system

Abstract

We provide the system. [Solution] A means for a wearable device to collect the user's vital data, A means of transmitting vital data to a communication terminal, The aforementioned communication terminal provides means for transmitting the collected vital data to a server, A server uses AI to analyze vital data, medical history, and prescription history to generate diagnostic results. A means for notifying the user's communication terminal of the aforementioned diagnostic results, A means by which users can receive a final diagnosis through a video call with a doctor, The means by which user feedback is collected and used by the server to train the AI, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a 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] In modern medical services, there are many problems in order to improve the quality of primary care. For example, processes such as medical appointment, consultation, and diagnosis take a long time, and it is difficult to efficiently utilize medical resources. Also, in general examinations, detailed vital data, past medical history, and prescription history of patients are often not fully utilized. Furthermore, there is no mechanism to effectively use patient feedback to improve diagnostic accuracy. It is required to solve such problems and provide higher-quality primary care.

Means for Solving the Problems

[0005] This invention provides a means for a wearable device to collect a user's vital data and transmit it to a communication terminal. The collected vital data is transmitted to a server via the communication terminal, where an AI analyzes the vital data, medical history, and prescription history to generate a diagnosis. The generated diagnosis is notified to the user's communication terminal, allowing the user to take appropriate action based on it. The invention also includes a function that allows the user to receive a final diagnosis via video call with a doctor if desired. Furthermore, by collecting feedback from the user and using it as training data for the AI, the accuracy of future diagnoses can be improved. This makes it possible to provide more efficient and accurate medical services.

[0006] A "wearable device" is an electronic device worn on the body by the user that collects vital data such as heart rate, body temperature, blood pressure, and steps taken.

[0007] "Vital data" refers to physiological indicators used to show a user's health status, including data such as heart rate, body temperature, blood pressure, and steps taken.

[0008] A "communication terminal" is an electronic device such as a smartphone or tablet used by a user, which has the function of transmitting vital data received from a wearable device to a server.

[0009] A "server" is a computer system that includes a central database and an AI processing system, and is a device that analyzes vital data, medical history, and prescription history to generate diagnostic results and transmit them to a communication terminal.

[0010] "AI" refers to artificial intelligence, a technology that uses specific algorithms to analyze vital data, medical history, and prescription history to evaluate the user's health status and generate diagnostic results.

[0011] "Diagnosis results" refer to the assessment of health status and recommended actions obtained by AI through analysis of vital data and other user information.

[0012] "Video calls with doctors" is a feature that allows users to communicate with doctors in real time using video and audio via a communication device, enabling them to receive direct consultations and ask questions.

[0013] "Feedback" refers to information that users input within the app, such as their reactions to diagnostic results and recommended actions, as well as their recovery status, and then send to the server.

[0014] "Training data" refers to data that AI uses for self-improvement and to enhance diagnostic accuracy, based on feedback information stored on the server. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of the data processing device and smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings.

[0017] First, the language used in the following description will be described.

[0018] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of 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.

[0019] 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.

[0020] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0021] 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).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] This invention is a system that combines a wearable device, a communication terminal, and a server to improve the quality of primary care provided to users. Specific embodiments for implementing this system are described below.

[0037] System Configuration

[0038] Wearable devices

[0039] The user wears a wearable device (e.g., a smartwatch). This wearable device has the function of monitoring vital data such as heart rate, body temperature, blood pressure, and steps in real time, and transmitting that data to a communication terminal.

[0040] Communication terminal

[0041] The user's communication device (e.g., a smartphone) receives and temporarily stores vital data transmitted from the wearable device. The communication device periodically sends this data to a server. It also provides an interface that allows the user to input symptoms and questions and send the data to the server.

[0042] server

[0043] The server receives vital data, medical history, and prescription history transmitted from the communication terminal and stores them in a central database. The AI ​​installed in the server analyzes this data, evaluates the user's health status, and generates a diagnosis. The generated diagnosis is then sent to the communication terminal and notified to the user.

[0044] System processing

[0045] Data collection

[0046] While the user is wearing the wearable device, the device periodically measures vital data such as heart rate, body temperature, blood pressure, and steps taken. This data is transmitted to a communication terminal using wireless communication such as Bluetooth. The communication terminal temporarily stores the received data and periodically sends it to a server.

[0047] Data analysis and generation of diagnostic results

[0048] The server is equipped with AI to analyze vital data sent by users in conjunction with their medical history and prescription history. Based on this data, the AI ​​assesses the user's health status and generates a diagnosis and recommended actions. The diagnosis may include assessments such as the possibility of a cold or the flu, and recommended actions may include purchasing over-the-counter medication, resting, and staying hydrated.

[0049] Notification of results and coordination with doctors

[0050] The diagnostic results generated by the server are sent to the communication terminal and notified to the user. The user can check the diagnostic results and take recommended actions through the app on the communication terminal. If the user wishes to have a more detailed consultation, they can use the communication terminal to schedule a video call with a doctor. The server provides the doctor with the accumulated data and AI-generated diagnostic results, which the doctor uses as a reference to make a final diagnosis.

[0051] Feedback and Learning

[0052] Users input feedback within the app regarding their recovery status after diagnosis and their impressions of the diagnosis. The communication device sends this feedback to the server, which stores it as training data for the AI. This improves the accuracy of the AI's diagnosis, enabling more accurate diagnoses in the future.

[0053] Specific example

[0054] Example 1: Daily health management

[0055] Users wear wearable devices daily to monitor their heart rate and body temperature. A communication terminal periodically sends this data to a server, where an AI analyzes it. For example, if the AI ​​detects an abnormal fluctuation in heart rate, a notification is sent to the communication terminal to alert the user.

[0056] Example 2: Diagnosis when symptoms appear

[0057] When a user experiences a sore throat, they open the app on their communication device and enter their symptoms. The server analyzes past vital data and symptoms and diagnoses a high probability of a cold. The user is then notified of recommended over-the-counter medications and lifestyle advice.

[0058] Example 3: Video call with a doctor

[0059] If a user experiences a worsening of symptoms and wishes to see a doctor, they can book a video call with a doctor using the app on their communication device. At the time of booking, the server provides the doctor with previous diagnostic results and vital data, and during the video call, the doctor makes a final diagnosis while asking additional questions.

[0060] In this way, the present invention realizes a system that provides users with high-quality primary care and improves the efficiency and accuracy of medical services through the cooperation of a wearable device, a communication terminal, and a server.

[0061] The following describes the processing flow.

[0062] Step 1: Data Collection

[0063] The user wears a wearable device (e.g., a smartwatch).

[0064] The device (wearable device) periodically measures vital data such as heart rate, body temperature, blood pressure, and steps taken.

[0065] The device (wearable device) measures vital data and transmits it to a communication terminal (smartphone) via wireless communication such as Bluetooth.

[0066] Step 2: Data transmission

[0067] The device (smartphone) temporarily stores vital data.

[0068] The device (smartphone) periodically (for example, every hour) sends stored vital data to the server.

[0069] Step 3: Appointment scheduling and medical interview

[0070] The user opens the dedicated app on their communication device (smartphone).

[0071] The user fills in their symptoms and questions in the app's input form.

[0072] The device (smartphone) sends the entered information and the latest vital data to the server.

[0073] Step 4: Data Analysis

[0074] The server retrieves the user's past vital data, medical history, and prescription history from a central database.

[0075] The AI ​​on the server analyzes this data and assesses the current health status of the user.

[0076] The server lists potential diagnoses and recommended actions based on the analysis results.

[0077] Step 5: Notification of diagnostic results

[0078] The server sends the analysis results to the communication terminal.

[0079] The device (smartphone) notifies the user of the diagnostic results and recommended actions.

[0080] The user reviews the diagnosis results and takes appropriate action (e.g., buys over-the-counter medication, takes rest).

[0081] Step 6: Collaboration with doctors

[0082] Users schedule video calls with doctors through the app.

[0083] The server provides doctors with AI analysis results and vital data at the scheduled time.

[0084] The doctor will use this data to ask additional questions and make a final diagnosis.

[0085] Step 7: Feedback and Learning

[0086] Users input their recovery status and evaluation after diagnosis within the app.

[0087] The device (smartphone) sends feedback information to the server.

[0088] The server saves the feedback as training data for the AI, which will be used to improve the accuracy of future diagnoses.

[0089] The above outlines the specific steps in the overall system processing flow and describes the actions performed at each step.

[0090] (Example 1)

[0091] 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."

[0092] In modern healthcare settings, there is a demand for monitoring individual health conditions and rapid diagnosis, but conventional systems are insufficient in terms of efficiency and accuracy. Furthermore, there is a lack of mechanisms to properly incorporate user feedback and continuously improve the system's diagnostic accuracy. As a result, users are often unable to receive accurate and timely healthcare services and are forced to rely on card-based medical services.

[0093] 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.

[0094] In this invention, the server includes means for generating diagnostic results using artificial intelligence that analyzes biometric data, medical history, and prescription history; means for presenting the diagnostic results to the individual's communication device; and means for collecting individual feedback and using it to train the artificial intelligence. This allows users to receive accurate and rapid diagnoses, improving the quality of medical services. Furthermore, the system can continuously learn from individual feedback, improving diagnostic accuracy.

[0095] A "biometric information sensing device" is a device used to measure an individual's biological data. Specifically, it refers to a device that has the function of measuring heart rate, body temperature, blood pressure, steps taken, etc.

[0096] A "communication device" is a device that has the function of receiving data transmitted from a biometric information sensing device and transferring it to a data processing device. Examples include mobile devices such as smartphones.

[0097] A "data processing device" is a device that receives biometric data transmitted from a communication device, stores it in a central database, and performs analysis. Typically, cloud servers and data centers fall into this category.

[0098] "Artificial intelligence" refers to algorithms and software used to analyze large amounts of data, recognize patterns, and generate diagnostic results. This primarily includes machine learning models and neural networks.

[0099] "Diagnosis results" refer to the assessment of health status and recommended actions obtained by artificial intelligence analyzing biometric data, medical history, and prescription history. Specifically, this refers to the identification of medical conditions and the suggestion of treatment methods.

[0100] "Video communication" is a means for users to interact with medical professionals remotely, and is usually conducted via video call applications.

[0101] "Feedback" refers to information in which users provide evaluations and opinions on diagnostic results and services. This is used to improve the system and enhance the accuracy of the diagnostics.

[0102] "Recommended actions" refer to behavioral guidelines suggested by artificial intelligence based on the diagnostic results, and include specific health management methods and countermeasures that users should follow.

[0103] "Training data" refers to a dataset used by a data processing device to allow artificial intelligence to learn from feedback and other data, thereby improving its diagnostic accuracy.

[0104] Through the above definitions, we clarify how this system combines its elements to provide users with high-quality primary care.

[0105] This invention is a system that combines a wearable device, a communication terminal, and a server to improve the quality of primary care provided to users. Specific embodiments for implementing the system of this invention are described below.

[0106] System Configuration

[0107] Wearable devices

[0108] The user wears a wearable device (e.g., a smartwatch). This allows for real-time monitoring of vital data such as heart rate, body temperature, blood pressure, and steps taken. The wearable device transmits this data to a communication terminal via wireless communication such as Bluetooth.

[0109] Communication terminal

[0110] The user's communication device (e.g., a smartphone) has the function of receiving and temporarily storing vital data transmitted from a wearable device. The communication device periodically sends this data to a server. It also provides an interface that allows the user to input symptoms and questions and send the data to the server.

[0111] server

[0112] The server receives vital data, medical history, and prescription history transmitted from the communication terminal and stores them in a central database. The server is equipped with AI (artificial intelligence) that analyzes this data to assess the user's health status and generates diagnostic results and recommended actions. The generated diagnostic results are sent to the communication terminal and notified to the user.

[0113] Specific example

[0114] Example 1: Daily health management

[0115] Users wear wearable devices daily to monitor their heart rate and body temperature. A communication terminal periodically sends this data to a server, where an AI analyzes it. For example, if the AI ​​detects an abnormal fluctuation in heart rate, a notification is sent to the communication terminal to alert the user.

[0116] Example of a prompt:

[0117] "An abnormally high heart rate was detected this morning. Please rest immediately and rehydrate."

[0118] Example 2: Diagnosis when symptoms appear

[0119] When a user experiences a sore throat, they open the app on their communication device and enter their symptoms. The server analyzes past vital data and symptoms to diagnose the possibility of a cold. The user is then notified of recommended over-the-counter medications and lifestyle advice.

[0120] Example of a prompt:

[0121] "If you have a sore throat, you may have a cold. Please use the over-the-counter medicine 'Pabron S Gold' and get plenty of rest."

[0122] Example 3: Video call with a doctor

[0123] If a user experiences a worsening of symptoms and wishes to see a doctor, they can book a video call with a doctor using the app on their communication device. At the time of booking, the server provides the doctor with previous diagnostic results and vital data, and during the video call, the doctor makes a final diagnosis while asking additional questions.

[0124] Example of a prompt:

[0125] "If your sore throat persists, please schedule a video consultation with a doctor. Your next appointment is tomorrow at 2 PM."

[0126] In this way, the present invention realizes a system that provides users with high-quality primary care and improves the efficiency and accuracy of medical services through the cooperation of a wearable device, a communication terminal, and a server.

[0127] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0128] Step 1:

[0129] Data collection

[0130] The user wears a wearable device that measures biometric data such as heart rate, body temperature, blood pressure, and steps taken in real time. This data is temporarily stored within the wearable device and transmitted to a communication terminal via wireless communication such as Bluetooth. The input is the user's biometric information, and the output is the biometric data transferred to the communication terminal. Specifically, while the user is jogging, the smartwatch measures their heart rate every minute and transmits it to their smartphone via Bluetooth.

[0131] Step 2:

[0132] Data transmission

[0133] The communication terminal (smartphone) receives biometric data sent from the wearable device and temporarily stores it locally. Furthermore, it periodically sends this data to the server. The input is the biometric data received from the wearable device, and the output is the data sent to the server. Specifically, at 10 PM every day, the smartphone synchronizes with the server and sends all of the day's biometric data at once.

[0134] Step 3:

[0135] Data Analysis

[0136] The server receives biometric data transmitted from communication terminals and stores it in a central database. Furthermore, the AI ​​within the server analyzes this data in combination with medical history and prescription history. The inputs are biometric data, medical history, and prescription history, and the output is the analysis results. Specifically, the AI ​​analyzes a large amount of data and detects whether there are any specific patterns (such as a sudden increase in heart rate).

[0137] Step 4:

[0138] Diagnosis result generation

[0139] The AI ​​on the server generates diagnostic results and recommended actions based on the analysis results. The diagnostic results include a specific assessment of health status and recommended actions. The input is the results of data analysis, and the output is the generated diagnostic results and recommended actions. Specifically, the AI ​​compares heart rate variability with past data and diagnoses that there is a 50% or higher probability of having a cold.

[0140] Step 5:

[0141] Result notification

[0142] The server sends the generated diagnostic results to the communication terminal, which then notifies the user. The input is the diagnostic results, and the output is the notification displayed on the communication terminal. Specifically, the smartphone displays a pop-up notification informing the user, "You may have a cold. Please rest and stay well-hydrated."

[0143] Step 6:

[0144] Collaboration with doctors

[0145] If a user wishes to have a detailed consultation, they can schedule a video call with a doctor using an app on their communication device. The server provides the doctor with the diagnosis results and past vital data. Inputs include the user's appointment request and related data, and output is the data provided to the doctor. Specifically, the user opens the app and schedules a video call with a doctor for 2 PM tomorrow. The server then sends the necessary data to the doctor.

[0146] Step 7:

[0147] Feedback Collection

[0148] Users input feedback into the app regarding their recovery status after diagnosis and their impressions of the system. The communication device sends this feedback to the server, which stores it as training data for the AI. The input is user feedback, and the output is AI training data. Specifically, the user reports within the app that their cold has cleared up and inputs an evaluation of whether the advice provided was helpful.

[0149] In this way, each step is organically linked, resulting in a system that can provide users with high-quality primary care.

[0150] (Application Example 1)

[0151] 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."

[0152] Traditional systems did not provide personalized meal suggestions based on the user's health status, making it difficult for users to easily select meals suitable for their health. Furthermore, the lack of a system that integrated health monitoring and meal suggestions meant users had to manage their health and diet separately. This resulted in inefficient health management and increased user effort.

[0153] 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.

[0154] In this invention, the server includes means for a wearable device to collect the user's vital data, means for transmitting the vital data to a communication terminal, means for the communication terminal to transmit the collected vital data to the server, means for the server to generate a diagnosis result using AI that analyzes the vital data, medical history, and prescription history, means for notifying the user's communication terminal of the diagnosis result, means for the user to receive a final diagnosis through a video call with a doctor, means for collecting user feedback that the server uses to train the AI, means for suggesting an optimal meal menu based on the analysis results, and means for ordering the suggested menu for delivery. This enables real-time monitoring of the user's health status and personalized meal suggestions based on the results. Furthermore, by instantly ordering the suggested meal for delivery, the efficiency of the user's health management and meal management is improved, and the effort involved is significantly reduced.

[0155] A "wearable device" is a small electronic device that a user wears to collect vital data such as heart rate, body temperature, and blood pressure in real time.

[0156] "Vital data" refers to data that indicates the user's biological state, such as heart rate, body temperature, blood pressure, and steps taken.

[0157] A "communication terminal" is an electronic device used to receive data transmitted from wearable devices such as smartphones and tablets, and to transmit that data to a server.

[0158] A "server" is a high-performance computer system that receives, stores, and analyzes data transmitted from communication terminals.

[0159] "AI" refers to artificial intelligence technology that analyzes data on a server and generates diagnostic results, meal menus, and other similar information.

[0160] "Diagnosis results" refer to information that shows an evaluation of the user's health status based on the results of AI analysis.

[0161] A "meal menu" refers to a combination of appropriate meals suggested by AI based on the user's health status and vital data.

[0162] "Delivery ordering" refers to the act of requesting a meal from a suggested menu via an information terminal and having the meal delivered.

[0163] "Feedback" refers to information that users report through the application, including their impressions of the system's diagnostic results and suggestions, as well as their actual actions.

[0164] "Analysis results" refer to the overall evaluation and diagnosis performed by the AI ​​based on vital data and other relevant data.

[0165] This invention is a system that combines a wearable device, a communication terminal, and a server to improve the quality of primary care provided to users. The system of this invention is specifically implemented as follows.

[0166] System Configuration

[0167] Wearable devices

[0168] The user wears a wearable device (e.g., a smartwatch). This wearable device monitors vital data such as heart rate, body temperature, blood pressure, and steps taken in real time and transmits it to a communication terminal via wireless communication such as Bluetooth.

[0169] Communication terminal

[0170] The user's communication device (e.g., a smartphone) receives and temporarily stores vital data transmitted from the wearable device. The communication device periodically sends this data to the server. It also provides an interface that allows the user to input questions about symptoms and diet and send the data to the server.

[0171] server

[0172] The server receives vital data, medical history, and prescription history transmitted from the communication terminal and stores them in a central database. The AI ​​installed in the server analyzes this data, evaluates the user's health status, and generates a diagnosis. It also has a function to suggest a personalized and optimal meal plan based on the generated diagnosis. The suggested menu is notified to the communication terminal, and the user can then place a delivery order directly.

[0173] System processing

[0174] Data collection

[0175] While the user is wearing the wearable device, the device periodically measures vital data such as heart rate, body temperature, blood pressure, and steps taken. This data is transmitted to a communication terminal using wireless communication such as Bluetooth. The communication terminal temporarily stores the received data and periodically sends it to a server.

[0176] Data analysis and generation of diagnostic results

[0177] The server is equipped with AI to analyze vital data sent by users in conjunction with their medical history and prescription history. Based on this data, the AI ​​assesses the user's health status and generates a diagnosis and recommended actions. The diagnosis may include assessments such as the possibility of a cold or the flu, and recommended actions may include purchasing over-the-counter medication, resting, and staying hydrated.

[0178] Suggestions for meal menus

[0179] Based on the analysis results, the AI ​​suggests a meal plan best suited to the user's health condition. The suggested menu is generated considering calories, nutritional balance, and allergy information, and is notified to the user's communication device. The user can then directly order the meal for delivery.

[0180] Notification of results and coordination with doctors

[0181] The diagnostic results and meal plans generated by the server are sent to the communication terminal and notified to the user. The user can check the diagnostic results and meal plans through the app on the communication terminal and take the recommended actions. If the user wishes to have a more detailed consultation, they can use the communication terminal to schedule a video call with a doctor. The server provides the doctor with the accumulated data and AI-generated diagnostic results, and the doctor uses this information to make a final diagnosis.

[0182] Feedback and Learning

[0183] Users input information within the app regarding their recovery status after diagnosis, their impressions of the diagnosis, and feedback on the suggested meal menu. The communication device sends this feedback to the server, which stores it as training data for the AI. This improves the accuracy of the AI's diagnosis and meal suggestions, enabling more accurate diagnoses and suggestions in the future.

[0184] Specific example

[0185] Example 1: Daily health management

[0186] Users wear wearable devices daily to monitor their heart rate and body temperature. A communication terminal periodically sends this data to a server, where an AI analyzes it. For example, if the AI ​​detects an abnormal fluctuation in heart rate, a notification is sent to the communication terminal to alert the user.

[0187] Example 2: Diagnosis when symptoms appear

[0188] When a user experiences a sore throat, they open the app on their communication device and enter their symptoms. The server analyzes their past vital data and symptoms and diagnoses a high probability of a cold. The user is then notified with information on recommended over-the-counter medications and lifestyle advice.

[0189] Example 3: Suggested meal menus and delivery orders

[0190] If a user wants to receive meal suggestions based on their health status, they send their current vital data to a server via a communication device. AI analyzes this data and suggests an optimal meal menu that takes into account calories, nutritional balance, and allergy information. The user can then order the suggested menu for delivery, improving the efficiency of their health management.

[0191] Example of a prompt

[0192] "Please suggest the optimal diet based on your health condition. Generate an AI module that analyzes your heart rate, body temperature, and blood pressure data from the past week and proposes an appropriate meal plan."

[0193] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0194] Step 1:

[0195] The user wears a wearable device (e.g., a smartwatch) and begins measuring vital data (heart rate, body temperature, blood pressure, steps, etc.). The input is the user's biometric information, and the output is real-time measured vital data. The wearable device transmits this data to a communication terminal via Bluetooth.

[0196] Step 2:

[0197] A communication terminal (e.g., a smartphone) receives vital data transmitted from a wearable device. The input is vital data transmitted via Bluetooth, and the output is vital data temporarily stored on the communication terminal. This allows for temporary storage and processing of the data.

[0198] Step 3:

[0199] The communication terminal periodically sends temporarily stored vital data to the server. The input is the temporarily stored vital data, and the output is the data sent to the server. Specifically, the data is uploaded from the communication terminal to the server via the internet.

[0200] Step 4:

[0201] The server stores the received vital data in a central database, which is then analyzed by AI along with the user's medical history and prescription history. The input consists of vital data, medical history, and prescription history sent to the server, while the output is the analysis results. Specifically, the AI ​​model evaluates the user's health status based on this data.

