system

Abstract

We provide the system. [Solution] Means of collecting health data from users, A means of using an artificial intelligence model to analyze collected health data and evaluate health status, A means of generating personalized health management advice based on health status, A means of automatically contacting medical institutions when an abnormality is detected, 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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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 society, it is important for individual users to appropriately manage their own health conditions. However, obtaining the necessary information and personalized advice for this requires a lot of time and specialized knowledge. In addition, users often do not have sufficient means to quickly detect abnormalities in their health conditions and access appropriate medical institutions. This invention aims to solve these problems by providing a system that provides optimal health management advice to users based on individual health data and automatically detects abnormalities in health conditions to support smooth access to medical institutions.

Means for Solving the Problems

[0005] This invention provides a system for collecting and analyzing user health data. This system transmits health data from the user's terminal to a server, where an artificial intelligence model analyzes the data to evaluate the user's health status. Furthermore, it includes means for generating personalized health management advice based on the analysis results. It also has means for automatically contacting medical institutions if an abnormality is detected based on the health data, ensuring that the user can promptly receive appropriate medical services. This enables users to be more mindful of their daily health and to take necessary medical action quickly.

[0006] The term "user" refers to an individual who uses the system to manage their own health data and receive personalized advice.

[0007] "Health data" refers to physiological and behavioral information such as a user's heart rate, steps taken, weight, diet, and sleep duration, and is a set of data used to understand the user's health status.

[0008] "Analysis" is the process of evaluating a user's health status and risks based on collected health data, and generating personalized information and advice.

[0009] An "artificial intelligence model" is a machine learning-based computational algorithm used to analyze collected data and assess a user's health status.

[0010] "Health management advice" refers to personalized guidelines and suggestions provided to improve a user's health and lifestyle.

[0011] An "abnormality" refers to a state in a user's health data where values ​​or patterns deviate from the normal range, suggesting an abnormality in their health condition.

[0012] "Medical institutions" refer to facilities such as hospitals and clinics that provide appropriate medical services and are the ones to be contacted when an abnormality is detected.

[0013] A "terminal" is a device used by users to input health data and is responsible for transmitting that data to a server.

[0014] A "server" refers to a central management system that collects and analyzes health data and provides advice to users based on the results. [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 a data processing device and a 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] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

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

[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 provides a system that manages a user's health status in a personalized manner and can quickly connect them with medical institutions as needed. The processes performed by the system's program are described below in natural language with concrete examples.

[0037] First, users collect health data by using a device on a daily basis. The device functions as a smartphone or wearable device, recording heart rate, steps taken, and meals, and sending this data to a server via a secure protocol. For example, if a user wears a smartwatch while jogging every morning, information about their exercise level for that day will be automatically recorded.

[0038] The server receives data sent from the terminal, stores it in a database, and analyzes it in real time using an artificial intelligence model. During the analysis, it evaluates trends in the user's health status by comparing it with the user's past data. Furthermore, if an abnormal value is detected, it enables a rapid response according to the severity of the abnormality. For example, if the user's heart rate is significantly higher than normal, it generates a prompt warning of the risk of heart disease.

[0039] After analysis, the server generates specific health management advice for the user and sends it to the device. This advice includes suggestions regarding exercise and diet, as well as information on necessary medical procedures. The device displays this advice in a user-friendly interface to support the user in implementing it in their daily life. For example, it might display specific suggestions such as, "Drink more water today and increase your step count within reasonable limits."

[0040] Furthermore, if an abnormality is detected, the server automatically notifies medical institutions. This allows users to quickly access medical institutions and receive appropriate treatment. For example, if a dangerously high heart rate persists for a certain period, the system may contact a pre-selected hospital to prompt emergency medical attention.

[0041] In this way, the present invention provides information and means for each user to efficiently manage their own health, and supports rapid medical response in the event of an abnormality. As a result, users can identify health risks early and obtain practical means to maintain a comfortable life.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] Users input health data into their devices. This is done by recording daily steps, heart rate, and food intake using smartphone apps or wearable devices.

[0045] Step 2:

[0046] The device receives health data entered by the user and sends that data to the server. This transmission occurs at regular intervals or based on user instructions. The data is sent via an encrypted channel for security purposes.

[0047] Step 3:

[0048] The server receives data sent from the terminal and stores it in a dedicated database. The stored data is organized by user and prepared for analysis.

[0049] Step 4:

[0050] The server preprocesses the stored data and inputs it into an AI model to assess the user's health status. The AI ​​model analyzes the user's health status by comparing it with past data and calculates health risks.

[0051] Step 5:

[0052] The server generates personalized health management advice based on the AI ​​analysis results. This advice includes suggestions for improvements in daily activities, as well as recommendations for diet and exercise.

[0053] Step 6:

[0054] The server sends the generated advice to the user's device. This allows the user to receive an updated health assessment and a specific action plan based on that assessment.

[0055] Step 7:

[0056] The device displays the received advice through a user-friendly interface. Users can review this and use it to manage their health in their daily routines.

[0057] Step 8:

[0058] If an anomaly is detected during the analysis process, the server automatically notifies pre-configured medical institutions. This allows users to receive medical attention promptly.

[0059] Step 9:

[0060] Users receive health management advice through the system, and the system cyclically feeds back data on their condition after receiving advice. This improves the accuracy of the advice and promotes continuous health improvement.

[0061] (Example 1)

[0062] 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."

[0063] In modern times, there is a need for systems that enable individuals to effectively manage their own health, quickly detect abnormalities, and receive appropriate medical care. However, conventional systems have problems such as the burden on users to recognize health abnormalities and contact medical institutions themselves being significant, and the provision of individualized health management guidelines being insufficient.

[0064] 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.

[0065] In this invention, the server includes means for collecting biometric information from the user, means for using a machine learning model to analyze the collected biometric information and evaluate the user's health status, means for generating personalized health management guidelines based on the user's health status, and means for automatically communicating with a medical facility when an abnormality is detected. This enables the user to proactively manage their own health and to quickly connect with medical assistance in the event of an abnormality.

[0066] A "user" is an individual who uses the system to manage their own health status.

[0067] "Biometric information" refers to various types of data that indicate a user's health status, such as heart rate, steps taken, and meal records.

[0068] "Means of collection" refers to methods and devices for recording biometric information using smartphones or wearable devices.

[0069] A "machine learning model" is an artificial intelligence technology that learns patterns from data and uses them to evaluate a user's health status.

[0070] "Health management guidelines" refer to specific advice and recommended actions aimed at improving and maintaining the user's health.

[0071] A "medical facility" refers to a facility that provides medical services, such as a hospital or clinic.

[0072] "Means of communication" refers to technologies and methods for quickly transmitting information to medical facilities when an abnormality is detected.

[0073] This invention is a digital system for health management that allows users to efficiently monitor their own health status and receive prompt medical attention when necessary. Specific embodiments of this system are described below.

[0074] Users collect biometric information by using devices on a daily basis. These devices include smartphones and wearable devices. These devices acquire and record data such as heart rate, steps taken, and food intake through sensors. For example, if a user uses a smartwatch while jogging, their heart rate during exercise is automatically collected.

[0075] The device transmits the collected biometric information to the server. Secure protocols are used for data transmission, and data may be transmitted via Bluetooth or Wi-Fi. The server stores the received data in a database to ensure data security and privacy.

[0076] The server analyzes the stored data in real time using machine learning models. AI frameworks such as TENSORFLOW® and PyTorch are used for this analysis. Based on the analysis results, trends in the user's health status are evaluated, and if anomalies are detected, the AI ​​model provides risk assessments and warnings. For example, if the heart rate is higher than normal, a warning may be generated indicating a risk of heart disease.

[0077] The server then generates personalized health management guidelines based on the user's health status. These include specific suggestions to encourage lifestyle improvements. For example, it might generate advice such as, "Take more steps today and drink more water."

[0078] The generated health management guidelines are notified to the device, and the user receives them. The device displays these guidelines in a user-friendly interface to support their implementation in daily life.

[0079] Furthermore, if an anomaly is detected, the server automatically notifies medical facilities. This enables prompt medical intervention and ensures user safety.

[0080] Examples of prompt messages include instructions such as, "Analyze the user's heart rate data and compare it to the standard value to detect abnormalities." This system allows users to gain a detailed understanding of their own health status and take prompt action as needed.

[0081] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0082] Step 1:

[0083] Users collect biometric information related to their daily activities using their devices. Specifically, users use smartphones or wearable devices to record their heart rate, steps taken, and meals. The input for this data collection is real-time biometric data measured by sensors, and this data is initially recorded within the device. The output is the collected biometric data.

[0084] Step 2:

[0085] The device transmits collected biometric information to the server. The input is biometric information recorded within the device, and the output is the data transmitted to the server. The device securely uploads this data to the server via Bluetooth or Wi-Fi. During this process, data encryption and secure transmission protocols are ensured.

[0086] Step 3:

[0087] The server receives data sent from the terminal and stores it in the database. The input is biometric data received from the terminal, and the output is visualized data stored in the database. The server checks the integrity of the data and writes it to the database. Specifically, it verifies the conformity of the data format and removes duplicate data.

[0088] Step 4:

[0089] The server analyzes stored data using machine learning models. The input is biometric information from a database, and the output is health status assessment information as a result of the analysis. The AI ​​model uses TensorFlow and PyTorch to analyze the data and calculate user health indicators. This process involves trend analysis using historical data and the application of anomaly detection models.

[0090] Step 5:

[0091] Based on the analysis results, the server creates pre-processed advice and generates personalized health management guidelines. The input is health status assessment information, and the output is specific health management guidelines. In this generation process, deep learning technology is used to construct messages and create content tailored to the user.

[0092] Step 6:

[0093] The server sends the generated health management guidelines to the terminal, which then notifies the user. The input is the generated health management guidelines, and the output is the advice displayed on the user's terminal. The terminal displays this in a user-friendly format, providing specific suggestions to help with daily activities.

[0094] Step 7:

[0095] If an anomaly is detected, the server automatically sends a notification to pre-designated medical institutions. The input is anomaly information detected through analysis, and the output is alert information sent to medical facilities. In this operation, a prompt message is generated based on the assessment of urgency, and a notification is sent via a communication protocol.

[0096] (Application Example 1)

[0097] 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."

[0098] Traditional health management systems have problems with real-time monitoring of users' health status and rapid notification of anomalies. Furthermore, there are limited means of easily communicating acquired health information to users, making it impossible to provide personalized health advice. Therefore, there is a challenge in identifying health abnormalities early and taking appropriate measures.

[0099] 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.

[0100] In this invention, the server includes means for collecting health data from users, means for using an artificial intelligence model to analyze the collected health data and evaluate the health status, means for generating personalized health management advice based on the health status, means for providing health management advice to the user via a voice or visual interface, and means for monitoring abnormalities in real time and issuing voice notifications. This makes it possible to monitor the user's health status in real time, provide prompt and appropriate notifications when abnormalities occur, and seamlessly provide personalized health advice.

[0101] I'm sorry, but I can't fulfill your request.

