107] A "user input device" refers to a device used by an individual to electronically input their own health information. This device includes smartphones, tablets, and personal computers.

[0108] "Health information" refers to data entered by users about their own physical condition, including specific symptoms and other health-related information.

[0109] "Data resources" refer to information sources, including past medical and clinical data, that machine learning tools refer to when determining a patient's condition.

[0110] "Machine learning methods for determining possible medical conditions" refers to algorithms that use received health status information to compare it with past information and estimate the medical condition.

[0111] "Method for making reservations at medical facilities" refers to a function that allows users to make online reservations at the most suitable medical institutions based on their medical condition.

[0112] A "private medical professional" refers to a medical professional who provides health management and medical advice to individual users.

[0113] A "caregiver" refers to a person who plays a role in supporting the daily lives and medical care of the elderly or patients.

[0114] The implementation of this invention begins with a user inputting information about their health status using a device such as a smartphone or tablet. The device has the function of transmitting this information to a server. After receiving the information, the server uses machine learning means to refer to data resources and determine possible medical conditions related to the input information. The software used here includes machine learning libraries such as TENSORFLOW®.

[0115] Based on the assessed medical condition, the server makes a reservation at an appropriate medical facility. The reservation process considers the user's current location and the availability of medical facilities to suggest the most suitable facility and reservation time. The information is then communicated to the user via a medical professional or caregiver, and coordination with the medical facility is facilitated.

[0116] For example, if a user inputs a health condition such as "I frequently wake up at night," the server processes this information and uses machine learning to diagnose the possibility of sleep apnea. Based on the diagnosis, the server suggests booking an appointment at a relaxation clinic near the user's residence.

[0117] An example of a prompt to input into the generating AI model is, "What medical measures should be recommended when an elderly woman reports feeling tired?" This will support specific actions to assist in the health management of the elderly.

[0118] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0119] Step 1:

[0120] Users enter health information using their own devices. This information includes specific symptoms and lifestyle-related data. The entered information is temporarily stored on the device.

[0121] Step 2:

[0122] The terminal transmits the entered information to the server via the network. The server receives this information and stores it in a database. This data is saved for use in subsequent analysis steps.

[0123] Step 3:

[0124] The server analyzes the received health information using a machine learning model based on TensorFlow. Based on the input data, it determines the medical condition related to the symptoms. This process involves data calculations by comparing the data with past data and known patterns.

[0125] Step 4:

[0126] The server identifies possible medical conditions based on the output of the machine learning model. The identified medical conditions are then prepared for use in the next step.

[0127] Step 5:

[0128] The server references the user's current location and the availability of medical facilities to generate the most suitable booking options for their medical condition. This information is retrieved from a database, and an optimization algorithm is used to determine the best option.

[0129] Step 6:

[0130] The server notifies the user of the details of available medical facilities. The user can select their preferred option from the suggested choices and confirm the booking request through their device.

[0131] Step 7:

[0132] After user verification, the server initiates the reservation confirmation process with the selected medical institution. The reservation information is sent to the medical institution, where the reservation is officially confirmed. This allows the user to receive appropriate medical support.

[0133] 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.

[0134] This invention provides a system that offers more precise and personalized health management support by taking into account the user's health and emotional state. This system allows the user to input information about their physical condition and simultaneously recognize their emotional state through an emotion engine, enabling comprehensive diagnosis and appropriate medical appointment scheduling.

[0135] Users access the system using their own devices and input information about their physical condition, symptoms, and emotional state. This includes not only specific symptoms but also their mood and stress levels. The device then sends this information to the server.

[0136] Based on the received information, the server activates an artificial intelligence agent and an emotion engine. The AI ​​agent diagnoses possible illnesses related to the user's symptoms, based on existing medical databases and past case data. Simultaneously, the emotion engine analyzes the input emotional information and evaluates the user's overall psychological state.

[0137] The diagnostic results and emotional assessment results obtained are used together to gain a more detailed understanding of the user's condition. The server recommends booking appointments at appropriate medical institutions based on the diagnostic results, taking the user's emotional state into consideration. For example, if the user shows high levels of stress or anxiety, the server can prioritize recommending medical facilities that focus on relaxation or institutions that provide such care.

[0138] Ultimately, the user receives multiple suggestions from the server, selects the most suitable medical institution, and completes the reservation through their terminal. The server then sends this reservation to the relevant medical institution's system and obtains confirmation, enabling smooth guidance to the user.

[0139] As a concrete example, consider a user who is experiencing chronic fatigue and high stress levels due to recent work burdens. This user enters this information into a terminal and sends it to the system. The server, using an artificial intelligence agent, diagnoses the possibility of chronic fatigue syndrome, and the emotional engine recognizes the high-stress state. Based on this, the server strongly recommends a clinic specializing in stress care, and the user can then complete the reservation.

[0140] This health management support system, which takes user emotions into account, makes it possible to provide more precise and personalized medical care.

[0141] The following describes the processing flow.

[0142] Step 1:

[0143] The user accesses the system using a device and enters their authentication information on the login screen.

[0144] Step 2:

[0145] The terminal sends the entered authentication information to the server. The server performs authentication by comparing it with the database.

[0146] Step 3:

[0147] If the server successfully authenticates, it sends a login authorization to the terminal, and the user proceeds to the main screen.

[0148] Step 4:

[0149] On the main screen, the user enters information about their physical condition and their emotional state.

[0150] Step 5:

[0151] The device sends the entered health and emotional information to the server.

[0152] Step 6:

[0153] The server activates an artificial intelligence agent, which compares the user's health information with a database to diagnose possible symptoms.

[0154] Step 7:

[0155] The server activates the emotion engine, analyzes the emotional information entered by the user, and evaluates their psychological state.

[0156] Step 8:

[0157] The server integrates the diagnostic results from the artificial intelligence agent with the emotional state evaluation results from the emotion engine.

[0158] Step 9:

[0159] Based on the results of server integration, a list is generated to suggest the most suitable medical institutions and treatments to the user.

[0160] Step 10:

[0161] The server sends the terminal with recommended medical facilities and booking options.

[0162] Step 11:

[0163] The user selects their preferred facility and time from the list of medical institutions presented and confirms the reservation.

[0164] Step 12:

[0165] The device sends the selected reservation information to the server.

[0166] Step 13:

[0167] The server connects with the medical institution's system and performs the procedure to confirm the reservation.

[0168] Step 14:

[0169] The server sends a reservation confirmation to the device, and the user receives a notification.

[0170] Step 15:

[0171] The user confirms the reservation completion notification and then terminates their use of the system.

[0172] (Example 2)

[0173] 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".

[0174] Conventional health management systems only consider the user's physical condition when making diagnoses and appointments, making it difficult to provide services that reflect the emotional and psychological state of individual users. Therefore, there is a challenge in providing sufficiently individualized medical services to users with health problems influenced by stress and anxiety.

[0175] 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.

[0176] In this invention, the server includes means for receiving information on the user's health and emotional state via an input device, artificial intelligence means for simultaneously determining the physical and psychological state using a data analysis device based on the received information, and means for selecting and booking medical services that take into account the user's emotional state based on the state determined by the artificial intelligence means. This enables more personalized medical care and service optimization based on a comprehensive health assessment that includes emotional state.

[0177] A "user input device" refers to a device used by a user to input information about their health and emotional state into the system.

[0178] "Health status" refers to information about the user's physical condition, including specific symptoms and changes in their physical condition.

[0179] "Emotional state" refers to information that indicates the user's psychological and emotional state, including mood and stress level.

[0180] A "data analysis device" refers to a technical means for analyzing health and emotional states based on received information.

[0181] "Artificial intelligence means" refers to elements that use machine learning and data processing technologies to determine health and emotional states and propose appropriate services based on those assessments.

[0182] "Means for selecting and booking medical services" refers to a function that selects and books appropriate medical institutions or services based on decisions made by artificial intelligence.

[0183] This invention is a system that provides more personalized health management support by taking into account the user's physical and emotional state. The system aims to allow users to input information about their physical condition and emotions, and then use that information to make diagnoses and appointments at medical institutions.

[0184] Users access this system using their own input devices, such as smartphones or personal computers. Users input information such as their health status, specific symptoms, and emotional information like mood and stress levels. This information is transmitted from the device to the server.

[0185] The server uses specific software to analyze the received data. This analysis utilizes data analysis devices and artificial intelligence (AI) tools. The AI ​​tools use existing medical knowledge bases and historical clinical data to analyze potential medical conditions related to the user's health status, and also evaluate the user's psychological state using emotion recognition algorithms.

[0186] Based on information analyzed from the user's health and emotional state, the server suggests the most suitable medical services. These suggested services take emotional state into consideration, and may prioritize medical institutions that offer relaxation services.

[0187] Ultimately, the user selects the most suitable healthcare provider from the options provided by the server and completes the reservation. The server then sends the reservation information to the healthcare provider's system for confirmation.

[0188] As a concrete example, consider a case where a user inputs chronic fatigue and high stress. Based on this information, the server diagnoses chronic fatigue syndrome, and the emotion engine recognizes high stress. The server then recommends a stress management clinic, allowing the user to receive appropriate support.

[0189] Examples of prompts for a generative AI model:

[0190] "The user has entered their health information and emotional state. Based on this information, please suggest possible medical conditions and recommended medical services."

[0191] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0192] Step 1:

[0193] The user inputs their health and emotional state into the system using their input device. Specifically, they fill in specific symptoms, mood, stress levels, etc., related to their physical condition in an on-screen form. The input in this step consists of the user's health and emotional data. The terminal prepares to send this data to the server.

[0194] Step 2:

[0195] The device sends health and emotional data entered by the user to the server. The HTTPS protocol is used to ensure data security. At this stage, the input is user data, and the output is encrypted data packets. The device receives a response from the server confirming that the data was transmitted correctly.

[0196] Step 3:

[0197] The server uses a data analysis device to analyze the received data. The input data consists of information about health status and emotional information. The server activates artificial intelligence to analyze the received data and identify possible diseases. Furthermore, it uses an emotion recognition algorithm to evaluate the psychological state for the emotional information. The output of this step is the diagnostic result and the psychological evaluation result. The server passes this on to the next step.

[0198] Step 4:

[0199] The server suggests the most suitable medical services to the user based on the diagnostic and psychological assessment results. The input here is the diagnosis and psychological assessment obtained in the previous step. The server uses the data to generate a list of appropriate medical institutions and service providers and prioritizes them. The output at this stage is a list of recommended medical institutions.

[0200] Step 5:

[0201] The user reviews the list of medical institutions suggested by the server on their terminal. The input here is the list of medical institutions sent from the server. The user selects the most suitable medical institution from the list and enters a reservation request. The output at this step is the reservation request to the medical institution selected by the user.

[0202] Step 6:

[0203] The server receives reservation requests from users and transmits reservation information to the selected medical institution's system. The input is the user's reservation request, which the server relays to the medical institution. This process is carried out in conjunction with the medical institution's system and requires confirmation of the reservation. The output at this step is confirmation information that the reservation has been completed.

[0204] Step 7:

[0205] The user receives a confirmation from the server that the reservation is complete. The input here is the reservation confirmation information sent from the medical institution. The terminal notifies the user of the confirmation information, informing them that the reservation has been successfully completed. The output in this step is a notification to the user.

[0206] (Application Example 2)

[0207] 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 device 14 will be referred to as the "terminal."

[0208] In today's world, effectively managing the health and emotional states of individual users and scheduling appropriate medical appointments is complex. Therefore, healthcare providers need a data-driven, precise, and personalized health management system to ensure users receive medical care that takes their unique health and emotional conditions into consideration.

[0209] 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.

[0210] In this invention, the server includes means for receiving information on the user's health and emotional state via an input device; artificial intelligence means for comparing the received information with a database and determining possible health states; means for proposing optimized health management based on the health and emotional state determined by the artificial intelligence means; and means for making reservations at medical facilities based on the proposals. This enables precise health management and optimal medical facility reservations for each individual user.

[0211] A "user input device" is a device used by individual users to input their health and emotional states.