[0202] Step 5:

[0203] The server generates diagnostic results and recommended actions based on AI analysis. The input is the AI ​​analysis results, and the output is the generated diagnostic results and recommended actions. The server uses prompt statements to generate the diagnostic results and inputs them into the generating AI model.

[0204] Step 6:

[0205] The server sends the diagnostic results and recommended actions to the communication terminal. The input is the generated diagnostic results and recommended actions, and the output is the result notified to the communication terminal. Specifically, a push notification is sent to the application on the communication terminal.

[0206] Step 7:

[0207] The application on the communication terminal displays diagnostic results and recommended actions to the user. The input is notification data sent from the server, and the output is information displayed on the user interface. Specifically, the user checks the diagnostic results and recommended actions on the app screen.

[0208] Step 8:

[0209] When a user utilizes the meal suggestion feature, they send their current vital data to the server using a communication terminal. The input is the current vital data, and the output is the data sent to the server.

[0210] Step 9:

[0211] The server analyzes vital data and suggests an optimal meal plan based on the user's health status. The input is the user's vital data, and the output is the suggested meal plan. Based on the analysis results, the menu is generated considering calories, nutritional balance, and allergy information.

[0212] Step 10:

[0213] The suggested meal menu is notified to the communication terminal, and the user places a delivery order directly through the app. The input is the suggested meal menu, and the output is a delivery order request. Specifically, the order data is sent from the application on the communication terminal to the partner delivery service.

[0214] Step 11:

[0215] The user inputs feedback on the diagnostic results and dietary suggestions into an application on a communication terminal. The input is the user's feedback, and the output is the feedback data sent to the server.

[0216] Step 12:

[0217] The server stores user feedback as AI training data, which will be used to improve diagnostic accuracy and meal recommendation accuracy in the future. The input is feedback data, and the output is updated training data for the AI ​​model. Specifically, by incorporating feedback data into the AI ​​model's learning algorithm, the accuracy of diagnoses and recommendations is improved.

[0218] 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.

[0219] This invention is a system that combines a wearable device, a communication terminal, a server, and an emotion engine to improve the quality of primary care provided to users. Specific embodiments for implementing the system of this invention are described below.

[0220] System Configuration

[0221] Wearable devices

[0222] The user wears a wearable device (e.g., a smartwatch). This wearable device has the function of monitoring vital data such as heart rate, body temperature, blood pressure, and steps in real time, and transmitting that data to a communication terminal.

[0223] Communication terminal

[0224] The user's communication device (e.g., a smartphone) receives and temporarily stores vital data transmitted from the wearable device. The communication device periodically sends this data to a server. It also provides an interface that allows the user to input symptoms and questions and send the data to the server.

[0225] server

[0226] The server receives vital data, medical history, and prescription history transmitted from the communication terminal and stores them in a central database. The AI ​​installed in the server analyzes this data, evaluates the user's health status, and generates a diagnosis. The generated diagnosis is then sent to the communication terminal and notified to the user.

[0227] Emotional Engine

[0228] The emotion engine is a device that recognizes emotions from the user's voice and facial expressions. The communication terminal uses the user's voice input and camera images to have the emotion engine perform emotional analysis. The emotional data obtained from the emotion engine is sent to a server and used to generate diagnostic results by AI.

[0229] System processing

[0230] Data collection

[0231] While the user is wearing the wearable device, the device periodically measures vital data such as heart rate, body temperature, blood pressure, and steps taken. This data is transmitted to a communication terminal using wireless communication such as Bluetooth. The communication terminal temporarily stores the received data and periodically sends it to a server.

[0232] Data analysis and generation of diagnostic results

[0233] The server is equipped with AI to analyze vital data sent by users in conjunction with their medical history and prescription history. Based on this data, the AI ​​assesses the user's health status and generates a diagnosis and recommended actions. The diagnosis may include assessments such as the possibility of a cold or the flu, and recommended actions may include purchasing over-the-counter medication, resting, and staying hydrated.

[0234] Improving the accuracy of emotion analysis and diagnosis

[0235] The communication terminal obtains analysis results from the emotion engine based on the voice input and camera images provided by the user. This emotion data, which indicates the user's stress level and emotional state, is sent to the server. The AI ​​analyzes this emotion data together with vital data to generate more accurate diagnostic results.

[0236] Notification of results and coordination with doctors

[0237] The diagnostic results generated by the server are sent to the communication terminal and notified to the user. The user can check the diagnostic results and take recommended actions through the app on the communication terminal. If the user wishes to have a more detailed consultation, they can use the communication terminal to schedule a video call with a doctor. The server provides the doctor with the accumulated data and AI-generated diagnostic results, which the doctor uses as a reference to make a final diagnosis.

[0238] Feedback and Learning

[0239] Users input feedback within the app regarding their recovery status after diagnosis and their impressions of the diagnosis. The communication device sends this feedback to the server, which stores it as training data for the AI. This improves the accuracy of the AI's diagnosis, enabling more accurate diagnoses in the future.

[0240] Specific example

[0241] Example 1: Daily health management

[0242] Users wear wearable devices daily to monitor their heart rate and body temperature. A communication terminal periodically sends this data to a server, where an AI analyzes it. For example, if the AI ​​detects an abnormal fluctuation in heart rate, a notification is sent to the communication terminal to alert the user. Additionally, an emotion engine recognizes the user's stress level from their voice and facial expressions, and this is reflected in the diagnostic results.

[0243] Example 2: Diagnosis when symptoms appear

[0244] When a user experiences a sore throat, they open the app on their communication device and enter their symptoms. The server analyzes past vital data and symptoms and diagnoses a high probability of a cold. The user is then notified of recommended over-the-counter medications and lifestyle advice. Additionally, if the user expresses pain or anxiety through voice or facial expressions, an emotion engine analyzes this data, which the AI ​​uses to improve the accuracy of the diagnosis.

[0245] Example 3: Video call with a doctor

[0246] If a user experiences a worsening of symptoms and wishes to see a doctor, they can book a video call with a doctor using the app on their communication device. At the time of booking, the server provides the doctor with previous diagnostic results, vital data, and emotional data, and during the video call, the doctor makes a final diagnosis while asking additional questions.

[0247] In this way, the present invention realizes a system that provides users with high-quality primary care and improves the efficiency and accuracy of medical services through the cooperation of a wearable device, a communication terminal, a server, and an emotion engine.

[0248] The following describes the processing flow.

[0249] Step 1: Data collection using wearable devices

[0250] The user wears a wearable device (e.g., a smartwatch).

[0251] The device (wearable device) periodically measures the user's vital data, such as heart rate, body temperature, blood pressure, and steps taken.

[0252] The device (wearable device) measures vital data and transmits it to the communication terminal (smartphone) using Bluetooth or similar technologies.

[0253] Step 2: Send vital data

[0254] The terminal (smartphone) temporarily stores vital data received from the wearable device.

[0255] The device (smartphone) periodically sends stored vital data to the server (for example, every hour).

[0256] Step 3: Appointment scheduling and medical interview

[0257] The user opens the dedicated app on their communication device (smartphone).

[0258] The user enters their symptoms and questions into the appointment booking form.

[0259] The device (smartphone) sends the entered information and the latest vital data to the server.

[0260] Step 4: Data Analysis

[0261] The server retrieves the user's past vital data, medical history, and prescription history from a central database.

[0262] Based on data acquired by the AI ​​on the server, the system evaluates the user's health status and generates diagnostic results and recommended actions.

[0263] Step 5: Generating diagnostic results

[0264] The emotion engine analyzes the user's emotions from their voice and facial expressions.

[0265] The device (smartphone) retrieves analysis results from the emotion engine and sends them to the server.

[0266] The AI ​​on the server integrates emotional data with vital data to improve diagnostic accuracy.

[0267] Step 6: Notification of diagnostic results

[0268] The server generates the final diagnostic results and recommended actions, and sends them to the communication terminal.

[0269] The device (smartphone) notifies the user of the diagnostic results and recommended actions.

[0270] Users can check their diagnostic results through an app on their communication device and take necessary actions (e.g., purchase over-the-counter medication, ensure they get enough rest).

[0271] Step 7: Video call coordination with the doctor

[0272] Users schedule video calls with doctors through apps on their communication devices.

[0273] The server provides the doctor with previous diagnostic results, vital data, and emotional analysis data at the scheduled time.

[0274] The doctor will conduct a video call to ask additional questions and make a final diagnosis.

[0275] Step 8: Feedback and Learning

[0276] Users enter their recovery status and evaluation after diagnosis into a feedback form within the app.

[0277] The device (smartphone) sends feedback to the server.

[0278] The server saves the feedback as training data for the AI ​​and uses it to improve diagnostic accuracy in the future.

[0279] The above outlines the specific processing steps of the system combining the emotion engine, and the specific actions performed by each entity within that process.

[0280] (Example 2)

[0281] 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".

[0282] In the conventional health management system, since diagnosis is performed only depending on the user's vital data, psychological factors such as emotions and stress levels are not considered. As a result, the accuracy of diagnosis may decrease, and it has been difficult to provide appropriate medical advice to the user. In addition, the mechanism for efficiently collecting feedback and utilizing it for AI learning was insufficient. Thereby, it has been difficult to improve the diagnostic accuracy of AI.

[0283] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following respective means. In this invention, the server includes: means for generating a diagnosis result using an AI that analyzes vital data, past history, prescription history, and emotion data; means for the emotion engine to recognize emotions from the user's voice and expression and transmit it to the server as data; means for the server to save the user's feedback as learning data. Thereby, it becomes possible to perform multi-faceted data analysis including the user's psychological factors, and it becomes possible to improve the diagnostic accuracy.

[0284] A "wearable device" is a device that a user wears and collects vital data such as heart rate, body temperature, blood pressure, and number of steps.

[0285] A "communication terminal" is a device for receiving vital data transmitted from a wearable device, temporarily storing it, and transmitting it to a server.

[0286] A "server" is a device that receives, analyzes vital data, past history, prescription history, and emotion data transmitted from a communication terminal, and generates a diagnosis result.

[0287] "AI" is an artificial intelligence technology for analyzing vital data, past history, prescription history, and emotion data, and generates a diagnosis result.

[0288] "Diagnosis results" refer to an assessment of the user's health status and recommended actions, generated based on the analysis of vital data, medical history, prescription history, and emotional data.

[0289] An "emotion engine" is a device that recognizes emotions from a user's voice and facial expressions and analyzes that data.

[0290] "Feedback" refers to information that users input regarding their recovery status after diagnosis and their impressions of the diagnosis.

[0291] A "video call" is a conversation between a user and a doctor face-to-face via a communication device, using both audio and video.

[0292] "Recommended actions" refer to advice and instructions on what actions to take that are presented to the user based on the diagnostic results.

[0293] Modes for carrying out the invention

[0294] This invention is a system that provides high-quality primary care to users by linking a wearable device, a communication terminal, a server, and an emotion engine. Specific embodiments of this system will be described below.

[0295] Hardware and software to use

[0296] Wearable devices

[0297] The user wears a wearable device (e.g., a smartwatch). This device monitors vital data such as heart rate, body temperature, blood pressure, and steps taken in real time and transmits the data to a communication terminal using Bluetooth.

[0298] Communication terminal

[0299] A communication terminal (e.g., a smartphone) owned by a user receives vital data transmitted from a wearable device and temporarily stores it. The communication terminal periodically transmits this data to a server. Also, through a dedicated application, it provides a function that allows the user to input symptoms and questions and transmit them to the server.

[0300] Server

[0301] The server receives vital data, past medical history, prescription history, and emotional data transmitted from the communication terminal and stores them in a central database. An AI model using TENSORFLOW (registered trademark), PyTorch, etc. is installed in the server, and this is used to comprehensively analyze the data and generate a diagnosis result.

[0302] Emotion engine

[0303] The emotion engine is a device for recognizing emotions from the user's voice and expression. The communication terminal sends data to the emotion engine based on voice input and camera video and receives the analysis result. This result is transmitted to the server and integrated into the AI analysis.

[0304] Specific examples and prompt sentences

[0305] Example 1: Daily health management

[0306] The user wears a wearable device daily, and thereby the heart rate and body temperature are monitored. The communication terminal periodically transmits this data to the server, and the server's AI analyzes the data. For example, when abnormal fluctuations in the heart rate are detected, a notification is sent to the communication terminal to alert the user. Also, the emotion engine recognizes the stress state from the user's voice and expression and reflects this in the diagnosis result.

[0307] Example of prompt sentence:

[0308] "Please analyze my heart rate monitoring results and let me know if there are any health problems. Also, please take my recent emotional state into consideration."

[0309] Example 2: Diagnosis when symptoms appear

[0310] When a user experiences a sore throat, they open an app on their communication device and enter their symptoms. The server analyzes past vital data and symptoms and diagnoses a high probability of a cold. The user is then notified of recommended over-the-counter medications and lifestyle advice. Additionally, if the user expresses pain or anxiety through voice or facial expressions, an emotion engine analyzes this data, which the AI ​​uses to improve the accuracy of the diagnosis.

[0311] Example of a prompt:

[0312] "I have a sore throat. Please provide a diagnosis and recommended actions based on my past health and emotional data."

[0313] Example 3: Video call with a doctor

[0314] If a user experiences a worsening of symptoms and wishes to see a doctor, they can book a video call with a doctor using the app on their communication device. At the time of booking, the server provides the doctor with previous diagnostic results, vital data, and emotional data, and during the video call, the doctor makes a final diagnosis while asking additional questions.

[0315] Example of a prompt:

[0316] "I would like to schedule a video call with a doctor. I would like to send my previous diagnostic data to the doctor and request an additional diagnosis during the video call."

[0317] This invention enables the realization of a system that provides high-quality primary care to users through the cooperation of a wearable device, a communication terminal, a server, and an emotion engine, thereby improving the efficiency and accuracy of medical services.

[0318] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0319] Step 1:

[0320] The user wears a wearable device that collects vital data such as heart rate, body temperature, blood pressure, and steps taken. Specifically, the device's sensors measure this data every minute and temporarily store it in its internal memory. The input is the user's vital data, and the output is the vital data stored in the device.

[0321] Step 2:

[0322] The wearable device uses Bluetooth to transmit collected vital data to a communication terminal. Specifically, the wearable device's data transmission function is activated, and a Bluetooth connection is established. The input is the vital data stored on the device, and the output is the vital data transmitted to the communication terminal.

[0323] Step 3:

[0324] The communication terminal receives vital data transmitted from the wearable device and stores it temporarily. This data is later sent to a server for analysis. Specifically, the communication terminal's application runs in the background, receiving data and saving it to its internal storage. The input is the transmitted vital data, and the output is the vital data stored in the communication terminal.

[0325] Step 4:

[0326] The communication terminal periodically sends stored vital data to the server. For example, it is set up so that data is sent in a batch every night. Specifically, the communication terminal's application executes a process to send data to the server at a set time. The input is the vital data stored on the communication terminal, and the output is the vital data sent to the server.

[0327] Step 5:

[0328] The server receives vital data transmitted from communication terminals and stores it in a central database. Specifically, the server's API endpoint receives a request and executes the process of saving the data to the database. The input is the vital data sent to the server, and the output is the data stored in the database.

[0329] Step 6:

[0330] The AI ​​installed on the server analyzes vital data, medical history, prescription history, and sentiment data. Specifically, the server runs machine learning models trained using TensorFlow or PyTorch to assess health status and generate diagnostic results. The input is vital data, medical history, prescription history, and sentiment data stored in a database, and the output is the generated diagnostic result.

[0331] Step 7:

[0332] The server sends the generated diagnostic results to the communication terminal and notifies the user. Specifically, the server sends a notification containing the diagnostic results to the application on the communication terminal, and the application displays a push notification. The input is the generated diagnostic results, and the output is the push notification on the communication terminal.

[0333] Step 8:

[0334] Users can view their diagnostic results and take recommended actions through an app on their communication device. If they wish to have a more detailed consultation, they can use the app to schedule a video call with a doctor. Specifically, the app on the communication device displays the diagnostic results to the user and provides a video call scheduling function. The input is the diagnostic results, and the output is the video call scheduling.

[0335] Step 9:

[0336] The emotion engine recognizes emotions from the user's voice and facial expressions and analyzes that data. Specifically, the communication terminal's application uses the microphone and camera to capture voice and facial expressions and sends them to the emotion engine. The engine then sends the analysis results to the server. The input is voice and facial expression data, and the output is the analyzed emotion data.

[0337] Step 10:

[0338] Users input feedback within the app regarding their recovery status after diagnosis and their impressions of the diagnosis. The communication device sends this feedback to the server, which stores it as training data for the AI. Specifically, the app on the communication device receives feedback from the user and sends the data to the server. The input is the user's feedback, and the output is the feedback data stored on the server.

[0339] This allows the system to perform multifaceted data analysis, provide users with highly accurate diagnostic results, and improve the efficiency and accuracy of medical services.

[0340] (Application Example 2)

[0341] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0342] Modern health management requires the collection and analysis of daily vital data to understand individual health conditions in real time and provide appropriate medical care and lifestyle guidance. However, previous systems often evaluated health conditions without considering the user's emotional state, resulting in inaccurate diagnoses and recommended behaviors. Furthermore, there was a lack of dietary suggestions based on the user's health condition, making it difficult to provide meals that met individual needs.

[0343] 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. In this invention, the server includes means for generating diagnostic results using AI that analyzes vital data, medical history, and prescription history; means for analyzing the user's emotional data using an emotion engine and reflecting it in the diagnostic results; and means for recommending the optimal diet based on health status and emotional data. This generates highly accurate diagnostic results that take into account the user's emotional state, and further enables dietary suggestions tailored to the individual's health condition.

[0344] A "wearable device" is a device worn by a user to collect vital data such as heart rate, body temperature, blood pressure, and steps taken.

[0345] A "communication terminal" is a device that has the function of receiving and storing vital data transmitted from a wearable device and sending it to a server.

[0346] A "server" is a device that stores vital data, medical history, and prescription history transmitted from communication terminals in a central database, analyzes it, and generates diagnostic results.

[0347] "AI" refers to an algorithm installed on a server that analyzes vital data, medical history, and prescription history to evaluate health status and generate diagnostic results.

[0348] "Diagnosis results" refer to an assessment of the user's health status generated by AI based on vital data and other information.

[0349] An "emotion engine" is a device that recognizes and analyzes emotions from a user's voice and facial expressions.

[0350] "Emotional data" refers to data that indicates the user's stress level and emotional state, analyzed by the emotion engine.

[0351] "Recommended actions" refer to information indicating the actions a user should take based on their diagnostic results.

[0352] "Health status" refers to the user's overall health condition, evaluated based on vital data such as heart rate, body temperature, blood pressure, and steps taken.

[0353] "Meal suggestions" refer to information that proposes the optimal meal plan based on the user's health status and emotional data.

[0354] This invention is a system that combines a wearable device, a communication terminal, a server, and an emotion engine to achieve more accurate user health management and dietary recommendations. Specific embodiments for carrying out the invention are described below.

[0355] System Configuration

[0356] Wearable devices

[0357] The user wears a wearable device (e.g., a smartwatch). This device measures vital data such as heart rate, body temperature, blood pressure, and steps in real time and transmits it to a communication terminal using wireless communication such as Bluetooth or Wi-Fi.

[0358] Communication terminal

[0359] The user's communication device (e.g., a smartphone) receives and temporarily stores vital data transmitted from the wearable device. Furthermore, it has the functionality to collect the user's voice and facial expressions using a camera and microphone and transmit them to an emotion engine. The communication device periodically sends this data to a server.

[0360] server

[0361] The server receives vital data, emotional data, medical history, and prescription history transmitted from the communication terminal and stores them in a central database. Furthermore, the server is equipped with AI, which analyzes this data to assess the user's health status and generate a diagnosis. The diagnosis is then communicated to the user via the communication terminal. The server also suggests an optimal diet based on the assessment.

[0362] Emotional Engine

[0363] The emotion engine recognizes emotions from the user's voice and facial expressions and generates data indicating their emotional state. The communication terminal transmits voice input and camera footage to the emotion engine for emotion analysis. The emotion data is sent to a server and used to generate diagnostic results.

[0364] Program processing

[0365] Data collection

[0366] A wearable device periodically measures the user's vital data, such as heart rate, body temperature, blood pressure, and steps taken, and transmits this data to a communication terminal. The communication terminal temporarily stores the vital data and then transmits it to a server.

[0367] Emotion analysis

[0368] The communication terminal uses an emotion engine to analyze the user's voice input and camera footage. The emotion engine analyzes this data and generates data indicating the user's emotional state. This emotional data is sent to a server and used to assess their health status.

[0369] Data analysis and generation of diagnostic results

[0370] The server is equipped with AI to analyze vital data, medical history, prescription history, and emotional data. Based on this data, the AI ​​assesses the user's health status and generates a diagnosis, recommended actions, and optimal dietary suggestions. This information is transmitted to the communication terminal and notified to the user.

[0371] Results notification and meal suggestions

[0372] The diagnostic results and meal suggestions generated by the server are sent to the communication terminal and notified to the user. The user can check the diagnostic results and meal suggestions through the app on the communication terminal and take the recommended actions.

[0373] Collaboration with doctors

[0374] If a user wishes to have a more detailed consultation, they can book a video call with a doctor using their communication device. At the time of booking, the server provides the doctor with previous diagnostic results, vital data, and emotional data, which will be used for the final diagnosis.

[0375] Specific example

[0376] For example, a user wears a wearable device daily to monitor their heart rate and body temperature. The communication terminal periodically sends this data to a server, where the server's AI analyzes it. If the AI ​​detects an abnormal fluctuation in heart rate, a notification is sent to the communication terminal to alert the user. In addition, an emotion engine recognizes the user's stress level from their voice and facial expressions, and this is reflected in the diagnostic results.

[0377] Example of a prompt

[0378] In the food delivery app you develop, please consider the following data to suggest the best meal options to users.

[0379] Input data:

[0380] Heart rate: 80

[0381] Body temperature: 36.5°C

[0382] Blood pressure: 120 / 80

[0383] Steps: 5000

[0384] Emotional state: Stress

[0385] output:

[0386] Please suggest meals that can help reduce the stress users are experiencing. The menu should be nutritionally balanced and include relaxing elements.

[0387] example:

[0388] Herbal tea

[0389] A light salad or a late-night snack

[0390] Low-sugar smoothies

[0391] The above describes a specific embodiment for carrying out the present invention. This enables highly accurate health management and dietary recommendations that take into account the user's emotional state.

[0392] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0393] Step 1:

[0394] The user wears a wearable device that collects vital data such as heart rate, body temperature, blood pressure, and steps taken. The wearable device transmits this data to a communication terminal using wireless communication such as Bluetooth.

[0395] Input: Vital data (heart rate, body temperature, blood pressure, steps)

[0396] Output: Data transmission to communication terminal

[0397] Specific operation: The wearable device measures vital data in real time and transmits the data to a communication terminal using Bluetooth.

[0398] Step 2:

[0399] The communication terminal receives and temporarily stores vital data transmitted from the wearable device. Simultaneously, it collects user emotion data using audio and camera footage.

[0400] Input: Vital data from wearable devices, user voice data, camera footage

[0401] Output: Temporary storage of data to send to the server

[0402] Specific operation: The communication terminal receives vital data via Bluetooth, and uses the microphone and camera to collect and temporarily store the user's voice and facial expression data.