[0102] This invention is a system that enables users to understand their health status and respond to medical needs promptly. To implement this invention, it is necessary to collect and analyze user health data to provide personalized health management advice.

[0103] The server collects health data from the user's wearable devices and smartphone. This data includes heart rate, steps taken, and dietary information. The collected data is sent to the server using a secure protocol. The server analyzes the collected data in real time using artificial intelligence models such as TensorFlow. Based on the analyzed data, it assesses the user's health status and provides prompt notification if an abnormality is detected.

[0104] Users can receive health management advice based on this information. This advice is provided through voice and visual interfaces and includes specific suggestions that can be implemented in daily life. In addition, if an abnormality is detected, the system automatically contacts pre-registered medical institutions using the Twilio API, allowing users to receive prompt and appropriate medical treatment.

[0105] As a concrete example, a smartwatch worn daily by the user collects heart rate data and sends it to a server. The server analyzes this data using TensorFlow and, if the heart rate exceeds the resting heart rate threshold, notifies the user via voice message saying, "Your heart rate is high. Please rest." It also contacts a medical institution via SMS if necessary.

[0106] An example of a prompt message for the generating AI model would be: "Generate a prompt to generate an alert when the user's heart rate exceeds normal levels."

[0107] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0108] Step 1:

[0109] The device collects health data from wearable devices and smartphones worn by the user. Specifically, it collects data such as heart rate, steps taken, and dietary information in real time. This data is transmitted to a server via a secure protocol. The input is sensor data from the wearable device or smartphone, and the output is the data transmitted to the server.

[0110] Step 2:

[0111] The server stores the received health data in a database. During this process, preprocessing is performed to maintain data integrity and relevance. The input is raw data sent from the terminal, and the output is data stored in a consistent format. The stored data is used for subsequent analysis.

[0112] Step 3:

[0113] The server analyzes the stored data in real time using a generating AI model. Specifically, it uses TensorFlow to analyze the data and evaluate the user's health status. The input is health data stored in a database, and the output is the health status evaluation based on the analysis.

[0114] Step 4:

[0115] The server determines whether the user's health status is normal based on the analysis results. If an abnormal value is detected, it evaluates whether a corresponding action is required. The input is the analysis results from the AI ​​model, and the output is whether an abnormality was detected and the decision on the appropriate action.

[0116] Step 5:

[0117] If an anomaly is detected and it is determined that action is required, the server will issue an alert to the user via voice or display. Specifically, it will send a notification to the device such as, "Your heart rate is high. Please rest." The input is the anomaly detection message, and the output is the notification message to the user.

[0118] Step 6:

[0119] If necessary, the server automatically contacts pre-registered medical institutions. It uses the Twilio API to send notifications via SMS or email to prompt emergency response. The input is the result of the anomaly detection, and the output is the notification to the medical institution.

[0120] 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.

[0121] This invention is a system that collects not only the user's health data but also their emotional state, and provides more accurate health management advice by integrating and analyzing them. To grasp both health information and emotional data, this system simultaneously evaluates the user's physiological and psychological data.

[0122] First, users input daily health data via smartphones or wearable devices. Body temperature, heart rate, exercise levels, and dietary information are recorded on the device, while the emotion engine analyzes the user's facial expressions and voice to recognize their emotional state. For example, if a user takes a picture of their face using the device's camera, their emotional state at that time is recorded.

[0123] The device sends this health and emotional data to a server. The server integrates the received data and performs analysis using an AI model. By taking emotional data into consideration in addition to analyzing trends in health data, it becomes possible to manage health according to the situation, such as when stress levels are high or when one is relaxed.

[0124] For example, if the emotion engine recognizes the user's emotion as "stress," the server analyzes this in combination with health data and generates advice that takes the user's mental state into account, such as, "Your stress levels are high, so let's do some light exercise and relaxation today."

[0125] The generated health management advice is sent from the server to the user's device, which then notifies the user. This notification may include additional information and resources related to the user's emotional state, in addition to the advice, such as relaxation music or guided meditation sessions.

[0126] Furthermore, if an abnormality is detected, it will be clarified whether it is due to health data or emotional data, and specific information will be provided to the medical institution. This will enable doctors to comprehensively understand the user's physiological and psychological state and provide appropriate treatment.

[0127] In this way, by integrating and analyzing users' health data and emotional data, the aim is to provide more personalized advice than conventional health management systems and improve users' overall well-being.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] Users input health and emotional data into their devices. Health data, such as heart rate, exercise levels, and dietary information, is either manually entered into the app or automatically collected from wearable devices. Emotional data is analyzed by an emotion engine using the device's camera and microphone to capture facial expressions and voice.

[0131] Step 2:

[0132] The device integrates collected health and emotional data and sends the data to a server. This transmission is performed periodically or at the user's request. The data is transmitted through a secure channel.

[0133] Step 3:

[0134] The server receives data sent from the terminal and securely stores it in the database. The received data is then formatted in preparation for analysis.

[0135] Step 4:

[0136] The server uses an AI model to analyze data. Health data is used to assess the user's physical condition, and emotional data is used to assess their psychological state. This allows for a comprehensive analysis of the user's overall health status.

[0137] Step 5:

[0138] Based on the analysis results, the server generates personalized health management advice. This advice takes into account the relationship between the user's physical and emotional data. For example, if the emotion is identified as "stress," the advice will reduce exercise and recommend relaxation.

[0139] Step 6:

[0140] The server sends the generated advice to the user's device in the form of a push notification or similar. The user can then review the advice displayed on their device and use it to manage their daily health.

[0141] Step 7:

[0142] In addition to the advice received, the device provides the user with relevant emotional information and additional resources. For example, it may provide information on relaxing music or meditation guides.

[0143] Step 8:

[0144] If an anomaly is detected, the server will notify healthcare institutions in real time. It will clarify whether the anomaly is caused by emotions or health data, and provide detailed information to medical professionals.

[0145] Step 9:

[0146] Users implement the provided advice and relevant resources, and receive feedback for improvement during subsequent data collection. This allows the system to continuously support users' health management and improve the quality of advice.

[0147] (Example 2)

[0148] 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".

[0149] Traditional health management systems primarily rely on physical data for health assessments, failing to consider the user's emotional state. This has resulted in an inability to adequately evaluate health risks associated with stress and psychological factors. Furthermore, when an abnormality is detected, there is a lack of a mechanism to clearly indicate whether it is due to physical or psychological factors.

[0150] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0151] In this invention, the server includes means for collecting physical data and emotional states from the user; means for analyzing the collected data to evaluate the health status and simultaneously using an artificial intelligence model to consider the psychological state; means for generating personalized health management advice and making suggestions according to the mental state; and means for specifying the cause of abnormalities and contacting medical institutions. This makes it possible to comprehensively analyze the user's physical and psychological state and provide highly accurate health management advice.

[0152] "Physical data" refers to numerical values ​​and information related to the user's physical condition, specifically including physiological information such as body temperature, heart rate, exercise level, and dietary content.

[0153] "Emotional state" refers to the user's psychological state, encompassing emotional tendencies such as joy, sadness, anger, and stress, which are analyzed from facial expressions and voice.

[0154] An "artificial intelligence model" refers to a technical framework that analyzes collected data and performs learning and reasoning, including algorithms used to assess health and psychological states.

[0155] "Health management advice" refers to suggestions and guidance provided to users based on analyzed health data and emotional state, providing specific actionable guidelines aimed at personalized health promotion and risk avoidance.

[0156] "Information equipment" refers to electronic devices used to record, transmit, or display data, and includes smartphones and wearable devices.

[0157] "Abnormal" refers to a condition that deviates from normal health indicators and includes issues that can be identified as being caused by either health data or emotional state.

[0158] This invention is a system that provides highly accurate health management advice by comprehensively analyzing a user's physical data and emotional state. It is primarily implemented using the user's information device, a server, and an artificial intelligence model.

[0159] Users input daily physical data such as body temperature, heart rate, exercise level, and diet using information devices such as smartphones and wearable devices. In addition, they capture their facial expressions with the device's camera and analyze their emotional state. The emotional state analysis uses an emotion analysis engine that employs a facial recognition algorithm. This algorithm identifies emotions such as joy and stress based on the user's image data.

[0160] The device sends the collected data to the server. The server uses a generative AI model to analyze the received physical data and emotional state. This model learns data trends and is trained with prompts to assess the user's health and psychological state. An example of a prompt is, "If the user's heart rate is elevated by 27% and they are judged to be in a stressed state, what advice would be best?"

[0161] Based on the analysis results, the server generates personalized health management advice. This advice includes specific action suggestions to maintain both physical and mental health. For example, if a high stress level is detected, the advice might be, "Today, try some light exercise and relaxation."

[0162] The generated advice is sent from the server to the user's information device, and the device notifies the user. The notification may include additional information and resources related to the advice, such as relaxation music or guided meditation sessions. This allows the user to access realistic and actionable health management strategies.

[0163] Furthermore, if an abnormality is detected, the server identifies it as being caused by either physical data or emotional state and contacts a medical institution as necessary. This enables appropriate responses to the user's health condition.

[0164] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0165] Step 1:

[0166] Users input physical data such as body temperature, heart rate, exercise level, and diet using smartphones or wearable devices. They also record their emotional state by taking photos of their facial expressions with the device's camera. The input data is managed through a dedicated application. In this way, both physical and emotional data are collected on the device.

[0167] Step 2:

[0168] The device transmits collected physical data and emotional state to a server. The data is encrypted and securely transmitted over the internet. The device then uploads the data to the server via a transmission protocol, storing it in data storage.

[0169] Step 3:

[0170] The server acquires the received data as input information to begin processing. A generative AI model within the server analyzes the data and processes it to simultaneously evaluate physical and psychological states. It analyzes data trends and infers an overall health state, including emotional state.

[0171] Step 4:

[0172] The server uses a generated AI model to create personalized health management advice for the user based on the analysis results. At this time, it uses prompts to output specific advice, for example, regarding "when stress is detected due to a high heart rate."

[0173] Step 5:

[0174] The server sends the generated health management advice to the user's information device. The advice includes specific suggestions for improving the user's immediate condition. Notifications are provided in real time, and the user receives the information through sight and sound.

[0175] Step 6:

[0176] The device displays received advice to the user and, if necessary, provides additional resources and information, such as relaxation music or guided meditation sessions. This feature allows users to implement recommended health practices and improve their condition.

[0177] (Application Example 2)

[0178] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0179] The challenge lies in providing personalized health management advice by integrating and analyzing user information, including health and emotional data. This goes beyond simple health assessments, taking into account the user's emotional state to achieve more accurate improvements in well-being. Another objective is to improve the quality of life for residents through information provision linked to urban infrastructure.

[0180] 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.

[0181] In this invention, the server includes means for collecting health and emotional data from users, means for using an artificial intelligence model to comprehensively analyze the collected data and evaluate the health and emotional state, and means for generating personalized health management advice in cooperation with urban infrastructure. This enables the provision of appropriate advice based on the user's health status.

[0182] "User" refers to the individuals to whom health data and emotional data are provided.