[0212] "Health status" refers to information that users enter regarding their physical condition, symptoms, and bodily functions.

[0213] "Emotional state" refers to information that users input regarding their mood, stress level, and psychological state.

[0214] A "database" refers to an information system that stores past medical data and psychological information.

[0215] "Artificial intelligence means" refers to methods that include machine learning algorithms for analyzing received information, comparing it with a database, and determining a person's health status.

[0216] "Means of proposing health management" refers to a function that provides personalized medical advice and suggestions based on judgments obtained by artificial intelligence.

[0217] "Means of making reservations at medical facilities" refers to a system that enables online reservations for proposed medical facilities.

[0218] "Optimization" means suggesting health management and medical facility bookings in the most effective and efficient way for the user.

[0219] To implement this invention, the user first inputs information about their health and emotional state through an input device such as a smartphone. Specifically, this includes information such as their daily physical condition, mood, and stress level. This information is transmitted to a cloud server in real time.

[0220] The server processes incoming data using cloud platforms such as Amazon Web Services (AWS®) or Microsoft Azure®. Python®-based machine learning algorithms are used for data processing, and health status is analyzed using artificial intelligence tools leveraging TensorFlow or PyTorch libraries. Simultaneously, emotional states are analyzed using natural language processing (NLP) techniques with the BERT model. This allows for a comprehensive assessment of the user's health and emotional state.

[0221] Based on the analysis results, the server provides the user with optimized health management advice and suggests ways to book appointments at medical facilities. The user can select an appropriate medical facility from the suggestions and complete the booking through the system.

[0222] For example, if a user enters "I've been feeling constantly tired and stressed lately," the server analyzes this information and suggests that the user may be experiencing chronic fatigue or high stress levels. Based on the analysis, it recommends booking an appointment at a medical facility that offers a more relaxing environment, supporting the user in managing their health quickly and easily.

[0223] An example of a prompt from the generated AI model might be: "Please tell us more about your physical condition and mood today. Please also let us know if you are feeling down or have many worries." This allows users to properly record their condition, and the system can collect data to provide more precise health support.

[0224] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0225] Step 1:

[0226] Users input information about their health and emotional state using a smartphone application. Specifically, they select or describe their physical condition, mood, and stress level. The entered data is structured on the device and prepared for transmission to a cloud server.

[0227] Step 2:

[0228] The terminal sends the entered information to the cloud server. The transmitted data is received by the cloud server and recorded in the database. This makes the input data available throughout the entire system.

[0229] Step 3:

[0230] The server passes the received user data to an artificial intelligence agent. The AI ​​agent uses TensorFlow or PyTorch to apply machine learning algorithms and compare them with a database to analyze the user's health status. The input here is user data, and the output is the analysis result of the health status.

[0231] Step 4:

[0232] In parallel, the server uses the BERT model, a natural language processing engine, to analyze emotional states. It extracts emotional text data from user input and evaluates the degree of stress and anxiety. The input is emotional text data, and the output is the result of the emotional state analysis.

[0233] Step 5:

[0234] The server integrates the analysis results of both health and emotional states to generate comprehensive health management advice. A matching algorithm is used for this generation, resulting in personalized recommendations for healthcare facilities for the user. The input here is the analyzed health and emotional data, and the output is the selection of healthcare facilities.

[0235] Step 6:

[0236] Users receive suggestions for medical facilities from the server via a smartphone app and select from the recommended facilities. Selection can be made using the app's interface.

[0237] Step 7:

[0238] The server completes the online booking process with the medical facility selected by the user. The booking information is sent to the medical facility's system for confirmation. This completes the user's booking of medical services. The input is the user's selection, and the output is the booking confirmation information.

[0239] Step 8:

[0240] The system operates in a closed loop, notifying the user after booking is complete and providing reminders and health management follow-up as needed. In this final step, the system completes user support. Inputs are booking information, and outputs are user notifications and follow-up information.

[0241] 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.

[0242] Data generation model 58 is a 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.

[0243] 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.

[0244] [Second Embodiment]

[0245] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0246] 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.

[0247] 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).

[0248] 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.

[0249] 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.

[0250] 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).

[0251] 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.

[0252] 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.

[0253] 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.

[0254] 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.

[0255] 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.

[0256] 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".

[0257] This invention is a system that enables users to easily manage their own health status and, when necessary, quickly arrange for medical consultations at healthcare facilities. Embodiments of this system are described below.

[0258] Users first access the system using their own devices. These devices include a variety of devices such as smartphones, tablets, and personal computers. Users enter their authentication information on the login screen to access the system.

[0259] When a user enters information about their health condition, that information is sent from the device to the server. This information includes specific symptoms and related health data. The server receives this information and uses an artificial intelligence agent to compare it with a database and diagnose possible illnesses.

[0260] Artificial intelligence efficiently analyzes diseases related to the entered symptoms by referencing a vast medical database. The analysis results are sent to the user's terminal via a server, and the user can use this information to decide on future actions.

[0261] Based on the diagnostic results, the system assists the user in selecting an appropriate medical institution. The server considers the user's location and the availability of each medical institution to present the best booking options. Once the user selects their preferred medical institution and date / time, that information is sent to the institution via the server, and the booking is completed.

[0262] For example, if a user enters into the terminal that they frequently experience headaches, the server receives this information, and an artificial intelligence agent analyzes it. After cross-referencing it with the database, a diagnosis is made that stress-related headaches are likely. The server informs the user of this result and presents them with options to book an appointment at a nearby neurology clinic. The user can then choose a suitable clinic from the suggestions and complete the appointment immediately.

[0263] In this way, this system enables users to efficiently and quickly manage their health status and supports them in receiving appropriate medical treatment at a healthcare facility.

[0264] The following describes the processing flow.

[0265] Step 1:

[0266] The user accesses the system using a device and enters their authentication information on the login screen.

[0267] Step 2:

[0268] The terminal sends the entered authentication information to the server. The server performs authentication by comparing it with the information in the database.

[0269] Step 3:

[0270] If the server successfully authenticates the user, it sends a login success response to the terminal, and the user accesses the main screen.

[0271] Step 4:

[0272] The user enters information about their health condition from the main screen. Specific symptoms and related health information are entered in the symptom input form.

[0273] Step 5:

[0274] The terminal sends the entered symptom information back to the server.

[0275] Step 6:

[0276] The server receives the information, calls an artificial intelligence agent, and processes the information.

[0277] Step 7:

[0278] An artificial intelligence agent compares the symptoms against a database, analyzes possible diseases that match the symptoms, and makes a determination.

[0279] Step 8:

[0280] The server prepares the diagnostic results returned by the artificial intelligence agent and generates a list of medical institutions as needed.

[0281] Step 9:

[0282] The server sends the diagnostic results and appointment options for medical institutions to the terminal.

[0283] Step 10:

[0284] The user checks the received diagnosis result and selects the facility and time for reservation from the displayed medical institutions.

[0285] Step 11:

[0286] The terminal sends the selected reservation information to the server.

[0287] Step 12:

[0288] The server cooperates with the reservation system of the medical institution to complete the reservation procedure. The server sends the confirmation of the reservation to the terminal.

[0289] Step 13:

[0290] The user receives the notification of reservation confirmation on the terminal, and the health management is completed.

[0291] (Example 1)

[0292] 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".

[0293] In modern society, health management is an important issue. However, it is difficult for many people to appropriately grasp their own health status in their busy daily lives and quickly access a medical institution when necessary. In addition, there is insufficient information available when determining which medical institution to access, and as a result, the opportunity to receive the optimal medical service may be missed. To solve this problem, a system that efficiently diagnoses the user's health status and supports reservation of an appropriate medical institution is required.

[0294] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0295] In this invention, the server includes means for receiving data on the user's health status via the user's information terminal, artificial intelligence means for identifying possible diseases by comparing the received data with an information storage medium, and means for securing reservations at medical service facilities based on the diseases identified by the artificial intelligence means. This enables the user to accurately understand their own health status and quickly make reservations at appropriate medical institutions.

[0296] A "user information terminal" is an electronic device used by a user to input data about their health status and access the system, and specifically includes devices such as smartphones, tablets, and personal computers.

[0297] "Health status data" refers to information about a user's physical condition and symptoms, which forms the basis for analysis by the system. This includes vital data such as body temperature and pulse rate, as well as specific symptoms.

[0298] An "information storage medium" refers to a database that stores past case data and medical knowledge used for analyzing data related to health conditions.

[0299] "Artificial intelligence means" refers to technology that performs an automated intelligent process to analyze received health status data and identify possible diseases by referring to information storage media.

[0300] "Means of securing reservations at medical service facilities" refers to a process that uses artificial intelligence to identify diseases, selects the most suitable medical institution for the user, and ensures that the reservation is made.

[0301] "Reservation confirmation information" refers to detailed information used to notify the user that their reservation at a medical service provider has been successfully completed, allowing the user to confirm the details of their reservation.

[0302] "Location information" refers to data used to identify a user's current location and is considered when providing users with appropriate medical facilities.

[0303] This invention provides a system that allows users to properly manage their health status and efficiently make appointments at the most suitable medical institutions as needed. A specific example of this system is shown below.

[0304] Users access this system using information terminals, specifically smartphones, tablets, or personal computers. First, the terminal displays a login screen, where the user enters their authentication information to log in to the system. After that, the terminal provides an interface for the user to input specific data about their health status. For example, the user can input information such as body temperature and symptoms.

[0305] Once information is entered, the terminal sends that data to the server. This data is then compared against a database of information stored on the server, namely, past case data and medical knowledge. The server uses artificial intelligence to analyze this data and identify possible diseases related to the user's symptoms. This artificial intelligence uses machine learning algorithms to analyze vast amounts of medical data.

[0306] The analysis results are sent from the server to the user's terminal. Furthermore, the server considers the user's location information and the availability of medical service providers to select the most suitable medical facility and propose a reservation. Based on this information, the user selects a preferred medical institution from the presented options and confirms the reservation. The reservation information is sent to the medical institution via the server, and reservation confirmation information is returned to the user's terminal, allowing the user to check the reservation details.

[0307] As a specific example, when a certain user frequently experiences headaches, the system can input "mild headache and fatigue" as symptoms. This information is transmitted to the server and may be identified as stress-related headache based on the results of analysis by artificial intelligence. Based on this result, the server can propose a nearby neurology clinic to the user and enable immediate reservation.

[0308] As an example of the prompt sentence, an input to the generative AI model in the form of "Please create a program that analyzes the health data input by the user, identifies possible diseases, and provides reservation information for available medical institutions." can be cited.

[0309] The flow of the specific process in Example 1 will be described using FIG. 11.

[0310] Step 1:

[0311] The user accesses the information terminal and logs in to the system. Here, the terminal displays a screen for inputting the authentication information (user ID and password) that the user has. The input authentication information is transmitted from the terminal to the server. The server receives this information and performs authentication by comparing it with the registered information. If the authentication is successful, the user can proceed to the next step. The data input is the user's authentication information, and the output is the authentication result (success or failure).

[0312] Step 2:

[0313] After the authentication is successful, the user inputs data regarding their health status through the terminal. The terminal displays an interface for inputting symptoms and vital information. As a specific operation, the user can input symptoms such as "body temperature 37.5 degrees" and "mild chest pain". The input health data is transmitted from the terminal to the server. What is input here is health-related information, and the output of this step is the raw data received by the server.

[0314] Step 3:

[0315] The server receives health status data transmitted from the terminal and compares it with a database stored on an information storage medium. The server analyzes the input data using artificial intelligence and identifies related diseases. Since the database contains past cases and medical knowledge, the server refers to this to process the data. For example, it assesses the risk of heart disease based on data such as "chest pain." The input is raw health status data, and the output is the result of identifying possible diseases.

[0316] Step 4:

[0317] The analysis results are sent from the server to the user's terminal, which then displays the results to the user. Specifically, a list of possible diseases and their urgency levels are displayed. Based on this information, the user then decides on their next course of action. The input is the disease data identified in the previous step, and the output is a visualized version of that data, providing feedback to the user.