[0403] Step 3:

[0404] The communication terminal periodically sends temporarily stored vital data and emotional data to the server.

[0405] Input: Temporarily stored vital data and emotional data

[0406] Output: Sending data to the server

[0407] Specific operation: The communication device uploads data to the server using Wi-Fi or mobile data communication.

[0408] Step 4:

[0409] The server uses AI to analyze vital and emotional data it receives. The AI ​​also analyzes medical history and prescription history to generate a diagnosis.

[0410] Input: Vital data, emotional data, medical history, prescription history

[0411] Output: Diagnostic results

[0412] Specific operation: The server uses an AI algorithm to analyze data, evaluate the user's health status, and generate a diagnostic result.

[0413] Step 5:

[0414] The server generates diagnostic results, which are then sent to the communication terminal to notify the user. Simultaneously, dietary suggestions based on the user's health status are also provided.

[0415] Input: Diagnostic result

[0416] Output: Sending diagnostic results and meal suggestions to the communication terminal.

[0417] Specific operation: The server generates diagnostic results and meal suggestions, and sends notifications to the communication terminal.

[0418] Step 6:

[0419] Users can view their diagnostic results and meal suggestions via a communication device. If necessary, they can also place orders based on the meal suggestions.

[0420] Input: Diagnostic results and dietary suggestions sent to the communication terminal.

[0421] Output: User confirmation and order

[0422] Specific operation: The user checks the notification on the app on their communication device and places an order based on the meal suggestion if necessary.

[0423] Step 7:

[0424] Users input feedback on their diagnostic results and meal suggestions through the app and send it to the server. The server stores this feedback as training data for the AI.

[0425] Input: User feedback

[0426] Output: AI training data

[0427] Specific operation: The user enters feedback in the app, and the server receives it and saves it as training data.

[0428] The above outlines the specific processing steps of the system based on the present invention. This enables user health management and personalized meal suggestions.

[0429] 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.

[0430] 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.

[0431] 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.

[0432] [Second Embodiment]

[0433] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0434] 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.

[0435] 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).

[0436] 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.

[0437] 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.

[0438] 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).

[0439] 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.

[0440] 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.

[0441] 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.

[0442] 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.

[0443] 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.

[0444] 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".

[0445] This invention is a system that combines a wearable device, a communication terminal, and a server to improve the quality of primary care provided to users. Specific embodiments for implementing this system are described below.

[0446] System Configuration

[0447] Wearable devices

[0448] The user wears a wearable device (e.g., a smartwatch). This wearable device has the function of monitoring vital data such as heart rate, body temperature, blood pressure, and steps in real time, and transmitting that data to a communication terminal.

[0449] Communication terminal

[0450] The user's communication device (e.g., a smartphone) receives and temporarily stores vital data transmitted from the wearable device. The communication device periodically sends this data to a server. It also provides an interface that allows the user to input symptoms and questions and send the data to the server.

[0451] server

[0452] The server receives vital data, medical history, and prescription history transmitted from the communication terminal and stores them in a central database. The AI ​​installed in the server analyzes this data, evaluates the user's health status, and generates a diagnosis. The generated diagnosis is then sent to the communication terminal and notified to the user.

[0453] System processing

[0454] Data collection

[0455] While the user is wearing the wearable device, the device periodically measures vital data such as heart rate, body temperature, blood pressure, and steps taken. This data is transmitted to a communication terminal using wireless communication such as Bluetooth. The communication terminal temporarily stores the received data and periodically sends it to a server.

[0456] Data analysis and generation of diagnostic results

[0457] The server is equipped with AI to analyze vital data sent by users in conjunction with their medical history and prescription history. Based on this data, the AI ​​assesses the user's health status and generates a diagnosis and recommended actions. The diagnosis may include assessments such as the possibility of a cold or the flu, and recommended actions may include purchasing over-the-counter medication, resting, and staying hydrated.

[0458] Notification of results and coordination with doctors

[0459] The diagnostic results generated by the server are sent to the communication terminal and notified to the user. The user can check the diagnostic results and take recommended actions through the app on the communication terminal. If the user wishes to have a more detailed consultation, they can use the communication terminal to schedule a video call with a doctor. The server provides the doctor with the accumulated data and AI-generated diagnostic results, which the doctor uses as a reference to make a final diagnosis.

[0460] Feedback and Learning

[0461] Users input feedback within the app regarding their recovery status after diagnosis and their impressions of the diagnosis. The communication device sends this feedback to the server, which stores it as training data for the AI. This improves the accuracy of the AI's diagnosis, enabling more accurate diagnoses in the future.

[0462] Specific example

[0463] Example 1: Daily health management

[0464] Users wear wearable devices daily to monitor their heart rate and body temperature. A communication terminal periodically sends this data to a server, where an AI analyzes it. For example, if the AI ​​detects an abnormal fluctuation in heart rate, a notification is sent to the communication terminal to alert the user.

[0465] Example 2: Diagnosis when symptoms appear

[0466] When a user experiences a sore throat, they open the app on their communication device and enter their symptoms. The server analyzes past vital data and symptoms and diagnoses a high probability of a cold. The user is then notified of recommended over-the-counter medications and lifestyle advice.

[0467] Example 3: Video call with a doctor

[0468] If a user experiences a worsening of symptoms and wishes to see a doctor, they can book a video call with a doctor using the app on their communication device. At the time of booking, the server provides the doctor with previous diagnostic results and vital data, and during the video call, the doctor makes a final diagnosis while asking additional questions.

[0469] In this way, the present invention realizes a system that provides users with high-quality primary care and improves the efficiency and accuracy of medical services through the cooperation of a wearable device, a communication terminal, and a server.

[0470] The following describes the processing flow.

[0471] Step 1: Data Collection

[0472] The user wears a wearable device (e.g., a smartwatch).

[0473] The device (wearable device) periodically measures vital data such as heart rate, body temperature, blood pressure, and steps taken.

[0474] The device (wearable device) measures vital data and transmits it to a communication terminal (smartphone) via wireless communication such as Bluetooth.

[0475] Step 2: Data transmission

[0476] The device (smartphone) temporarily stores vital data.

[0477] The device (smartphone) periodically (for example, every hour) sends stored vital data to the server.

[0478] Step 3: Appointment scheduling and medical interview

[0479] The user opens the dedicated app on their communication device (smartphone).

[0480] The user fills in their symptoms and questions in the app's input form.

[0481] The device (smartphone) sends the entered information and the latest vital data to the server.

[0482] Step 4: Data Analysis

[0483] The server retrieves the user's past vital data, medical history, and prescription history from a central database.

[0484] The AI ​​on the server analyzes this data and assesses the current health status of the user.

[0485] The server lists potential diagnoses and recommended actions based on the analysis results.

[0486] Step 5: Notification of diagnostic results

[0487] The server sends the analysis results to the communication terminal.

[0488] The device (smartphone) notifies the user of the diagnostic results and recommended actions.

[0489] The user reviews the diagnosis results and takes appropriate action (e.g., buys over-the-counter medication, takes rest).

[0490] Step 6: Collaboration with doctors

[0491] Users schedule video calls with doctors through the app.

[0492] The server provides doctors with AI analysis results and vital data at the scheduled time.

[0493] The doctor will use this data to ask additional questions and make a final diagnosis.

[0494] Step 7: Feedback and Learning

[0495] Users input their recovery status and evaluation after diagnosis within the app.

[0496] The device (smartphone) sends feedback information to the server.

[0497] The server saves the feedback as training data for the AI, which will be used to improve the accuracy of future diagnoses.

[0498] The above outlines the specific steps in the overall system processing flow and describes the actions performed at each step.

[0499] (Example 1)

[0500] 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 glasses 214 will be referred to as the "terminal."

[0501] In modern healthcare settings, there is a demand for monitoring individual health conditions and rapid diagnosis, but conventional systems are insufficient in terms of efficiency and accuracy. Furthermore, there is a lack of mechanisms to properly incorporate user feedback and continuously improve the system's diagnostic accuracy. As a result, users are often unable to receive accurate and timely healthcare services and are forced to rely on card-based medical services.

[0502] 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.

[0503] In this invention, the server includes means for generating diagnostic results using artificial intelligence that analyzes biometric data, medical history, and prescription history; means for presenting the diagnostic results to the individual's communication device; and means for collecting individual feedback and using it to train the artificial intelligence. This allows users to receive accurate and rapid diagnoses, improving the quality of medical services. Furthermore, the system can continuously learn from individual feedback, improving diagnostic accuracy.

[0504] A "biometric information sensing device" is a device used to measure an individual's biological data. Specifically, it refers to a device that has the function of measuring heart rate, body temperature, blood pressure, steps taken, etc.

[0505] A "communication device" is a device that has the function of receiving data transmitted from a biometric information sensing device and transferring it to a data processing device. Examples include mobile devices such as smartphones.

[0506] A "data processing device" is a device that receives biometric data transmitted from a communication device, stores it in a central database, and performs analysis. Typically, cloud servers and data centers fall into this category.

[0507] "Artificial intelligence" refers to algorithms and software used to analyze large amounts of data, recognize patterns, and generate diagnostic results. This primarily includes machine learning models and neural networks.

[0508] "Diagnosis results" refer to the assessment of health status and recommended actions obtained by artificial intelligence analyzing biometric data, medical history, and prescription history. Specifically, this refers to the identification of medical conditions and the suggestion of treatment methods.

[0509] "Video communication" is a means for users to interact with medical professionals remotely, and is usually conducted via video call applications.

[0510] "Feedback" refers to information in which users provide evaluations and opinions on diagnostic results and services. This is used to improve the system and enhance the accuracy of the diagnostics.

[0511] "Recommended actions" refer to behavioral guidelines suggested by artificial intelligence based on the diagnostic results, and include specific health management methods and countermeasures that users should follow.

[0512] "Training data" refers to a dataset used by a data processing device to allow artificial intelligence to learn from feedback and other data, thereby improving its diagnostic accuracy.

[0513] Through the above definitions, we clarify how this system combines its elements to provide users with high-quality primary care.

[0514] This invention is a system that combines a wearable device, a communication terminal, and a server to improve the quality of primary care provided to users. Specific embodiments for implementing the system of this invention are described below.

[0515] System Configuration

[0516] Wearable devices

[0517] The user wears a wearable device (e.g., a smartwatch). This allows for real-time monitoring of vital data such as heart rate, body temperature, blood pressure, and steps taken. The wearable device transmits this data to a communication terminal via wireless communication such as Bluetooth.

[0518] Communication terminal

[0519] The user's communication device (e.g., a smartphone) has the function of receiving and temporarily storing vital data transmitted from a wearable device. The communication device periodically sends this data to a server. It also provides an interface that allows the user to input symptoms and questions and send the data to the server.

[0520] server

[0521] The server receives vital data, medical history, and prescription history transmitted from the communication terminal and stores them in a central database. The server is equipped with AI (artificial intelligence) that analyzes this data to assess the user's health status and generates diagnostic results and recommended actions. The generated diagnostic results are sent to the communication terminal and notified to the user.

[0522] Specific example

[0523] Example 1: Daily health management

[0524] Users wear wearable devices daily to monitor their heart rate and body temperature. A communication terminal periodically sends this data to a server, where an AI analyzes it. For example, if the AI ​​detects an abnormal fluctuation in heart rate, a notification is sent to the communication terminal to alert the user.

[0525] Example of a prompt:

[0526] "An abnormally high heart rate was detected this morning. Please rest immediately and rehydrate."

[0527] Example 2: Diagnosis when symptoms appear

[0528] When a user experiences a sore throat, they open the app on their communication device and enter their symptoms. The server analyzes past vital data and symptoms to diagnose the possibility of a cold. The user is then notified of recommended over-the-counter medications and lifestyle advice.

[0529] Example of a prompt:

[0530] "If you have a sore throat, you may have a cold. Please use the over-the-counter medicine 'Pabron S Gold' and get plenty of rest."

[0531] Example 3: Video call with a doctor

[0532] If a user experiences a worsening of symptoms and wishes to see a doctor, they can book a video call with a doctor using the app on their communication device. At the time of booking, the server provides the doctor with previous diagnostic results and vital data, and during the video call, the doctor makes a final diagnosis while asking additional questions.

[0533] Example of a prompt:

[0534] "If your sore throat persists, please schedule a video consultation with a doctor. Your next appointment is tomorrow at 2 PM."

[0535] In this way, the present invention realizes a system that provides users with high-quality primary care and improves the efficiency and accuracy of medical services through the cooperation of a wearable device, a communication terminal, and a server.

[0536] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0537] Step 1:

[0538] Data collection

[0539] The user wears a wearable device that measures biometric data such as heart rate, body temperature, blood pressure, and steps taken in real time. This data is temporarily stored within the wearable device and transmitted to a communication terminal via wireless communication such as Bluetooth. The input is the user's biometric information, and the output is the biometric data transferred to the communication terminal. Specifically, while the user is jogging, the smartwatch measures their heart rate every minute and transmits it to their smartphone via Bluetooth.

[0540] Step 2:

[0541] Data transmission

[0542] The communication terminal (smartphone) receives biometric data sent from the wearable device and temporarily stores it locally. Furthermore, it periodically sends this data to the server. The input is the biometric data received from the wearable device, and the output is the data sent to the server. Specifically, at 10 PM every day, the smartphone synchronizes with the server and sends all of the day's biometric data at once.

[0543] Step 3:

[0544] Data Analysis

[0545] The server receives biometric data transmitted from communication terminals and stores it in a central database. Furthermore, the AI ​​within the server analyzes this data in combination with medical history and prescription history. The inputs are biometric data, medical history, and prescription history, and the output is the analysis results. Specifically, the AI ​​analyzes a large amount of data and detects whether there are any specific patterns (such as a sudden increase in heart rate).

[0546] Step 4:

[0547] Diagnosis result generation

[0548] The AI ​​on the server generates diagnostic results and recommended actions based on the analysis results. The diagnostic results include a specific assessment of health status and recommended actions. The input is the results of data analysis, and the output is the generated diagnostic results and recommended actions. Specifically, the AI ​​compares heart rate variability with past data and diagnoses that there is a 50% or higher probability of having a cold.

[0549] Step 5:

[0550] Result notification

[0551] The server sends the generated diagnostic results to the communication terminal, which then notifies the user. The input is the diagnostic results, and the output is the notification displayed on the communication terminal. Specifically, the smartphone displays a pop-up notification informing the user, "You may have a cold. Please rest and stay well-hydrated."

[0552] Step 6:

[0553] Collaboration with doctors

[0554] If a user wishes to have a detailed consultation, they can schedule a video call with a doctor using an app on their communication device. The server provides the doctor with the diagnosis results and past vital data. Inputs include the user's appointment request and related data, and output is the data provided to the doctor. Specifically, the user opens the app and schedules a video call with a doctor for 2 PM tomorrow. The server then sends the necessary data to the doctor.

[0555] Step 7:

[0556] Feedback Collection

[0557] Users input feedback into the app regarding their recovery status after diagnosis and their impressions of the system. The communication device sends this feedback to the server, which stores it as training data for the AI. The input is user feedback, and the output is AI training data. Specifically, the user reports within the app that their cold has cleared up and inputs an evaluation of whether the advice provided was helpful.

[0558] In this way, each step is organically linked, resulting in a system that can provide users with high-quality primary care.

[0559] (Application Example 1)

[0560] 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."

[0561] Traditional systems did not provide personalized meal suggestions based on the user's health status, making it difficult for users to easily select meals suitable for their health. Furthermore, the lack of a system that integrated health monitoring and meal suggestions meant users had to manage their health and diet separately. This resulted in inefficient health management and increased user effort.

[0562] 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.

[0563] In this invention, the server includes means for a wearable device to collect the user's vital data, means for transmitting the vital data to a communication terminal, means for the communication terminal to transmit the collected vital data to the server, means for the server to generate a diagnosis result using AI that analyzes the vital data, medical history, and prescription history, means for notifying the user's communication terminal of the diagnosis result, means for the user to receive a final diagnosis through a video call with a doctor, means for collecting user feedback that the server uses to train the AI, means for suggesting an optimal meal menu based on the analysis results, and means for ordering the suggested menu for delivery. This enables real-time monitoring of the user's health status and personalized meal suggestions based on the results. Furthermore, by instantly ordering the suggested meal for delivery, the efficiency of the user's health management and meal management is improved, and the effort involved is significantly reduced.

[0564] A "wearable device" is a small electronic device that a user wears to collect vital data such as heart rate, body temperature, and blood pressure in real time.

[0565] "Vital data" refers to data that indicates the user's biological state, such as heart rate, body temperature, blood pressure, and steps taken.

[0566] A "communication terminal" is an electronic device used to receive data transmitted from wearable devices such as smartphones and tablets, and to transmit that data to a server.

[0567] A "server" is a high-performance computer system that receives, stores, and analyzes data transmitted from communication terminals.

[0568] "AI" refers to artificial intelligence technology that analyzes data on a server and generates diagnostic results, meal menus, and other similar information.

[0569] "Diagnosis results" refer to information that shows an evaluation of the user's health status based on the results of AI analysis.

[0570] A "meal menu" refers to a combination of appropriate meals suggested by AI based on the user's health status and vital data.

[0571] "Delivery ordering" refers to the act of requesting a meal from a suggested menu via an information terminal and having the meal delivered.

[0572] "Feedback" refers to information that users report through the application, including their impressions of the system's diagnostic results and suggestions, as well as their actual actions.

[0573] "Analysis results" refer to the overall evaluation and diagnosis performed by the AI ​​based on vital data and other relevant data.

[0574] This invention is a system that combines a wearable device, a communication terminal, and a server to improve the quality of primary care provided to users. The system of this invention is specifically implemented as follows.

[0575] System Configuration

[0576] Wearable devices

[0577] The user wears a wearable device (e.g., a smartwatch). This wearable device monitors vital data such as heart rate, body temperature, blood pressure, and steps taken in real time and transmits it to a communication terminal via wireless communication such as Bluetooth.

[0578] Communication terminal

[0579] The user's communication device (e.g., a smartphone) receives and temporarily stores vital data transmitted from the wearable device. The communication device periodically sends this data to the server. It also provides an interface that allows the user to input questions about symptoms and diet and send the data to the server.

[0580] server

[0581] The server receives vital data, medical history, and prescription history transmitted from the communication terminal and stores them in a central database. The AI ​​installed in the server analyzes this data, evaluates the user's health status, and generates a diagnosis. It also has a function to suggest a personalized and optimal meal plan based on the generated diagnosis. The suggested menu is notified to the communication terminal, and the user can then place a delivery order directly.

[0582] System processing

[0583] Data collection

[0584] While the user is wearing the wearable device, the device periodically measures vital data such as heart rate, body temperature, blood pressure, and steps taken. This data is transmitted to a communication terminal using wireless communication such as Bluetooth. The communication terminal temporarily stores the received data and periodically sends it to a server.

[0585] Data analysis and generation of diagnostic results

[0586] The server is equipped with AI to analyze vital data sent by users in conjunction with their medical history and prescription history. Based on this data, the AI ​​assesses the user's health status and generates a diagnosis and recommended actions. The diagnosis may include assessments such as the possibility of a cold or the flu, and recommended actions may include purchasing over-the-counter medication, resting, and staying hydrated.

[0587] Suggestions for meal menus

[0588] Based on the analysis results, the AI ​​suggests a meal plan best suited to the user's health condition. The suggested menu is generated considering calories, nutritional balance, and allergy information, and is notified to the user's communication device. The user can then directly order the meal for delivery.

[0589] Notification of results and coordination with doctors

[0590] The diagnostic results and meal plans generated by the server are sent to the communication terminal and notified to the user. The user can check the diagnostic results and meal plans through the app on the communication terminal and take the recommended actions. If the user wishes to have a more detailed consultation, they can use the communication terminal to schedule a video call with a doctor. The server provides the doctor with the accumulated data and AI-generated diagnostic results, and the doctor uses this information to make a final diagnosis.

[0591] Feedback and Learning

[0592] Users input information within the app regarding their recovery status after diagnosis, their impressions of the diagnosis, and feedback on the suggested meal menu. The communication device sends this feedback to the server, which stores it as training data for the AI. This improves the accuracy of the AI's diagnosis and meal suggestions, enabling more accurate diagnoses and suggestions in the future.

[0593] Specific example

[0594] Example 1: Daily health management

[0595] Users wear wearable devices daily to monitor their heart rate and body temperature. A communication terminal periodically sends this data to a server, where an AI analyzes it. For example, if the AI ​​detects an abnormal fluctuation in heart rate, a notification is sent to the communication terminal to alert the user.

[0596] Example 2: Diagnosis when symptoms appear

[0597] When a user experiences a sore throat, they open the app on their communication device and enter their symptoms. The server analyzes their past vital data and symptoms and diagnoses a high probability of a cold. The user is then notified with information on recommended over-the-counter medications and lifestyle advice.

[0598] Example 3: Suggested meal menus and delivery orders

[0599] If a user wants to receive meal suggestions based on their health status, they send their current vital data to a server via a communication device. AI analyzes this data and suggests an optimal meal menu that takes into account calories, nutritional balance, and allergy information. The user can then order the suggested menu for delivery, improving the efficiency of their health management.

[0600] Example of a prompt

[0601] "Please suggest the optimal diet based on your health condition. Generate an AI module that analyzes your heart rate, body temperature, and blood pressure data from the past week and proposes an appropriate meal plan."

[0602] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0603] Step 1:

[0604] The user wears a wearable device (e.g., a smartwatch) and begins measuring vital data (heart rate, body temperature, blood pressure, steps, etc.). The input is the user's biometric information, and the output is real-time measured vital data. The wearable device transmits this data to a communication terminal via Bluetooth.

[0605] Step 2:

[0606] A communication terminal (e.g., a smartphone) receives vital data transmitted from a wearable device. The input is vital data transmitted via Bluetooth, and the output is vital data temporarily stored on the communication terminal. This allows for temporary storage and processing of the data.

[0607] Step 3:

[0608] The communication terminal periodically sends temporarily stored vital data to the server. The input is the temporarily stored vital data, and the output is the data sent to the server. Specifically, the data is uploaded from the communication terminal to the server via the internet.

[0609] Step 4:

[0610] The server stores the received vital data in a central database, which is then analyzed by AI along with the user's medical history and prescription history. The input consists of vital data, medical history, and prescription history sent to the server, while the output is the analysis results. Specifically, the AI ​​model evaluates the user's health status based on this data.

[0611] Step 5:

[0612] The server generates diagnostic results and recommended actions based on AI analysis. The input is the AI ​​analysis results, and the output is the generated diagnostic results and recommended actions. The server uses prompt statements to generate the diagnostic results and inputs them into the generating AI model.

[0613] Step 6:

[0614] The server sends the diagnostic results and recommended actions to the communication terminal. The input is the generated diagnostic results and recommended actions, and the output is the result notified to the communication terminal. Specifically, a push notification is sent to the application on the communication terminal.

[0615] Step 7:

[0616] The application on the communication terminal displays diagnostic results and recommended actions to the user. The input is notification data sent from the server, and the output is information displayed on the user interface. Specifically, the user checks the diagnostic results and recommended actions on the app screen.