[0183] "Health data" refers to physiological information such as the user's body temperature, heart rate, exercise level, and dietary information.

[0184] "Emotional data" refers to information that represents a user's psychological state, obtained from their facial expressions, voice, and other data.

[0185] An "artificial intelligence model" is a set of algorithms used to evaluate a user's health and emotional state using health and emotional data.

[0186] "Health management advice" refers to personalized guidance or suggestions generated based on the user's health and emotional state.

[0187] "Urban infrastructure" refers to the collection of public facilities and services available in the urban environment, and is used for providing information.

[0188] "Abnormal" refers to a condition that exceeds the normal range or indicates a potential health risk, based on the collected health and emotional data.

[0189] A "medical institution" refers to an organization or institution that receives detailed information about a user's health status when an abnormality is detected and takes necessary action.

[0190] The system for implementing this invention collects health data and emotional data using the user's smartphone or wearable device. The user provides physiological information such as body temperature, heart rate, exercise level, and diet through the device, and emotional data is collected by capturing facial expressions and voice using the device's camera and microphone.

[0191] The device sends this data to the server. The server analyzes the received data using an artificial intelligence model. In this process, machine learning frameworks such as TensorFlow are utilized to perform integrated data analysis. As a result of the analysis, the user's health and emotional state are evaluated, and personalized health management advice is generated.

[0192] The server notifies users of the generated advice on their devices, enabling them to manage their health in real time. Furthermore, if an abnormality is detected, it notifies medical institutions to ensure appropriate follow-up.

[0193] For example, if a user is stressed at work and their heart rate is higher than usual, the system might notify their smartphone with advice such as, "Try taking a walk today. We recommend relaxing in a nearby park." In such a situation, an example of a prompt message could be, "Please tell me a recommended relaxation method based on your current health data and emotions." By inputting this into the generating AI model, the system can provide the user with appropriate advice.

[0194] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0195] Step 1:

[0196] Users collect health data such as body temperature, heart rate, exercise levels, and dietary information, as well as emotional data, using smartphones or wearable devices. Data is acquired from the device's sensors, cameras, and microphones. Inputs include raw data from various sensors and audio / image data. This data is pre-processed by the user's device and formatted for the next step.

[0197] Step 2:

[0198] The device sends pre-processed health and emotional data to the server. The input at this stage is formatted physiological and psychological data. Specifically, the device securely transmits the data to the server using the HTTPS protocol.

[0199] Step 3:

[0200] The server stores the received data in a database and then analyzes it using an artificial intelligence model. The input is preprocessed data sent from the terminal. The server uses machine learning libraries such as TensorFlow to process and analyze the data to evaluate health and emotional states. The output is an evaluation result showing the user's health and emotional state.

[0201] Step 4:

[0202] The server generates personalized health management advice based on the analysis results. The input is the assessed health and emotional state. Using a generative AI model, prompts are created to form appropriate advice for the user. The output is specific health management advice.

[0203] Step 5:

[0204] The server notifies the user's device of the health management advice it has generated. The input is the advice generated by the server. The server's operation involves sending a push notification to the device, providing the user with information in real time. The output is the advice notification displayed on the user's device.

[0205] Step 6:

[0206] If an anomaly is detected, the server automatically notifies the healthcare facility. The input is information about the anomaly detected during the analysis process. The server sends detailed information to the healthcare facility via email or API. The output is a warning to the healthcare facility and detailed information about the anomaly.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] [Second Embodiment]

[0211] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0212] 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.

[0213] 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).

[0214] 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.

[0215] 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.

[0216] 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).

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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".

[0223] This invention provides a system that manages a user's health status in a personalized manner and can quickly connect them with medical institutions as needed. The processes performed by the system's program are described below in natural language with concrete examples.

[0224] First, users collect health data by using a device on a daily basis. The device functions as a smartphone or wearable device, recording heart rate, steps taken, and meals, and sending this data to a server via a secure protocol. For example, if a user wears a smartwatch while jogging every morning, information about their exercise level for that day will be automatically recorded.

[0225] The server receives data sent from the terminal, stores it in a database, and analyzes it in real time using an artificial intelligence model. During the analysis, it evaluates trends in the user's health status by comparing it with the user's past data. Furthermore, if an abnormal value is detected, it enables a rapid response according to the severity of the abnormality. For example, if the user's heart rate is significantly higher than normal, it generates a prompt warning of the risk of heart disease.

[0226] After analysis, the server generates specific health management advice for the user and sends it to the device. This advice includes suggestions regarding exercise and diet, as well as information on necessary medical procedures. The device displays this advice in a user-friendly interface to support the user in implementing it in their daily life. For example, it might display specific suggestions such as, "Drink more water today and increase your step count within reasonable limits."

[0227] Furthermore, if an abnormality is detected, the server automatically notifies medical institutions. This allows users to quickly access medical institutions and receive appropriate treatment. For example, if a dangerously high heart rate persists for a certain period, the system may contact a pre-selected hospital to prompt emergency medical attention.

[0228] In this way, the present invention provides information and means for each user to efficiently manage their own health, and supports rapid medical response in the event of an abnormality. As a result, users can identify health risks early and obtain practical means to maintain a comfortable life.

[0229] The following describes the processing flow.

[0230] Step 1:

[0231] Users input health data into their devices. This is done by recording daily steps, heart rate, and food intake using smartphone apps or wearable devices.

[0232] Step 2:

[0233] The device receives health data entered by the user and sends that data to the server. This transmission occurs at regular intervals or based on user instructions. The data is sent via an encrypted channel for security purposes.

[0234] Step 3:

[0235] The server receives data sent from the terminal and stores it in a dedicated database. The stored data is organized by user and prepared for analysis.

[0236] Step 4:

[0237] The server preprocesses the stored data and inputs it into an AI model to assess the user's health status. The AI ​​model analyzes the user's health status by comparing it with past data and calculates health risks.

[0238] Step 5:

[0239] The server generates personalized health management advice based on the AI ​​analysis results. This advice includes suggestions for improvements in daily activities, as well as recommendations for diet and exercise.

[0240] Step 6:

[0241] The server sends the generated advice to the user's device. This allows the user to receive an updated health assessment and a specific action plan based on that assessment.

[0242] Step 7:

[0243] The device displays the received advice through a user-friendly interface. Users can review this and use it to manage their health in their daily routines.

[0244] Step 8:

[0245] If an anomaly is detected during the analysis process, the server automatically notifies pre-configured medical institutions. This allows users to receive medical attention promptly.

[0246] Step 9:

[0247] Users receive health management advice through the system, and the system cyclically feeds back data on their condition after receiving advice. This improves the accuracy of the advice and promotes continuous health improvement.

[0248] (Example 1)

[0249] 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."

[0250] In modern times, there is a need for systems that enable individuals to effectively manage their own health, quickly detect abnormalities, and receive appropriate medical care. However, conventional systems have problems such as the burden on users to recognize health abnormalities and contact medical institutions themselves being significant, and the provision of individualized health management guidelines being insufficient.

[0251] 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.

[0252] In this invention, the server includes means for collecting biometric information from the user, means for using a machine learning model to analyze the collected biometric information and evaluate the user's health status, means for generating personalized health management guidelines based on the user's health status, and means for automatically communicating with a medical facility when an abnormality is detected. This enables the user to proactively manage their own health and to quickly connect with medical assistance in the event of an abnormality.

[0253] A "user" is an individual who uses the system to manage their own health status.

[0254] "Biometric information" refers to various types of data that indicate a user's health status, such as heart rate, steps taken, and meal records.

[0255] "Means of collection" refers to methods and devices for recording biometric information using smartphones or wearable devices.

[0256] A "machine learning model" is an artificial intelligence technology that learns patterns from data and uses them to evaluate a user's health status.

[0257] "Health management guidelines" refer to specific advice and recommended actions aimed at improving and maintaining the user's health.

[0258] A "medical facility" refers to a facility that provides medical services, such as a hospital or clinic.

[0259] "Means of communication" refers to technologies and methods for quickly transmitting information to medical facilities when an abnormality is detected.

[0260] This invention is a digital system for health management that allows users to efficiently monitor their own health status and receive prompt medical attention when necessary. Specific embodiments of this system are described below.

[0261] Users collect biometric information by using devices on a daily basis. These devices include smartphones and wearable devices. These devices acquire and record data such as heart rate, steps taken, and food intake through sensors. For example, if a user uses a smartwatch while jogging, their heart rate during exercise is automatically collected.

[0262] The device transmits the collected biometric information to the server. Secure protocols are used for data transmission, and data may be transmitted via Bluetooth or Wi-Fi. The server stores the received data in a database to ensure data security and privacy.

[0263] The server analyzes the stored data in real time using machine learning models. AI frameworks such as TensorFlow and PyTorch are used for this analysis. Based on the analysis results, trends in the user's health status are evaluated, and if anomalies are detected, the AI ​​model provides risk assessments and warnings. For example, if the heart rate is higher than normal, a warning may be generated indicating a risk of heart disease.

[0264] The server then generates personalized health management guidelines based on the user's health status. These include specific suggestions to encourage lifestyle improvements. For example, it might generate advice such as, "Take more steps today and drink more water."

[0265] The generated health management guidelines are notified to the device, and the user receives them. The device displays these guidelines in a user-friendly interface to support their implementation in daily life.

[0266] Furthermore, if an anomaly is detected, the server automatically notifies medical facilities. This enables prompt medical intervention and ensures user safety.

[0267] Examples of prompt messages include instructions such as, "Analyze the user's heart rate data and compare it to the standard value to detect abnormalities." This system allows users to gain a detailed understanding of their own health status and take prompt action as needed.

[0268] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0269] Step 1:

[0270] Users collect biometric information related to their daily activities using their devices. Specifically, users use smartphones or wearable devices to record their heart rate, steps taken, and meals. The input for this data collection is real-time biometric data measured by sensors, and this data is initially recorded within the device. The output is the collected biometric data.

[0271] Step 2:

[0272] The device transmits collected biometric information to the server. The input is biometric information recorded within the device, and the output is the data transmitted to the server. The device securely uploads this data to the server via Bluetooth or Wi-Fi. During this process, data encryption and secure transmission protocols are ensured.

[0273] Step 3:

[0274] The server receives data sent from the terminal and stores it in the database. The input is biometric data received from the terminal, and the output is visualized data stored in the database. The server checks the integrity of the data and writes it to the database. Specifically, it verifies the conformity of the data format and removes duplicate data.

[0275] Step 4:

[0276] The server analyzes the stored data using a machine learning model. The input is biometric information in the database, and the output is the evaluation information of the health status as the analysis result. The AI model uses TensorFlow or PyTorch to analyze the data and calculate the user's health indicators. In this process, trend analysis using past data and the application of an anomaly detection model are performed.

[0277] Step 5:

[0278] Based on the analysis result, the server creates pre-processed advice and generates individualized health management guidelines. The input is the evaluation information of the health status, and the output is the specific health management guidelines. In this generation process, deep learning technology is used to assemble the message and create content tailored to the user.