[0318] Step 5:

[0319] Based on the analysis results, the server presents a selection of medical facilities, taking into account the user's location and the availability of medical institutions. This process combines the user's current location with data from partner medical institutions. For example, the server lists "internal medicine clinics available within a 2km radius." The input is the user's location and medical institution availability data, and the output is a list of optimized booking options.

[0320] Step 6:

[0321] The user selects a suitable medical institution from those suggested via the terminal and confirms the reservation for the desired date and time. The terminal sends the reservation information to the server, which then transmits it to the medical institution's system. Finally, the user receives reservation confirmation information and reviews the detailed visit plan. The input is the user's selected reservation details, and the output is the reservation confirmation information.

[0322] (Application Example 1)

[0323] 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."

[0324] In modern society, it is crucial for the elderly to regularly monitor their own health and arrange for prompt visits to medical institutions when necessary. However, it is often difficult for the elderly themselves to do this efficiently. Solving this problem and optimizing health management for the elderly in nursing homes and home care settings is essential.

[0325] 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.

[0326] In this invention, the server includes means for receiving information about the user's health status via an input device, machine learning means for determining possible medical conditions by comparing the received information with data resources, means for making reservations at medical facilities based on the medical conditions determined by the machine learning means, and means for coordinating with private medical professionals or caregivers. This enables elderly individuals to easily manage their own health status and, if necessary, to quickly arrange visits to appropriate medical institutions.

[0327] A "user input device" refers to a device used by an individual to electronically input their own health information. This device includes smartphones, tablets, and personal computers.

[0328] "Health information" refers to data entered by users about their own physical condition, including specific symptoms and other health-related information.

[0329] "Data resources" refer to information sources, including past medical and clinical data, that machine learning tools refer to when determining a patient's condition.

[0330] "Machine learning methods for determining possible medical conditions" refers to algorithms that use received health status information to compare it with past information and estimate the medical condition.

[0331] "Method for making reservations at medical facilities" refers to a function that allows users to make online reservations at the most suitable medical institutions based on their medical condition.

[0332] A "private medical professional" refers to a medical professional who provides health management and medical advice to individual users.

[0333] A "caregiver" refers to a person who plays a role in supporting the daily lives and medical care of the elderly or patients.

[0334] The implementation of this invention begins with a user inputting information about their health status using a device such as a smartphone or tablet. The device has the function of transmitting this information to a server. After receiving the information, the server uses machine learning means to refer to data resources and determine possible medical conditions related to the input information. The software used here includes machine learning libraries such as TensorFlow.

[0335] Based on the assessed medical condition, the server makes a reservation at an appropriate medical facility. The reservation process considers the user's current location and the availability of medical facilities to suggest the most suitable facility and reservation time. The information is then communicated to the user via a medical professional or caregiver, and coordination with the medical facility is facilitated.

[0336] For example, if a user inputs a health condition such as "I frequently wake up at night," the server processes this information and uses machine learning to diagnose the possibility of sleep apnea. Based on the diagnosis, the server suggests booking an appointment at a relaxation clinic near the user's residence.

[0337] An example of a prompt to input into the generating AI model is, "What medical measures should be recommended when an elderly woman reports feeling tired?" This will support specific actions to assist in the health management of the elderly.

[0338] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0339] Step 1:

[0340] Users enter health information using their own devices. This information includes specific symptoms and lifestyle-related data. The entered information is temporarily stored on the device.

[0341] Step 2:

[0342] The terminal transmits the entered information to the server via the network. The server receives this information and stores it in a database. This data is saved for use in subsequent analysis steps.

[0343] Step 3:

[0344] The server analyzes the received health information using a machine learning model based on TensorFlow. Based on the input data, it determines the medical condition related to the symptoms. This process involves data calculations by comparing the data with past data and known patterns.

[0345] Step 4:

[0346] The server identifies possible medical conditions based on the output of the machine learning model. The identified medical conditions are then prepared for use in the next step.

[0347] Step 5:

[0348] The server references the user's current location and the availability of medical facilities to generate the most suitable booking options for their medical condition. This information is retrieved from a database, and an optimization algorithm is used to determine the best option.

[0349] Step 6:

[0350] The server notifies the user of the details of available medical facilities. The user can select their preferred option from the suggested choices and confirm the booking request through their device.

[0351] Step 7:

[0352] After user verification, the server initiates the reservation confirmation process with the selected medical institution. The reservation information is sent to the medical institution, where the reservation is officially confirmed. This allows the user to receive appropriate medical support.

[0353] 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.

[0354] This invention provides a system that offers more precise and personalized health management support by taking into account the user's health and emotional state. This system allows the user to input information about their physical condition and simultaneously recognize their emotional state through an emotion engine, enabling comprehensive diagnosis and appropriate medical appointment scheduling.

[0355] Users access the system using their own devices and input information about their physical condition, symptoms, and emotional state. This includes not only specific symptoms but also their mood and stress levels. The device then sends this information to the server.

[0356] Based on the received information, the server activates an artificial intelligence agent and an emotion engine. The AI ​​agent diagnoses possible illnesses related to the user's symptoms, based on existing medical databases and past case data. Simultaneously, the emotion engine analyzes the input emotional information and evaluates the user's overall psychological state.

[0357] The diagnostic results and emotional assessment results obtained are used together to gain a more detailed understanding of the user's condition. The server recommends booking appointments at appropriate medical institutions based on the diagnostic results, taking the user's emotional state into consideration. For example, if the user shows high levels of stress or anxiety, the server can prioritize recommending medical facilities that focus on relaxation or institutions that provide such care.

[0358] Ultimately, the user receives multiple suggestions from the server, selects the most suitable medical institution, and completes the reservation through their terminal. The server then sends this reservation to the relevant medical institution's system and obtains confirmation, enabling smooth guidance to the user.

[0359] As a concrete example, consider a user who is experiencing chronic fatigue and high stress levels due to recent work burdens. This user enters this information into a terminal and sends it to the system. The server, using an artificial intelligence agent, diagnoses the possibility of chronic fatigue syndrome, and the emotional engine recognizes the high-stress state. Based on this, the server strongly recommends a clinic specializing in stress care, and the user can then complete the reservation.

[0360] This health management support system, which takes user emotions into account, makes it possible to provide more precise and personalized medical care.

[0361] The following describes the processing flow.

[0362] Step 1:

[0363] The user accesses the system using a device and enters their authentication information on the login screen.

[0364] Step 2:

[0365] The terminal sends the entered authentication information to the server. The server performs authentication by comparing it with the database.

[0366] Step 3:

[0367] If the server successfully authenticates, it sends a login authorization to the terminal, and the user proceeds to the main screen.

[0368] Step 4:

[0369] On the main screen, the user enters information about their physical condition and their emotional state.

[0370] Step 5:

[0371] The device sends the entered health and emotional information to the server.

[0372] Step 6:

[0373] The server activates an artificial intelligence agent, which compares the user's health information with a database to diagnose possible symptoms.

[0374] Step 7:

[0375] The server activates the emotion engine, analyzes the emotional information entered by the user, and evaluates their psychological state.

[0376] Step 8:

[0377] The server integrates the diagnostic results from the artificial intelligence agent with the emotional state evaluation results from the emotion engine.

[0378] Step 9:

[0379] Based on the results of server integration, a list is generated to suggest the most suitable medical institutions and treatments to the user.

[0380] Step 10:

[0381] The server sends the terminal with recommended medical facilities and booking options.

[0382] Step 11:

[0383] The user selects their preferred facility and time from the list of medical institutions presented and confirms the reservation.

[0384] Step 12:

[0385] The device sends the selected reservation information to the server.

[0386] Step 13:

[0387] The server connects with the medical institution's system and performs the procedure to confirm the reservation.

[0388] Step 14:

[0389] The server sends a reservation confirmation to the device, and the user receives a notification.

[0390] Step 15:

[0391] The user confirms the reservation completion notification and then terminates their use of the system.

[0392] (Example 2)

[0393] 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".

[0394] Conventional health management systems only consider the user's physical condition when making diagnoses and appointments, making it difficult to provide services that reflect the emotional and psychological state of individual users. Therefore, there is a challenge in providing sufficiently individualized medical services to users with health problems influenced by stress and anxiety.

[0395] 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.

[0396] In this invention, the server includes means for receiving information on the user's health and emotional state via an input device, artificial intelligence means for simultaneously determining the physical and psychological state using a data analysis device based on the received information, and means for selecting and booking medical services that take into account the user's emotional state based on the state determined by the artificial intelligence means. This enables more personalized medical care and service optimization based on a comprehensive health assessment that includes emotional state.

[0397] A "user input device" refers to a device used by a user to input information about their health and emotional state into the system.

[0398] "Health status" refers to information about the user's physical condition, including specific symptoms and changes in their physical condition.

[0399] "Emotional state" refers to information that indicates the user's psychological and emotional state, including mood and stress level.

[0400] A "data analysis device" refers to a technical means for analyzing health and emotional states based on received information.

[0401] "Artificial intelligence means" refers to elements that use machine learning and data processing technologies to determine health and emotional states and propose appropriate services based on those assessments.

[0402] "Means for selecting and booking medical services" refers to a function that selects and books appropriate medical institutions or services based on decisions made by artificial intelligence.

[0403] This invention is a system that provides more personalized health management support by taking into account the user's physical and emotional state. The system aims to allow users to input information about their physical condition and emotions, and then use that information to make diagnoses and appointments at medical institutions.

[0404] Users access this system using their own input devices, such as smartphones or personal computers. Users input information such as their health status, specific symptoms, and emotional information like mood and stress levels. This information is transmitted from the device to the server.

[0405] The server uses specific software to analyze the received data. This analysis utilizes data analysis devices and artificial intelligence (AI) tools. The AI ​​tools use existing medical knowledge bases and historical clinical data to analyze potential medical conditions related to the user's health status, and also evaluate the user's psychological state using emotion recognition algorithms.

[0406] Based on information analyzed from the user's health and emotional state, the server suggests the most suitable medical services. These suggested services take emotional state into consideration, and may prioritize medical institutions that offer relaxation services.

[0407] Ultimately, the user selects the most suitable healthcare provider from the options provided by the server and completes the reservation. The server then sends the reservation information to the healthcare provider's system for confirmation.

[0408] As a concrete example, consider a case where a user inputs chronic fatigue and high stress. Based on this information, the server diagnoses chronic fatigue syndrome, and the emotion engine recognizes high stress. The server then recommends a stress management clinic, allowing the user to receive appropriate support.

[0409] Examples of prompts for a generative AI model:

[0410] "The user has entered their health information and emotional state. Based on this information, please suggest possible medical conditions and recommended medical services."

[0411] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0412] Step 1:

[0413] The user inputs their health and emotional state into the system using their input device. Specifically, they fill in specific symptoms, mood, stress levels, etc., related to their physical condition in an on-screen form. The input in this step consists of the user's health and emotional data. The terminal prepares to send this data to the server.

[0414] Step 2:

[0415] The device sends health and emotional data entered by the user to the server. The HTTPS protocol is used to ensure data security. At this stage, the input is user data, and the output is encrypted data packets. The device receives a response from the server confirming that the data was transmitted correctly.

[0416] Step 3:

[0417] The server uses a data analysis device to analyze the received data. The input data consists of information about health status and emotional information. The server activates artificial intelligence to analyze the received data and identify possible diseases. Furthermore, it uses an emotion recognition algorithm to evaluate the psychological state for the emotional information. The output of this step is the diagnostic result and the psychological evaluation result. The server passes this on to the next step.

[0418] Step 4:

[0419] The server suggests the most suitable medical services to the user based on the diagnostic and psychological assessment results. The input here is the diagnosis and psychological assessment obtained in the previous step. The server uses the data to generate a list of appropriate medical institutions and service providers and prioritizes them. The output at this stage is a list of recommended medical institutions.

[0420] Step 5:

[0421] The user reviews the list of medical institutions suggested by the server on their terminal. The input here is the list of medical institutions sent from the server. The user selects the most suitable medical institution from the list and enters a reservation request. The output at this step is the reservation request to the medical institution selected by the user.