[0617] Step 8:

[0618] When a user utilizes the meal suggestion feature, they send their current vital data to the server using a communication terminal. The input is the current vital data, and the output is the data sent to the server.

[0619] Step 9:

[0620] The server analyzes vital data and suggests an optimal meal plan based on the user's health status. The input is the user's vital data, and the output is the suggested meal plan. Based on the analysis results, the menu is generated considering calories, nutritional balance, and allergy information.

[0621] Step 10:

[0622] The suggested meal menu is notified to the communication terminal, and the user places a delivery order directly through the app. The input is the suggested meal menu, and the output is a delivery order request. Specifically, the order data is sent from the application on the communication terminal to the partner delivery service.

[0623] Step 11:

[0624] The user inputs feedback on the diagnostic results and dietary suggestions into an application on a communication terminal. The input is the user's feedback, and the output is the feedback data sent to the server.

[0625] Step 12:

[0626] The server stores user feedback as AI training data, which will be used to improve diagnostic accuracy and meal recommendation accuracy in the future. The input is feedback data, and the output is updated training data for the AI ​​model. Specifically, by incorporating feedback data into the AI ​​model's learning algorithm, the accuracy of diagnoses and recommendations is improved.

[0627] 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.

[0628] This invention is a system that combines a wearable device, a communication terminal, a server, and an emotion engine to improve the quality of primary care provided to users. Specific embodiments for implementing the system of this invention are described below.

[0629] System Configuration

[0630] Wearable devices

[0631] The user wears a wearable device (e.g., a smartwatch). This wearable device has the function of monitoring vital data such as heart rate, body temperature, blood pressure, and steps in real time, and transmitting that data to a communication terminal.

[0632] Communication terminal

[0633] The user's communication device (e.g., a smartphone) receives and temporarily stores vital data transmitted from the wearable device. The communication device periodically sends this data to a server. It also provides an interface that allows the user to input symptoms and questions and send the data to the server.

[0634] server

[0635] The server receives vital data, medical history, and prescription history transmitted from the communication terminal and stores them in a central database. The AI ​​installed in the server analyzes this data, evaluates the user's health status, and generates a diagnosis. The generated diagnosis is then sent to the communication terminal and notified to the user.

[0636] Emotional Engine

[0637] The emotion engine is a device that recognizes emotions from the user's voice and facial expressions. The communication terminal uses the user's voice input and camera images to have the emotion engine perform emotional analysis. The emotional data obtained from the emotion engine is sent to a server and used to generate diagnostic results by AI.

[0638] System processing

[0639] Data collection

[0640] While the user is wearing the wearable device, the device periodically measures vital data such as heart rate, body temperature, blood pressure, and steps taken. This data is transmitted to a communication terminal using wireless communication such as Bluetooth. The communication terminal temporarily stores the received data and periodically sends it to a server.

[0641] Data analysis and generation of diagnostic results

[0642] The server is equipped with AI to analyze vital data sent by users in conjunction with their medical history and prescription history. Based on this data, the AI ​​assesses the user's health status and generates a diagnosis and recommended actions. The diagnosis may include assessments such as the possibility of a cold or the flu, and recommended actions may include purchasing over-the-counter medication, resting, and staying hydrated.

[0643] Improving the accuracy of emotion analysis and diagnosis

[0644] The communication terminal obtains analysis results from the emotion engine based on the voice input and camera images provided by the user. This emotion data, which indicates the user's stress level and emotional state, is sent to the server. The AI ​​analyzes this emotion data together with vital data to generate more accurate diagnostic results.

[0645] Notification of results and coordination with doctors

[0646] The diagnostic results generated by the server are sent to the communication terminal and notified to the user. The user can check the diagnostic results and take recommended actions through the app on the communication terminal. If the user wishes to have a more detailed consultation, they can use the communication terminal to schedule a video call with a doctor. The server provides the doctor with the accumulated data and AI-generated diagnostic results, which the doctor uses as a reference to make a final diagnosis.

[0647] Feedback and Learning

[0648] Users input feedback within the app regarding their recovery status after diagnosis and their impressions of the diagnosis. The communication device sends this feedback to the server, which stores it as training data for the AI. This improves the accuracy of the AI's diagnosis, enabling more accurate diagnoses in the future.

[0649] Specific example

[0650] Example 1: Daily health management

[0651] Users wear wearable devices daily to monitor their heart rate and body temperature. A communication terminal periodically sends this data to a server, where an AI analyzes it. For example, if the AI ​​detects an abnormal fluctuation in heart rate, a notification is sent to the communication terminal to alert the user. Additionally, an emotion engine recognizes the user's stress level from their voice and facial expressions, and this is reflected in the diagnostic results.

[0652] Example 2: Diagnosis when symptoms appear

[0653] When a user experiences a sore throat, they open the app on their communication device and enter their symptoms. The server analyzes past vital data and symptoms and diagnoses a high probability of a cold. The user is then notified of recommended over-the-counter medications and lifestyle advice. Additionally, if the user expresses pain or anxiety through voice or facial expressions, an emotion engine analyzes this data, which the AI ​​uses to improve the accuracy of the diagnosis.

[0654] Example 3: Video call with a doctor

[0655] If a user experiences a worsening of symptoms and wishes to see a doctor, they can book a video call with a doctor using the app on their communication device. At the time of booking, the server provides the doctor with previous diagnostic results, vital data, and emotional data, and during the video call, the doctor makes a final diagnosis while asking additional questions.

[0656] In this way, the present invention realizes a system that provides users with high-quality primary care and improves the efficiency and accuracy of medical services through the cooperation of a wearable device, a communication terminal, a server, and an emotion engine.

[0657] The following describes the processing flow.

[0658] Step 1: Data collection using wearable devices

[0659] The user wears a wearable device (e.g., a smartwatch).

[0660] The device (wearable device) periodically measures the user's vital data, such as heart rate, body temperature, blood pressure, and steps taken.

[0661] The device (wearable device) measures vital data and transmits it to the communication terminal (smartphone) using Bluetooth or similar technologies.

[0662] Step 2: Send vital data

[0663] The terminal (smartphone) temporarily stores vital data received from the wearable device.

[0664] The device (smartphone) periodically sends stored vital data to the server (for example, every hour).

[0665] Step 3: Appointment scheduling and medical interview

[0666] The user opens the dedicated app on their communication device (smartphone).

[0667] The user enters their symptoms and questions into the appointment booking form.

[0668] The device (smartphone) sends the entered information and the latest vital data to the server.

[0669] Step 4: Data Analysis

[0670] The server retrieves the user's past vital data, medical history, and prescription history from a central database.

[0671] Based on data acquired by the AI ​​on the server, the system evaluates the user's health status and generates diagnostic results and recommended actions.

[0672] Step 5: Generating diagnostic results

[0673] The emotion engine analyzes the user's emotions from their voice and facial expressions.

[0674] The device (smartphone) retrieves analysis results from the emotion engine and sends them to the server.

[0675] The AI ​​on the server integrates emotional data with vital data to improve diagnostic accuracy.

[0676] Step 6: Notification of diagnostic results

[0677] The server generates the final diagnostic results and recommended actions, and sends them to the communication terminal.

[0678] The device (smartphone) notifies the user of the diagnostic results and recommended actions.

[0679] Users can check their diagnostic results through an app on their communication device and take necessary actions (e.g., purchase over-the-counter medication, ensure they get enough rest).

[0680] Step 7: Video call coordination with the doctor

[0681] Users schedule video calls with doctors through apps on their communication devices.

[0682] The server provides the doctor with previous diagnostic results, vital data, and emotional analysis data at the scheduled time.

[0683] The doctor will conduct a video call to ask additional questions and make a final diagnosis.

[0684] Step 8: Feedback and Learning

[0685] Users enter their recovery status and evaluation after diagnosis into a feedback form within the app.

[0686] The device (smartphone) sends feedback to the server.

[0687] The server saves the feedback as training data for the AI ​​and uses it to improve diagnostic accuracy in the future.

[0688] The above outlines the specific processing steps of the system combining the emotion engine, and the specific actions performed by each entity within that process.

[0689] (Example 2)

[0690] 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".

[0691] Traditional health management systems rely solely on users' vital data for diagnosis, neglecting psychological factors such as emotions and stress levels. This can lead to decreased diagnostic accuracy and difficulty in providing appropriate medical advice to users. Furthermore, the mechanisms for efficiently collecting feedback and utilizing it for AI learning were insufficient, hindering improvements in AI diagnostic accuracy.

[0692] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for generating a diagnosis result using AI that analyzes vital data, medical history, prescription history, and emotional data; means for an emotional engine to recognize emotions from the user's voice and facial expressions and transmit them to the server as data; and means for the server to store the user's feedback as learning data. This makes it possible to perform multifaceted data analysis including the user's psychological elements, thereby improving diagnostic accuracy.

[0693] A "wearable device" is a device that a user wears to collect vital data such as heart rate, body temperature, blood pressure, and steps taken.

[0694] A "communication terminal" is a device that receives vital data transmitted from a wearable device, temporarily stores it, and sends it to a server.

[0695] A "server" is a device that receives vital data, medical history, prescription history, and emotional data transmitted from a communication terminal, analyzes them, and generates diagnostic results.

[0696] "AI" refers to artificial intelligence technology used to analyze vital data, medical history, prescription history, and emotional data, and to generate diagnostic results.

[0697] "Diagnosis results" refer to an assessment of the user's health status and recommended actions, generated based on the analysis of vital data, medical history, prescription history, and emotional data.

[0698] An "emotion engine" is a device that recognizes emotions from a user's voice and facial expressions and analyzes that data.

[0699] "Feedback" refers to information that users input regarding their recovery status after diagnosis and their impressions of the diagnosis.

[0700] A "video call" is a conversation between a user and a doctor face-to-face via a communication device, using both audio and video.

[0701] "Recommended actions" refer to advice and instructions on what actions to take that are presented to the user based on the diagnostic results.

[0702] Modes for carrying out the invention

[0703] This invention is a system that provides high-quality primary care to users by linking a wearable device, a communication terminal, a server, and an emotion engine. Specific embodiments of this system will be described below.

[0704] Hardware and software to use

[0705] Wearable devices

[0706] The user wears a wearable device (e.g., a smartwatch). This device monitors vital data such as heart rate, body temperature, blood pressure, and steps taken in real time and transmits the data to a communication terminal using Bluetooth.

[0707] Communication terminal

[0708] The user's communication device (e.g., a smartphone) receives and temporarily stores vital data transmitted from the wearable device. The communication device periodically sends this data to a server. It also provides a function to allow the user to input symptoms and questions through a dedicated application and send this information to the server.

[0709] server

[0710] The server receives vital data, medical history, prescription history, and emotional data transmitted from communication terminals and stores them in a central database. The server is equipped with AI models using TensorFlow, PyTorch, etc., which are used to comprehensively analyze the data and generate diagnostic results.

[0711] Emotional Engine

[0712] The emotion engine is a device that recognizes emotions from the user's voice and facial expressions. The communication terminal sends data to the emotion engine based on voice input and camera footage, and receives the analysis results. These results are sent to a server and integrated into the AI ​​analysis.

[0713] Specific examples and prompt statements

[0714] Example 1: Daily health management

[0715] Users wear a wearable device daily, which monitors their heart rate and body temperature. A communication terminal periodically sends this data to a server, where the server's AI analyzes it. For example, if an abnormal fluctuation in heart rate is detected, a notification is sent to the communication terminal to alert the user. In addition, an emotion engine recognizes the user's stress level from their voice and facial expressions and reflects this in the diagnostic results.

[0716] Example of a prompt:

[0717] "Please analyze my heart rate monitoring results and let me know if there are any health problems. Also, please take my recent emotional state into consideration."

[0718] Example 2: Diagnosis when symptoms appear

[0719] When a user experiences a sore throat, they open an app on their communication device and enter their symptoms. The server analyzes past vital data and symptoms and diagnoses a high probability of a cold. The user is then notified of recommended over-the-counter medications and lifestyle advice. Additionally, if the user expresses pain or anxiety through voice or facial expressions, an emotion engine analyzes this data, which the AI ​​uses to improve the accuracy of the diagnosis.

[0720] Example of a prompt:

[0721] "I have a sore throat. Please provide a diagnosis and recommended actions based on my past health and emotional data."

[0722] Example 3: Video call with a doctor

[0723] If a user experiences a worsening of symptoms and wishes to see a doctor, they can book a video call with a doctor using the app on their communication device. At the time of booking, the server provides the doctor with previous diagnostic results, vital data, and emotional data, and during the video call, the doctor makes a final diagnosis while asking additional questions.

[0724] Example of a prompt:

[0725] "I would like to schedule a video call with a doctor. I would like to send my previous diagnostic data to the doctor and request an additional diagnosis during the video call."

[0726] This invention enables the realization of a system that provides high-quality primary care to users through the cooperation of a wearable device, a communication terminal, a server, and an emotion engine, thereby improving the efficiency and accuracy of medical services.

[0727] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0728] Step 1:

[0729] The user wears a wearable device that collects vital data such as heart rate, body temperature, blood pressure, and steps taken. Specifically, the device's sensors measure this data every minute and temporarily store it in its internal memory. The input is the user's vital data, and the output is the vital data stored in the device.

[0730] Step 2:

[0731] The wearable device uses Bluetooth to transmit collected vital data to a communication terminal. Specifically, the wearable device's data transmission function is activated, and a Bluetooth connection is established. The input is the vital data stored on the device, and the output is the vital data transmitted to the communication terminal.

[0732] Step 3:

[0733] The communication terminal receives vital data transmitted from the wearable device and stores it temporarily. This data is later sent to a server for analysis. Specifically, the communication terminal's application runs in the background, receiving data and saving it to its internal storage. The input is the transmitted vital data, and the output is the vital data stored in the communication terminal.

[0734] Step 4:

[0735] The communication terminal periodically sends stored vital data to the server. For example, it is set up so that data is sent in a batch every night. Specifically, the communication terminal's application executes a process to send data to the server at a set time. The input is the vital data stored on the communication terminal, and the output is the vital data sent to the server.

[0736] Step 5:

[0737] The server receives vital data transmitted from communication terminals and stores it in a central database. Specifically, the server's API endpoint receives a request and executes the process of saving the data to the database. The input is the vital data sent to the server, and the output is the data stored in the database.

[0738] Step 6:

[0739] The AI ​​installed on the server analyzes vital data, medical history, prescription history, and sentiment data. Specifically, the server runs machine learning models trained using TensorFlow or PyTorch to assess health status and generate diagnostic results. The input is vital data, medical history, prescription history, and sentiment data stored in a database, and the output is the generated diagnostic result.

[0740] Step 7:

[0741] The server sends the generated diagnostic results to the communication terminal and notifies the user. Specifically, the server sends a notification containing the diagnostic results to the application on the communication terminal, and the application displays a push notification. The input is the generated diagnostic results, and the output is the push notification on the communication terminal.

[0742] Step 8:

[0743] Users can view their diagnostic results and take recommended actions through an app on their communication device. If they wish to have a more detailed consultation, they can use the app to schedule a video call with a doctor. Specifically, the app on the communication device displays the diagnostic results to the user and provides a video call scheduling function. The input is the diagnostic results, and the output is the video call scheduling.

[0744] Step 9:

[0745] The emotion engine recognizes emotions from the user's voice and facial expressions and analyzes that data. Specifically, the communication terminal's application uses the microphone and camera to capture voice and facial expressions and sends them to the emotion engine. The engine then sends the analysis results to the server. The input is voice and facial expression data, and the output is the analyzed emotion data.

[0746] Step 10:

[0747] Users input feedback within the app regarding their recovery status after diagnosis and their impressions of the diagnosis. The communication device sends this feedback to the server, which stores it as training data for the AI. Specifically, the app on the communication device receives feedback from the user and sends the data to the server. The input is the user's feedback, and the output is the feedback data stored on the server.

[0748] This allows the system to perform multifaceted data analysis, provide users with highly accurate diagnostic results, and improve the efficiency and accuracy of medical services.

[0749] (Application Example 2)

[0750] 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."

[0751] Modern health management requires the collection and analysis of daily vital data to understand individual health conditions in real time and provide appropriate medical care and lifestyle guidance. However, previous systems often evaluated health conditions without considering the user's emotional state, resulting in inaccurate diagnoses and recommended behaviors. Furthermore, there was a lack of dietary suggestions based on the user's health condition, making it difficult to provide meals that met individual needs.

[0752] 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. In this invention, the server includes means for generating diagnostic results using AI that analyzes vital data, medical history, and prescription history; means for analyzing the user's emotional data using an emotion engine and reflecting it in the diagnostic results; and means for recommending the optimal diet based on health status and emotional data. This generates highly accurate diagnostic results that take into account the user's emotional state, and further enables dietary suggestions tailored to the individual's health condition.

[0753] A "wearable device" is a device worn by a user to collect vital data such as heart rate, body temperature, blood pressure, and steps taken.

[0754] A "communication terminal" is a device that has the function of receiving and storing vital data transmitted from a wearable device and sending it to a server.

[0755] A "server" is a device that stores vital data, medical history, and prescription history transmitted from communication terminals in a central database, analyzes it, and generates diagnostic results.

[0756] "AI" refers to an algorithm installed on a server that analyzes vital data, medical history, and prescription history to evaluate health status and generate diagnostic results.

[0757] "Diagnosis results" refer to an assessment of the user's health status generated by AI based on vital data and other information.

[0758] An "emotion engine" is a device that recognizes and analyzes emotions from a user's voice and facial expressions.

[0759] "Emotional data" refers to data that indicates the user's stress level and emotional state, analyzed by the emotion engine.

[0760] "Recommended actions" refer to information indicating the actions a user should take based on their diagnostic results.

[0761] "Health status" refers to the user's overall health condition, evaluated based on vital data such as heart rate, body temperature, blood pressure, and steps taken.

[0762] "Meal suggestions" refer to information that proposes the optimal meal plan based on the user's health status and emotional data.

[0763] This invention is a system that combines a wearable device, a communication terminal, a server, and an emotion engine to achieve more accurate user health management and dietary recommendations. Specific embodiments for carrying out the invention are described below.

[0764] System Configuration

[0765] Wearable devices

[0766] The user wears a wearable device (e.g., a smartwatch). This device measures vital data such as heart rate, body temperature, blood pressure, and steps in real time and transmits it to a communication terminal using wireless communication such as Bluetooth or Wi-Fi.

[0767] Communication terminal

[0768] The user's communication device (e.g., a smartphone) receives and temporarily stores vital data transmitted from the wearable device. Furthermore, it has the functionality to collect the user's voice and facial expressions using a camera and microphone and transmit them to an emotion engine. The communication device periodically sends this data to a server.

[0769] server

[0770] The server receives vital data, emotional data, medical history, and prescription history transmitted from the communication terminal and stores them in a central database. Furthermore, the server is equipped with AI, which analyzes this data to assess the user's health status and generate a diagnosis. The diagnosis is then communicated to the user via the communication terminal. The server also suggests an optimal diet based on the assessment.

[0771] Emotional Engine

[0772] The emotion engine recognizes emotions from the user's voice and facial expressions and generates data indicating their emotional state. The communication terminal transmits voice input and camera footage to the emotion engine for emotion analysis. The emotion data is sent to a server and used to generate diagnostic results.

[0773] Program processing

[0774] Data collection

[0775] A wearable device periodically measures the user's vital data, such as heart rate, body temperature, blood pressure, and steps taken, and transmits this data to a communication terminal. The communication terminal temporarily stores the vital data and then transmits it to a server.

[0776] Emotion analysis

[0777] The communication terminal uses an emotion engine to analyze the user's voice input and camera footage. The emotion engine analyzes this data and generates data indicating the user's emotional state. This emotional data is sent to a server and used to assess their health status.

[0778] Data analysis and generation of diagnostic results

[0779] The server is equipped with AI to analyze vital data, medical history, prescription history, and emotional data. Based on this data, the AI ​​assesses the user's health status and generates a diagnosis, recommended actions, and optimal dietary suggestions. This information is transmitted to the communication terminal and notified to the user.

[0780] Results notification and meal suggestions

[0781] The diagnostic results and meal suggestions generated by the server are sent to the communication terminal and notified to the user. The user can check the diagnostic results and meal suggestions through the app on the communication terminal and take the recommended actions.

[0782] Collaboration with doctors

[0783] If a user wishes to have a more detailed consultation, they can book a video call with a doctor using their communication device. At the time of booking, the server provides the doctor with previous diagnostic results, vital data, and emotional data, which will be used for the final diagnosis.

[0784] Specific example

[0785] For example, a user wears a wearable device daily to monitor their heart rate and body temperature. The communication terminal periodically sends this data to a server, where the server's AI analyzes it. If the AI ​​detects an abnormal fluctuation in heart rate, a notification is sent to the communication terminal to alert the user. In addition, an emotion engine recognizes the user's stress level from their voice and facial expressions, and this is reflected in the diagnostic results.

[0786] Example of a prompt

[0787] In the food delivery app you develop, please consider the following data to suggest the best meal options to users.

[0788] Input data:

[0789] Heart rate: 80

[0790] Body temperature: 36.5°C

[0791] Blood pressure: 120 / 80

[0792] Steps: 5000

[0793] Emotional state: Stress

[0794] output:

[0795] Please suggest meals that can help reduce the stress users are experiencing. The menu should be nutritionally balanced and include relaxing elements.

[0796] example:

[0797] Herbal tea

[0798] A light salad or a late-night snack

[0799] Low-sugar smoothies

[0800] The above describes a specific embodiment for carrying out the present invention. This enables highly accurate health management and dietary recommendations that take into account the user's emotional state.

[0801] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0802] Step 1:

[0803] The user wears a wearable device that collects vital data such as heart rate, body temperature, blood pressure, and steps taken. The wearable device transmits this data to a communication terminal using wireless communication such as Bluetooth.

[0804] Input: Vital data (heart rate, body temperature, blood pressure, steps)

[0805] Output: Data transmission to communication terminal

[0806] Specific operation: The wearable device measures vital data in real time and transmits the data to a communication terminal using Bluetooth.

[0807] Step 2:

[0808] The communication terminal receives and temporarily stores vital data transmitted from the wearable device. Simultaneously, it collects user emotion data using audio and camera footage.

[0809] Input: Vital data from wearable devices, user voice data, camera footage

[0810] Output: Temporary storage of data to send to the server

[0811] Specific operation: The communication terminal receives vital data via Bluetooth, and uses the microphone and camera to collect and temporarily store the user's voice and facial expression data.

[0812] Step 3:

[0813] The communication terminal periodically sends temporarily stored vital data and emotional data to the server.

[0814] Input: Temporarily stored vital data and emotional data

[0815] Output: Sending data to the server

[0816] Specific operation: The communication device uploads data to the server using Wi-Fi or mobile data communication.

[0817] Step 4:

[0818] The server uses AI to analyze vital and emotional data it receives. The AI ​​also analyzes medical history and prescription history to generate a diagnosis.

[0819] Input: Vital data, emotional data, medical history, prescription history

[0820] Output: Diagnostic results

[0821] Specific operation: The server uses an AI algorithm to analyze data, evaluate the user's health status, and generate a diagnostic result.

[0822] Step 5:

[0823] The server generates diagnostic results, which are then sent to the communication terminal to notify the user. Simultaneously, dietary suggestions based on the user's health status are also provided.