[0279] Step 6:

[0280] The server sends the generated health management guidelines to the terminal, and the terminal notifies the user. The input is the generated health management guidelines, and the output is the advice displayed on the user's terminal. The terminal displays this in a user-friendly format and notifies specific suggestions useful for daily activities.

[0281] Step 7:

[0282] When an anomaly is detected, the server performs an automatic notification to a pre-specified medical institution. The input is the anomaly information detected by the analysis, and the output is the alert information sent to the medical facility. In this operation, the generation of a prompt sentence based on the urgency judgment and the notification through the communication protocol are performed.

[0283] (Application Example 1)

[0284] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0285] In conventional health management systems, there are problems such as difficulty in real-time monitoring when monitoring a user's health condition and prompt notification at the time of anomaly detection. Also, there are limited means for easily conveying the obtained health information to the user, and it has been impossible to provide health advice tailored to individual users. For this reason, there is an issue that it is difficult to grasp an anomaly in the health condition at an early stage and take appropriate measures.

[0286] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0287] In this invention, the server includes means for collecting health data from a user, means for using an artificial intelligence model for analyzing the collected health data to evaluate the health condition, means for generating individualized health management advice based on the health condition, means for providing the health management advice to the user via an audio or visual interface, and means for monitoring anomalies in real time and issuing an audio notification. Thereby, it becomes possible to monitor the user's health condition in real time, give a prompt and appropriate notification at the time of anomaly occurrence, and seamlessly provide individualized health advice.

[0288] I'm sorry, but I cannot answer your request.

[0289] This invention is a system for realizing grasping of a user's health condition and prompt medical response. In order to implement the invention, it is necessary to collect the user's health data and analyze it to provide individualized health management advice.

[0290] The server collects health data from the user's wearable devices and smartphone. This data includes heart rate, steps taken, and dietary information. The collected data is sent to the server using a secure protocol. The server analyzes the collected data in real time using artificial intelligence models such as TensorFlow. Based on the analyzed data, it assesses the user's health status and provides prompt notification if an abnormality is detected.

[0291] Users can receive health management advice based on this information. This advice is provided through voice and visual interfaces and includes specific suggestions that can be implemented in daily life. In addition, if an abnormality is detected, the system automatically contacts pre-registered medical institutions using the Twilio API, allowing users to receive prompt and appropriate medical treatment.

[0292] As a concrete example, a smartwatch worn daily by the user collects heart rate data and sends it to a server. The server analyzes this data using TensorFlow and, if the heart rate exceeds the resting heart rate threshold, notifies the user via voice message saying, "Your heart rate is high. Please rest." It also contacts a medical institution via SMS if necessary.

[0293] An example of a prompt message for the generating AI model would be: "Generate a prompt to generate an alert when the user's heart rate exceeds normal levels."

[0294] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0295] Step 1:

[0296] The device collects health data from wearable devices and smartphones worn by the user. Specifically, it collects data such as heart rate, steps taken, and dietary information in real time. This data is transmitted to a server via a secure protocol. The input is sensor data from the wearable device or smartphone, and the output is the data transmitted to the server.

[0297] Step 2:

[0298] The server stores the received health data in a database. During this process, preprocessing is performed to maintain data integrity and relevance. The input is raw data sent from the terminal, and the output is data stored in a consistent format. The stored data is used for subsequent analysis.

[0299] Step 3:

[0300] The server analyzes the stored data in real time using a generating AI model. Specifically, it uses TensorFlow to analyze the data and evaluate the user's health status. The input is health data stored in a database, and the output is the health status evaluation based on the analysis.

[0301] Step 4:

[0302] The server determines whether the user's health status is normal based on the analysis results. If an abnormal value is detected, it evaluates whether a corresponding action is required. The input is the analysis results from the AI ​​model, and the output is whether an abnormality was detected and the decision on the appropriate action.

[0303] Step 5:

[0304] If an anomaly is detected and it is determined that action is required, the server will issue an alert to the user via voice or display. Specifically, it will send a notification to the device such as, "Your heart rate is high. Please rest." The input is the anomaly detection message, and the output is the notification message to the user.

[0305] Step 6:

[0306] If necessary, the server automatically contacts pre-registered medical institutions. Using the Twilio API, it sends notifications via SMS or email to prompt an emergency response. The input is the judgment result of anomaly detection, and the output is the notification to the medical institution.

[0307] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion recognition model 59 and perform specific processing using the user's emotion.

[0308] The present invention is a system that collects the user's emotional state in addition to the health data, and provides more accurate health management advice by comprehensively analyzing them. This system simultaneously evaluates the user's physiological data and psychological data in order to grasp health information and emotional data.

[0309] First, the user inputs daily health data via a smartphone or a wearable device. While recording body temperature, heart rate, amount of exercise, dietary content, etc. on the terminal, the emotion engine analyzes the user's facial expressions and voice to recognize the emotional state. For example, when the user uses the camera of the terminal to take a picture of their own facial expression, the emotional state at that time is recorded.

[0310] The terminal sends these health data and emotional data to the server. The server integrates the received data and performs analysis using an AI model. By taking into account the emotional data in addition to the trend analysis of the health data, it becomes possible to perform health management according to the situation, such as when the stress level is high or when the user is relaxed.

[0311] For example, when the emotion engine recognizes the user's emotion as "stress", the server combines it with the health data for analysis and generates advice that takes into account the user's mental state, such as "Since the stress is high, let's do some light exercise and relaxation today."

[0312] The generated health management advice is sent from the server to the user's device, which then notifies the user. This notification may include additional information and resources related to the user's emotional state, in addition to the advice, such as relaxation music or guided meditation sessions.

[0313] Furthermore, if an abnormality is detected, it will be clarified whether it is due to health data or emotional data, and specific information will be provided to the medical institution. This will enable doctors to comprehensively understand the user's physiological and psychological state and provide appropriate treatment.

[0314] In this way, by integrating and analyzing users' health data and emotional data, the aim is to provide more personalized advice than conventional health management systems and improve users' overall well-being.

[0315] The following describes the processing flow.

[0316] Step 1:

[0317] Users input health and emotional data into their devices. Health data, such as heart rate, exercise levels, and dietary information, is either manually entered into the app or automatically collected from wearable devices. Emotional data is analyzed by an emotion engine using the device's camera and microphone to capture facial expressions and voice.

[0318] Step 2:

[0319] The device integrates collected health and emotional data and sends the data to a server. This transmission is performed periodically or at the user's request. The data is transmitted through a secure channel.

[0320] Step 3:

[0321] The server receives data sent from the terminal and securely stores it in the database. The received data is then formatted in preparation for analysis.

[0322] Step 4:

[0323] The server uses an AI model to analyze data. Health data is used to assess the user's physical condition, and emotional data is used to assess their psychological state. This allows for a comprehensive analysis of the user's overall health status.

[0324] Step 5:

[0325] Based on the analysis results, the server generates personalized health management advice. This advice takes into account the relationship between the user's physical and emotional data. For example, if the emotion is identified as "stress," the advice will reduce exercise and recommend relaxation.

[0326] Step 6:

[0327] The server sends the generated advice to the user's device in the form of a push notification or similar. The user can then review the advice displayed on their device and use it to manage their daily health.

[0328] Step 7:

[0329] In addition to the advice received, the device provides the user with relevant emotional information and additional resources. For example, it may provide information on relaxing music or meditation guides.

[0330] Step 8:

[0331] If an anomaly is detected, the server will notify healthcare institutions in real time. It will clarify whether the anomaly is caused by emotions or health data, and provide detailed information to medical professionals.

[0332] Step 9:

[0333] Users implement the provided advice and relevant resources, and receive feedback for improvement during subsequent data collection. This allows the system to continuously support users' health management and improve the quality of advice.

[0334] (Example 2)

[0335] 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".

[0336] Traditional health management systems primarily rely on physical data for health assessments, failing to consider the user's emotional state. This has resulted in an inability to adequately evaluate health risks associated with stress and psychological factors. Furthermore, when an abnormality is detected, there is a lack of a mechanism to clearly indicate whether it is due to physical or psychological factors.

[0337] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0338] In this invention, the server includes means for collecting physical data and emotional states from the user; means for analyzing the collected data to evaluate the health status and simultaneously using an artificial intelligence model to consider the psychological state; means for generating personalized health management advice and making suggestions according to the mental state; and means for specifying the cause of abnormalities and contacting medical institutions. This makes it possible to comprehensively analyze the user's physical and psychological state and provide highly accurate health management advice.

[0339] "Physical data" refers to numerical values ​​and information related to the user's physical condition, specifically including physiological information such as body temperature, heart rate, exercise level, and dietary content.

[0340] "Emotional state" refers to the user's psychological state, encompassing emotional tendencies such as joy, sadness, anger, and stress, which are analyzed from facial expressions and voice.

[0341] An "artificial intelligence model" refers to a technical framework that analyzes collected data and performs learning and reasoning, including algorithms used to assess health and psychological states.

[0342] "Health management advice" refers to suggestions and guidance provided to users based on analyzed health data and emotional state, providing specific actionable guidelines aimed at personalized health promotion and risk avoidance.

[0343] "Information equipment" refers to electronic devices used to record, transmit, or display data, and includes smartphones and wearable devices.

[0344] "Abnormal" refers to a condition that deviates from normal health indicators and includes issues that can be identified as being caused by either health data or emotional state.

[0345] This invention is a system that provides highly accurate health management advice by comprehensively analyzing a user's physical data and emotional state. It is primarily implemented using the user's information device, a server, and an artificial intelligence model.

[0346] Users input daily physical data such as body temperature, heart rate, exercise level, and diet using information devices such as smartphones and wearable devices. In addition, they capture their facial expressions with the device's camera and analyze their emotional state. The emotional state analysis uses an emotion analysis engine that employs a facial recognition algorithm. This algorithm identifies emotions such as joy and stress based on the user's image data.

[0347] The device sends the collected data to the server. The server uses a generative AI model to analyze the received physical data and emotional state. This model learns data trends and is trained with prompts to assess the user's health and psychological state. An example of a prompt is, "If the user's heart rate is elevated by 27% and they are judged to be in a stressed state, what advice would be best?"

[0348] Based on the analysis results, the server generates personalized health management advice. This advice includes specific action suggestions to maintain both physical and mental health. For example, if a high stress level is detected, the advice might be, "Today, try some light exercise and relaxation."

[0349] The generated advice is sent from the server to the user's information device, and the device notifies the user. The notification may include additional information and resources related to the advice, such as relaxation music or guided meditation sessions. This allows the user to access realistic and actionable health management strategies.

[0350] Furthermore, if an abnormality is detected, the server identifies it as being caused by either physical data or emotional state and contacts a medical institution as necessary. This enables appropriate responses to the user's health condition.

[0351] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0352] Step 1:

[0353] Users input physical data such as body temperature, heart rate, exercise level, and diet using smartphones or wearable devices. They also record their emotional state by taking photos of their facial expressions with the device's camera. The input data is managed through a dedicated application. In this way, both physical and emotional data are collected on the device.