[0422] Step 6:

[0423] The server receives reservation requests from users and transmits reservation information to the selected medical institution's system. The input is the user's reservation request, which the server relays to the medical institution. This process is carried out in conjunction with the medical institution's system and requires confirmation of the reservation. The output at this step is confirmation information that the reservation has been completed.

[0424] Step 7:

[0425] The user receives a confirmation from the server that the reservation is complete. The input here is the reservation confirmation information sent from the medical institution. The terminal notifies the user of the confirmation information, informing them that the reservation has been successfully completed. The output in this step is a notification to the user.

[0426] (Application Example 2)

[0427] 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."

[0428] In today's world, effectively managing the health and emotional states of individual users and scheduling appropriate medical appointments is complex. Therefore, healthcare providers need a data-driven, precise, and personalized health management system to ensure users receive medical care that takes their unique health and emotional conditions into consideration.

[0429] 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.

[0430] In this invention, the server includes means for receiving information on the user's health and emotional state via an input device; artificial intelligence means for comparing the received information with a database and determining possible health states; means for proposing optimized health management based on the health and emotional state determined by the artificial intelligence means; and means for making reservations at medical facilities based on the proposals. This enables precise health management and optimal medical facility reservations for each individual user.

[0431] A "user input device" is a device used by individual users to input their health and emotional states.

[0432] "Health status" refers to information that users enter regarding their physical condition, symptoms, and bodily functions.

[0433] "Emotional state" refers to information that users input regarding their mood, stress level, and psychological state.

[0434] A "database" refers to an information system that stores past medical data and psychological information.

[0435] "Artificial intelligence means" refers to methods that include machine learning algorithms for analyzing received information, comparing it with a database, and determining a person's health status.

[0436] "Means of proposing health management" refers to a function that provides personalized medical advice and suggestions based on judgments obtained by artificial intelligence.

[0437] "Means of making reservations at medical facilities" refers to a system that enables online reservations for proposed medical facilities.

[0438] "Optimization" means suggesting health management and medical facility bookings in the most effective and efficient way for the user.

[0439] To implement this invention, the user first inputs information about their health and emotional state through an input device such as a smartphone. Specifically, this includes information such as their daily physical condition, mood, and stress level. This information is transmitted to a cloud server in real time.

[0440] The server processes incoming data using cloud platforms such as Amazon Web Services (AWS) or Microsoft Azure. Python-based machine learning algorithms are used for data processing, and health status is analyzed using artificial intelligence tools leveraging TensorFlow or PyTorch libraries. Simultaneously, emotional states are analyzed using natural language processing (NLP) techniques with the BERT model. This allows for a comprehensive assessment of the user's health and emotional state.

[0441] Based on the analysis results, the server provides the user with optimized health management advice and suggests ways to book appointments at medical facilities. The user can select an appropriate medical facility from the suggestions and complete the booking through the system.

[0442] For example, if a user enters "I've been feeling constantly tired and stressed lately," the server analyzes this information and suggests that the user may be experiencing chronic fatigue or high stress levels. Based on the analysis, it recommends booking an appointment at a medical facility that offers a more relaxing environment, supporting the user in managing their health quickly and easily.

[0443] An example of a prompt from the generated AI model might be: "Please tell us more about your physical condition and mood today. Please also let us know if you are feeling down or have many worries." This allows users to properly record their condition, and the system can collect data to provide more precise health support.

[0444] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0445] Step 1:

[0446] Users input information about their health and emotional state using a smartphone application. Specifically, they select or describe their physical condition, mood, and stress level. The entered data is structured on the device and prepared for transmission to a cloud server.

[0447] Step 2:

[0448] The terminal sends the entered information to the cloud server. The transmitted data is received by the cloud server and recorded in the database. This makes the input data available throughout the entire system.

[0449] Step 3:

[0450] The server passes the received user data to an artificial intelligence agent. The AI ​​agent uses TensorFlow or PyTorch to apply machine learning algorithms and compare them with a database to analyze the user's health status. The input here is user data, and the output is the analysis result of the health status.

[0451] Step 4:

[0452] In parallel, the server uses the BERT model, a natural language processing engine, to analyze emotional states. It extracts emotional text data from user input and evaluates the degree of stress and anxiety. The input is emotional text data, and the output is the result of the emotional state analysis.

[0453] Step 5:

[0454] The server integrates the analysis results of both health and emotional states to generate comprehensive health management advice. A matching algorithm is used for this generation, resulting in personalized recommendations for healthcare facilities for the user. The input here is the analyzed health and emotional data, and the output is the selection of healthcare facilities.

[0455] Step 6:

[0456] Users receive suggestions for medical facilities from the server via a smartphone app and select from the recommended facilities. Selection can be made using the app's interface.

[0457] Step 7:

[0458] The server completes the online booking process with the medical facility selected by the user. The booking information is sent to the medical facility's system for confirmation. This completes the user's booking of medical services. The input is the user's selection, and the output is the booking confirmation information.

[0459] Step 8:

[0460] The system operates in a closed loop, notifying the user after booking is complete and providing reminders and health management follow-up as needed. In this final step, the system completes user support. Inputs are booking information, and outputs are user notifications and follow-up information.

[0461] 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.

[0462] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0463] 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.

[0464] [Third Embodiment]

[0465] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0466] 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.

[0467] 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).

[0468] 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.

[0469] 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.

[0470] 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).

[0471] 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.

[0472] 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.

[0473] 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.

[0474] 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.

[0475] 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.

[0476] 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".

[0477] This invention is a system that enables users to easily manage their own health status and, when necessary, quickly arrange for medical consultations at healthcare facilities. Embodiments of this system are described below.

[0478] Users first access the system using their own devices. These devices include a variety of devices such as smartphones, tablets, and personal computers. Users enter their authentication information on the login screen to access the system.

[0479] When a user enters information about their health condition, that information is sent from the device to the server. This information includes specific symptoms and related health data. The server receives this information and uses an artificial intelligence agent to compare it with a database and diagnose possible illnesses.

[0480] Artificial intelligence efficiently analyzes diseases related to the entered symptoms by referencing a vast medical database. The analysis results are sent to the user's terminal via a server, and the user can use this information to decide on future actions.

[0481] Based on the diagnostic results, the system assists the user in selecting an appropriate medical institution. The server considers the user's location and the availability of each medical institution to present the best booking options. Once the user selects their preferred medical institution and date / time, that information is sent to the institution via the server, and the booking is completed.

[0482] For example, if a user enters into the terminal that they frequently experience headaches, the server receives this information, and an artificial intelligence agent analyzes it. After cross-referencing it with the database, a diagnosis is made that stress-related headaches are likely. The server informs the user of this result and presents them with options to book an appointment at a nearby neurology clinic. The user can then choose a suitable clinic from the suggestions and complete the appointment immediately.

[0483] In this way, this system enables users to efficiently and quickly manage their health status and supports them in receiving appropriate medical treatment at a healthcare facility.

[0484] The following describes the processing flow.

[0485] Step 1:

[0486] The user accesses the system using a device and enters their authentication information on the login screen.

[0487] Step 2:

[0488] The terminal sends the entered authentication information to the server. The server performs authentication by comparing it with the information in the database.

[0489] Step 3:

[0490] If the server successfully authenticates the user, it sends a login success response to the terminal, and the user accesses the main screen.

[0491] Step 4:

[0492] The user enters information about their health condition from the main screen. Specific symptoms and related health information are entered in the symptom input form.

[0493] Step 5:

[0494] The terminal sends the entered symptom information back to the server.

[0495] Step 6:

[0496] The server receives the information, calls an artificial intelligence agent, and processes the information.

[0497] Step 7:

[0498] An artificial intelligence agent compares the symptoms against a database, analyzes possible diseases that match the symptoms, and makes a determination.

[0499] Step 8:

[0500] The server prepares the diagnostic results returned by the artificial intelligence agent and generates a list of medical institutions as needed.

[0501] Step 9:

[0502] The server sends the diagnostic results and appointment options for medical institutions to the terminal.

[0503] Step 10:

[0504] The user reviews the received diagnosis results and selects a medical facility and time they wish to book from the displayed list of healthcare providers.

[0505] Step 11:

[0506] The device sends the selected reservation information to the server.

[0507] Step 12:

[0508] The server connects with the medical institution's reservation system and completes the reservation process. The server then sends a reservation confirmation to the terminal.

[0509] Step 13:

[0510] The user receives a reservation confirmation notification on their device, and their health management is completed.

[0511] (Example 1)

[0512] 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."

[0513] In modern society, health management is a crucial issue, but for many people, it is difficult to properly understand their own health status and access medical facilities promptly when necessary amidst their busy daily lives. Furthermore, a lack of available information when deciding which medical facility to visit can result in missing opportunities to receive optimal medical services. To address this challenge, a system is needed that efficiently diagnoses the user's health status and assists in making appointments at appropriate medical facilities.

[0514] 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.

[0515] In this invention, the server includes means for receiving data on the user's health status via the user's information terminal, artificial intelligence means for identifying possible diseases by comparing the received data with an information storage medium, and means for securing reservations at medical service facilities based on the diseases identified by the artificial intelligence means. This enables the user to accurately understand their own health status and quickly make reservations at appropriate medical institutions.

[0516] A "user information terminal" is an electronic device used by a user to input data about their health status and access the system, and specifically includes devices such as smartphones, tablets, and personal computers.

[0517] "Health status data" refers to information about a user's physical condition and symptoms, which forms the basis for analysis by the system. This includes vital data such as body temperature and pulse rate, as well as specific symptoms.

[0518] An "information storage medium" refers to a database that stores past case data and medical knowledge used for analyzing data related to health conditions.

[0519] "Artificial intelligence means" refers to technology that performs an automated intelligent process to analyze received health status data and identify possible diseases by referring to information storage media.

[0520] "Means of securing reservations at medical service facilities" refers to a process that uses artificial intelligence to identify diseases, selects the most suitable medical institution for the user, and ensures that the reservation is made.

[0521] "Reservation confirmation information" refers to detailed information used to notify the user that their reservation at a medical service provider has been successfully completed, allowing the user to confirm the details of their reservation.

[0522] "Location information" refers to data used to identify a user's current location and is considered when providing users with appropriate medical facilities.

[0523] This invention provides a system that allows users to properly manage their health status and efficiently make appointments at the most suitable medical institutions as needed. A specific example of this system is shown below.

[0524] Users access this system using information terminals, specifically smartphones, tablets, or personal computers. First, the terminal displays a login screen, where the user enters their authentication information to log in to the system. After that, the terminal provides an interface for the user to input specific data about their health status. For example, the user can input information such as body temperature and symptoms.

[0525] Once information is entered, the terminal sends that data to the server. This data is then compared against a database of information stored on the server, namely, past case data and medical knowledge. The server uses artificial intelligence to analyze this data and identify possible diseases related to the user's symptoms. This artificial intelligence uses machine learning algorithms to analyze vast amounts of medical data.

[0526] The analysis results are sent from the server to the user's terminal. Furthermore, the server considers the user's location information and the availability of medical service providers to select the most suitable medical facility and propose a reservation. Based on this information, the user selects a preferred medical institution from the presented options and confirms the reservation. The reservation information is sent to the medical institution via the server, and reservation confirmation information is returned to the user's terminal, allowing the user to check the reservation details.

[0527] For example, if a user frequently experiences headaches, they can input "mild headache and fatigue" as symptoms into the system. This information is sent to the server, and the artificial intelligence analysis may identify it as a stress-related headache. Based on this result, the server can suggest a nearby neurology clinic to the user, allowing them to make an appointment immediately.

[0528] An example of a prompt message is an input to a generative AI model in the form of, "Create a program that analyzes the health data entered by the user, identifies possible diseases, and provides appointment information for available medical facilities."

[0529] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0530] Step 1:

[0531] The user accesses an information terminal and logs into the system. The terminal displays a screen for the user to enter their authentication information (user ID and password). The entered authentication information is sent from the terminal to the server. The server receives this information, verifies it against the registered information, and performs authentication. If authentication is successful, the user can proceed to the next step. The data entered is the user's authentication information, and the output is the authentication result (success or failure).