[0824] Input: Diagnostic result

[0825] Output: Sending diagnostic results and meal suggestions to the communication terminal.

[0826] Specific operation: The server generates diagnostic results and meal suggestions, and sends notifications to the communication terminal.

[0827] Step 6:

[0828] Users can view their diagnostic results and meal suggestions via a communication device. If necessary, they can also place orders based on the meal suggestions.

[0829] Input: Diagnostic results and dietary suggestions sent to the communication terminal.

[0830] Output: User confirmation and order

[0831] Specific operation: The user checks the notification on the app on their communication device and places an order based on the meal suggestion if necessary.

[0832] Step 7:

[0833] Users input feedback on their diagnostic results and meal suggestions through the app and send it to the server. The server stores this feedback as training data for the AI.

[0834] Input: User feedback

[0835] Output: AI training data

[0836] Specific operation: The user enters feedback in the app, and the server receives it and saves it as training data.

[0837] The above outlines the specific processing steps of the system based on the present invention. This enables user health management and personalized meal suggestions.

[0838] 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.

[0839] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0840] 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.

[0841] [Third Embodiment]

[0842] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0843] 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.

[0844] 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).

[0845] 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.

[0846] 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.

[0847] 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).

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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".

[0854] This invention is a system that combines a wearable device, a communication terminal, and a server to improve the quality of primary care provided to users. Specific embodiments for implementing this system are described below.

[0855] System Configuration

[0856] Wearable devices

[0857] The user wears a wearable device (e.g., a smartwatch). This wearable device has the function of monitoring vital data such as heart rate, body temperature, blood pressure, and steps in real time, and transmitting that data to a communication terminal.

[0858] Communication terminal

[0859] The user's communication device (e.g., a smartphone) receives and temporarily stores vital data transmitted from the wearable device. The communication device periodically sends this data to a server. It also provides an interface that allows the user to input symptoms and questions and send the data to the server.

[0860] server

[0861] The server receives vital data, medical history, and prescription history transmitted from the communication terminal and stores them in a central database. The AI ​​installed in the server analyzes this data, evaluates the user's health status, and generates a diagnosis. The generated diagnosis is then sent to the communication terminal and notified to the user.

[0862] System processing

[0863] Data collection

[0864] While the user is wearing the wearable device, the device periodically measures vital data such as heart rate, body temperature, blood pressure, and steps taken. This data is transmitted to a communication terminal using wireless communication such as Bluetooth. The communication terminal temporarily stores the received data and periodically sends it to a server.

[0865] Data analysis and generation of diagnostic results

[0866] The server is equipped with AI to analyze vital data sent by users in conjunction with their medical history and prescription history. Based on this data, the AI ​​assesses the user's health status and generates a diagnosis and recommended actions. The diagnosis may include assessments such as the possibility of a cold or the flu, and recommended actions may include purchasing over-the-counter medication, resting, and staying hydrated.

[0867] Notification of results and coordination with doctors

[0868] The diagnostic results generated by the server are sent to the communication terminal and notified to the user. The user can check the diagnostic results and take recommended actions through the app on the communication terminal. If the user wishes to have a more detailed consultation, they can use the communication terminal to schedule a video call with a doctor. The server provides the doctor with the accumulated data and AI-generated diagnostic results, which the doctor uses as a reference to make a final diagnosis.

[0869] Feedback and Learning

[0870] Users input feedback within the app regarding their recovery status after diagnosis and their impressions of the diagnosis. The communication device sends this feedback to the server, which stores it as training data for the AI. This improves the accuracy of the AI's diagnosis, enabling more accurate diagnoses in the future.

[0871] Specific example

[0872] Example 1: Daily health management

[0873] Users wear wearable devices daily to monitor their heart rate and body temperature. A communication terminal periodically sends this data to a server, where an AI analyzes it. For example, if the AI ​​detects an abnormal fluctuation in heart rate, a notification is sent to the communication terminal to alert the user.

[0874] Example 2: Diagnosis when symptoms appear

[0875] When a user experiences a sore throat, they open the app on their communication device and enter their symptoms. The server analyzes past vital data and symptoms and diagnoses a high probability of a cold. The user is then notified of recommended over-the-counter medications and lifestyle advice.

[0876] Example 3: Video call with a doctor

[0877] If a user experiences a worsening of symptoms and wishes to see a doctor, they can book a video call with a doctor using the app on their communication device. At the time of booking, the server provides the doctor with previous diagnostic results and vital data, and during the video call, the doctor makes a final diagnosis while asking additional questions.

[0878] In this way, the present invention realizes a system that provides users with high-quality primary care and improves the efficiency and accuracy of medical services through the cooperation of a wearable device, a communication terminal, and a server.

[0879] The following describes the processing flow.

[0880] Step 1: Data Collection

[0881] The user wears a wearable device (e.g., a smartwatch).

[0882] The device (wearable device) periodically measures vital data such as heart rate, body temperature, blood pressure, and steps taken.

[0883] The device (wearable device) measures vital data and transmits it to a communication terminal (smartphone) via wireless communication such as Bluetooth.

[0884] Step 2: Data transmission

[0885] The device (smartphone) temporarily stores vital data.

[0886] The device (smartphone) periodically (for example, every hour) sends stored vital data to the server.

[0887] Step 3: Appointment scheduling and medical interview

[0888] The user opens the dedicated app on their communication device (smartphone).

[0889] The user fills in their symptoms and questions in the app's input form.

[0890] The device (smartphone) sends the entered information and the latest vital data to the server.

[0891] Step 4: Data Analysis

[0892] The server retrieves the user's past vital data, medical history, and prescription history from a central database.

[0893] The AI ​​on the server analyzes this data and assesses the current health status of the user.

[0894] The server lists potential diagnoses and recommended actions based on the analysis results.

[0895] Step 5: Notification of diagnostic results

[0896] The server sends the analysis results to the communication terminal.

[0897] The device (smartphone) notifies the user of the diagnostic results and recommended actions.

[0898] The user reviews the diagnosis results and takes appropriate action (e.g., buys over-the-counter medication, takes rest).

[0899] Step 6: Collaboration with doctors

[0900] Users schedule video calls with doctors through the app.

[0901] The server provides doctors with AI analysis results and vital data at the scheduled time.

[0902] The doctor will use this data to ask additional questions and make a final diagnosis.

[0903] Step 7: Feedback and Learning

[0904] Users input their recovery status and evaluation after diagnosis within the app.

[0905] The device (smartphone) sends feedback information to the server.

[0906] The server saves the feedback as training data for the AI, which will be used to improve the accuracy of future diagnoses.

[0907] The above outlines the specific steps in the overall system processing flow and describes the actions performed at each step.

[0908] (Example 1)

[0909] 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."

[0910] In modern healthcare settings, there is a demand for monitoring individual health conditions and rapid diagnosis, but conventional systems are insufficient in terms of efficiency and accuracy. Furthermore, there is a lack of mechanisms to properly incorporate user feedback and continuously improve the system's diagnostic accuracy. As a result, users are often unable to receive accurate and timely healthcare services and are forced to rely on card-based medical services.

[0911] 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.

[0912] In this invention, the server includes means for generating diagnostic results using artificial intelligence that analyzes biometric data, medical history, and prescription history; means for presenting the diagnostic results to the individual's communication device; and means for collecting individual feedback and using it to train the artificial intelligence. This allows users to receive accurate and rapid diagnoses, improving the quality of medical services. Furthermore, the system can continuously learn from individual feedback, improving diagnostic accuracy.

[0913] A "biometric information sensing device" is a device used to measure an individual's biological data. Specifically, it refers to a device that has the function of measuring heart rate, body temperature, blood pressure, steps taken, etc.

[0914] A "communication device" is a device that has the function of receiving data transmitted from a biometric information sensing device and transferring it to a data processing device. Examples include mobile devices such as smartphones.

[0915] A "data processing device" is a device that receives biometric data transmitted from a communication device, stores it in a central database, and performs analysis. Typically, cloud servers and data centers fall into this category.

[0916] "Artificial intelligence" refers to algorithms and software used to analyze large amounts of data, recognize patterns, and generate diagnostic results. This primarily includes machine learning models and neural networks.

[0917] "Diagnosis results" refer to the assessment of health status and recommended actions obtained by artificial intelligence analyzing biometric data, medical history, and prescription history. Specifically, this refers to the identification of medical conditions and the suggestion of treatment methods.

[0918] "Video communication" is a means for users to interact with medical professionals remotely, and is usually conducted via video call applications.

[0919] "Feedback" refers to information in which users provide evaluations and opinions on diagnostic results and services. This is used to improve the system and enhance the accuracy of the diagnostics.

[0920] "Recommended actions" refer to behavioral guidelines suggested by artificial intelligence based on the diagnostic results, and include specific health management methods and countermeasures that users should follow.

[0921] "Training data" refers to a dataset used by a data processing device to allow artificial intelligence to learn from feedback and other data, thereby improving its diagnostic accuracy.

[0922] Through the above definitions, we clarify how this system combines its elements to provide users with high-quality primary care.

[0923] This invention is a system that combines a wearable device, a communication terminal, and a server to improve the quality of primary care provided to users. Specific embodiments for implementing the system of this invention are described below.

[0924] System Configuration

[0925] Wearable devices

[0926] The user wears a wearable device (e.g., a smartwatch). This allows for real-time monitoring of vital data such as heart rate, body temperature, blood pressure, and steps taken. The wearable device transmits this data to a communication terminal via wireless communication such as Bluetooth.

[0927] Communication terminal

[0928] The user's communication device (e.g., a smartphone) has the function of receiving and temporarily storing vital data transmitted from a wearable device. The communication device periodically sends this data to a server. It also provides an interface that allows the user to input symptoms and questions and send the data to the server.

[0929] server

[0930] The server receives vital data, medical history, and prescription history transmitted from the communication terminal and stores them in a central database. The server is equipped with AI (artificial intelligence) that analyzes this data to assess the user's health status and generates diagnostic results and recommended actions. The generated diagnostic results are sent to the communication terminal and notified to the user.

[0931] Specific example

[0932] Example 1: Daily health management

[0933] Users wear wearable devices daily to monitor their heart rate and body temperature. A communication terminal periodically sends this data to a server, where an AI analyzes it. For example, if the AI ​​detects an abnormal fluctuation in heart rate, a notification is sent to the communication terminal to alert the user.

[0934] Example of a prompt:

[0935] "An abnormally high heart rate was detected this morning. Please rest immediately and rehydrate."

[0936] Example 2: Diagnosis when symptoms appear

[0937] When a user experiences a sore throat, they open the app on their communication device and enter their symptoms. The server analyzes past vital data and symptoms to diagnose the possibility of a cold. The user is then notified of recommended over-the-counter medications and lifestyle advice.

[0938] Example of a prompt:

[0939] "If you have a sore throat, you may have a cold. Please use the over-the-counter medicine 'Pabron S Gold' and get plenty of rest."

[0940] Example 3: Video call with a doctor

[0941] If a user experiences a worsening of symptoms and wishes to see a doctor, they can book a video call with a doctor using the app on their communication device. At the time of booking, the server provides the doctor with previous diagnostic results and vital data, and during the video call, the doctor makes a final diagnosis while asking additional questions.

[0942] Example of a prompt:

[0943] "If your sore throat persists, please schedule a video consultation with a doctor. Your next appointment is tomorrow at 2 PM."

[0944] In this way, the present invention realizes a system that provides users with high-quality primary care and improves the efficiency and accuracy of medical services through the cooperation of a wearable device, a communication terminal, and a server.

[0945] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0946] Step 1:

[0947] Data collection

[0948] The user wears a wearable device that measures biometric data such as heart rate, body temperature, blood pressure, and steps taken in real time. This data is temporarily stored within the wearable device and transmitted to a communication terminal via wireless communication such as Bluetooth. The input is the user's biometric information, and the output is the biometric data transferred to the communication terminal. Specifically, while the user is jogging, the smartwatch measures their heart rate every minute and transmits it to their smartphone via Bluetooth.

[0949] Step 2:

[0950] Data transmission

[0951] The communication terminal (smartphone) receives biometric data sent from the wearable device and temporarily stores it locally. Furthermore, it periodically sends this data to the server. The input is the biometric data received from the wearable device, and the output is the data sent to the server. Specifically, at 10 PM every day, the smartphone synchronizes with the server and sends all of the day's biometric data at once.

[0952] Step 3:

[0953] Data Analysis

[0954] The server receives biometric data transmitted from communication terminals and stores it in a central database. Furthermore, the AI ​​within the server analyzes this data in combination with medical history and prescription history. The inputs are biometric data, medical history, and prescription history, and the output is the analysis results. Specifically, the AI ​​analyzes a large amount of data and detects whether there are any specific patterns (such as a sudden increase in heart rate).

[0955] Step 4:

[0956] Diagnosis result generation

[0957] The AI ​​on the server generates diagnostic results and recommended actions based on the analysis results. The diagnostic results include a specific assessment of health status and recommended actions. The input is the results of data analysis, and the output is the generated diagnostic results and recommended actions. Specifically, the AI ​​compares heart rate variability with past data and diagnoses that there is a 50% or higher probability of having a cold.

[0958] Step 5:

[0959] Result notification

[0960] The server sends the generated diagnostic results to the communication terminal, which then notifies the user. The input is the diagnostic results, and the output is the notification displayed on the communication terminal. Specifically, the smartphone displays a pop-up notification informing the user, "You may have a cold. Please rest and stay well-hydrated."

[0961] Step 6:

[0962] Collaboration with doctors

[0963] If a user wishes to have a detailed consultation, they can schedule a video call with a doctor using an app on their communication device. The server provides the doctor with the diagnosis results and past vital data. Inputs include the user's appointment request and related data, and output is the data provided to the doctor. Specifically, the user opens the app and schedules a video call with a doctor for 2 PM tomorrow. The server then sends the necessary data to the doctor.

[0964] Step 7:

[0965] Feedback Collection

[0966] Users input feedback into the app regarding their recovery status after diagnosis and their impressions of the system. The communication device sends this feedback to the server, which stores it as training data for the AI. The input is user feedback, and the output is AI training data. Specifically, the user reports within the app that their cold has cleared up and inputs an evaluation of whether the advice provided was helpful.

[0967] In this way, each step is organically linked, resulting in a system that can provide users with high-quality primary care.

[0968] (Application Example 1)

[0969] 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."

[0970] Traditional systems did not provide personalized meal suggestions based on the user's health status, making it difficult for users to easily select meals suitable for their health. Furthermore, the lack of a system that integrated health monitoring and meal suggestions meant users had to manage their health and diet separately. This resulted in inefficient health management and increased user effort.

[0971] 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.

[0972] In this invention, the server includes means for a wearable device to collect the user's vital data, means for transmitting the vital data to a communication terminal, means for the communication terminal to transmit the collected vital data to the server, means for the server to generate a diagnosis result using AI that analyzes the vital data, medical history, and prescription history, means for notifying the user's communication terminal of the diagnosis result, means for the user to receive a final diagnosis through a video call with a doctor, means for collecting user feedback that the server uses to train the AI, means for suggesting an optimal meal menu based on the analysis results, and means for ordering the suggested menu for delivery. This enables real-time monitoring of the user's health status and personalized meal suggestions based on the results. Furthermore, by instantly ordering the suggested meal for delivery, the efficiency of the user's health management and meal management is improved, and the effort involved is significantly reduced.

[0973] A "wearable device" is a small electronic device that a user wears to collect vital data such as heart rate, body temperature, and blood pressure in real time.

[0974] "Vital data" refers to data that indicates the user's biological state, such as heart rate, body temperature, blood pressure, and steps taken.

[0975] A "communication terminal" is an electronic device used to receive data transmitted from wearable devices such as smartphones and tablets, and to transmit that data to a server.

[0976] A "server" is a high-performance computer system that receives, stores, and analyzes data transmitted from communication terminals.

[0977] "AI" refers to artificial intelligence technology that analyzes data on a server and generates diagnostic results, meal menus, and other similar information.

[0978] "Diagnosis results" refer to information that shows an evaluation of the user's health status based on the results of AI analysis.

[0979] A "meal menu" refers to a combination of appropriate meals suggested by AI based on the user's health status and vital data.

[0980] "Delivery ordering" refers to the act of requesting a meal from a suggested menu via an information terminal and having the meal delivered.

[0981] "Feedback" refers to information that users report through the application, including their impressions of the system's diagnostic results and suggestions, as well as their actual actions.

[0982] "Analysis results" refer to the overall evaluation and diagnosis performed by the AI ​​based on vital data and other relevant data.

[0983] This invention is a system that combines a wearable device, a communication terminal, and a server to improve the quality of primary care provided to users. The system of this invention is specifically implemented as follows.

[0984] System Configuration

[0985] Wearable devices

[0986] The user wears a wearable device (e.g., a smartwatch). This wearable device monitors vital data such as heart rate, body temperature, blood pressure, and steps taken in real time and transmits it to a communication terminal via wireless communication such as Bluetooth.

[0987] Communication terminal

[0988] The user's communication device (e.g., a smartphone) receives and temporarily stores vital data transmitted from the wearable device. The communication device periodically sends this data to the server. It also provides an interface that allows the user to input questions about symptoms and diet and send the data to the server.

[0989] server

[0990] The server receives vital data, medical history, and prescription history transmitted from the communication terminal and stores them in a central database. The AI ​​installed in the server analyzes this data, evaluates the user's health status, and generates a diagnosis. It also has a function to suggest a personalized and optimal meal plan based on the generated diagnosis. The suggested menu is notified to the communication terminal, and the user can then place a delivery order directly.

[0991] System processing

[0992] Data collection

[0993] While the user is wearing the wearable device, the device periodically measures vital data such as heart rate, body temperature, blood pressure, and steps taken. This data is transmitted to a communication terminal using wireless communication such as Bluetooth. The communication terminal temporarily stores the received data and periodically sends it to a server.

[0994] Data analysis and generation of diagnostic results

[0995] The server is equipped with AI to analyze vital data sent by users in conjunction with their medical history and prescription history. Based on this data, the AI ​​assesses the user's health status and generates a diagnosis and recommended actions. The diagnosis may include assessments such as the possibility of a cold or the flu, and recommended actions may include purchasing over-the-counter medication, resting, and staying hydrated.

[0996] Suggestions for meal menus

[0997] Based on the analysis results, the AI ​​suggests a meal plan best suited to the user's health condition. The suggested menu is generated considering calories, nutritional balance, and allergy information, and is notified to the user's communication device. The user can then directly order the meal for delivery.

[0998] Notification of results and coordination with doctors

[0999] The diagnostic results and meal plans generated by the server are sent to the communication terminal and notified to the user. The user can check the diagnostic results and meal plans through the app on the communication terminal and take the recommended actions. If the user wishes to have a more detailed consultation, they can use the communication terminal to schedule a video call with a doctor. The server provides the doctor with the accumulated data and AI-generated diagnostic results, and the doctor uses this information to make a final diagnosis.

[1000] Feedback and Learning

[1001] Users input information within the app regarding their recovery status after diagnosis, their impressions of the diagnosis, and feedback on the suggested meal menu. The communication device sends this feedback to the server, which stores it as training data for the AI. This improves the accuracy of the AI's diagnosis and meal suggestions, enabling more accurate diagnoses and suggestions in the future.

[1002] Specific example

[1003] Example 1: Daily health management

[1004] Users wear wearable devices daily to monitor their heart rate and body temperature. A communication terminal periodically sends this data to a server, where an AI analyzes it. For example, if the AI ​​detects an abnormal fluctuation in heart rate, a notification is sent to the communication terminal to alert the user.

[1005] Example 2: Diagnosis when symptoms appear

[1006] When a user experiences a sore throat, they open the app on their communication device and enter their symptoms. The server analyzes their past vital data and symptoms and diagnoses a high probability of a cold. The user is then notified with information on recommended over-the-counter medications and lifestyle advice.

[1007] Example 3: Suggested meal menus and delivery orders

[1008] If a user wants to receive meal suggestions based on their health status, they send their current vital data to a server via a communication device. AI analyzes this data and suggests an optimal meal menu that takes into account calories, nutritional balance, and allergy information. The user can then order the suggested menu for delivery, improving the efficiency of their health management.

[1009] Example of a prompt

[1010] "Please suggest the optimal diet based on your health condition. Generate an AI module that analyzes your heart rate, body temperature, and blood pressure data from the past week and proposes an appropriate meal plan."

[1011] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1012] Step 1:

[1013] The user wears a wearable device (e.g., a smartwatch) and begins measuring vital data (heart rate, body temperature, blood pressure, steps, etc.). The input is the user's biometric information, and the output is real-time measured vital data. The wearable device transmits this data to a communication terminal via Bluetooth.

[1014] Step 2:

[1015] A communication terminal (e.g., a smartphone) receives vital data transmitted from a wearable device. The input is vital data transmitted via Bluetooth, and the output is vital data temporarily stored on the communication terminal. This allows for temporary storage and processing of the data.

[1016] Step 3:

[1017] The communication terminal periodically sends temporarily stored vital data to the server. The input is the temporarily stored vital data, and the output is the data sent to the server. Specifically, the data is uploaded from the communication terminal to the server via the internet.

[1018] Step 4:

[1019] The server stores the received vital data in a central database, which is then analyzed by AI along with the user's medical history and prescription history. The input consists of vital data, medical history, and prescription history sent to the server, while the output is the analysis results. Specifically, the AI ​​model evaluates the user's health status based on this data.

[1020] Step 5:

[1021] The server generates diagnostic results and recommended actions based on AI analysis. The input is the AI ​​analysis results, and the output is the generated diagnostic results and recommended actions. The server uses prompt statements to generate the diagnostic results and inputs them into the generating AI model.

[1022] Step 6:

[1023] The server sends the diagnostic results and recommended actions to the communication terminal. The input is the generated diagnostic results and recommended actions, and the output is the result notified to the communication terminal. Specifically, a push notification is sent to the application on the communication terminal.

[1024] Step 7:

[1025] The application on the communication terminal displays diagnostic results and recommended actions to the user. The input is notification data sent from the server, and the output is information displayed on the user interface. Specifically, the user checks the diagnostic results and recommended actions on the app screen.

[1026] Step 8:

[1027] When a user utilizes the meal suggestion feature, they send their current vital data to the server using a communication terminal. The input is the current vital data, and the output is the data sent to the server.

[1028] Step 9:

[1029] The server analyzes vital data and suggests an optimal meal plan based on the user's health status. The input is the user's vital data, and the output is the suggested meal plan. Based on the analysis results, the menu is generated considering calories, nutritional balance, and allergy information.

[1030] Step 10:

[1031] The suggested meal menu is notified to the communication terminal, and the user places a delivery order directly through the app. The input is the suggested meal menu, and the output is a delivery order request. Specifically, the order data is sent from the application on the communication terminal to the partner delivery service.

[1032] Step 11:

[1033] The user inputs feedback on the diagnostic results and dietary suggestions into an application on a communication terminal. The input is the user's feedback, and the output is the feedback data sent to the server.

[1034] Step 12:

[1035] The server stores user feedback as AI training data, which will be used to improve diagnostic accuracy and meal recommendation accuracy in the future. The input is feedback data, and the output is updated training data for the AI ​​model. Specifically, by incorporating feedback data into the AI ​​model's learning algorithm, the accuracy of diagnoses and recommendations is improved.