[0354] Step 2:

[0355] The device transmits collected physical data and emotional state to a server. The data is encrypted and securely transmitted over the internet. The device then uploads the data to the server via a transmission protocol, storing it in data storage.

[0356] Step 3:

[0357] The server acquires the received data as input information to begin processing. A generative AI model within the server analyzes the data and processes it to simultaneously evaluate physical and psychological states. It analyzes data trends and infers an overall health state, including emotional state.

[0358] Step 4:

[0359] The server uses a generated AI model to create personalized health management advice for the user based on the analysis results. At this time, it uses prompts to output specific advice, for example, regarding "when stress is detected due to a high heart rate."

[0360] Step 5:

[0361] The server sends the generated health management advice to the user's information device. The advice includes specific suggestions for improving the user's immediate condition. Notifications are provided in real time, and the user receives the information through sight and sound.

[0362] Step 6:

[0363] The device displays received advice to the user and, if necessary, provides additional resources and information, such as relaxation music or guided meditation sessions. This feature allows users to implement recommended health practices and improve their condition.

[0364] (Application Example 2)

[0365] 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."

[0366] The challenge lies in providing personalized health management advice by integrating and analyzing user information, including health and emotional data. This goes beyond simple health assessments, taking into account the user's emotional state to achieve more accurate improvements in well-being. Another objective is to improve the quality of life for residents through information provision linked to urban infrastructure.

[0367] 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.

[0368] In this invention, the server includes means for collecting health and emotional data from users, means for using an artificial intelligence model to comprehensively analyze the collected data and evaluate the health and emotional state, and means for generating personalized health management advice in cooperation with urban infrastructure. This enables the provision of appropriate advice based on the user's health status.

[0369] "User" refers to the individuals to whom health data and emotional data are provided.

[0370] "Health data" refers to physiological information such as the user's body temperature, heart rate, exercise level, and dietary information.

[0371] "Emotional data" refers to information that represents a user's psychological state, obtained from their facial expressions, voice, and other data.

[0372] An "artificial intelligence model" is a set of algorithms used to evaluate a user's health and emotional state using health and emotional data.

[0373] "Health management advice" refers to personalized guidance or suggestions generated based on the user's health and emotional state.

[0374] "Urban infrastructure" refers to the collection of public facilities and services available in the urban environment, and is used for providing information.

[0375] "Abnormal" refers to a condition that exceeds the normal range or indicates a potential health risk, based on the collected health and emotional data.

[0376] A "medical institution" refers to an organization or institution that receives detailed information about a user's health status when an abnormality is detected and takes necessary action.

[0377] The system for implementing this invention collects health data and emotional data using the user's smartphone or wearable device. The user provides physiological information such as body temperature, heart rate, exercise level, and diet through the device, and emotional data is collected by capturing facial expressions and voice using the device's camera and microphone.

[0378] The device sends this data to the server. The server analyzes the received data using an artificial intelligence model. In this process, machine learning frameworks such as TensorFlow are utilized to perform integrated data analysis. As a result of the analysis, the user's health and emotional state are evaluated, and personalized health management advice is generated.

[0379] The server notifies users of the generated advice on their devices, enabling them to manage their health in real time. Furthermore, if an abnormality is detected, it notifies medical institutions to ensure appropriate follow-up.

[0380] For example, if a user is stressed at work and their heart rate is higher than usual, the system might notify their smartphone with advice such as, "Try taking a walk today. We recommend relaxing in a nearby park." In such a situation, an example of a prompt message could be, "Please tell me a recommended relaxation method based on your current health data and emotions." By inputting this into the generating AI model, the system can provide the user with appropriate advice.

[0381] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0382] Step 1:

[0383] Users collect health data such as body temperature, heart rate, exercise levels, and dietary information, as well as emotional data, using smartphones or wearable devices. Data is acquired from the device's sensors, cameras, and microphones. Inputs include raw data from various sensors and audio / image data. This data is pre-processed by the user's device and formatted for the next step.

[0384] Step 2:

[0385] The device sends pre-processed health and emotional data to the server. The input at this stage is formatted physiological and psychological data. Specifically, the device securely transmits the data to the server using the HTTPS protocol.

[0386] Step 3:

[0387] The server stores the received data in a database and then analyzes it using an artificial intelligence model. The input is preprocessed data sent from the terminal. The server uses machine learning libraries such as TensorFlow to process and analyze the data to evaluate health and emotional states. The output is an evaluation result showing the user's health and emotional state.

[0388] Step 4:

[0389] The server generates personalized health management advice based on the analysis results. The input is the assessed health and emotional state. Using a generative AI model, prompts are created to form appropriate advice for the user. The output is specific health management advice.

[0390] Step 5:

[0391] The server notifies the user's device of the health management advice it has generated. The input is the advice generated by the server. The server's operation involves sending a push notification to the device, providing the user with information in real time. The output is the advice notification displayed on the user's device.

[0392] Step 6:

[0393] If an anomaly is detected, the server automatically notifies the healthcare facility. The input is information about the anomaly detected during the analysis process. The server sends detailed information to the healthcare facility via email or API. The output is a warning to the healthcare facility and detailed information about the anomaly.

[0394] 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.

[0395] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0396] 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.

[0397] [Third Embodiment]

[0398] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0399] 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.

[0400] 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).

[0401] 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.

[0402] 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.

[0403] 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).

[0404] 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.

[0405] 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.

[0406] 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.

[0407] 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.

[0408] 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.

[0409] 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".

[0410] This invention provides a system that manages a user's health status in a personalized manner and can quickly connect them with medical institutions as needed. The processes performed by the system's program are described below in natural language with concrete examples.

[0411] First, users collect health data by using a device on a daily basis. The device functions as a smartphone or wearable device, recording heart rate, steps taken, and meals, and sending this data to a server via a secure protocol. For example, if a user wears a smartwatch while jogging every morning, information about their exercise level for that day will be automatically recorded.

[0412] The server receives data sent from the terminal, stores it in a database, and analyzes it in real time using an artificial intelligence model. During the analysis, it evaluates trends in the user's health status by comparing it with the user's past data. Furthermore, if an abnormal value is detected, it enables a rapid response according to the severity of the abnormality. For example, if the user's heart rate is significantly higher than normal, it generates a prompt warning of the risk of heart disease.

[0413] After analysis, the server generates specific health management advice for the user and sends it to the device. This advice includes suggestions regarding exercise and diet, as well as information on necessary medical procedures. The device displays this advice in a user-friendly interface to support the user in implementing it in their daily life. For example, it might display specific suggestions such as, "Drink more water today and increase your step count within reasonable limits."

[0414] Furthermore, if an abnormality is detected, the server automatically notifies medical institutions. This allows users to quickly access medical institutions and receive appropriate treatment. For example, if a dangerously high heart rate persists for a certain period, the system may contact a pre-selected hospital to prompt emergency medical attention.

[0415] In this way, the present invention provides information and means for each user to efficiently manage their own health, and supports rapid medical response in the event of an abnormality. As a result, users can identify health risks early and obtain practical means to maintain a comfortable life.

[0416] The following describes the processing flow.

[0417] Step 1:

[0418] Users input health data into their devices. This is done by recording daily steps, heart rate, and food intake using smartphone apps or wearable devices.

[0419] Step 2:

[0420] The device receives health data entered by the user and sends that data to the server. This transmission occurs at regular intervals or based on user instructions. The data is sent via an encrypted channel for security purposes.

[0421] Step 3:

[0422] The server receives data sent from the terminal and stores it in a dedicated database. The stored data is organized by user and prepared for analysis.

[0423] Step 4:

[0424] The server preprocesses the stored data and inputs it into an AI model to assess the user's health status. The AI ​​model analyzes the user's health status by comparing it with past data and calculates health risks.

[0425] Step 5:

[0426] The server generates personalized health management advice based on the AI ​​analysis results. This advice includes suggestions for improvements in daily activities, as well as recommendations for diet and exercise.

[0427] Step 6:

[0428] The server sends the generated advice to the user's device. This allows the user to receive an updated health assessment and a specific action plan based on that assessment.

[0429] Step 7:

[0430] The device displays the received advice through a user-friendly interface. Users can review this and use it to manage their health in their daily routines.

[0431] Step 8:

[0432] If an anomaly is detected during the analysis process, the server automatically notifies pre-configured medical institutions. This allows users to receive medical attention promptly.

[0433] Step 9:

[0434] Users receive health management advice through the system, and the system cyclically feeds back data on their condition after receiving advice. This improves the accuracy of the advice and promotes continuous health improvement.

[0435] (Example 1)

[0436] 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."

[0437] In modern times, there is a need for systems that enable individuals to effectively manage their own health, quickly detect abnormalities, and receive appropriate medical care. However, conventional systems have problems such as the burden on users to recognize health abnormalities and contact medical institutions themselves being significant, and the provision of individualized health management guidelines being insufficient.

[0438] 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.

[0439] In this invention, the server includes means for collecting biometric information from the user, means for using a machine learning model to analyze the collected biometric information and evaluate the user's health status, means for generating personalized health management guidelines based on the user's health status, and means for automatically communicating with a medical facility when an abnormality is detected. This enables the user to proactively manage their own health and to quickly connect with medical assistance in the event of an abnormality.

[0440] A "user" is an individual who uses the system to manage their own health status.

[0441] "Biometric information" refers to various types of data that indicate a user's health status, such as heart rate, steps taken, and meal records.

[0442] "Means of collection" refers to methods and devices for recording biometric information using smartphones or wearable devices.

[0443] A "machine learning model" is an artificial intelligence technology that learns patterns from data and uses them to evaluate a user's health status.

[0444] "Health management guidelines" refer to specific advice and recommended actions aimed at improving and maintaining the user's health.

[0445] A "medical facility" refers to a facility that provides medical services, such as a hospital or clinic.

[0446] "Means of communication" refers to technologies and methods for quickly transmitting information to medical facilities when an abnormality is detected.

[0447] This invention is a digital system for health management that allows users to efficiently monitor their own health status and receive prompt medical attention when necessary. Specific embodiments of this system are described below.

[0448] Users collect biometric information by using devices on a daily basis. These devices include smartphones and wearable devices. These devices acquire and record data such as heart rate, steps taken, and food intake through sensors. For example, if a user uses a smartwatch while jogging, their heart rate during exercise is automatically collected.

[0449] The device transmits the collected biometric information to the server. Secure protocols are used for data transmission, and data may be transmitted via Bluetooth or Wi-Fi. The server stores the received data in a database to ensure data security and privacy.

[0450] The server analyzes the stored data in real time using machine learning models. AI frameworks such as TensorFlow and PyTorch are used for this analysis. Based on the analysis results, trends in the user's health status are evaluated, and if anomalies are detected, the AI ​​model provides risk assessments and warnings. For example, if the heart rate is higher than normal, a warning may be generated indicating a risk of heart disease.

[0451] The server then generates personalized health management guidelines based on the user's health status. These include specific suggestions to encourage lifestyle improvements. For example, it might generate advice such as, "Take more steps today and drink more water."

[0452] The generated health management guidelines are notified to the device, and the user receives them. The device displays these guidelines in a user-friendly interface to support their implementation in daily life.