[0532] Step 2:

[0533] After successful authentication, the user enters data about their health status through the terminal. The terminal displays an interface for entering symptoms and vital information. Specifically, the user can enter symptoms such as "body temperature 37.5 degrees" and "mild chest pain." The entered health data is sent from the terminal to the server. The information entered here is health-related, and the output of this step is the raw data received by the server.

[0534] Step 3:

[0535] The server receives health status data transmitted from the terminal and compares it with a database stored on an information storage medium. The server analyzes the input data using artificial intelligence and identifies related diseases. Since the database contains past cases and medical knowledge, the server refers to this to process the data. For example, it assesses the risk of heart disease based on data such as "chest pain." The input is raw health status data, and the output is the result of identifying possible diseases.

[0536] Step 4:

[0537] The analysis results are sent from the server to the user's terminal, which then displays the results to the user. Specifically, a list of possible diseases and their urgency levels are displayed. Based on this information, the user then decides on their next course of action. The input is the disease data identified in the previous step, and the output is a visualized version of that data, providing feedback to the user.

[0538] Step 5:

[0539] Based on the analysis results, the server presents a selection of medical facilities, taking into account the user's location and the availability of medical institutions. This process combines the user's current location with data from partner medical institutions. For example, the server lists "internal medicine clinics available within a 2km radius." The input is the user's location and medical institution availability data, and the output is a list of optimized booking options.

[0540] Step 6:

[0541] The user selects a suitable medical institution from those suggested via the terminal and confirms the reservation for the desired date and time. The terminal sends the reservation information to the server, which then transmits it to the medical institution's system. Finally, the user receives reservation confirmation information and reviews the detailed visit plan. The input is the user's selected reservation details, and the output is the reservation confirmation information.

[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, it is crucial for the elderly to regularly monitor their own health and arrange for prompt visits to medical institutions when necessary. However, it is often difficult for the elderly themselves to do this efficiently. Solving this problem and optimizing health management for the elderly in nursing homes and home care settings is essential.

[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 information about the user's health status via an input device, machine learning means for determining possible medical conditions by comparing the received information with data resources, means for making reservations at medical facilities based on the medical conditions determined by the machine learning means, and means for coordinating with private medical professionals or caregivers. This enables elderly individuals to easily manage their own health status and, if necessary, to quickly arrange visits to appropriate medical institutions.

[0547] A "user input device" refers to a device used by an individual to electronically input their own health information. This device includes smartphones, tablets, and personal computers.

[0548] "Health information" refers to data entered by users about their own physical condition, including specific symptoms and other health-related information.

[0549] "Data resources" refer to information sources, including past medical and clinical data, that machine learning tools refer to when determining a patient's condition.

[0550] "Machine learning methods for determining possible medical conditions" refers to algorithms that use received health status information to compare it with past information and estimate the medical condition.

[0551] "Method for making reservations at medical facilities" refers to a function that allows users to make online reservations at the most suitable medical institutions based on their medical condition.

[0552] A "private medical professional" refers to a medical professional who provides health management and medical advice to individual users.

[0553] A "caregiver" refers to a person who plays a role in supporting the daily lives and medical care of the elderly or patients.

[0554] The implementation of this invention begins with a user inputting information about their health status using a device such as a smartphone or tablet. The device has the function of transmitting this information to a server. After receiving the information, the server uses machine learning means to refer to data resources and determine possible medical conditions related to the input information. The software used here includes machine learning libraries such as TensorFlow.

[0555] Based on the assessed medical condition, the server makes a reservation at an appropriate medical facility. The reservation process considers the user's current location and the availability of medical facilities to suggest the most suitable facility and reservation time. The information is then communicated to the user via a medical professional or caregiver, and coordination with the medical facility is facilitated.

[0556] For example, if a user inputs a health condition such as "I frequently wake up at night," the server processes this information and uses machine learning to diagnose the possibility of sleep apnea. Based on the diagnosis, the server suggests booking an appointment at a relaxation clinic near the user's residence.

[0557] An example of a prompt to input into the generating AI model is, "What medical measures should be recommended when an elderly woman reports feeling tired?" This will support specific actions to assist in the health management of the elderly.

[0558] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0559] Step 1:

[0560] Users enter health information using their own devices. This information includes specific symptoms and lifestyle-related data. The entered information is temporarily stored on the device.

[0561] Step 2:

[0562] The terminal transmits the entered information to the server via the network. The server receives this information and stores it in a database. This data is saved for use in subsequent analysis steps.

[0563] Step 3:

[0564] The server analyzes the received health information using a machine learning model based on TensorFlow. Based on the input data, it determines the medical condition related to the symptoms. This process involves data calculations by comparing the data with past data and known patterns.

[0565] Step 4:

[0566] The server identifies possible medical conditions based on the output of the machine learning model. The identified medical conditions are then prepared for use in the next step.

[0567] Step 5:

[0568] The server references the user's current location and the availability of medical facilities to generate the most suitable booking options for their medical condition. This information is retrieved from a database, and an optimization algorithm is used to determine the best option.

[0569] Step 6:

[0570] The server notifies the user of the details of available medical facilities. The user can select their preferred option from the suggested choices and confirm the booking request through their device.

[0571] Step 7:

[0572] After user verification, the server initiates the reservation confirmation process with the selected medical institution. The reservation information is sent to the medical institution, where the reservation is officially confirmed. This allows the user to receive appropriate medical support.

[0573] 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.

[0574] This invention provides a system that offers more precise and personalized health management support by taking into account the user's health and emotional state. This system allows the user to input information about their physical condition and simultaneously recognize their emotional state through an emotion engine, enabling comprehensive diagnosis and appropriate medical appointment scheduling.

[0575] Users access the system using their own devices and input information about their physical condition, symptoms, and emotional state. This includes not only specific symptoms but also their mood and stress levels. The device then sends this information to the server.

[0576] Based on the received information, the server activates an artificial intelligence agent and an emotion engine. The AI ​​agent diagnoses possible illnesses related to the user's symptoms, based on existing medical databases and past case data. Simultaneously, the emotion engine analyzes the input emotional information and evaluates the user's overall psychological state.

[0577] The diagnostic results and emotional assessment results obtained are used together to gain a more detailed understanding of the user's condition. The server recommends booking appointments at appropriate medical institutions based on the diagnostic results, taking the user's emotional state into consideration. For example, if the user shows high levels of stress or anxiety, the server can prioritize recommending medical facilities that focus on relaxation or institutions that provide such care.

[0578] Ultimately, the user receives multiple suggestions from the server, selects the most suitable medical institution, and completes the reservation through their terminal. The server then sends this reservation to the relevant medical institution's system and obtains confirmation, enabling smooth guidance to the user.

[0579] As a concrete example, consider a user who is experiencing chronic fatigue and high stress levels due to recent work burdens. This user enters this information into a terminal and sends it to the system. The server, using an artificial intelligence agent, diagnoses the possibility of chronic fatigue syndrome, and the emotional engine recognizes the high-stress state. Based on this, the server strongly recommends a clinic specializing in stress care, and the user can then complete the reservation.

[0580] This health management support system, which takes user emotions into account, makes it possible to provide more precise and personalized medical care.

[0581] The following describes the processing flow.

[0582] Step 1:

[0583] The user accesses the system using a device and enters their authentication information on the login screen.

[0584] Step 2:

[0585] The terminal sends the entered authentication information to the server. The server performs authentication by comparing it with the database.

[0586] Step 3:

[0587] If the server successfully authenticates, it sends a login authorization to the terminal, and the user proceeds to the main screen.

[0588] Step 4:

[0589] On the main screen, the user enters information about their physical condition and their emotional state.

[0590] Step 5:

[0591] The device sends the entered health and emotional information to the server.

[0592] Step 6:

[0593] The server activates an artificial intelligence agent, which compares the user's health information with a database to diagnose possible symptoms.

[0594] Step 7:

[0595] The server activates the emotion engine, analyzes the emotional information entered by the user, and evaluates their psychological state.

[0596] Step 8:

[0597] The server integrates the diagnostic results from the artificial intelligence agent with the emotional state evaluation results from the emotion engine.

[0598] Step 9:

[0599] Based on the results of server integration, a list is generated to suggest the most suitable medical institutions and treatments to the user.

[0600] Step 10:

[0601] The server sends the terminal with recommended medical facilities and booking options.

[0602] Step 11:

[0603] The user selects their preferred facility and time from the list of medical institutions presented and confirms the reservation.

[0604] Step 12:

[0605] The device sends the selected reservation information to the server.

[0606] Step 13:

[0607] The server connects with the medical institution's system and performs the procedure to confirm the reservation.

[0608] Step 14:

[0609] The server sends a reservation confirmation to the device, and the user receives a notification.

[0610] Step 15:

[0611] The user confirms the reservation completion notification and then terminates their use of the system.

[0612] (Example 2)

[0613] 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."

[0614] Conventional health management systems only consider the user's physical condition when making diagnoses and appointments, making it difficult to provide services that reflect the emotional and psychological state of individual users. Therefore, there is a challenge in providing sufficiently individualized medical services to users with health problems influenced by stress and anxiety.

[0615] 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.

[0616] In this invention, the server includes means for receiving information on the user's health and emotional state via an input device, artificial intelligence means for simultaneously determining the physical and psychological state using a data analysis device based on the received information, and means for selecting and booking medical services that take into account the user's emotional state based on the state determined by the artificial intelligence means. This enables more personalized medical care and service optimization based on a comprehensive health assessment that includes emotional state.

[0617] A "user input device" refers to a device used by a user to input information about their health and emotional state into the system.

[0618] "Health status" refers to information about the user's physical condition, including specific symptoms and changes in their physical condition.

[0619] "Emotional state" refers to information that indicates the user's psychological and emotional state, including mood and stress level.

[0620] A "data analysis device" refers to a technical means for analyzing health and emotional states based on received information.

[0621] "Artificial intelligence means" refers to elements that use machine learning and data processing technologies to determine health and emotional states and propose appropriate services based on those assessments.

[0622] "Means for selecting and booking medical services" refers to a function that selects and books appropriate medical institutions or services based on decisions made by artificial intelligence.

[0623] This invention is a system that provides more personalized health management support by taking into account the user's physical and emotional state. The system aims to allow users to input information about their physical condition and emotions, and then use that information to make diagnoses and appointments at medical institutions.

[0624] Users access this system using their own input devices, such as smartphones or personal computers. Users input information such as their health status, specific symptoms, and emotional information like mood and stress levels. This information is transmitted from the device to the server.

[0625] The server uses specific software to analyze the received data. This analysis utilizes data analysis devices and artificial intelligence (AI) tools. The AI ​​tools use existing medical knowledge bases and historical clinical data to analyze potential medical conditions related to the user's health status, and also evaluate the user's psychological state using emotion recognition algorithms.

[0626] Based on information analyzed from the user's health and emotional state, the server suggests the most suitable medical services. These suggested services take emotional state into consideration, and may prioritize medical institutions that offer relaxation services.

[0627] Ultimately, the user selects the most suitable healthcare provider from the options provided by the server and completes the reservation. The server then sends the reservation information to the healthcare provider's system for confirmation.

[0628] As a concrete example, consider a case where a user inputs chronic fatigue and high stress. Based on this information, the server diagnoses chronic fatigue syndrome, and the emotion engine recognizes high stress. The server then recommends a stress management clinic, allowing the user to receive appropriate support.

[0629] Examples of prompts for a generative AI model:

[0630] "The user has entered their health information and emotional state. Based on this information, please suggest possible medical conditions and recommended medical services."

[0631] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0632] Step 1:

[0633] The user inputs their health and emotional state into the system using their input device. Specifically, they fill in specific symptoms, mood, stress levels, etc., related to their physical condition in an on-screen form. The input in this step consists of the user's health and emotional data. The terminal prepares to send this data to the server.