[1036] 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.

[1037] This invention is a system that combines a wearable device, a communication terminal, a server, and an emotion engine to improve the quality of primary care provided to users. Specific embodiments for implementing the system of this invention are described below.

[1038] System Configuration

[1039] Wearable devices

[1040] The user wears a wearable device (e.g., a smartwatch). This wearable device has the function of monitoring vital data such as heart rate, body temperature, blood pressure, and steps in real time, and transmitting that data to a communication terminal.

[1041] Communication terminal

[1042] The user's communication device (e.g., a smartphone) receives and temporarily stores vital data transmitted from the wearable device. The communication device periodically sends this data to a server. It also provides an interface that allows the user to input symptoms and questions and send the data to the server.

[1043] server

[1044] The server receives vital data, medical history, and prescription history transmitted from the communication terminal and stores them in a central database. The AI ​​installed in the server analyzes this data, evaluates the user's health status, and generates a diagnosis. The generated diagnosis is then sent to the communication terminal and notified to the user.

[1045] Emotional Engine

[1046] The emotion engine is a device that recognizes emotions from the user's voice and facial expressions. The communication terminal uses the user's voice input and camera images to have the emotion engine perform emotional analysis. The emotional data obtained from the emotion engine is sent to a server and used to generate diagnostic results by AI.

[1047] System processing

[1048] Data collection

[1049] While the user is wearing the wearable device, the device periodically measures vital data such as heart rate, body temperature, blood pressure, and steps taken. This data is transmitted to a communication terminal using wireless communication such as Bluetooth. The communication terminal temporarily stores the received data and periodically sends it to a server.

[1050] Data analysis and generation of diagnostic results

[1051] The server is equipped with AI to analyze vital data sent by users in conjunction with their medical history and prescription history. Based on this data, the AI ​​assesses the user's health status and generates a diagnosis and recommended actions. The diagnosis may include assessments such as the possibility of a cold or the flu, and recommended actions may include purchasing over-the-counter medication, resting, and staying hydrated.

[1052] Improving the accuracy of emotion analysis and diagnosis

[1053] The communication terminal obtains analysis results from the emotion engine based on the voice input and camera images provided by the user. This emotion data, which indicates the user's stress level and emotional state, is sent to the server. The AI ​​analyzes this emotion data together with vital data to generate more accurate diagnostic results.

[1054] Notification of results and coordination with doctors

[1055] The diagnostic results generated by the server are sent to the communication terminal and notified to the user. The user can check the diagnostic results and take recommended actions through the app on the communication terminal. If the user wishes to have a more detailed consultation, they can use the communication terminal to schedule a video call with a doctor. The server provides the doctor with the accumulated data and AI-generated diagnostic results, which the doctor uses as a reference to make a final diagnosis.

[1056] Feedback and Learning

[1057] Users input feedback within the app regarding their recovery status after diagnosis and their impressions of the diagnosis. The communication device sends this feedback to the server, which stores it as training data for the AI. This improves the accuracy of the AI's diagnosis, enabling more accurate diagnoses in the future.

[1058] Specific example

[1059] Example 1: Daily health management

[1060] Users wear wearable devices daily to monitor their heart rate and body temperature. A communication terminal periodically sends this data to a server, where an AI analyzes it. For example, if the AI ​​detects an abnormal fluctuation in heart rate, a notification is sent to the communication terminal to alert the user. Additionally, an emotion engine recognizes the user's stress level from their voice and facial expressions, and this is reflected in the diagnostic results.

[1061] Example 2: Diagnosis when symptoms appear

[1062] When a user experiences a sore throat, they open the app on their communication device and enter their symptoms. The server analyzes past vital data and symptoms and diagnoses a high probability of a cold. The user is then notified of recommended over-the-counter medications and lifestyle advice. Additionally, if the user expresses pain or anxiety through voice or facial expressions, an emotion engine analyzes this data, which the AI ​​uses to improve the accuracy of the diagnosis.

[1063] Example 3: Video call with a doctor

[1064] If a user experiences a worsening of symptoms and wishes to see a doctor, they can book a video call with a doctor using the app on their communication device. At the time of booking, the server provides the doctor with previous diagnostic results, vital data, and emotional data, and during the video call, the doctor makes a final diagnosis while asking additional questions.

[1065] In this way, the present invention realizes a system that provides users with high-quality primary care and improves the efficiency and accuracy of medical services through the cooperation of a wearable device, a communication terminal, a server, and an emotion engine.

[1066] The following describes the processing flow.

[1067] Step 1: Data collection using wearable devices

[1068] The user wears a wearable device (e.g., a smartwatch).

[1069] The device (wearable device) periodically measures the user's vital data, such as heart rate, body temperature, blood pressure, and steps taken.

[1070] The device (wearable device) measures vital data and transmits it to the communication terminal (smartphone) using Bluetooth or similar technologies.

[1071] Step 2: Send vital data

[1072] The terminal (smartphone) temporarily stores vital data received from the wearable device.

[1073] The device (smartphone) periodically sends stored vital data to the server (for example, every hour).

[1074] Step 3: Appointment scheduling and medical interview

[1075] The user opens the dedicated app on their communication device (smartphone).

[1076] The user enters their symptoms and questions into the appointment booking form.

[1077] The device (smartphone) sends the entered information and the latest vital data to the server.

[1078] Step 4: Data Analysis

[1079] The server retrieves the user's past vital data, medical history, and prescription history from a central database.

[1080] Based on data acquired by the AI ​​on the server, the system evaluates the user's health status and generates diagnostic results and recommended actions.

[1081] Step 5: Generating diagnostic results

[1082] The emotion engine analyzes the user's emotions from their voice and facial expressions.

[1083] The device (smartphone) retrieves analysis results from the emotion engine and sends them to the server.

[1084] The AI ​​on the server integrates emotional data with vital data to improve diagnostic accuracy.

[1085] Step 6: Notification of diagnostic results

[1086] The server generates the final diagnostic results and recommended actions, and sends them to the communication terminal.

[1087] The device (smartphone) notifies the user of the diagnostic results and recommended actions.

[1088] Users can check their diagnostic results through an app on their communication device and take necessary actions (e.g., purchase over-the-counter medication, ensure they get enough rest).

[1089] Step 7: Video call coordination with the doctor

[1090] Users schedule video calls with doctors through apps on their communication devices.

[1091] The server provides the doctor with previous diagnostic results, vital data, and emotional analysis data at the scheduled time.

[1092] The doctor will conduct a video call to ask additional questions and make a final diagnosis.

[1093] Step 8: Feedback and Learning

[1094] Users enter their recovery status and evaluation after diagnosis into a feedback form within the app.

[1095] The device (smartphone) sends feedback to the server.

[1096] The server saves the feedback as training data for the AI ​​and uses it to improve diagnostic accuracy in the future.

[1097] The above outlines the specific processing steps of the system combining the emotion engine, and the specific actions performed by each entity within that process.

[1098] (Example 2)

[1099] 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."

[1100] Traditional health management systems rely solely on users' vital data for diagnosis, neglecting psychological factors such as emotions and stress levels. This can lead to decreased diagnostic accuracy and difficulty in providing appropriate medical advice to users. Furthermore, the mechanisms for efficiently collecting feedback and utilizing it for AI learning were insufficient, hindering improvements in AI diagnostic accuracy.

[1101] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for generating a diagnosis result using AI that analyzes vital data, medical history, prescription history, and emotional data; means for an emotional engine to recognize emotions from the user's voice and facial expressions and transmit them to the server as data; and means for the server to store the user's feedback as learning data. This makes it possible to perform multifaceted data analysis including the user's psychological elements, thereby improving diagnostic accuracy.

[1102] A "wearable device" is a device that a user wears to collect vital data such as heart rate, body temperature, blood pressure, and steps taken.

[1103] A "communication terminal" is a device that receives vital data transmitted from a wearable device, temporarily stores it, and sends it to a server.

[1104] A "server" is a device that receives vital data, medical history, prescription history, and emotional data transmitted from a communication terminal, analyzes them, and generates diagnostic results.

[1105] "AI" refers to artificial intelligence technology used to analyze vital data, medical history, prescription history, and emotional data, and to generate diagnostic results.

[1106] "Diagnosis results" refer to an assessment of the user's health status and recommended actions, generated based on the analysis of vital data, medical history, prescription history, and emotional data.

[1107] An "emotion engine" is a device that recognizes emotions from a user's voice and facial expressions and analyzes that data.

[1108] "Feedback" refers to information that users input regarding their recovery status after diagnosis and their impressions of the diagnosis.

[1109] A "video call" is a conversation between a user and a doctor face-to-face via a communication device, using both audio and video.

[1110] "Recommended actions" refer to advice and instructions on what actions to take that are presented to the user based on the diagnostic results.

[1111] Modes for carrying out the invention

[1112] This invention is a system that provides high-quality primary care to users by linking a wearable device, a communication terminal, a server, and an emotion engine. Specific embodiments of this system will be described below.

[1113] Hardware and software to use

[1114] Wearable devices

[1115] The user wears a wearable device (e.g., a smartwatch). This device monitors vital data such as heart rate, body temperature, blood pressure, and steps taken in real time and transmits the data to a communication terminal using Bluetooth.

[1116] Communication terminal

[1117] The user's communication device (e.g., a smartphone) receives and temporarily stores vital data transmitted from the wearable device. The communication device periodically sends this data to a server. It also provides a function to allow the user to input symptoms and questions through a dedicated application and send this information to the server.

[1118] server

[1119] The server receives vital data, medical history, prescription history, and emotional data transmitted from communication terminals and stores them in a central database. The server is equipped with AI models using TensorFlow, PyTorch, etc., which are used to comprehensively analyze the data and generate diagnostic results.

[1120] Emotional Engine

[1121] The emotion engine is a device that recognizes emotions from the user's voice and facial expressions. The communication terminal sends data to the emotion engine based on voice input and camera footage, and receives the analysis results. These results are sent to a server and integrated into the AI ​​analysis.

[1122] Specific examples and prompt statements

[1123] Example 1: Daily health management

[1124] Users wear a wearable device daily, which monitors their heart rate and body temperature. A communication terminal periodically sends this data to a server, where the server's AI analyzes it. For example, if an abnormal fluctuation in heart rate is detected, a notification is sent to the communication terminal to alert the user. In addition, an emotion engine recognizes the user's stress level from their voice and facial expressions and reflects this in the diagnostic results.

[1125] Example of a prompt:

[1126] "Please analyze my heart rate monitoring results and let me know if there are any health problems. Also, please take my recent emotional state into consideration."

[1127] Example 2: Diagnosis when symptoms appear

[1128] When a user experiences a sore throat, they open an app on their communication device and enter their symptoms. The server analyzes past vital data and symptoms and diagnoses a high probability of a cold. The user is then notified of recommended over-the-counter medications and lifestyle advice. Additionally, if the user expresses pain or anxiety through voice or facial expressions, an emotion engine analyzes this data, which the AI ​​uses to improve the accuracy of the diagnosis.

[1129] Example of a prompt:

[1130] "I have a sore throat. Please provide a diagnosis and recommended actions based on my past health and emotional data."

[1131] Example 3: Video call with a doctor

[1132] If a user experiences a worsening of symptoms and wishes to see a doctor, they can book a video call with a doctor using the app on their communication device. At the time of booking, the server provides the doctor with previous diagnostic results, vital data, and emotional data, and during the video call, the doctor makes a final diagnosis while asking additional questions.

[1133] Example of a prompt:

[1134] "I would like to schedule a video call with a doctor. I would like to send my previous diagnostic data to the doctor and request an additional diagnosis during the video call."

[1135] This invention enables the realization of a system that provides high-quality primary care to users through the cooperation of a wearable device, a communication terminal, a server, and an emotion engine, thereby improving the efficiency and accuracy of medical services.

[1136] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1137] Step 1:

[1138] The user wears a wearable device that collects vital data such as heart rate, body temperature, blood pressure, and steps taken. Specifically, the device's sensors measure this data every minute and temporarily store it in its internal memory. The input is the user's vital data, and the output is the vital data stored in the device.

[1139] Step 2:

[1140] The wearable device uses Bluetooth to transmit collected vital data to a communication terminal. Specifically, the wearable device's data transmission function is activated, and a Bluetooth connection is established. The input is the vital data stored on the device, and the output is the vital data transmitted to the communication terminal.

[1141] Step 3:

[1142] The communication terminal receives vital data transmitted from the wearable device and stores it temporarily. This data is later sent to a server for analysis. Specifically, the communication terminal's application runs in the background, receiving data and saving it to its internal storage. The input is the transmitted vital data, and the output is the vital data stored in the communication terminal.

[1143] Step 4:

[1144] The communication terminal periodically sends stored vital data to the server. For example, it is set up so that data is sent in a batch every night. Specifically, the communication terminal's application executes a process to send data to the server at a set time. The input is the vital data stored on the communication terminal, and the output is the vital data sent to the server.

[1145] Step 5:

[1146] The server receives vital data transmitted from communication terminals and stores it in a central database. Specifically, the server's API endpoint receives a request and executes the process of saving the data to the database. The input is the vital data sent to the server, and the output is the data stored in the database.

[1147] Step 6:

[1148] The AI ​​installed on the server analyzes vital data, medical history, prescription history, and sentiment data. Specifically, the server runs machine learning models trained using TensorFlow or PyTorch to assess health status and generate diagnostic results. The input is vital data, medical history, prescription history, and sentiment data stored in a database, and the output is the generated diagnostic result.

[1149] Step 7:

[1150] The server sends the generated diagnostic results to the communication terminal and notifies the user. Specifically, the server sends a notification containing the diagnostic results to the application on the communication terminal, and the application displays a push notification. The input is the generated diagnostic results, and the output is the push notification on the communication terminal.

[1151] Step 8:

[1152] Users can view their diagnostic results and take recommended actions through an app on their communication device. If they wish to have a more detailed consultation, they can use the app to schedule a video call with a doctor. Specifically, the app on the communication device displays the diagnostic results to the user and provides a video call scheduling function. The input is the diagnostic results, and the output is the video call scheduling.

[1153] Step 9:

[1154] The emotion engine recognizes emotions from the user's voice and facial expressions and analyzes that data. Specifically, the communication terminal's application uses the microphone and camera to capture voice and facial expressions and sends them to the emotion engine. The engine then sends the analysis results to the server. The input is voice and facial expression data, and the output is the analyzed emotion data.

[1155] Step 10:

[1156] Users input feedback within the app regarding their recovery status after diagnosis and their impressions of the diagnosis. The communication device sends this feedback to the server, which stores it as training data for the AI. Specifically, the app on the communication device receives feedback from the user and sends the data to the server. The input is the user's feedback, and the output is the feedback data stored on the server.

[1157] This allows the system to perform multifaceted data analysis, provide users with highly accurate diagnostic results, and improve the efficiency and accuracy of medical services.

[1158] (Application Example 2)

[1159] 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."

[1160] Modern health management requires the collection and analysis of daily vital data to understand individual health conditions in real time and provide appropriate medical care and lifestyle guidance. However, previous systems often evaluated health conditions without considering the user's emotional state, resulting in inaccurate diagnoses and recommended behaviors. Furthermore, there was a lack of dietary suggestions based on the user's health condition, making it difficult to provide meals that met individual needs.

[1161] 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. In this invention, the server includes means for generating diagnostic results using AI that analyzes vital data, medical history, and prescription history; means for analyzing the user's emotional data using an emotion engine and reflecting it in the diagnostic results; and means for recommending the optimal diet based on health status and emotional data. This generates highly accurate diagnostic results that take into account the user's emotional state, and further enables dietary suggestions tailored to the individual's health condition.

[1162] A "wearable device" is a device worn by a user to collect vital data such as heart rate, body temperature, blood pressure, and steps taken.

[1163] A "communication terminal" is a device that has the function of receiving and storing vital data transmitted from a wearable device and sending it to a server.

[1164] A "server" is a device that stores vital data, medical history, and prescription history transmitted from communication terminals in a central database, analyzes it, and generates diagnostic results.

[1165] "AI" refers to an algorithm installed on a server that analyzes vital data, medical history, and prescription history to evaluate health status and generate diagnostic results.

[1166] "Diagnosis results" refer to an assessment of the user's health status generated by AI based on vital data and other information.

[1167] An "emotion engine" is a device that recognizes and analyzes emotions from a user's voice and facial expressions.

[1168] "Emotional data" refers to data that indicates the user's stress level and emotional state, analyzed by the emotion engine.

[1169] "Recommended actions" refer to information indicating the actions a user should take based on their diagnostic results.

[1170] "Health status" refers to the user's overall health condition, evaluated based on vital data such as heart rate, body temperature, blood pressure, and steps taken.

[1171] "Meal suggestions" refer to information that proposes the optimal meal plan based on the user's health status and emotional data.

[1172] This invention is a system that combines a wearable device, a communication terminal, a server, and an emotion engine to achieve more accurate user health management and dietary recommendations. Specific embodiments for carrying out the invention are described below.

[1173] System Configuration

[1174] Wearable devices

[1175] The user wears a wearable device (e.g., a smartwatch). This device measures vital data such as heart rate, body temperature, blood pressure, and steps in real time and transmits it to a communication terminal using wireless communication such as Bluetooth or Wi-Fi.

[1176] Communication terminal

[1177] The user's communication device (e.g., a smartphone) receives and temporarily stores vital data transmitted from the wearable device. Furthermore, it has the functionality to collect the user's voice and facial expressions using a camera and microphone and transmit them to an emotion engine. The communication device periodically sends this data to a server.

[1178] server

[1179] The server receives vital data, emotional data, medical history, and prescription history transmitted from the communication terminal and stores them in a central database. Furthermore, the server is equipped with AI, which analyzes this data to assess the user's health status and generate a diagnosis. The diagnosis is then communicated to the user via the communication terminal. The server also suggests an optimal diet based on the assessment.

[1180] Emotional Engine

[1181] The emotion engine recognizes emotions from the user's voice and facial expressions and generates data indicating their emotional state. The communication terminal transmits voice input and camera footage to the emotion engine for emotion analysis. The emotion data is sent to a server and used to generate diagnostic results.

[1182] Program processing

[1183] Data collection

[1184] A wearable device periodically measures the user's vital data, such as heart rate, body temperature, blood pressure, and steps taken, and transmits this data to a communication terminal. The communication terminal temporarily stores the vital data and then transmits it to a server.

[1185] Emotion analysis

[1186] The communication terminal uses an emotion engine to analyze the user's voice input and camera footage. The emotion engine analyzes this data and generates data indicating the user's emotional state. This emotional data is sent to a server and used to assess their health status.

[1187] Data analysis and generation of diagnostic results

[1188] The server is equipped with AI to analyze vital data, medical history, prescription history, and emotional data. Based on this data, the AI ​​assesses the user's health status and generates a diagnosis, recommended actions, and optimal dietary suggestions. This information is transmitted to the communication terminal and notified to the user.

[1189] Results notification and meal suggestions

[1190] The diagnostic results and meal suggestions generated by the server are sent to the communication terminal and notified to the user. The user can check the diagnostic results and meal suggestions through the app on the communication terminal and take the recommended actions.

[1191] Collaboration with doctors

[1192] If a user wishes to have a more detailed consultation, they can book a video call with a doctor using their communication device. At the time of booking, the server provides the doctor with previous diagnostic results, vital data, and emotional data, which will be used for the final diagnosis.

[1193] Specific example

[1194] For example, a user wears a wearable device daily to monitor their heart rate and body temperature. The communication terminal periodically sends this data to a server, where the server's AI analyzes it. If the AI ​​detects an abnormal fluctuation in heart rate, a notification is sent to the communication terminal to alert the user. In addition, an emotion engine recognizes the user's stress level from their voice and facial expressions, and this is reflected in the diagnostic results.

[1195] Example of a prompt

[1196] In the food delivery app you develop, please consider the following data to suggest the best meal options to users.

[1197] Input data:

[1198] Heart rate: 80

[1199] Body temperature: 36.5°C

[1200] Blood pressure: 120 / 80

[1201] Steps: 5000

[1202] Emotional state: Stress

[1203] output:

[1204] Please suggest meals that can help reduce the stress users are experiencing. The menu should be nutritionally balanced and include relaxing elements.

[1205] example:

[1206] Herbal tea

[1207] A light salad or a late-night snack

[1208] Low-sugar smoothies

[1209] The above describes a specific embodiment for carrying out the present invention. This enables highly accurate health management and dietary recommendations that take into account the user's emotional state.

[1210] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1211] Step 1:

[1212] The user wears a wearable device that collects vital data such as heart rate, body temperature, blood pressure, and steps taken. The wearable device transmits this data to a communication terminal using wireless communication such as Bluetooth.

[1213] Input: Vital data (heart rate, body temperature, blood pressure, steps)

[1214] Output: Data transmission to communication terminal

[1215] Specific operation: The wearable device measures vital data in real time and transmits the data to a communication terminal using Bluetooth.

[1216] Step 2:

[1217] The communication terminal receives and temporarily stores vital data transmitted from the wearable device. Simultaneously, it collects user emotion data using audio and camera footage.

[1218] Input: Vital data from wearable devices, user voice data, camera footage

[1219] Output: Temporary storage of data to send to the server

[1220] Specific operation: The communication terminal receives vital data via Bluetooth, and uses the microphone and camera to collect and temporarily store the user's voice and facial expression data.

[1221] Step 3:

[1222] The communication terminal periodically sends temporarily stored vital data and emotional data to the server.

[1223] Input: Temporarily stored vital data and emotional data

[1224] Output: Sending data to the server

[1225] Specific operation: The communication device uploads data to the server using Wi-Fi or mobile data communication.

[1226] Step 4:

[1227] The server uses AI to analyze vital and emotional data it receives. The AI ​​also analyzes medical history and prescription history to generate a diagnosis.

[1228] Input: Vital data, emotional data, medical history, prescription history

[1229] Output: Diagnostic results

[1230] Specific operation: The server uses an AI algorithm to analyze data, evaluate the user's health status, and generate a diagnostic result.

[1231] Step 5:

[1232] The server generates diagnostic results, which are then sent to the communication terminal to notify the user. Simultaneously, dietary suggestions based on the user's health status are also provided.

[1233] Input: Diagnostic result

[1234] Output: Sending diagnostic results and meal suggestions to the communication terminal.

[1235] Specific operation: The server generates diagnostic results and meal suggestions, and sends notifications to the communication terminal.

[1236] Step 6:

[1237] Users can view their diagnostic results and meal suggestions via a communication device. If necessary, they can also place orders based on the meal suggestions.

[1238] Input: Diagnostic results and dietary suggestions sent to the communication terminal.

[1239] Output: User confirmation and order

[1240] Specific operation: The user checks the notification on the app on their communication device and places an order based on the meal suggestion if necessary.

[1241] Step 7:

[1242] Users input feedback on their diagnostic results and meal suggestions through the app and send it to the server. The server stores this feedback as training data for the AI.

[1243] Input: User feedback

[1244] Output: AI training data

[1245] Specific operation: The user enters feedback in the app, and the server receives it and saves it as training data.

[1246] The above outlines the specific processing steps of the system based on the present invention. This enables user health management and personalized meal suggestions.