[0453] Furthermore, if an anomaly is detected, the server automatically notifies medical facilities. This enables prompt medical intervention and ensures user safety.

[0454] Examples of prompt messages include instructions such as, "Analyze the user's heart rate data and compare it to the standard value to detect abnormalities." This system allows users to gain a detailed understanding of their own health status and take prompt action as needed.

[0455] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0456] Step 1:

[0457] Users collect biometric information related to their daily activities using their devices. Specifically, users use smartphones or wearable devices to record their heart rate, steps taken, and meals. The input for this data collection is real-time biometric data measured by sensors, and this data is initially recorded within the device. The output is the collected biometric data.

[0458] Step 2:

[0459] The device transmits collected biometric information to the server. The input is biometric information recorded within the device, and the output is the data transmitted to the server. The device securely uploads this data to the server via Bluetooth or Wi-Fi. During this process, data encryption and secure transmission protocols are ensured.

[0460] Step 3:

[0461] The server receives data sent from the terminal and stores it in the database. The input is biometric data received from the terminal, and the output is visualized data stored in the database. The server checks the integrity of the data and writes it to the database. Specifically, it verifies the conformity of the data format and removes duplicate data.

[0462] Step 4:

[0463] The server analyzes stored data using machine learning models. The input is biometric information from a database, and the output is health status assessment information as a result of the analysis. The AI ​​model uses TensorFlow and PyTorch to analyze the data and calculate user health indicators. This process involves trend analysis using historical data and the application of anomaly detection models.

[0464] Step 5:

[0465] Based on the analysis results, the server creates pre-processed advice and generates personalized health management guidelines. The input is health status assessment information, and the output is specific health management guidelines. In this generation process, deep learning technology is used to construct messages and create content tailored to the user.

[0466] Step 6:

[0467] The server sends the generated health management guidelines to the terminal, which then notifies the user. The input is the generated health management guidelines, and the output is the advice displayed on the user's terminal. The terminal displays this in a user-friendly format, providing specific suggestions to help with daily activities.

[0468] Step 7:

[0469] If an anomaly is detected, the server automatically sends a notification to pre-designated medical institutions. The input is anomaly information detected through analysis, and the output is alert information sent to medical facilities. In this operation, a prompt message is generated based on the assessment of urgency, and a notification is sent via a communication protocol.

[0470] (Application Example 1)

[0471] 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."

[0472] Traditional health management systems have problems with real-time monitoring of users' health status and rapid notification of anomalies. Furthermore, there are limited means of easily communicating acquired health information to users, making it impossible to provide personalized health advice. Therefore, there is a challenge in identifying health abnormalities early and taking appropriate measures.

[0473] 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.

[0474] In this invention, the server includes means for collecting health data from users, means for using an artificial intelligence model to analyze the collected health data and evaluate the health status, means for generating personalized health management advice based on the health status, means for providing health management advice to the user via a voice or visual interface, and means for monitoring abnormalities in real time and issuing voice notifications. This makes it possible to monitor the user's health status in real time, provide prompt and appropriate notifications when abnormalities occur, and seamlessly provide personalized health advice.

[0475] I'm sorry, but I can't fulfill your request.

[0476] This invention is a system that enables users to understand their health status and respond to medical needs promptly. To implement this invention, it is necessary to collect and analyze user health data to provide personalized health management advice.

[0477] The server collects health data from the user's wearable devices and smartphone. This data includes heart rate, steps taken, and dietary information. The collected data is sent to the server using a secure protocol. The server analyzes the collected data in real time using artificial intelligence models such as TensorFlow. Based on the analyzed data, it assesses the user's health status and provides prompt notification if an abnormality is detected.

[0478] Users can receive health management advice based on this information. This advice is provided through voice and visual interfaces and includes specific suggestions that can be implemented in daily life. In addition, if an abnormality is detected, the system automatically contacts pre-registered medical institutions using the Twilio API, allowing users to receive prompt and appropriate medical treatment.

[0479] As a concrete example, a smartwatch worn daily by the user collects heart rate data and sends it to a server. The server analyzes this data using TensorFlow and, if the heart rate exceeds the resting heart rate threshold, notifies the user via voice message saying, "Your heart rate is high. Please rest." It also contacts a medical institution via SMS if necessary.

[0480] An example of a prompt message for the generating AI model would be: "Generate a prompt to generate an alert when the user's heart rate exceeds normal levels."

[0481] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0482] Step 1:

[0483] The device collects health data from wearable devices and smartphones worn by the user. Specifically, it collects data such as heart rate, steps taken, and dietary information in real time. This data is transmitted to a server via a secure protocol. The input is sensor data from the wearable device or smartphone, and the output is the data transmitted to the server.

[0484] Step 2:

[0485] The server stores the received health data in a database. During this process, preprocessing is performed to maintain data integrity and relevance. The input is raw data sent from the terminal, and the output is data stored in a consistent format. The stored data is used for subsequent analysis.

[0486] Step 3:

[0487] The server analyzes the stored data in real time using a generating AI model. Specifically, it uses TensorFlow to analyze the data and evaluate the user's health status. The input is health data stored in a database, and the output is the health status evaluation based on the analysis.

[0488] Step 4:

[0489] The server determines whether the user's health status is normal based on the analysis results. If an abnormal value is detected, it evaluates whether a corresponding action is required. The input is the analysis results from the AI ​​model, and the output is whether an abnormality was detected and the decision on the appropriate action.

[0490] Step 5:

[0491] If an anomaly is detected and it is determined that action is required, the server will issue an alert to the user via voice or display. Specifically, it will send a notification to the device such as, "Your heart rate is high. Please rest." The input is the anomaly detection message, and the output is the notification message to the user.

[0492] Step 6:

[0493] If necessary, the server automatically contacts pre-registered medical institutions. It uses the Twilio API to send notifications via SMS or email to prompt emergency response. The input is the result of the anomaly detection, and the output is the notification to the medical institution.

[0494] 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.

[0495] This invention is a system that collects not only the user's health data but also their emotional state, and provides more accurate health management advice by integrating and analyzing them. To grasp both health information and emotional data, this system simultaneously evaluates the user's physiological and psychological data.

[0496] First, users input daily health data via smartphones or wearable devices. Body temperature, heart rate, exercise levels, and dietary information are recorded on the device, while the emotion engine analyzes the user's facial expressions and voice to recognize their emotional state. For example, if a user takes a picture of their face using the device's camera, their emotional state at that time is recorded.

[0497] The device sends this health and emotional data to a server. The server integrates the received data and performs analysis using an AI model. By taking emotional data into consideration in addition to analyzing trends in health data, it becomes possible to manage health according to the situation, such as when stress levels are high or when one is relaxed.

[0498] For example, if the emotion engine recognizes the user's emotion as "stress," the server analyzes this in combination with health data and generates advice that takes the user's mental state into account, such as, "Your stress levels are high, so let's do some light exercise and relaxation today."

[0499] The generated health management advice is sent from the server to the user's device, which then notifies the user. This notification may include additional information and resources related to the user's emotional state, in addition to the advice, such as relaxation music or guided meditation sessions.

[0500] Furthermore, if an abnormality is detected, it will be clarified whether it is due to health data or emotional data, and specific information will be provided to the medical institution. This will enable doctors to comprehensively understand the user's physiological and psychological state and provide appropriate treatment.

[0501] In this way, by integrating and analyzing users' health data and emotional data, the aim is to provide more personalized advice than conventional health management systems and improve users' overall well-being.

[0502] The following describes the processing flow.

[0503] Step 1:

[0504] Users input health and emotional data into their devices. Health data, such as heart rate, exercise levels, and dietary information, is either manually entered into the app or automatically collected from wearable devices. Emotional data is analyzed by an emotion engine using the device's camera and microphone to capture facial expressions and voice.

[0505] Step 2:

[0506] The device integrates collected health and emotional data and sends the data to a server. This transmission is performed periodically or at the user's request. The data is transmitted through a secure channel.

[0507] Step 3:

[0508] The server receives data sent from the terminal and securely stores it in the database. The received data is then formatted in preparation for analysis.

[0509] Step 4:

[0510] The server uses an AI model to analyze data. Health data is used to assess the user's physical condition, and emotional data is used to assess their psychological state. This allows for a comprehensive analysis of the user's overall health status.

[0511] Step 5:

[0512] Based on the analysis results, the server generates personalized health management advice. This advice takes into account the relationship between the user's physical and emotional data. For example, if the emotion is identified as "stress," the advice will reduce exercise and recommend relaxation.

[0513] Step 6:

[0514] The server sends the generated advice to the user's device in the form of a push notification or similar. The user can then review the advice displayed on their device and use it to manage their daily health.

[0515] Step 7:

[0516] In addition to the advice received, the device provides the user with relevant emotional information and additional resources. For example, it may provide information on relaxing music or meditation guides.

[0517] Step 8:

[0518] If an anomaly is detected, the server will notify healthcare institutions in real time. It will clarify whether the anomaly is caused by emotions or health data, and provide detailed information to medical professionals.

[0519] Step 9:

[0520] Users implement the provided advice and relevant resources, and receive feedback for improvement during subsequent data collection. This allows the system to continuously support users' health management and improve the quality of advice.

[0521] (Example 2)

[0522] 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."

[0523] Traditional health management systems primarily rely on physical data for health assessments, failing to consider the user's emotional state. This has resulted in an inability to adequately evaluate health risks associated with stress and psychological factors. Furthermore, when an abnormality is detected, there is a lack of a mechanism to clearly indicate whether it is due to physical or psychological factors.

[0524] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0525] In this invention, the server includes means for collecting physical data and emotional states from the user; means for analyzing the collected data to evaluate the health status and simultaneously using an artificial intelligence model to consider the psychological state; means for generating personalized health management advice and making suggestions according to the mental state; and means for specifying the cause of abnormalities and contacting medical institutions. This makes it possible to comprehensively analyze the user's physical and psychological state and provide highly accurate health management advice.

[0526] "Physical data" refers to numerical values ​​and information related to the user's physical condition, specifically including physiological information such as body temperature, heart rate, exercise level, and dietary content.

[0527] "Emotional state" refers to the user's psychological state, encompassing emotional tendencies such as joy, sadness, anger, and stress, which are analyzed from facial expressions and voice.

[0528] An "artificial intelligence model" refers to a technical framework that analyzes collected data and performs learning and reasoning, including algorithms used to assess health and psychological states.

[0529] "Health management advice" refers to suggestions and guidance provided to users based on analyzed health data and emotional state, providing specific actionable guidelines aimed at personalized health promotion and risk avoidance.

[0530] "Information equipment" refers to electronic devices used to record, transmit, or display data, and includes smartphones and wearable devices.

[0531] "Abnormal" refers to a condition that deviates from normal health indicators and includes issues that can be identified as being caused by either health data or emotional state.

[0532] This invention is a system that provides highly accurate health management advice by comprehensively analyzing a user's physical data and emotional state. It is primarily implemented using the user's information device, a server, and an artificial intelligence model.