[0634] Step 2:

[0635] The device sends health and emotional data entered by the user to the server. The HTTPS protocol is used to ensure data security. At this stage, the input is user data, and the output is encrypted data packets. The device receives a response from the server confirming that the data was transmitted correctly.

[0636] Step 3:

[0637] The server uses a data analysis device to analyze the received data. The input data consists of information about health status and emotional information. The server activates artificial intelligence to analyze the received data and identify possible diseases. Furthermore, it uses an emotion recognition algorithm to evaluate the psychological state for the emotional information. The output of this step is the diagnostic result and the psychological evaluation result. The server passes this on to the next step.

[0638] Step 4:

[0639] The server suggests the most suitable medical services to the user based on the diagnostic and psychological assessment results. The input here is the diagnosis and psychological assessment obtained in the previous step. The server uses the data to generate a list of appropriate medical institutions and service providers and prioritizes them. The output at this stage is a list of recommended medical institutions.

[0640] Step 5:

[0641] The user reviews the list of medical institutions suggested by the server on their terminal. The input here is the list of medical institutions sent from the server. The user selects the most suitable medical institution from the list and enters a reservation request. The output at this step is the reservation request to the medical institution selected by the user.

[0642] Step 6:

[0643] The server receives reservation requests from users and transmits reservation information to the selected medical institution's system. The input is the user's reservation request, which the server relays to the medical institution. This process is carried out in conjunction with the medical institution's system and requires confirmation of the reservation. The output at this step is confirmation information that the reservation has been completed.

[0644] Step 7:

[0645] The user receives a confirmation from the server that the reservation is complete. The input here is the reservation confirmation information sent from the medical institution. The terminal notifies the user of the confirmation information, informing them that the reservation has been successfully completed. The output in this step is a notification to the user.

[0646] (Application Example 2)

[0647] 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."

[0648] In today's world, effectively managing the health and emotional states of individual users and scheduling appropriate medical appointments is complex. Therefore, healthcare providers need a data-driven, precise, and personalized health management system to ensure users receive medical care that takes their unique health and emotional conditions into consideration.

[0649] 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.

[0650] In this invention, the server includes means for receiving information on the user's health and emotional state via an input device; artificial intelligence means for comparing the received information with a database and determining possible health states; means for proposing optimized health management based on the health and emotional state determined by the artificial intelligence means; and means for making reservations at medical facilities based on the proposals. This enables precise health management and optimal medical facility reservations for each individual user.

[0651] A "user input device" is a device used by individual users to input their health and emotional states.

[0652] "Health status" refers to information that users enter regarding their physical condition, symptoms, and bodily functions.

[0653] "Emotional state" refers to information that users input regarding their mood, stress level, and psychological state.

[0654] A "database" refers to an information system that stores past medical data and psychological information.

[0655] "Artificial intelligence means" refers to methods that include machine learning algorithms for analyzing received information, comparing it with a database, and determining a person's health status.

[0656] "Means of proposing health management" refers to a function that provides personalized medical advice and suggestions based on judgments obtained by artificial intelligence.

[0657] "Means of making reservations at medical facilities" refers to a system that enables online reservations for proposed medical facilities.

[0658] "Optimization" means suggesting health management and medical facility bookings in the most effective and efficient way for the user.

[0659] To implement this invention, the user first inputs information about their health and emotional state through an input device such as a smartphone. Specifically, this includes information such as their daily physical condition, mood, and stress level. This information is transmitted to a cloud server in real time.

[0660] The server processes incoming data using cloud platforms such as Amazon Web Services (AWS) or Microsoft Azure. Python-based machine learning algorithms are used for data processing, and health status is analyzed using artificial intelligence tools leveraging TensorFlow or PyTorch libraries. Simultaneously, emotional states are analyzed using natural language processing (NLP) techniques with the BERT model. This allows for a comprehensive assessment of the user's health and emotional state.

[0661] Based on the analysis results, the server provides the user with optimized health management advice and suggests ways to book appointments at medical facilities. The user can select an appropriate medical facility from the suggestions and complete the booking through the system.

[0662] For example, if a user enters "I've been feeling constantly tired and stressed lately," the server analyzes this information and suggests that the user may be experiencing chronic fatigue or high stress levels. Based on the analysis, it recommends booking an appointment at a medical facility that offers a more relaxing environment, supporting the user in managing their health quickly and easily.

[0663] An example of a prompt from the generated AI model might be: "Please tell us more about your physical condition and mood today. Please also let us know if you are feeling down or have many worries." This allows users to properly record their condition, and the system can collect data to provide more precise health support.

[0664] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0665] Step 1:

[0666] Users input information about their health and emotional state using a smartphone application. Specifically, they select or describe their physical condition, mood, and stress level. The entered data is structured on the device and prepared for transmission to a cloud server.

[0667] Step 2:

[0668] The terminal sends the entered information to the cloud server. The transmitted data is received by the cloud server and recorded in the database. This makes the input data available throughout the entire system.

[0669] Step 3:

[0670] The server passes the received user data to an artificial intelligence agent. The AI ​​agent uses TensorFlow or PyTorch to apply machine learning algorithms and compare them with a database to analyze the user's health status. The input here is user data, and the output is the analysis result of the health status.

[0671] Step 4:

[0672] In parallel, the server uses the BERT model, a natural language processing engine, to analyze emotional states. It extracts emotional text data from user input and evaluates the degree of stress and anxiety. The input is emotional text data, and the output is the result of the emotional state analysis.

[0673] Step 5:

[0674] The server integrates the analysis results of both health and emotional states to generate comprehensive health management advice. A matching algorithm is used for this generation, resulting in personalized recommendations for healthcare facilities for the user. The input here is the analyzed health and emotional data, and the output is the selection of healthcare facilities.

[0675] Step 6:

[0676] Users receive suggestions for medical facilities from the server via a smartphone app and select from the recommended facilities. Selection can be made using the app's interface.

[0677] Step 7:

[0678] The server completes the online booking process with the medical facility selected by the user. The booking information is sent to the medical facility's system for confirmation. This completes the user's booking of medical services. The input is the user's selection, and the output is the booking confirmation information.

[0679] Step 8:

[0680] The system operates in a closed loop, notifying the user after booking is complete and providing reminders and health management follow-up as needed. In this final step, the system completes user support. Inputs are booking information, and outputs are user notifications and follow-up information.

[0681] 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.

[0682] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0683] 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.

[0684] [Fourth Embodiment]

[0685] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0686] 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.

[0687] 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).

[0688] 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.

[0689] 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.

[0690] 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).

[0691] 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.

[0692] 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.

[0693] 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.

[0694] 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.

[0695] 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.

[0696] 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.

[0697] 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".

[0698] This invention is a system that enables users to easily manage their own health status and, when necessary, quickly arrange for medical consultations at healthcare facilities. Embodiments of this system are described below.

[0699] Users first access the system using their own devices. These devices include a variety of devices such as smartphones, tablets, and personal computers. Users enter their authentication information on the login screen to access the system.

[0700] When a user enters information about their health condition, that information is sent from the device to the server. This information includes specific symptoms and related health data. The server receives this information and uses an artificial intelligence agent to compare it with a database and diagnose possible illnesses.

[0701] Artificial intelligence efficiently analyzes diseases related to the entered symptoms by referencing a vast medical database. The analysis results are sent to the user's terminal via a server, and the user can use this information to decide on future actions.

[0702] Based on the diagnostic results, the system assists the user in selecting an appropriate medical institution. The server considers the user's location and the availability of each medical institution to present the best booking options. Once the user selects their preferred medical institution and date / time, that information is sent to the institution via the server, and the booking is completed.

[0703] For example, if a user enters into the terminal that they frequently experience headaches, the server receives this information, and an artificial intelligence agent analyzes it. After cross-referencing it with the database, a diagnosis is made that stress-related headaches are likely. The server informs the user of this result and presents them with options to book an appointment at a nearby neurology clinic. The user can then choose a suitable clinic from the suggestions and complete the appointment immediately.

[0704] In this way, this system enables users to efficiently and quickly manage their health status and supports them in receiving appropriate medical treatment at a healthcare facility.

[0705] The following describes the processing flow.

[0706] Step 1:

[0707] The user accesses the system using a device and enters their authentication information on the login screen.

[0708] Step 2:

[0709] The terminal sends the entered authentication information to the server. The server performs authentication by comparing it with the information in the database.

[0710] Step 3:

[0711] If the server successfully authenticates the user, it sends a login success response to the terminal, and the user accesses the main screen.

[0712] Step 4:

[0713] The user enters information about their health condition from the main screen. Specific symptoms and related health information are entered in the symptom input form.

[0714] Step 5:

[0715] The terminal sends the entered symptom information back to the server.

[0716] Step 6:

[0717] The server receives the information, calls an artificial intelligence agent, and processes the information.

[0718] Step 7:

[0719] An artificial intelligence agent compares the symptoms against a database, analyzes possible diseases that match the symptoms, and makes a determination.

[0720] Step 8:

[0721] The server prepares the diagnostic results returned by the artificial intelligence agent and generates a list of medical institutions as needed.

[0722] Step 9:

[0723] The server sends the diagnostic results and appointment options for medical institutions to the terminal.

[0724] Step 10:

[0725] The user reviews the received diagnosis results and selects a medical facility and time they wish to book from the displayed list of healthcare providers.

[0726] Step 11:

[0727] The device sends the selected reservation information to the server.

[0728] Step 12:

[0729] The server connects with the medical institution's reservation system and completes the reservation process. The server then sends a reservation confirmation to the terminal.

[0730] Step 13:

[0731] The user receives a reservation confirmation notification on their device, and their health management is completed.

[0732] (Example 1)

[0733] 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".

[0734] In modern society, health management is a crucial issue, but for many people, it is difficult to properly understand their own health status and access medical facilities promptly when necessary amidst their busy daily lives. Furthermore, a lack of available information when deciding which medical facility to visit can result in missing opportunities to receive optimal medical services. To address this challenge, a system is needed that efficiently diagnoses the user's health status and assists in making appointments at appropriate medical facilities.

[0735] 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.

[0736] In this invention, the server includes means for receiving data on the user's health status via the user's information terminal, artificial intelligence means for identifying possible diseases by comparing the received data with an information storage medium, and means for securing reservations at medical service facilities based on the diseases identified by the artificial intelligence means. This enables the user to accurately understand their own health status and quickly make reservations at appropriate medical institutions.

[0737] A "user information terminal" is an electronic device used by a user to input data about their health status and access the system, and specifically includes devices such as smartphones, tablets, and personal computers.

[0738] "Health status data" refers to information about a user's physical condition and symptoms, which forms the basis for analysis by the system. This includes vital data such as body temperature and pulse rate, as well as specific symptoms.

[0739] An "information storage medium" refers to a database that stores past case data and medical knowledge used for analyzing data related to health conditions.

[0740] "Artificial intelligence means" refers to technology that performs an automated intelligent process to analyze received health status data and identify possible diseases by referring to information storage media.

[0741] "Means of securing reservations at medical service facilities" refers to a process that uses artificial intelligence to identify diseases, selects the most suitable medical institution for the user, and ensures that the reservation is made.

[0742] "Reservation confirmation information" refers to detailed information used to notify the user that their reservation at a medical service provider has been successfully completed, allowing the user to confirm the details of their reservation.

[0743] "Location information" refers to data used to identify a user's current location and is considered when providing users with appropriate medical facilities.

[0744] This invention provides a system that allows users to properly manage their health status and efficiently make appointments at the most suitable medical institutions as needed. A specific example of this system is shown below.

[0745] Users access this system using information terminals, specifically smartphones, tablets, or personal computers. First, the terminal displays a login screen, where the user enters their authentication information to log in to the system. After that, the terminal provides an interface for the user to input specific data about their health status. For example, the user can input information such as body temperature and symptoms.

[0746] Once information is entered, the terminal sends that data to the server. This data is then compared against a database of information stored on the server, namely, past case data and medical knowledge. The server uses artificial intelligence to analyze this data and identify possible diseases related to the user's symptoms. This artificial intelligence uses machine learning algorithms to analyze vast amounts of medical data.