[1247] 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.

[1248] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1249] 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.

[1250] [Fourth Embodiment]

[1251] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1252] 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.

[1253] 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).

[1254] 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.

[1255] 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.

[1256] 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).

[1257] 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.

[1258] 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.

[1259] 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.

[1260] 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.

[1261] 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.

[1262] 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.

[1263] 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".

[1264] This invention is a system that combines a wearable device, a communication terminal, and a server to improve the quality of primary care provided to users. Specific embodiments for implementing this system are described below.

[1265] System Configuration

[1266] Wearable devices

[1267] The user wears a wearable device (e.g., a smartwatch). This wearable device has the function of monitoring vital data such as heart rate, body temperature, blood pressure, and steps in real time, and transmitting that data to a communication terminal.

[1268] Communication terminal

[1269] The user's communication device (e.g., a smartphone) receives and temporarily stores vital data transmitted from the wearable device. The communication device periodically sends this data to a server. It also provides an interface that allows the user to input symptoms and questions and send the data to the server.

[1270] server

[1271] The server receives vital data, medical history, and prescription history transmitted from the communication terminal and stores them in a central database. The AI ​​installed in the server analyzes this data, evaluates the user's health status, and generates a diagnosis. The generated diagnosis is then sent to the communication terminal and notified to the user.

[1272] System processing

[1273] Data collection

[1274] While the user is wearing the wearable device, the device periodically measures vital data such as heart rate, body temperature, blood pressure, and steps taken. This data is transmitted to a communication terminal using wireless communication such as Bluetooth. The communication terminal temporarily stores the received data and periodically sends it to a server.

[1275] Data analysis and generation of diagnostic results

[1276] The server is equipped with AI to analyze vital data sent by users in conjunction with their medical history and prescription history. Based on this data, the AI ​​assesses the user's health status and generates a diagnosis and recommended actions. The diagnosis may include assessments such as the possibility of a cold or the flu, and recommended actions may include purchasing over-the-counter medication, resting, and staying hydrated.

[1277] Notification of results and coordination with doctors

[1278] The diagnostic results generated by the server are sent to the communication terminal and notified to the user. The user can check the diagnostic results and take recommended actions through the app on the communication terminal. If the user wishes to have a more detailed consultation, they can use the communication terminal to schedule a video call with a doctor. The server provides the doctor with the accumulated data and AI-generated diagnostic results, which the doctor uses as a reference to make a final diagnosis.

[1279] Feedback and Learning

[1280] Users input feedback within the app regarding their recovery status after diagnosis and their impressions of the diagnosis. The communication device sends this feedback to the server, which stores it as training data for the AI. This improves the accuracy of the AI's diagnosis, enabling more accurate diagnoses in the future.

[1281] Specific example

[1282] Example 1: Daily health management

[1283] Users wear wearable devices daily to monitor their heart rate and body temperature. A communication terminal periodically sends this data to a server, where an AI analyzes it. For example, if the AI ​​detects an abnormal fluctuation in heart rate, a notification is sent to the communication terminal to alert the user.

[1284] Example 2: Diagnosis when symptoms appear

[1285] When a user experiences a sore throat, they open the app on their communication device and enter their symptoms. The server analyzes past vital data and symptoms and diagnoses a high probability of a cold. The user is then notified of recommended over-the-counter medications and lifestyle advice.

[1286] Example 3: Video call with a doctor

[1287] If a user experiences a worsening of symptoms and wishes to see a doctor, they can book a video call with a doctor using the app on their communication device. At the time of booking, the server provides the doctor with previous diagnostic results and vital data, and during the video call, the doctor makes a final diagnosis while asking additional questions.

[1288] In this way, the present invention realizes a system that provides users with high-quality primary care and improves the efficiency and accuracy of medical services through the cooperation of a wearable device, a communication terminal, and a server.

[1289] The following describes the processing flow.

[1290] Step 1: Data Collection

[1291] The user wears a wearable device (e.g., a smartwatch).

[1292] The device (wearable device) periodically measures vital data such as heart rate, body temperature, blood pressure, and steps taken.

[1293] The device (wearable device) measures vital data and transmits it to a communication terminal (smartphone) via wireless communication such as Bluetooth.

[1294] Step 2: Data transmission

[1295] The device (smartphone) temporarily stores vital data.

[1296] The device (smartphone) periodically (for example, every hour) sends stored vital data to the server.

[1297] Step 3: Appointment scheduling and medical interview

[1298] The user opens the dedicated app on their communication device (smartphone).

[1299] The user fills in their symptoms and questions in the app's input form.

[1300] The device (smartphone) sends the entered information and the latest vital data to the server.

[1301] Step 4: Data Analysis

[1302] The server retrieves the user's past vital data, medical history, and prescription history from a central database.

[1303] The AI ​​on the server analyzes this data and assesses the current health status of the user.

[1304] The server lists potential diagnoses and recommended actions based on the analysis results.

[1305] Step 5: Notification of diagnostic results

[1306] The server sends the analysis results to the communication terminal.

[1307] The device (smartphone) notifies the user of the diagnostic results and recommended actions.

[1308] The user reviews the diagnosis results and takes appropriate action (e.g., buys over-the-counter medication, takes rest).

[1309] Step 6: Collaboration with doctors

[1310] Users schedule video calls with doctors through the app.

[1311] The server provides doctors with AI analysis results and vital data at the scheduled time.

[1312] The doctor will use this data to ask additional questions and make a final diagnosis.

[1313] Step 7: Feedback and Learning

[1314] Users input their recovery status and evaluation after diagnosis within the app.

[1315] The device (smartphone) sends feedback information to the server.

[1316] The server saves the feedback as training data for the AI, which will be used to improve the accuracy of future diagnoses.

[1317] The above outlines the specific steps in the overall system processing flow and describes the actions performed at each step.

[1318] (Example 1)

[1319] 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".

[1320] In modern healthcare settings, there is a demand for monitoring individual health conditions and rapid diagnosis, but conventional systems are insufficient in terms of efficiency and accuracy. Furthermore, there is a lack of mechanisms to properly incorporate user feedback and continuously improve the system's diagnostic accuracy. As a result, users are often unable to receive accurate and timely healthcare services and are forced to rely on card-based medical services.

[1321] 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.

[1322] In this invention, the server includes means for generating diagnostic results using artificial intelligence that analyzes biometric data, medical history, and prescription history; means for presenting the diagnostic results to the individual's communication device; and means for collecting individual feedback and using it to train the artificial intelligence. This allows users to receive accurate and rapid diagnoses, improving the quality of medical services. Furthermore, the system can continuously learn from individual feedback, improving diagnostic accuracy.

[1323] A "biometric information sensing device" is a device used to measure an individual's biological data. Specifically, it refers to a device that has the function of measuring heart rate, body temperature, blood pressure, steps taken, etc.

[1324] A "communication device" is a device that has the function of receiving data transmitted from a biometric information sensing device and transferring it to a data processing device. Examples include mobile devices such as smartphones.

[1325] A "data processing device" is a device that receives biometric data transmitted from a communication device, stores it in a central database, and performs analysis. Typically, cloud servers and data centers fall into this category.

[1326] "Artificial intelligence" refers to algorithms and software used to analyze large amounts of data, recognize patterns, and generate diagnostic results. This primarily includes machine learning models and neural networks.

[1327] "Diagnosis results" refer to the assessment of health status and recommended actions obtained by artificial intelligence analyzing biometric data, medical history, and prescription history. Specifically, this refers to the identification of medical conditions and the suggestion of treatment methods.

[1328] "Video communication" is a means for users to interact with medical professionals remotely, and is usually conducted via video call applications.

[1329] "Feedback" refers to information in which users provide evaluations and opinions on diagnostic results and services. This is used to improve the system and enhance the accuracy of the diagnostics.

[1330] "Recommended actions" refer to behavioral guidelines suggested by artificial intelligence based on the diagnostic results, and include specific health management methods and countermeasures that users should follow.

[1331] "Training data" refers to a dataset used by a data processing device to allow artificial intelligence to learn from feedback and other data, thereby improving its diagnostic accuracy.

[1332] Through the above definitions, we clarify how this system combines its elements to provide users with high-quality primary care.

[1333] This invention is a system that combines a wearable device, a communication terminal, and a server to improve the quality of primary care provided to users. Specific embodiments for implementing the system of this invention are described below.

[1334] System Configuration

[1335] Wearable devices

[1336] The user wears a wearable device (e.g., a smartwatch). This allows for real-time monitoring of vital data such as heart rate, body temperature, blood pressure, and steps taken. The wearable device transmits this data to a communication terminal via wireless communication such as Bluetooth.

[1337] Communication terminal

[1338] The user's communication device (e.g., a smartphone) has the function of receiving and temporarily storing vital data transmitted from a wearable device. The communication device periodically sends this data to a server. It also provides an interface that allows the user to input symptoms and questions and send the data to the server.

[1339] server

[1340] The server receives vital data, medical history, and prescription history transmitted from the communication terminal and stores them in a central database. The server is equipped with AI (artificial intelligence) that analyzes this data to assess the user's health status and generates diagnostic results and recommended actions. The generated diagnostic results are sent to the communication terminal and notified to the user.

[1341] Specific example

[1342] Example 1: Daily health management

[1343] Users wear wearable devices daily to monitor their heart rate and body temperature. A communication terminal periodically sends this data to a server, where an AI analyzes it. For example, if the AI ​​detects an abnormal fluctuation in heart rate, a notification is sent to the communication terminal to alert the user.

[1344] Example of a prompt:

[1345] "An abnormally high heart rate was detected this morning. Please rest immediately and rehydrate."

[1346] Example 2: Diagnosis when symptoms appear

[1347] When a user experiences a sore throat, they open the app on their communication device and enter their symptoms. The server analyzes past vital data and symptoms to diagnose the possibility of a cold. The user is then notified of recommended over-the-counter medications and lifestyle advice.

[1348] Example of a prompt:

[1349] "If you have a sore throat, you may have a cold. Please use the over-the-counter medicine 'Pabron S Gold' and get plenty of rest."

[1350] Example 3: Video call with a doctor

[1351] If a user experiences a worsening of symptoms and wishes to see a doctor, they can book a video call with a doctor using the app on their communication device. At the time of booking, the server provides the doctor with previous diagnostic results and vital data, and during the video call, the doctor makes a final diagnosis while asking additional questions.

[1352] Example of a prompt:

[1353] "If your sore throat persists, please schedule a video consultation with a doctor. Your next appointment is tomorrow at 2 PM."

[1354] In this way, the present invention realizes a system that provides users with high-quality primary care and improves the efficiency and accuracy of medical services through the cooperation of a wearable device, a communication terminal, and a server.

[1355] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1356] Step 1:

[1357] Data collection

[1358] The user wears a wearable device that measures biometric data such as heart rate, body temperature, blood pressure, and steps taken in real time. This data is temporarily stored within the wearable device and transmitted to a communication terminal via wireless communication such as Bluetooth. The input is the user's biometric information, and the output is the biometric data transferred to the communication terminal. Specifically, while the user is jogging, the smartwatch measures their heart rate every minute and transmits it to their smartphone via Bluetooth.

[1359] Step 2:

[1360] Data transmission

[1361] The communication terminal (smartphone) receives biometric data sent from the wearable device and temporarily stores it locally. Furthermore, it periodically sends this data to the server. The input is the biometric data received from the wearable device, and the output is the data sent to the server. Specifically, at 10 PM every day, the smartphone synchronizes with the server and sends all of the day's biometric data at once.

[1362] Step 3:

[1363] Data Analysis

[1364] The server receives biometric data transmitted from communication terminals and stores it in a central database. Furthermore, the AI ​​within the server analyzes this data in combination with medical history and prescription history. The inputs are biometric data, medical history, and prescription history, and the output is the analysis results. Specifically, the AI ​​analyzes a large amount of data and detects whether there are any specific patterns (such as a sudden increase in heart rate).

[1365] Step 4:

[1366] Diagnosis result generation

[1367] The AI ​​on the server generates diagnostic results and recommended actions based on the analysis results. The diagnostic results include a specific assessment of health status and recommended actions. The input is the results of data analysis, and the output is the generated diagnostic results and recommended actions. Specifically, the AI ​​compares heart rate variability with past data and diagnoses that there is a 50% or higher probability of having a cold.

[1368] Step 5:

[1369] Result notification

[1370] The server sends the generated diagnostic results to the communication terminal, which then notifies the user. The input is the diagnostic results, and the output is the notification displayed on the communication terminal. Specifically, the smartphone displays a pop-up notification informing the user, "You may have a cold. Please rest and stay well-hydrated."

[1371] Step 6:

[1372] Collaboration with doctors

[1373] If a user wishes to have a detailed consultation, they can schedule a video call with a doctor using an app on their communication device. The server provides the doctor with the diagnosis results and past vital data. Inputs include the user's appointment request and related data, and output is the data provided to the doctor. Specifically, the user opens the app and schedules a video call with a doctor for 2 PM tomorrow. The server then sends the necessary data to the doctor.

[1374] Step 7:

[1375] Feedback Collection

[1376] Users input feedback into the app regarding their recovery status after diagnosis and their impressions of the system. The communication device sends this feedback to the server, which stores it as training data for the AI. The input is user feedback, and the output is AI training data. Specifically, the user reports within the app that their cold has cleared up and inputs an evaluation of whether the advice provided was helpful.

[1377] In this way, each step is organically linked, resulting in a system that can provide users with high-quality primary care.

[1378] (Application Example 1)

[1379] 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".

[1380] Traditional systems did not provide personalized meal suggestions based on the user's health status, making it difficult for users to easily select meals suitable for their health. Furthermore, the lack of a system that integrated health monitoring and meal suggestions meant users had to manage their health and diet separately. This resulted in inefficient health management and increased user effort.

[1381] 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.

[1382] In this invention, the server includes means for a wearable device to collect the user's vital data, means for transmitting the vital data to a communication terminal, means for the communication terminal to transmit the collected vital data to the server, means for the server to generate a diagnosis result using AI that analyzes the vital data, medical history, and prescription history, means for notifying the user's communication terminal of the diagnosis result, means for the user to receive a final diagnosis through a video call with a doctor, means for collecting user feedback that the server uses to train the AI, means for suggesting an optimal meal menu based on the analysis results, and means for ordering the suggested menu for delivery. This enables real-time monitoring of the user's health status and personalized meal suggestions based on the results. Furthermore, by instantly ordering the suggested meal for delivery, the efficiency of the user's health management and meal management is improved, and the effort involved is significantly reduced.

[1383] A "wearable device" is a small electronic device that a user wears to collect vital data such as heart rate, body temperature, and blood pressure in real time.

[1384] "Vital data" refers to data that indicates the user's biological state, such as heart rate, body temperature, blood pressure, and steps taken.

[1385] A "communication terminal" is an electronic device used to receive data transmitted from wearable devices such as smartphones and tablets, and to transmit that data to a server.

[1386] A "server" is a high-performance computer system that receives, stores, and analyzes data transmitted from communication terminals.

[1387] "AI" refers to artificial intelligence technology that analyzes data on a server and generates diagnostic results, meal menus, and other similar information.

[1388] "Diagnosis results" refer to information that shows an evaluation of the user's health status based on the results of AI analysis.

[1389] A "meal menu" refers to a combination of appropriate meals suggested by AI based on the user's health status and vital data.

[1390] "Delivery ordering" refers to the act of requesting a meal from a suggested menu via an information terminal and having the meal delivered.

[1391] "Feedback" refers to information that users report through the application, including their impressions of the system's diagnostic results and suggestions, as well as their actual actions.

[1392] "Analysis results" refer to the overall evaluation and diagnosis performed by the AI ​​based on vital data and other relevant data.

[1393] This invention is a system that combines a wearable device, a communication terminal, and a server to improve the quality of primary care provided to users. The system of this invention is specifically implemented as follows.

[1394] System Configuration

[1395] Wearable devices

[1396] The user wears a wearable device (e.g., a smartwatch). This wearable device monitors vital data such as heart rate, body temperature, blood pressure, and steps taken in real time and transmits it to a communication terminal via wireless communication such as Bluetooth.

[1397] Communication terminal

[1398] The user's communication device (e.g., a smartphone) receives and temporarily stores vital data transmitted from the wearable device. The communication device periodically sends this data to the server. It also provides an interface that allows the user to input questions about symptoms and diet and send the data to the server.

[1399] server

[1400] The server receives vital data, medical history, and prescription history transmitted from the communication terminal and stores them in a central database. The AI ​​installed in the server analyzes this data, evaluates the user's health status, and generates a diagnosis. It also has a function to suggest a personalized and optimal meal plan based on the generated diagnosis. The suggested menu is notified to the communication terminal, and the user can then place a delivery order directly.

[1401] System processing

[1402] Data collection

[1403] While the user is wearing the wearable device, the device periodically measures vital data such as heart rate, body temperature, blood pressure, and steps taken. This data is transmitted to a communication terminal using wireless communication such as Bluetooth. The communication terminal temporarily stores the received data and periodically sends it to a server.

[1404] Data analysis and generation of diagnostic results

[1405] The server is equipped with AI to analyze vital data sent by users in conjunction with their medical history and prescription history. Based on this data, the AI ​​assesses the user's health status and generates a diagnosis and recommended actions. The diagnosis may include assessments such as the possibility of a cold or the flu, and recommended actions may include purchasing over-the-counter medication, resting, and staying hydrated.

[1406] Suggestions for meal menus

[1407] Based on the analysis results, the AI ​​suggests a meal plan best suited to the user's health condition. The suggested menu is generated considering calories, nutritional balance, and allergy information, and is notified to the user's communication device. The user can then directly order the meal for delivery.

[1408] Notification of results and coordination with doctors

[1409] The diagnostic results and meal plans generated by the server are sent to the communication terminal and notified to the user. The user can check the diagnostic results and meal plans through the app on the communication terminal and take the recommended actions. If the user wishes to have a more detailed consultation, they can use the communication terminal to schedule a video call with a doctor. The server provides the doctor with the accumulated data and AI-generated diagnostic results, and the doctor uses this information to make a final diagnosis.

[1410] Feedback and Learning

[1411] Users input information within the app regarding their recovery status after diagnosis, their impressions of the diagnosis, and feedback on the suggested meal menu. The communication device sends this feedback to the server, which stores it as training data for the AI. This improves the accuracy of the AI's diagnosis and meal suggestions, enabling more accurate diagnoses and suggestions in the future.

[1412] Specific example

[1413] Example 1: Daily health management

[1414] Users wear wearable devices daily to monitor their heart rate and body temperature. A communication terminal periodically sends this data to a server, where an AI analyzes it. For example, if the AI ​​detects an abnormal fluctuation in heart rate, a notification is sent to the communication terminal to alert the user.

[1415] Example 2: Diagnosis when symptoms appear

[1416] When a user experiences a sore throat, they open the app on their communication device and enter their symptoms. The server analyzes their past vital data and symptoms and diagnoses a high probability of a cold. The user is then notified with information on recommended over-the-counter medications and lifestyle advice.

[1417] Example 3: Suggested meal menus and delivery orders

[1418] If a user wants to receive meal suggestions based on their health status, they send their current vital data to a server via a communication device. AI analyzes this data and suggests an optimal meal menu that takes into account calories, nutritional balance, and allergy information. The user can then order the suggested menu for delivery, improving the efficiency of their health management.

[1419] Example of a prompt

[1420] "Please suggest the optimal diet based on your health condition. Generate an AI module that analyzes your heart rate, body temperature, and blood pressure data from the past week and proposes an appropriate meal plan."

[1421] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1422] Step 1:

[1423] The user wears a wearable device (e.g., a smartwatch) and begins measuring vital data (heart rate, body temperature, blood pressure, steps, etc.). The input is the user's biometric information, and the output is real-time measured vital data. The wearable device transmits this data to a communication terminal via Bluetooth.

[1424] Step 2:

[1425] A communication terminal (e.g., a smartphone) receives vital data transmitted from a wearable device. The input is vital data transmitted via Bluetooth, and the output is vital data temporarily stored on the communication terminal. This allows for temporary storage and processing of the data.

[1426] Step 3:

[1427] The communication terminal periodically sends temporarily stored vital data to the server. The input is the temporarily stored vital data, and the output is the data sent to the server. Specifically, the data is uploaded from the communication terminal to the server via the internet.

[1428] Step 4:

[1429] The server stores the received vital data in a central database, which is then analyzed by AI along with the user's medical history and prescription history. The input consists of vital data, medical history, and prescription history sent to the server, while the output is the analysis results. Specifically, the AI ​​model evaluates the user's health status based on this data.

[1430] Step 5:

[1431] The server generates diagnostic results and recommended actions based on AI analysis. The input is the AI ​​analysis results, and the output is the generated diagnostic results and recommended actions. The server uses prompt statements to generate the diagnostic results and inputs them into the generating AI model.

[1432] Step 6:

[1433] The server sends the diagnostic results and recommended actions to the communication terminal. The input is the generated diagnostic results and recommended actions, and the output is the result notified to the communication terminal. Specifically, a push notification is sent to the application on the communication terminal.

[1434] Step 7:

[1435] The application on the communication terminal displays diagnostic results and recommended actions to the user. The input is notification data sent from the server, and the output is information displayed on the user interface. Specifically, the user checks the diagnostic results and recommended actions on the app screen.

[1436] Step 8:

[1437] When a user utilizes the meal suggestion feature, they send their current vital data to the server using a communication terminal. The input is the current vital data, and the output is the data sent to the server.

[1438] Step 9:

[1439] The server analyzes vital data and suggests an optimal meal plan based on the user's health status. The input is the user's vital data, and the output is the suggested meal plan. Based on the analysis results, the menu is generated considering calories, nutritional balance, and allergy information.

[1440] Step 10:

[1441] The suggested meal menu is notified to the communication terminal, and the user places a delivery order directly through the app. The input is the suggested meal menu, and the output is a delivery order request. Specifically, the order data is sent from the application on the communication terminal to the partner delivery service.

[1442] Step 11:

[1443] The user inputs feedback on the diagnostic results and dietary suggestions into an application on a communication terminal. The input is the user's feedback, and the output is the feedback data sent to the server.

[1444] Step 12:

[1445] The server stores user feedback as AI training data, which will be used to improve diagnostic accuracy and meal recommendation accuracy in the future. The input is feedback data, and the output is updated training data for the AI ​​model. Specifically, by incorporating feedback data into the AI ​​model's learning algorithm, the accuracy of diagnoses and recommendations is improved.

[1446] 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.

[1447] This invention is a system that combines a wearable device, a communication terminal, a server, and an emotion engine to improve the quality of primary care provided to users. Specific embodiments for implementing the system of this invention are described below.

[1448] System Configuration

[1449] Wearable devices

[1450] The user wears a wearable device (e.g., a smartwatch). This wearable device has the function of monitoring vital data such as heart rate, body temperature, blood pressure, and steps in real time, and transmitting that data to a communication terminal.

[1451] Communication terminal

[1452] The user's communication device (e.g., a smartphone) receives and temporarily stores vital data transmitted from the wearable device. The communication device periodically sends this data to a server. It also provides an interface that allows the user to input symptoms and questions and send the data to the server.