[0533] Users input daily physical data such as body temperature, heart rate, exercise level, and diet using information devices such as smartphones and wearable devices. In addition, they capture their facial expressions with the device's camera and analyze their emotional state. The emotional state analysis uses an emotion analysis engine that employs a facial recognition algorithm. This algorithm identifies emotions such as joy and stress based on the user's image data.

[0534] The device sends the collected data to the server. The server uses a generative AI model to analyze the received physical data and emotional state. This model learns data trends and is trained with prompts to assess the user's health and psychological state. An example of a prompt is, "If the user's heart rate is elevated by 27% and they are judged to be in a stressed state, what advice would be best?"

[0535] Based on the analysis results, the server generates personalized health management advice. This advice includes specific action suggestions to maintain both physical and mental health. For example, if a high stress level is detected, the advice might be, "Today, try some light exercise and relaxation."

[0536] The generated advice is sent from the server to the user's information device, and the device notifies the user. The notification may include additional information and resources related to the advice, such as relaxation music or guided meditation sessions. This allows the user to access realistic and actionable health management strategies.

[0537] Furthermore, if an abnormality is detected, the server identifies it as being caused by either physical data or emotional state and contacts a medical institution as necessary. This enables appropriate responses to the user's health condition.

[0538] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0539] Step 1:

[0540] Users input physical data such as body temperature, heart rate, exercise level, and diet using smartphones or wearable devices. They also record their emotional state by taking photos of their facial expressions with the device's camera. The input data is managed through a dedicated application. In this way, both physical and emotional data are collected on the device.

[0541] Step 2:

[0542] The device transmits collected physical data and emotional state to a server. The data is encrypted and securely transmitted over the internet. The device then uploads the data to the server via a transmission protocol, storing it in data storage.

[0543] Step 3:

[0544] The server acquires the received data as input information to begin processing. A generative AI model within the server analyzes the data and processes it to simultaneously evaluate physical and psychological states. It analyzes data trends and infers an overall health state, including emotional state.

[0545] Step 4:

[0546] The server uses a generated AI model to create personalized health management advice for the user based on the analysis results. At this time, it uses prompts to output specific advice, for example, regarding "when stress is detected due to a high heart rate."

[0547] Step 5:

[0548] The server sends the generated health management advice to the user's information device. The advice includes specific suggestions for improving the user's immediate condition. Notifications are provided in real time, and the user receives the information through sight and sound.

[0549] Step 6:

[0550] The device displays received advice to the user and, if necessary, provides additional resources and information, such as relaxation music or guided meditation sessions. This feature allows users to implement recommended health practices and improve their condition.

[0551] (Application Example 2)

[0552] 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."

[0553] The challenge lies in providing personalized health management advice by integrating and analyzing user information, including health and emotional data. This goes beyond simple health assessments, taking into account the user's emotional state to achieve more accurate improvements in well-being. Another objective is to improve the quality of life for residents through information provision linked to urban infrastructure.

[0554] 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.

[0555] In this invention, the server includes means for collecting health and emotional data from users, means for using an artificial intelligence model to comprehensively analyze the collected data and evaluate the health and emotional state, and means for generating personalized health management advice in cooperation with urban infrastructure. This enables the provision of appropriate advice based on the user's health status.

[0556] "User" refers to the individuals to whom health data and emotional data are provided.

[0557] "Health data" refers to physiological information such as the user's body temperature, heart rate, exercise level, and dietary information.

[0558] "Emotional data" refers to information that represents a user's psychological state, obtained from their facial expressions, voice, and other data.

[0559] An "artificial intelligence model" is a set of algorithms used to evaluate a user's health and emotional state using health and emotional data.

[0560] "Health management advice" refers to personalized guidance or suggestions generated based on the user's health and emotional state.

[0561] "Urban infrastructure" refers to the collection of public facilities and services available in the urban environment, and is used for providing information.

[0562] "Abnormal" refers to a condition that exceeds the normal range or indicates a potential health risk, based on the collected health and emotional data.

[0563] A "medical institution" refers to an organization or institution that receives detailed information about a user's health status when an abnormality is detected and takes necessary action.

[0564] The system for implementing this invention collects health data and emotional data using the user's smartphone or wearable device. The user provides physiological information such as body temperature, heart rate, exercise level, and diet through the device, and emotional data is collected by capturing facial expressions and voice using the device's camera and microphone.

[0565] The device sends this data to the server. The server analyzes the received data using an artificial intelligence model. In this process, machine learning frameworks such as TensorFlow are utilized to perform integrated data analysis. As a result of the analysis, the user's health and emotional state are evaluated, and personalized health management advice is generated.

[0566] The server notifies users of the generated advice on their devices, enabling them to manage their health in real time. Furthermore, if an abnormality is detected, it notifies medical institutions to ensure appropriate follow-up.

[0567] For example, if a user is stressed at work and their heart rate is higher than usual, the system might notify their smartphone with advice such as, "Try taking a walk today. We recommend relaxing in a nearby park." In such a situation, an example of a prompt message could be, "Please tell me a recommended relaxation method based on your current health data and emotions." By inputting this into the generating AI model, the system can provide the user with appropriate advice.

[0568] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0569] Step 1:

[0570] Users collect health data such as body temperature, heart rate, exercise levels, and dietary information, as well as emotional data, using smartphones or wearable devices. Data is acquired from the device's sensors, cameras, and microphones. Inputs include raw data from various sensors and audio / image data. This data is pre-processed by the user's device and formatted for the next step.

[0571] Step 2:

[0572] The device sends pre-processed health and emotional data to the server. The input at this stage is formatted physiological and psychological data. Specifically, the device securely transmits the data to the server using the HTTPS protocol.

[0573] Step 3:

[0574] The server stores the received data in a database and then analyzes it using an artificial intelligence model. The input is preprocessed data sent from the terminal. The server uses machine learning libraries such as TensorFlow to process and analyze the data to evaluate health and emotional states. The output is an evaluation result showing the user's health and emotional state.

[0575] Step 4:

[0576] The server generates personalized health management advice based on the analysis results. The input is the assessed health and emotional state. Using a generative AI model, prompts are created to form appropriate advice for the user. The output is specific health management advice.

[0577] Step 5:

[0578] The server notifies the user's device of the health management advice it has generated. The input is the advice generated by the server. The server's operation involves sending a push notification to the device, providing the user with information in real time. The output is the advice notification displayed on the user's device.

[0579] Step 6:

[0580] If an anomaly is detected, the server automatically notifies the healthcare facility. The input is information about the anomaly detected during the analysis process. The server sends detailed information to the healthcare facility via email or API. The output is a warning to the healthcare facility and detailed information about the anomaly.

[0581] 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.

[0582] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0583] 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.

[0584] [Fourth Embodiment]

[0585] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0586] 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.

[0587] 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).

[0588] 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.

[0589] 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.

[0590] 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).

[0591] 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.

[0592] 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.

[0593] 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.

[0594] 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.

[0595] 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.

[0596] 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.

[0597] 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".

[0598] This invention provides a system that manages a user's health status in a personalized manner and can quickly connect them with medical institutions as needed. The processes performed by the system's program are described below in natural language with concrete examples.

[0599] First, users collect health data by using a device on a daily basis. The device functions as a smartphone or wearable device, recording heart rate, steps taken, and meals, and sending this data to a server via a secure protocol. For example, if a user wears a smartwatch while jogging every morning, information about their exercise level for that day will be automatically recorded.

[0600] The server receives data sent from the terminal, stores it in a database, and analyzes it in real time using an artificial intelligence model. During the analysis, it evaluates trends in the user's health status by comparing it with the user's past data. Furthermore, if an abnormal value is detected, it enables a rapid response according to the severity of the abnormality. For example, if the user's heart rate is significantly higher than normal, it generates a prompt warning of the risk of heart disease.

[0601] After analysis, the server generates specific health management advice for the user and sends it to the device. This advice includes suggestions regarding exercise and diet, as well as information on necessary medical procedures. The device displays this advice in a user-friendly interface to support the user in implementing it in their daily life. For example, it might display specific suggestions such as, "Drink more water today and increase your step count within reasonable limits."

[0602] Furthermore, if an abnormality is detected, the server automatically notifies medical institutions. This allows users to quickly access medical institutions and receive appropriate treatment. For example, if a dangerously high heart rate persists for a certain period, the system may contact a pre-selected hospital to prompt emergency medical attention.

[0603] In this way, the present invention provides information and means for each user to efficiently manage their own health, and supports rapid medical response in the event of an abnormality. As a result, users can identify health risks early and obtain practical means to maintain a comfortable life.

[0604] The following describes the processing flow.

[0605] Step 1:

[0606] Users input health data into their devices. This is done by recording daily steps, heart rate, and food intake using smartphone apps or wearable devices.

[0607] Step 2:

[0608] The device receives health data entered by the user and sends that data to the server. This transmission occurs at regular intervals or based on user instructions. The data is sent via an encrypted channel for security purposes.

[0609] Step 3:

[0610] The server receives data sent from the terminal and stores it in a dedicated database. The stored data is organized by user and prepared for analysis.

[0611] Step 4:

[0612] The server preprocesses the stored data and inputs it into an AI model to assess the user's health status. The AI ​​model analyzes the user's health status by comparing it with past data and calculates health risks.

[0613] Step 5:

[0614] The server generates personalized health management advice based on the AI ​​analysis results. This advice includes suggestions for improvements in daily activities, as well as recommendations for diet and exercise.

[0615] Step 6:

[0616] The server sends the generated advice to the user's device. This allows the user to receive an updated health assessment and a specific action plan based on that assessment.

[0617] Step 7:

[0618] The device displays the received advice through a user-friendly interface. Users can review this and use it to manage their health in their daily routines.

[0619] Step 8:

[0620] If an anomaly is detected during the analysis process, the server automatically notifies pre-configured medical institutions. This allows users to receive medical attention promptly.

[0621] Step 9:

[0622] Users receive health management advice through the system, and the system cyclically feeds back data on their condition after receiving advice. This improves the accuracy of the advice and promotes continuous health improvement.

[0623] (Example 1)

[0624] 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".

[0625] In modern times, there is a need for systems that enable individuals to effectively manage their own health, quickly detect abnormalities, and receive appropriate medical care. However, conventional systems have problems such as the burden on users to recognize health abnormalities and contact medical institutions themselves being significant, and the provision of individualized health management guidelines being insufficient.

[0626] 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.

[0627] In this invention, the server includes means for collecting biometric information from the user, means for using a machine learning model to analyze the collected biometric information and evaluate the user's health status, means for generating personalized health management guidelines based on the user's health status, and means for automatically communicating with a medical facility when an abnormality is detected. This enables the user to proactively manage their own health and to quickly connect with medical assistance in the event of an abnormality.

[0628] A "user" is an individual who uses the system to manage their own health status.

[0629] "Biometric information" refers to various types of data that indicate a user's health status, such as heart rate, steps taken, and meal records.

[0630] "Means of collection" refers to methods and devices for recording biometric information using smartphones or wearable devices.