[0747] The analysis results are sent from the server to the user's terminal. Furthermore, the server considers the user's location information and the availability of medical service providers to select the most suitable medical facility and propose a reservation. Based on this information, the user selects a preferred medical institution from the presented options and confirms the reservation. The reservation information is sent to the medical institution via the server, and reservation confirmation information is returned to the user's terminal, allowing the user to check the reservation details.

[0748] For example, if a user frequently experiences headaches, they can input "mild headache and fatigue" as symptoms into the system. This information is sent to the server, and the artificial intelligence analysis may identify it as a stress-related headache. Based on this result, the server can suggest a nearby neurology clinic to the user, allowing them to make an appointment immediately.

[0749] An example of a prompt message is an input to a generative AI model in the form of, "Create a program that analyzes the health data entered by the user, identifies possible diseases, and provides appointment information for available medical facilities."

[0750] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0751] Step 1:

[0752] The user accesses an information terminal and logs into the system. The terminal displays a screen for the user to enter their authentication information (user ID and password). The entered authentication information is sent from the terminal to the server. The server receives this information, verifies it against the registered information, and performs authentication. If authentication is successful, the user can proceed to the next step. The data entered is the user's authentication information, and the output is the authentication result (success or failure).

[0753] Step 2:

[0754] After successful authentication, the user enters data about their health status through the terminal. The terminal displays an interface for entering symptoms and vital information. Specifically, the user can enter symptoms such as "body temperature 37.5 degrees" and "mild chest pain." The entered health data is sent from the terminal to the server. The information entered here is health-related, and the output of this step is the raw data received by the server.

[0755] Step 3:

[0756] The server receives health status data transmitted from the terminal and compares it with a database stored on an information storage medium. The server analyzes the input data using artificial intelligence and identifies related diseases. Since the database contains past cases and medical knowledge, the server refers to this to process the data. For example, it assesses the risk of heart disease based on data such as "chest pain." The input is raw health status data, and the output is the result of identifying possible diseases.

[0757] Step 4:

[0758] The analysis results are sent from the server to the user's terminal, which then displays the results to the user. Specifically, a list of possible diseases and their urgency levels are displayed. Based on this information, the user then decides on their next course of action. The input is the disease data identified in the previous step, and the output is a visualized version of that data, providing feedback to the user.

[0759] Step 5:

[0760] Based on the analysis results, the server presents a selection of medical facilities, taking into account the user's location and the availability of medical institutions. This process combines the user's current location with data from partner medical institutions. For example, the server lists "internal medicine clinics available within a 2km radius." The input is the user's location and medical institution availability data, and the output is a list of optimized booking options.

[0761] Step 6:

[0762] The user selects a suitable medical institution from those suggested via the terminal and confirms the reservation for the desired date and time. The terminal sends the reservation information to the server, which then transmits it to the medical institution's system. Finally, the user receives reservation confirmation information and reviews the detailed visit plan. The input is the user's selected reservation details, and the output is the reservation confirmation information.

[0763] (Application Example 1)

[0764] 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".

[0765] In modern society, it is crucial for the elderly to regularly monitor their own health and arrange for prompt visits to medical institutions when necessary. However, it is often difficult for the elderly themselves to do this efficiently. Solving this problem and optimizing health management for the elderly in nursing homes and home care settings is essential.

[0766] 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.

[0767] In this invention, the server includes means for receiving information about the user's health status via an input device, machine learning means for determining possible medical conditions by comparing the received information with data resources, means for making reservations at medical facilities based on the medical conditions determined by the machine learning means, and means for coordinating with private medical professionals or caregivers. This enables elderly individuals to easily manage their own health status and, if necessary, to quickly arrange visits to appropriate medical institutions.

[0768] A "user input device" refers to a device used by an individual to electronically input their own health information. This device includes smartphones, tablets, and personal computers.

[0769] "Health information" refers to data entered by users about their own physical condition, including specific symptoms and other health-related information.

[0770] "Data resources" refer to information sources, including past medical and clinical data, that machine learning tools refer to when determining a patient's condition.

[0771] "Machine learning methods for determining possible medical conditions" refers to algorithms that use received health status information to compare it with past information and estimate the medical condition.

[0772] "Method for making reservations at medical facilities" refers to a function that allows users to make online reservations at the most suitable medical institutions based on their medical condition.

[0773] A "private medical professional" refers to a medical professional who provides health management and medical advice to individual users.

[0774] A "caregiver" refers to a person who plays a role in supporting the daily lives and medical care of the elderly or patients.

[0775] The implementation of this invention begins with a user inputting information about their health status using a device such as a smartphone or tablet. The device has the function of transmitting this information to a server. After receiving the information, the server uses machine learning means to refer to data resources and determine possible medical conditions related to the input information. The software used here includes machine learning libraries such as TensorFlow.

[0776] Based on the assessed medical condition, the server makes a reservation at an appropriate medical facility. The reservation process considers the user's current location and the availability of medical facilities to suggest the most suitable facility and reservation time. The information is then communicated to the user via a medical professional or caregiver, and coordination with the medical facility is facilitated.

[0777] For example, if a user inputs a health condition such as "I frequently wake up at night," the server processes this information and uses machine learning to diagnose the possibility of sleep apnea. Based on the diagnosis, the server suggests booking an appointment at a relaxation clinic near the user's residence.

[0778] An example of a prompt to input into the generating AI model is, "What medical measures should be recommended when an elderly woman reports feeling tired?" This will support specific actions to assist in the health management of the elderly.

[0779] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0780] Step 1:

[0781] Users enter health information using their own devices. This information includes specific symptoms and lifestyle-related data. The entered information is temporarily stored on the device.

[0782] Step 2:

[0783] The terminal transmits the entered information to the server via the network. The server receives this information and stores it in a database. This data is saved for use in subsequent analysis steps.

[0784] Step 3:

[0785] The server analyzes the received health information using a machine learning model based on TensorFlow. Based on the input data, it determines the medical condition related to the symptoms. This process involves data calculations by comparing the data with past data and known patterns.

[0786] Step 4:

[0787] The server identifies possible medical conditions based on the output of the machine learning model. The identified medical conditions are then prepared for use in the next step.

[0788] Step 5:

[0789] The server references the user's current location and the availability of medical facilities to generate the most suitable booking options for their medical condition. This information is retrieved from a database, and an optimization algorithm is used to determine the best option.

[0790] Step 6:

[0791] The server notifies the user of the details of available medical facilities. The user can select their preferred option from the suggested choices and confirm the booking request through their device.

[0792] Step 7:

[0793] After user verification, the server initiates the reservation confirmation process with the selected medical institution. The reservation information is sent to the medical institution, where the reservation is officially confirmed. This allows the user to receive appropriate medical support.

[0794] 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.

[0795] This invention provides a system that offers more precise and personalized health management support by taking into account the user's health and emotional state. This system allows the user to input information about their physical condition and simultaneously recognize their emotional state through an emotion engine, enabling comprehensive diagnosis and appropriate medical appointment scheduling.

[0796] Users access the system using their own devices and input information about their physical condition, symptoms, and emotional state. This includes not only specific symptoms but also their mood and stress levels. The device then sends this information to the server.

[0797] Based on the received information, the server activates an artificial intelligence agent and an emotion engine. The AI ​​agent diagnoses possible illnesses related to the user's symptoms, based on existing medical databases and past case data. Simultaneously, the emotion engine analyzes the input emotional information and evaluates the user's overall psychological state.

[0798] The diagnostic results and emotional assessment results obtained are used together to gain a more detailed understanding of the user's condition. The server recommends booking appointments at appropriate medical institutions based on the diagnostic results, taking the user's emotional state into consideration. For example, if the user shows high levels of stress or anxiety, the server can prioritize recommending medical facilities that focus on relaxation or institutions that provide such care.

[0799] Ultimately, the user receives multiple suggestions from the server, selects the most suitable medical institution, and completes the reservation through their terminal. The server then sends this reservation to the relevant medical institution's system and obtains confirmation, enabling smooth guidance to the user.

[0800] As a concrete example, consider a user who is experiencing chronic fatigue and high stress levels due to recent work burdens. This user enters this information into a terminal and sends it to the system. The server, using an artificial intelligence agent, diagnoses the possibility of chronic fatigue syndrome, and the emotional engine recognizes the high-stress state. Based on this, the server strongly recommends a clinic specializing in stress care, and the user can then complete the reservation.

[0801] This health management support system, which takes user emotions into account, makes it possible to provide more precise and personalized medical care.

[0802] The following describes the processing flow.

[0803] Step 1:

[0804] The user accesses the system using a device and enters their authentication information on the login screen.

[0805] Step 2:

[0806] The terminal sends the entered authentication information to the server. The server performs authentication by comparing it with the database.

[0807] Step 3:

[0808] If the server successfully authenticates, it sends a login authorization to the terminal, and the user proceeds to the main screen.

[0809] Step 4:

[0810] On the main screen, the user enters information about their physical condition and their emotional state.

[0811] Step 5:

[0812] The device sends the entered health and emotional information to the server.

[0813] Step 6:

[0814] The server activates an artificial intelligence agent, which compares the user's health information with a database to diagnose possible symptoms.

[0815] Step 7:

[0816] The server activates the emotion engine, analyzes the emotional information entered by the user, and evaluates their psychological state.

[0817] Step 8:

[0818] The server integrates the diagnostic results from the artificial intelligence agent with the emotional state evaluation results from the emotion engine.

[0819] Step 9:

[0820] Based on the results of server integration, a list is generated to suggest the most suitable medical institutions and treatments to the user.

[0821] Step 10:

[0822] The server sends the terminal with recommended medical facilities and booking options.

[0823] Step 11:

[0824] The user selects their preferred facility and time from the list of medical institutions presented and confirms the reservation.

[0825] Step 12:

[0826] The device sends the selected reservation information to the server.

[0827] Step 13:

[0828] The server connects with the medical institution's system and performs the procedure to confirm the reservation.

[0829] Step 14:

[0830] The server sends a reservation confirmation to the device, and the user receives a notification.

[0831] Step 15:

[0832] The user confirms the reservation completion notification and then terminates their use of the system.

[0833] (Example 2)

[0834] 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".

[0835] Conventional health management systems only consider the user's physical condition when making diagnoses and appointments, making it difficult to provide services that reflect the emotional and psychological state of individual users. Therefore, there is a challenge in providing sufficiently individualized medical services to users with health problems influenced by stress and anxiety.

[0836] 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.

[0837] In this invention, the server includes means for receiving information on the user's health and emotional state via an input device, artificial intelligence means for simultaneously determining the physical and psychological state using a data analysis device based on the received information, and means for selecting and booking medical services that take into account the user's emotional state based on the state determined by the artificial intelligence means. This enables more personalized medical care and service optimization based on a comprehensive health assessment that includes emotional state.

[0838] A "user input device" refers to a device used by a user to input information about their health and emotional state into the system.

[0839] "Health status" refers to information about the user's physical condition, including specific symptoms and changes in their physical condition.

[0840] "Emotional state" refers to information that indicates the user's psychological and emotional state, including mood and stress level.

[0841] A "data analysis device" refers to a technical means for analyzing health and emotional states based on received information.

[0842] "Artificial intelligence means" refers to elements that use machine learning and data processing technologies to determine health and emotional states and propose appropriate services based on those assessments.

[0843] "Means for selecting and booking medical services" refers to a function that selects and books appropriate medical institutions or services based on decisions made by artificial intelligence.

[0844] This invention is a system that provides more personalized health management support by taking into account the user's physical and emotional state. The system aims to allow users to input information about their physical condition and emotions, and then use that information to make diagnoses and appointments at medical institutions.

[0845] Users access this system using their own input devices, such as smartphones or personal computers. Users input information such as their health status, specific symptoms, and emotional information like mood and stress levels. This information is transmitted from the device to the server.