[1453] server

[1454] The server receives vital data, medical history, and prescription history transmitted from the communication terminal and stores them in a central database. The AI ​​installed in the server analyzes this data, evaluates the user's health status, and generates a diagnosis. The generated diagnosis is then sent to the communication terminal and notified to the user.

[1455] Emotional Engine

[1456] The emotion engine is a device that recognizes emotions from the user's voice and facial expressions. The communication terminal uses the user's voice input and camera images to have the emotion engine perform emotional analysis. The emotional data obtained from the emotion engine is sent to a server and used to generate diagnostic results by AI.

[1457] System processing

[1458] Data collection

[1459] While the user is wearing the wearable device, the device periodically measures vital data such as heart rate, body temperature, blood pressure, and steps taken. This data is transmitted to a communication terminal using wireless communication such as Bluetooth. The communication terminal temporarily stores the received data and periodically sends it to a server.

[1460] Data analysis and generation of diagnostic results

[1461] The server is equipped with AI to analyze vital data sent by users in conjunction with their medical history and prescription history. Based on this data, the AI ​​assesses the user's health status and generates a diagnosis and recommended actions. The diagnosis may include assessments such as the possibility of a cold or the flu, and recommended actions may include purchasing over-the-counter medication, resting, and staying hydrated.

[1462] Improving the accuracy of emotion analysis and diagnosis

[1463] The communication terminal obtains analysis results from the emotion engine based on the voice input and camera images provided by the user. This emotion data, which indicates the user's stress level and emotional state, is sent to the server. The AI ​​analyzes this emotion data together with vital data to generate more accurate diagnostic results.

[1464] Notification of results and coordination with doctors

[1465] The diagnostic results generated by the server are sent to the communication terminal and notified to the user. The user can check the diagnostic results and take recommended actions through the app on the communication terminal. If the user wishes to have a more detailed consultation, they can use the communication terminal to schedule a video call with a doctor. The server provides the doctor with the accumulated data and AI-generated diagnostic results, which the doctor uses as a reference to make a final diagnosis.

[1466] Feedback and Learning

[1467] Users input feedback within the app regarding their recovery status after diagnosis and their impressions of the diagnosis. The communication device sends this feedback to the server, which stores it as training data for the AI. This improves the accuracy of the AI's diagnosis, enabling more accurate diagnoses in the future.

[1468] Specific example

[1469] Example 1: Daily health management

[1470] Users wear wearable devices daily to monitor their heart rate and body temperature. A communication terminal periodically sends this data to a server, where an AI analyzes it. For example, if the AI ​​detects an abnormal fluctuation in heart rate, a notification is sent to the communication terminal to alert the user. Additionally, an emotion engine recognizes the user's stress level from their voice and facial expressions, and this is reflected in the diagnostic results.

[1471] Example 2: Diagnosis when symptoms appear

[1472] When a user experiences a sore throat, they open the app on their communication device and enter their symptoms. The server analyzes past vital data and symptoms and diagnoses a high probability of a cold. The user is then notified of recommended over-the-counter medications and lifestyle advice. Additionally, if the user expresses pain or anxiety through voice or facial expressions, an emotion engine analyzes this data, which the AI ​​uses to improve the accuracy of the diagnosis.

[1473] Example 3: Video call with a doctor

[1474] If a user experiences a worsening of symptoms and wishes to see a doctor, they can book a video call with a doctor using the app on their communication device. At the time of booking, the server provides the doctor with previous diagnostic results, vital data, and emotional data, and during the video call, the doctor makes a final diagnosis while asking additional questions.

[1475] In this way, the present invention realizes a system that provides users with high-quality primary care and improves the efficiency and accuracy of medical services through the cooperation of a wearable device, a communication terminal, a server, and an emotion engine.

[1476] The following describes the processing flow.

[1477] Step 1: Data collection using wearable devices

[1478] The user wears a wearable device (e.g., a smartwatch).

[1479] The device (wearable device) periodically measures the user's vital data, such as heart rate, body temperature, blood pressure, and steps taken.

[1480] The device (wearable device) measures vital data and transmits it to the communication terminal (smartphone) using Bluetooth or similar technologies.

[1481] Step 2: Send vital data

[1482] The terminal (smartphone) temporarily stores vital data received from the wearable device.

[1483] The device (smartphone) periodically sends stored vital data to the server (for example, every hour).

[1484] Step 3: Appointment scheduling and medical interview

[1485] The user opens the dedicated app on their communication device (smartphone).

[1486] The user enters their symptoms and questions into the appointment booking form.

[1487] The device (smartphone) sends the entered information and the latest vital data to the server.

[1488] Step 4: Data Analysis

[1489] The server retrieves the user's past vital data, medical history, and prescription history from a central database.

[1490] Based on data acquired by the AI ​​on the server, the system evaluates the user's health status and generates diagnostic results and recommended actions.

[1491] Step 5: Generating diagnostic results

[1492] The emotion engine analyzes the user's emotions from their voice and facial expressions.

[1493] The device (smartphone) retrieves analysis results from the emotion engine and sends them to the server.

[1494] The AI ​​on the server integrates emotional data with vital data to improve diagnostic accuracy.

[1495] Step 6: Notification of diagnostic results

[1496] The server generates the final diagnostic results and recommended actions, and sends them to the communication terminal.

[1497] The device (smartphone) notifies the user of the diagnostic results and recommended actions.

[1498] Users can check their diagnostic results through an app on their communication device and take necessary actions (e.g., purchase over-the-counter medication, ensure they get enough rest).

[1499] Step 7: Video call coordination with the doctor

[1500] Users schedule video calls with doctors through apps on their communication devices.

[1501] The server provides the doctor with previous diagnostic results, vital data, and emotional analysis data at the scheduled time.

[1502] The doctor will conduct a video call to ask additional questions and make a final diagnosis.

[1503] Step 8: Feedback and Learning

[1504] Users enter their recovery status and evaluation after diagnosis into a feedback form within the app.

[1505] The device (smartphone) sends feedback to the server.

[1506] The server saves the feedback as training data for the AI ​​and uses it to improve diagnostic accuracy in the future.

[1507] The above outlines the specific processing steps of the system combining the emotion engine, and the specific actions performed by each entity within that process.

[1508] (Example 2)

[1509] 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".

[1510] Traditional health management systems rely solely on users' vital data for diagnosis, neglecting psychological factors such as emotions and stress levels. This can lead to decreased diagnostic accuracy and difficulty in providing appropriate medical advice to users. Furthermore, the mechanisms for efficiently collecting feedback and utilizing it for AI learning were insufficient, hindering improvements in AI diagnostic accuracy.

[1511] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for generating a diagnosis result using AI that analyzes vital data, medical history, prescription history, and emotional data; means for an emotional engine to recognize emotions from the user's voice and facial expressions and transmit them to the server as data; and means for the server to store the user's feedback as learning data. This makes it possible to perform multifaceted data analysis including the user's psychological elements, thereby improving diagnostic accuracy.

[1512] A "wearable device" is a device that a user wears to collect vital data such as heart rate, body temperature, blood pressure, and steps taken.

[1513] A "communication terminal" is a device that receives vital data transmitted from a wearable device, temporarily stores it, and sends it to a server.

[1514] A "server" is a device that receives vital data, medical history, prescription history, and emotional data transmitted from a communication terminal, analyzes them, and generates diagnostic results.

[1515] "AI" refers to artificial intelligence technology used to analyze vital data, medical history, prescription history, and emotional data, and to generate diagnostic results.

[1516] "Diagnosis results" refer to an assessment of the user's health status and recommended actions, generated based on the analysis of vital data, medical history, prescription history, and emotional data.

[1517] An "emotion engine" is a device that recognizes emotions from a user's voice and facial expressions and analyzes that data.

[1518] "Feedback" refers to information that users input regarding their recovery status after diagnosis and their impressions of the diagnosis.

[1519] A "video call" is a conversation between a user and a doctor face-to-face via a communication device, using both audio and video.

[1520] "Recommended actions" refer to advice and instructions on what actions to take that are presented to the user based on the diagnostic results.

[1521] Modes for carrying out the invention

[1522] This invention is a system that provides high-quality primary care to users by linking a wearable device, a communication terminal, a server, and an emotion engine. Specific embodiments of this system will be described below.

[1523] Hardware and software to use

[1524] Wearable devices

[1525] The user wears a wearable device (e.g., a smartwatch). This device monitors vital data such as heart rate, body temperature, blood pressure, and steps taken in real time and transmits the data to a communication terminal using Bluetooth.

[1526] Communication terminal

[1527] The user's communication device (e.g., a smartphone) receives and temporarily stores vital data transmitted from the wearable device. The communication device periodically sends this data to a server. It also provides a function to allow the user to input symptoms and questions through a dedicated application and send this information to the server.

[1528] server

[1529] The server receives vital data, medical history, prescription history, and emotional data transmitted from communication terminals and stores them in a central database. The server is equipped with AI models using TensorFlow, PyTorch, etc., which are used to comprehensively analyze the data and generate diagnostic results.

[1530] Emotional Engine

[1531] The emotion engine is a device that recognizes emotions from the user's voice and facial expressions. The communication terminal sends data to the emotion engine based on voice input and camera footage, and receives the analysis results. These results are sent to a server and integrated into the AI ​​analysis.

[1532] Specific examples and prompt statements

[1533] Example 1: Daily health management

[1534] Users wear a wearable device daily, which monitors their heart rate and body temperature. A communication terminal periodically sends this data to a server, where the server's AI analyzes it. For example, if an abnormal fluctuation in heart rate is detected, a notification is sent to the communication terminal to alert the user. In addition, an emotion engine recognizes the user's stress level from their voice and facial expressions and reflects this in the diagnostic results.

[1535] Example of a prompt:

[1536] "Please analyze my heart rate monitoring results and let me know if there are any health problems. Also, please take my recent emotional state into consideration."

[1537] Example 2: Diagnosis when symptoms appear

[1538] When a user experiences a sore throat, they open an app on their communication device and enter their symptoms. The server analyzes past vital data and symptoms and diagnoses a high probability of a cold. The user is then notified of recommended over-the-counter medications and lifestyle advice. Additionally, if the user expresses pain or anxiety through voice or facial expressions, an emotion engine analyzes this data, which the AI ​​uses to improve the accuracy of the diagnosis.

[1539] Example of a prompt:

[1540] "I have a sore throat. Please provide a diagnosis and recommended actions based on my past health and emotional data."

[1541] Example 3: Video call with a doctor

[1542] If a user experiences a worsening of symptoms and wishes to see a doctor, they can book a video call with a doctor using the app on their communication device. At the time of booking, the server provides the doctor with previous diagnostic results, vital data, and emotional data, and during the video call, the doctor makes a final diagnosis while asking additional questions.

[1543] Example of a prompt:

[1544] "I would like to schedule a video call with a doctor. I would like to send my previous diagnostic data to the doctor and request an additional diagnosis during the video call."

[1545] This invention enables the realization of a system that provides high-quality primary care to users through the cooperation of a wearable device, a communication terminal, a server, and an emotion engine, thereby improving the efficiency and accuracy of medical services.

[1546] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1547] Step 1:

[1548] The user wears a wearable device that collects vital data such as heart rate, body temperature, blood pressure, and steps taken. Specifically, the device's sensors measure this data every minute and temporarily store it in its internal memory. The input is the user's vital data, and the output is the vital data stored in the device.

[1549] Step 2:

[1550] The wearable device uses Bluetooth to transmit collected vital data to a communication terminal. Specifically, the wearable device's data transmission function is activated, and a Bluetooth connection is established. The input is the vital data stored on the device, and the output is the vital data transmitted to the communication terminal.

[1551] Step 3:

[1552] The communication terminal receives vital data transmitted from the wearable device and stores it temporarily. This data is later sent to a server for analysis. Specifically, the communication terminal's application runs in the background, receiving data and saving it to its internal storage. The input is the transmitted vital data, and the output is the vital data stored in the communication terminal.

[1553] Step 4:

[1554] The communication terminal periodically sends stored vital data to the server. For example, it is set up so that data is sent in a batch every night. Specifically, the communication terminal's application executes a process to send data to the server at a set time. The input is the vital data stored on the communication terminal, and the output is the vital data sent to the server.

[1555] Step 5:

[1556] The server receives vital data transmitted from communication terminals and stores it in a central database. Specifically, the server's API endpoint receives a request and executes the process of saving the data to the database. The input is the vital data sent to the server, and the output is the data stored in the database.

[1557] Step 6:

[1558] The AI ​​installed on the server analyzes vital data, medical history, prescription history, and sentiment data. Specifically, the server runs machine learning models trained using TensorFlow or PyTorch to assess health status and generate diagnostic results. The input is vital data, medical history, prescription history, and sentiment data stored in a database, and the output is the generated diagnostic result.

[1559] Step 7:

[1560] The server sends the generated diagnostic results to the communication terminal and notifies the user. Specifically, the server sends a notification containing the diagnostic results to the application on the communication terminal, and the application displays a push notification. The input is the generated diagnostic results, and the output is the push notification on the communication terminal.

[1561] Step 8:

[1562] Users can view their diagnostic results and take recommended actions through an app on their communication device. If they wish to have a more detailed consultation, they can use the app to schedule a video call with a doctor. Specifically, the app on the communication device displays the diagnostic results to the user and provides a video call scheduling function. The input is the diagnostic results, and the output is the video call scheduling.

[1563] Step 9:

[1564] The emotion engine recognizes emotions from the user's voice and facial expressions and analyzes that data. Specifically, the communication terminal's application uses the microphone and camera to capture voice and facial expressions and sends them to the emotion engine. The engine then sends the analysis results to the server. The input is voice and facial expression data, and the output is the analyzed emotion data.

[1565] Step 10:

[1566] Users input feedback within the app regarding their recovery status after diagnosis and their impressions of the diagnosis. The communication device sends this feedback to the server, which stores it as training data for the AI. Specifically, the app on the communication device receives feedback from the user and sends the data to the server. The input is the user's feedback, and the output is the feedback data stored on the server.

[1567] This allows the system to perform multifaceted data analysis, provide users with highly accurate diagnostic results, and improve the efficiency and accuracy of medical services.

[1568] (Application Example 2)

[1569] 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".

[1570] Modern health management requires the collection and analysis of daily vital data to understand individual health conditions in real time and provide appropriate medical care and lifestyle guidance. However, previous systems often evaluated health conditions without considering the user's emotional state, resulting in inaccurate diagnoses and recommended behaviors. Furthermore, there was a lack of dietary suggestions based on the user's health condition, making it difficult to provide meals that met individual needs.

[1571] 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. In this invention, the server includes means for generating diagnostic results using AI that analyzes vital data, medical history, and prescription history; means for analyzing the user's emotional data using an emotion engine and reflecting it in the diagnostic results; and means for recommending the optimal diet based on health status and emotional data. This generates highly accurate diagnostic results that take into account the user's emotional state, and further enables dietary suggestions tailored to the individual's health condition.

[1572] A "wearable device" is a device worn by a user to collect vital data such as heart rate, body temperature, blood pressure, and steps taken.

[1573] A "communication terminal" is a device that has the function of receiving and storing vital data transmitted from a wearable device and sending it to a server.

[1574] A "server" is a device that stores vital data, medical history, and prescription history transmitted from communication terminals in a central database, analyzes it, and generates diagnostic results.

[1575] "AI" refers to an algorithm installed on a server that analyzes vital data, medical history, and prescription history to evaluate health status and generate diagnostic results.

[1576] "Diagnosis results" refer to an assessment of the user's health status generated by AI based on vital data and other information.

[1577] An "emotion engine" is a device that recognizes and analyzes emotions from a user's voice and facial expressions.

[1578] "Emotional data" refers to data that indicates the user's stress level and emotional state, analyzed by the emotion engine.

[1579] "Recommended actions" refer to information indicating the actions a user should take based on their diagnostic results.

[1580] "Health status" refers to the user's overall health condition, evaluated based on vital data such as heart rate, body temperature, blood pressure, and steps taken.

[1581] "Meal suggestions" refer to information that proposes the optimal meal plan based on the user's health status and emotional data.

[1582] This invention is a system that combines a wearable device, a communication terminal, a server, and an emotion engine to achieve more accurate user health management and dietary recommendations. Specific embodiments for carrying out the invention are described below.

[1583] System Configuration

[1584] Wearable devices

[1585] The user wears a wearable device (e.g., a smartwatch). This device measures vital data such as heart rate, body temperature, blood pressure, and steps in real time and transmits it to a communication terminal using wireless communication such as Bluetooth or Wi-Fi.

[1586] Communication terminal

[1587] The user's communication device (e.g., a smartphone) receives and temporarily stores vital data transmitted from the wearable device. Furthermore, it has the functionality to collect the user's voice and facial expressions using a camera and microphone and transmit them to an emotion engine. The communication device periodically sends this data to a server.

[1588] server

[1589] The server receives vital data, emotional data, medical history, and prescription history transmitted from the communication terminal and stores them in a central database. Furthermore, the server is equipped with AI, which analyzes this data to assess the user's health status and generate a diagnosis. The diagnosis is then communicated to the user via the communication terminal. The server also suggests an optimal diet based on the assessment.

[1590] Emotional Engine

[1591] The emotion engine recognizes emotions from the user's voice and facial expressions and generates data indicating their emotional state. The communication terminal transmits voice input and camera footage to the emotion engine for emotion analysis. The emotion data is sent to a server and used to generate diagnostic results.

[1592] Program processing

[1593] Data collection

[1594] A wearable device periodically measures the user's vital data, such as heart rate, body temperature, blood pressure, and steps taken, and transmits this data to a communication terminal. The communication terminal temporarily stores the vital data and then transmits it to a server.

[1595] Emotion analysis

[1596] The communication terminal uses an emotion engine to analyze the user's voice input and camera footage. The emotion engine analyzes this data and generates data indicating the user's emotional state. This emotional data is sent to a server and used to assess their health status.

[1597] Data analysis and generation of diagnostic results

[1598] The server is equipped with AI to analyze vital data, medical history, prescription history, and emotional data. Based on this data, the AI ​​assesses the user's health status and generates a diagnosis, recommended actions, and optimal dietary suggestions. This information is transmitted to the communication terminal and notified to the user.

[1599] Results notification and meal suggestions

[1600] The diagnostic results and meal suggestions generated by the server are sent to the communication terminal and notified to the user. The user can check the diagnostic results and meal suggestions through the app on the communication terminal and take the recommended actions.

[1601] Collaboration with doctors

[1602] If a user wishes to have a more detailed consultation, they can book a video call with a doctor using their communication device. At the time of booking, the server provides the doctor with previous diagnostic results, vital data, and emotional data, which will be used for the final diagnosis.

[1603] Specific example

[1604] For example, a user wears a wearable device daily to monitor their heart rate and body temperature. The communication terminal periodically sends this data to a server, where the server's AI analyzes it. If the AI ​​detects an abnormal fluctuation in heart rate, a notification is sent to the communication terminal to alert the user. In addition, an emotion engine recognizes the user's stress level from their voice and facial expressions, and this is reflected in the diagnostic results.

[1605] Example of a prompt

[1606] In the food delivery app you develop, please consider the following data to suggest the best meal options to users.

[1607] Input data:

[1608] Heart rate: 80

[1609] Body temperature: 36.5°C

[1610] Blood pressure: 120 / 80

[1611] Steps: 5000

[1612] Emotional state: Stress

[1613] output:

[1614] Please suggest meals that can help reduce the stress users are experiencing. The menu should be nutritionally balanced and include relaxing elements.

[1615] example:

[1616] Herbal tea

[1617] A light salad or a late-night snack

[1618] Low-sugar smoothies

[1619] The above describes a specific embodiment for carrying out the present invention. This enables highly accurate health management and dietary recommendations that take into account the user's emotional state.

[1620] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1621] Step 1:

[1622] The user wears a wearable device that collects vital data such as heart rate, body temperature, blood pressure, and steps taken. The wearable device transmits this data to a communication terminal using wireless communication such as Bluetooth.

[1623] Input: Vital data (heart rate, body temperature, blood pressure, steps)

[1624] Output: Data transmission to communication terminal

[1625] Specific operation: The wearable device measures vital data in real time and transmits the data to a communication terminal using Bluetooth.

[1626] Step 2:

[1627] The communication terminal receives and temporarily stores vital data transmitted from the wearable device. Simultaneously, it collects user emotion data using audio and camera footage.

[1628] Input: Vital data from wearable devices, user voice data, camera footage

[1629] Output: Temporary storage of data to send to the server

[1630] Specific operation: The communication terminal receives vital data via Bluetooth, and uses the microphone and camera to collect and temporarily store the user's voice and facial expression data.

[1631] Step 3:

[1632] The communication terminal periodically sends temporarily stored vital data and emotional data to the server.

[1633] Input: Temporarily stored vital data and emotional data

[1634] Output: Sending data to the server

[1635] Specific operation: The communication device uploads data to the server using Wi-Fi or mobile data communication.

[1636] Step 4:

[1637] The server uses AI to analyze vital and emotional data it receives. The AI ​​also analyzes medical history and prescription history to generate a diagnosis.

[1638] Input: Vital data, emotional data, medical history, prescription history

[1639] Output: Diagnostic results

[1640] Specific operation: The server uses an AI algorithm to analyze data, evaluate the user's health status, and generate a diagnostic result.

[1641] Step 5:

[1642] The server generates diagnostic results, which are then sent to the communication terminal to notify the user. Simultaneously, dietary suggestions based on the user's health status are also provided.

[1643] Input: Diagnostic result

[1644] Output: Sending diagnostic results and meal suggestions to the communication terminal.

[1645] Specific operation: The server generates diagnostic results and meal suggestions, and sends notifications to the communication terminal.

[1646] Step 6:

[1647] Users can view their diagnostic results and meal suggestions via a communication device. If necessary, they can also place orders based on the meal suggestions.

[1648] Input: Diagnostic results and dietary suggestions sent to the communication terminal.

[1649] Output: User confirmation and order

[1650] Specific operation: The user checks the notification on the app on their communication device and places an order based on the meal suggestion if necessary.

[1651] Step 7:

[1652] Users input feedback on their diagnostic results and meal suggestions through the app and send it to the server. The server stores this feedback as training data for the AI.

[1653] Input: User feedback

[1654] Output: AI training data

[1655] Specific operation: The user enters feedback in the app, and the server receives it and saves it as training data.

[1656] The above outlines the specific processing steps of the system based on the present invention. This enables user health management and personalized meal suggestions.

[1657] 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.

[1658] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1659] 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.

[1660] 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.

[1661] 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.

[1662] 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.

[1663] 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.

[1664] 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.

[1665] 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."

[1666] 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.

[1667] 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.

[1668] 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.

[1669] 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 progra...

Claims

1. Means by which wearable devices collect users' vital data, A means of transmitting vital data to a communication terminal, The aforementioned communication terminal provides means for transmitting the collected vital data to a server, A server uses AI to analyze vital data, medical history, and prescription history to generate diagnostic results. A means for notifying the user's communication terminal of the aforementioned diagnostic results, A means by which users can receive a final diagnosis through a video call with a doctor, The means by which user feedback is collected and used by the server to train the AI, A system that includes this.

2. The system according to claim 1, comprising means for displaying recommended actions along with the diagnostic results.

3. The system according to claim 1, wherein the server includes means for storing user feedback as learning data.

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