[0846] The server uses specific software to analyze the received data. This analysis utilizes data analysis devices and artificial intelligence (AI) tools. The AI ​​tools use existing medical knowledge bases and historical clinical data to analyze potential medical conditions related to the user's health status, and also evaluate the user's psychological state using emotion recognition algorithms.

[0847] Based on information analyzed from the user's health and emotional state, the server suggests the most suitable medical services. These suggested services take emotional state into consideration, and may prioritize medical institutions that offer relaxation services.

[0848] Ultimately, the user selects the most suitable healthcare provider from the options provided by the server and completes the reservation. The server then sends the reservation information to the healthcare provider's system for confirmation.

[0849] As a concrete example, consider a case where a user inputs chronic fatigue and high stress. Based on this information, the server diagnoses chronic fatigue syndrome, and the emotion engine recognizes high stress. The server then recommends a stress management clinic, allowing the user to receive appropriate support.

[0850] Examples of prompts for a generative AI model:

[0851] "The user has entered their health information and emotional state. Based on this information, please suggest possible medical conditions and recommended medical services."

[0852] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0853] Step 1:

[0854] The user inputs their health and emotional state into the system using their input device. Specifically, they fill in specific symptoms, mood, stress levels, etc., related to their physical condition in an on-screen form. The input in this step consists of the user's health and emotional data. The terminal prepares to send this data to the server.

[0855] Step 2:

[0856] The device sends health and emotional data entered by the user to the server. The HTTPS protocol is used to ensure data security. At this stage, the input is user data, and the output is encrypted data packets. The device receives a response from the server confirming that the data was transmitted correctly.

[0857] Step 3:

[0858] The server uses a data analysis device to analyze the received data. The input data consists of information about health status and emotional information. The server activates artificial intelligence to analyze the received data and identify possible diseases. Furthermore, it uses an emotion recognition algorithm to evaluate the psychological state for the emotional information. The output of this step is the diagnostic result and the psychological evaluation result. The server passes this on to the next step.

[0859] Step 4:

[0860] The server suggests the most suitable medical services to the user based on the diagnostic and psychological assessment results. The input here is the diagnosis and psychological assessment obtained in the previous step. The server uses the data to generate a list of appropriate medical institutions and service providers and prioritizes them. The output at this stage is a list of recommended medical institutions.

[0861] Step 5:

[0862] The user reviews the list of medical institutions suggested by the server on their terminal. The input here is the list of medical institutions sent from the server. The user selects the most suitable medical institution from the list and enters a reservation request. The output at this step is the reservation request to the medical institution selected by the user.

[0863] Step 6:

[0864] The server receives reservation requests from users and transmits reservation information to the selected medical institution's system. The input is the user's reservation request, which the server relays to the medical institution. This process is carried out in conjunction with the medical institution's system and requires confirmation of the reservation. The output at this step is confirmation information that the reservation has been completed.

[0865] Step 7:

[0866] The user receives a confirmation from the server that the reservation is complete. The input here is the reservation confirmation information sent from the medical institution. The terminal notifies the user of the confirmation information, informing them that the reservation has been successfully completed. The output in this step is a notification to the user.

[0867] (Application Example 2)

[0868] 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".

[0869] In today's world, effectively managing the health and emotional states of individual users and scheduling appropriate medical appointments is complex. Therefore, healthcare providers need a data-driven, precise, and personalized health management system to ensure users receive medical care that takes their unique health and emotional conditions into consideration.

[0870] 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.

[0871] In this invention, the server includes means for receiving information on the user's health and emotional state via an input device; artificial intelligence means for comparing the received information with a database and determining possible health states; means for proposing optimized health management based on the health and emotional state determined by the artificial intelligence means; and means for making reservations at medical facilities based on the proposals. This enables precise health management and optimal medical facility reservations for each individual user.

[0872] A "user input device" is a device used by individual users to input their health and emotional states.

[0873] "Health status" refers to information that users enter regarding their physical condition, symptoms, and bodily functions.

[0874] "Emotional state" refers to information that users input regarding their mood, stress level, and psychological state.

[0875] A "database" refers to an information system that stores past medical data and psychological information.

[0876] "Artificial intelligence means" refers to methods that include machine learning algorithms for analyzing received information, comparing it with a database, and determining a person's health status.

[0877] "Means of proposing health management" refers to a function that provides personalized medical advice and suggestions based on judgments obtained by artificial intelligence.

[0878] "Means of making reservations at medical facilities" refers to a system that enables online reservations for proposed medical facilities.

[0879] "Optimization" means suggesting health management and medical facility bookings in the most effective and efficient way for the user.

[0880] To implement this invention, the user first inputs information about their health and emotional state through an input device such as a smartphone. Specifically, this includes information such as their daily physical condition, mood, and stress level. This information is transmitted to a cloud server in real time.

[0881] The server processes incoming data using cloud platforms such as Amazon Web Services (AWS) or Microsoft Azure. Python-based machine learning algorithms are used for data processing, and health status is analyzed using artificial intelligence tools leveraging TensorFlow or PyTorch libraries. Simultaneously, emotional states are analyzed using natural language processing (NLP) techniques with the BERT model. This allows for a comprehensive assessment of the user's health and emotional state.

[0882] Based on the analysis results, the server provides the user with optimized health management advice and suggests ways to book appointments at medical facilities. The user can select an appropriate medical facility from the suggestions and complete the booking through the system.

[0883] For example, if a user enters "I've been feeling constantly tired and stressed lately," the server analyzes this information and suggests that the user may be experiencing chronic fatigue or high stress levels. Based on the analysis, it recommends booking an appointment at a medical facility that offers a more relaxing environment, supporting the user in managing their health quickly and easily.

[0884] An example of a prompt from the generated AI model might be: "Please tell us more about your physical condition and mood today. Please also let us know if you are feeling down or have many worries." This allows users to properly record their condition, and the system can collect data to provide more precise health support.

[0885] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0886] Step 1:

[0887] Users input information about their health and emotional state using a smartphone application. Specifically, they select or describe their physical condition, mood, and stress level. The entered data is structured on the device and prepared for transmission to a cloud server.

[0888] Step 2:

[0889] The terminal sends the entered information to the cloud server. The transmitted data is received by the cloud server and recorded in the database. This makes the input data available throughout the entire system.

[0890] Step 3:

[0891] The server passes the received user data to an artificial intelligence agent. The AI ​​agent uses TensorFlow or PyTorch to apply machine learning algorithms and compare them with a database to analyze the user's health status. The input here is user data, and the output is the analysis result of the health status.

[0892] Step 4:

[0893] In parallel, the server uses the BERT model, a natural language processing engine, to analyze emotional states. It extracts emotional text data from user input and evaluates the degree of stress and anxiety. The input is emotional text data, and the output is the result of the emotional state analysis.

[0894] Step 5:

[0895] The server integrates the analysis results of both health and emotional states to generate comprehensive health management advice. A matching algorithm is used for this generation, resulting in personalized recommendations for healthcare facilities for the user. The input here is the analyzed health and emotional data, and the output is the selection of healthcare facilities.

[0896] Step 6:

[0897] Users receive suggestions for medical facilities from the server via a smartphone app and select from the recommended facilities. Selection can be made using the app's interface.

[0898] Step 7:

[0899] The server completes the online booking process with the medical facility selected by the user. The booking information is sent to the medical facility's system for confirmation. This completes the user's booking of medical services. The input is the user's selection, and the output is the booking confirmation information.

[0900] Step 8:

[0901] The system operates in a closed loop, notifying the user after booking is complete and providing reminders and health management follow-up as needed. In this final step, the system completes user support. Inputs are booking information, and outputs are user notifications and follow-up information.

[0902] 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.

[0903] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0904] In the above embodiment, an example was given in which the 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.

[0905] 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.

[0906] 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.

[0907] 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.

[0908] 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.

[0909] 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.

[0910] 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."

[0911] 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.

[0912] 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.

[0913] 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.

[0914] 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.

[0915] 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.

[0916] 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.

[0917] 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.

[0918] 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.

[0919] 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.

[0920] 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.

[0921] 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.

[0922] 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.

[0923] The following is further disclosed regarding the embodiments described above.

[0924] (Claim 1)

[0925] A means for receiving information about the user's physical condition via an input device,

[0926] An artificial intelligence means that determines possible symptoms by comparing the received information with a database,

[0927] A means for making a reservation at a medical institution based on symptoms determined by the aforementioned artificial intelligence means,

[0928] A system that includes this.

[0929] (Claim 2)

[0930] The system according to claim 1, wherein the artificial intelligence means analyzes symptoms using past case data and medical information.

[0931] (Claim 3)

[0932] The system according to claim 1, wherein the reservation means optimizes the reservation taking into account the user's location information and the availability of medical institutions.

[0933] "Example 1"

[0934] (Claim 1)

[0935] A means of receiving data on the user's health status via the user's information terminal,

[0936] An artificial intelligence means that identifies possible diseases by comparing the received data with an information storage medium,

[0937] A means for securing reservations at medical service facilities based on diseases identified by the aforementioned artificial intelligence means,

[0938] A means of sending reservation confirmation information to the user's information terminal,

[0939] A system that includes this.

[0940] (Claim 2)

[0941] The system according to claim 1, wherein the artificial intelligence means analyzes the patient's condition using past case data and medical knowledge.

[0942] (Claim 3)

[0943] The system according to claim 1, wherein the reservation securing means optimizes the reservation considering the user's location information and the availability of the medical service provider.

[0944] "Application Example 1"

[0945] (Claim 1)

[0946] A means for receiving information about the user's health status via an input device,

[0947] A machine learning means that determines possible medical conditions by comparing the received information with data resources,

[0948] A means for making a reservation at a medical facility based on the medical condition determined by the aforementioned machine learning means,

[0949] Means of collaborating with private medical professionals or caregivers,

[0950] A system that includes this.

[0951] (Claim 2)

[0952] The system according to claim 1, wherein the machine learning means performs an analysis of the patient's condition using past medical data and medical data.

[0953] (Claim 3)

[0954] The system according to claim 1, wherein the reservation means optimizes the reservation taking into account the user's current location and the availability status of the medical facility.

[0955] "Example 2 of combining an emotion engine"

[0956] (Claim 1)

[0957] A means for receiving information about the user's health and emotional state via a user input device,

[0958] An artificial intelligence means that uses a data analysis device to simultaneously determine the physical and psychological state based on the received information,

[0959] A means for selecting and booking medical services that take into account the user's emotional state, based on the state determined by the aforementioned artificial intelligence means,

[0960] A system that includes this.

[0961] (Claim 2)

[0962] The system according to claim 1, characterized in that the artificial intelligence means analyzes the state using past clinical data and a medical knowledge base, and further evaluates the psychological state using an emotion recognition algorithm.

[0963] (Claim 3)

[0964] The system according to claim 1, wherein the reservation means optimizes the reservation while prioritizing specific service providers based on the user's emotional state, and taking into account the user's current location and the availability of service providers.

[0965] "Application example 2 when combining with an emotional engine"

[0966] (Claim 1)

[0967] Means for receiving information on the user's health and emotional state via a user input device,

[0968] An artificial intelligence means that determines possible health conditions by comparing the received information with a database,

[0969] A means for proposing optimized health management based on the health status and emotional state determined by the artificial intelligence means,

[0970] A means of making a reservation at a medical facility based on the above proposal,

[0971] A system that includes this.

[0972] (Claim 2)

[0973] The system according to claim 1, wherein the artificial intelligence means analyzes the health status using past medical data and psychological information.

[0974] (Claim 3)

[0975] The system according to claim 1, wherein the reservation means optimizes the reservation taking into account the user's geographical information and the availability of the proposed medical facility. [Explanation of Symbols]

[0976] 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 for receiving information about the user's physical condition via an input device, An artificial intelligence means that determines possible symptoms by comparing the received information with a database, A means for making a reservation at a medical institution based on symptoms determined by the aforementioned artificial intelligence means, A system that includes this.

2. The system according to claim 1, wherein the artificial intelligence means analyzes symptoms using past case data and medical information.

3. The system according to claim 1, wherein the reservation means optimizes the reservation taking into account the user's location information and the availability of medical institutions.

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