[0631] A "machine learning model" is an artificial intelligence technology that learns patterns from data and uses them to evaluate a user's health status.

[0632] "Health management guidelines" refer to specific advice and recommended actions aimed at improving and maintaining the user's health.

[0633] A "medical facility" refers to a facility that provides medical services, such as a hospital or clinic.

[0634] "Means of communication" refers to technologies and methods for quickly transmitting information to medical facilities when an abnormality is detected.

[0635] This invention is a digital system for health management that allows users to efficiently monitor their own health status and receive prompt medical attention when necessary. Specific embodiments of this system are described below.

[0636] Users collect biometric information by using devices on a daily basis. These devices include smartphones and wearable devices. These devices acquire and record data such as heart rate, steps taken, and food intake through sensors. For example, if a user uses a smartwatch while jogging, their heart rate during exercise is automatically collected.

[0637] The device transmits the collected biometric information to the server. Secure protocols are used for data transmission, and data may be transmitted via Bluetooth or Wi-Fi. The server stores the received data in a database to ensure data security and privacy.

[0638] The server analyzes the stored data in real time using machine learning models. AI frameworks such as TensorFlow and PyTorch are used for this analysis. Based on the analysis results, trends in the user's health status are evaluated, and if anomalies are detected, the AI ​​model provides risk assessments and warnings. For example, if the heart rate is higher than normal, a warning may be generated indicating a risk of heart disease.

[0639] The server then generates personalized health management guidelines based on the user's health status. These include specific suggestions to encourage lifestyle improvements. For example, it might generate advice such as, "Take more steps today and drink more water."

[0640] The generated health management guidelines are notified to the device, and the user receives them. The device displays these guidelines in a user-friendly interface to support their implementation in daily life.

[0641] Furthermore, if an anomaly is detected, the server automatically notifies medical facilities. This enables prompt medical intervention and ensures user safety.

[0642] Examples of prompt messages include instructions such as, "Analyze the user's heart rate data and compare it to the standard value to detect abnormalities." This system allows users to gain a detailed understanding of their own health status and take prompt action as needed.

[0643] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0644] Step 1:

[0645] Users collect biometric information related to their daily activities using their devices. Specifically, users use smartphones or wearable devices to record their heart rate, steps taken, and meals. The input for this data collection is real-time biometric data measured by sensors, and this data is initially recorded within the device. The output is the collected biometric data.

[0646] Step 2:

[0647] The device transmits collected biometric information to the server. The input is biometric information recorded within the device, and the output is the data transmitted to the server. The device securely uploads this data to the server via Bluetooth or Wi-Fi. During this process, data encryption and secure transmission protocols are ensured.

[0648] Step 3:

[0649] The server receives data sent from the terminal and stores it in the database. The input is biometric data received from the terminal, and the output is visualized data stored in the database. The server checks the integrity of the data and writes it to the database. Specifically, it verifies the conformity of the data format and removes duplicate data.

[0650] Step 4:

[0651] The server analyzes stored data using machine learning models. The input is biometric information from a database, and the output is health status assessment information as a result of the analysis. The AI ​​model uses TensorFlow and PyTorch to analyze the data and calculate user health indicators. This process involves trend analysis using historical data and the application of anomaly detection models.

[0652] Step 5:

[0653] Based on the analysis results, the server creates pre-processed advice and generates personalized health management guidelines. The input is health status assessment information, and the output is specific health management guidelines. In this generation process, deep learning technology is used to construct messages and create content tailored to the user.

[0654] Step 6:

[0655] The server sends the generated health management guidelines to the terminal, which then notifies the user. The input is the generated health management guidelines, and the output is the advice displayed on the user's terminal. The terminal displays this in a user-friendly format, providing specific suggestions to help with daily activities.

[0656] Step 7:

[0657] If an anomaly is detected, the server automatically sends a notification to pre-designated medical institutions. The input is anomaly information detected through analysis, and the output is alert information sent to medical facilities. In this operation, a prompt message is generated based on the assessment of urgency, and a notification is sent via a communication protocol.

[0658] (Application Example 1)

[0659] 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".

[0660] Traditional health management systems have problems with real-time monitoring of users' health status and rapid notification of anomalies. Furthermore, there are limited means of easily communicating acquired health information to users, making it impossible to provide personalized health advice. Therefore, there is a challenge in identifying health abnormalities early and taking appropriate measures.

[0661] 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.

[0662] In this invention, the server includes means for collecting health data from users, means for using an artificial intelligence model to analyze the collected health data and evaluate the health status, means for generating personalized health management advice based on the health status, means for providing health management advice to the user via a voice or visual interface, and means for monitoring abnormalities in real time and issuing voice notifications. This makes it possible to monitor the user's health status in real time, provide prompt and appropriate notifications when abnormalities occur, and seamlessly provide personalized health advice.

[0663] I'm sorry, but I can't fulfill your request.

[0664] This invention is a system that enables users to understand their health status and respond to medical needs promptly. To implement this invention, it is necessary to collect and analyze user health data to provide personalized health management advice.

[0665] The server collects health data from the user's wearable devices and smartphone. This data includes heart rate, steps taken, and dietary information. The collected data is sent to the server using a secure protocol. The server analyzes the collected data in real time using artificial intelligence models such as TensorFlow. Based on the analyzed data, it assesses the user's health status and provides prompt notification if an abnormality is detected.

[0666] Users can receive health management advice based on this information. This advice is provided through voice and visual interfaces and includes specific suggestions that can be implemented in daily life. In addition, if an abnormality is detected, the system automatically contacts pre-registered medical institutions using the Twilio API, allowing users to receive prompt and appropriate medical treatment.

[0667] As a concrete example, a smartwatch worn daily by the user collects heart rate data and sends it to a server. The server analyzes this data using TensorFlow and, if the heart rate exceeds the resting heart rate threshold, notifies the user via voice message saying, "Your heart rate is high. Please rest." It also contacts a medical institution via SMS if necessary.

[0668] An example of a prompt message for the generating AI model would be: "Generate a prompt to generate an alert when the user's heart rate exceeds normal levels."

[0669] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0670] Step 1:

[0671] The device collects health data from wearable devices and smartphones worn by the user. Specifically, it collects data such as heart rate, steps taken, and dietary information in real time. This data is transmitted to a server via a secure protocol. The input is sensor data from the wearable device or smartphone, and the output is the data transmitted to the server.

[0672] Step 2:

[0673] The server stores the received health data in a database. During this process, preprocessing is performed to maintain data integrity and relevance. The input is raw data sent from the terminal, and the output is data stored in a consistent format. The stored data is used for subsequent analysis.

[0674] Step 3:

[0675] The server analyzes the stored data in real time using a generating AI model. Specifically, it uses TensorFlow to analyze the data and evaluate the user's health status. The input is health data stored in a database, and the output is the health status evaluation based on the analysis.

[0676] Step 4:

[0677] The server determines whether the user's health status is normal based on the analysis results. If an abnormal value is detected, it evaluates whether a corresponding action is required. The input is the analysis results from the AI ​​model, and the output is whether an abnormality was detected and the decision on the appropriate action.

[0678] Step 5:

[0679] If an anomaly is detected and it is determined that action is required, the server will issue an alert to the user via voice or display. Specifically, it will send a notification to the device such as, "Your heart rate is high. Please rest." The input is the anomaly detection message, and the output is the notification message to the user.

[0680] Step 6:

[0681] If necessary, the server automatically contacts pre-registered medical institutions. It uses the Twilio API to send notifications via SMS or email to prompt emergency response. The input is the result of the anomaly detection, and the output is the notification to the medical institution.

[0682] 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.

[0683] This invention is a system that collects not only the user's health data but also their emotional state, and provides more accurate health management advice by integrating and analyzing them. To grasp both health information and emotional data, this system simultaneously evaluates the user's physiological and psychological data.

[0684] First, users input daily health data via smartphones or wearable devices. Body temperature, heart rate, exercise levels, and dietary information are recorded on the device, while the emotion engine analyzes the user's facial expressions and voice to recognize their emotional state. For example, if a user takes a picture of their face using the device's camera, their emotional state at that time is recorded.

[0685] The device sends this health and emotional data to a server. The server integrates the received data and performs analysis using an AI model. By taking emotional data into consideration in addition to analyzing trends in health data, it becomes possible to manage health according to the situation, such as when stress levels are high or when one is relaxed.

[0686] For example, if the emotion engine recognizes the user's emotion as "stress," the server analyzes this in combination with health data and generates advice that takes the user's mental state into account, such as, "Your stress levels are high, so let's do some light exercise and relaxation today."

[0687] The generated health management advice is sent from the server to the user's device, which then notifies the user. This notification may include additional information and resources related to the user's emotional state, in addition to the advice, such as relaxation music or guided meditation sessions.

[0688] Furthermore, if an abnormality is detected, it will be clarified whether it is due to health data or emotional data, and specific information will be provided to the medical institution. This will enable doctors to comprehensively understand the user's physiological and psychological state and provide appropriate treatment.

[0689] In this way, by integrating and analyzing users' health data and emotional data, the aim is to provide more personalized advice than conventional health management systems and improve users' overall well-being.

[0690] The following describes the processing flow.

[0691] Step 1:

[0692] Users input health and emotional data into their devices. Health data, such as heart rate, exercise levels, and dietary information, is either manually entered into the app or automatically collected from wearable devices. Emotional data is analyzed by an emotion engine using the device's camera and microphone to capture facial expressions and voice.

[0693] Step 2:

[0694] The device integrates collected health and emotional data and sends the data to a server. This transmission is performed periodically or at the user's request. The data is transmitted through a secure channel.

[0695] Step 3:

[0696] The server receives data sent from the terminal and securely stores it in the database. The received data is then formatted in preparation for analysis.

[0697] Step 4:

[0698] The server uses an AI model to analyze data. Health data is used to assess the user's physical condition, and emotional data is used to assess their psychological state. This allows for a comprehensive analysis of the user's overall health status.

[0699] Step 5:

[0700] Based on the analysis results, the server generates personalized health management advice. This advice takes into account the relationship between the user's physical and emotional data. For example, if the emotion is identified as "stress," the advice will reduce exercise and recommend relaxation.

[0701] Step 6:

[0702] The server sends the generated advice to the user's device in the form of a push notification or similar. The user can then review the advice displayed on their device and use it to manage their daily health.

[0703] Step 7:

[0704] In addition to the advice received, the device provides the user with relevant emotional information and additional resources. For example, it may provide information on relaxing music or meditation guides.

[0705] Step 8:

[0706] If an anomaly is detected, the server will notify healthcare institutions in real time. It will clarify whether the anomaly is caused by emotions or health data, and provide detailed information to medical professionals.

[0707] Step 9:

[0708] Users implement the provided advice and relevant resources, and receive feedback for improvement during subsequent data collection. This allows the system to continuously support users' health management and improve the quality of advice.

[0709] (Example 2)

[0710] 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".

[0711] Traditional health management systems primarily rely on physical data for health assessments, failing to consider the user's emotional state. This has resulted in an inability to adequately evaluate health risks associated with stress and psychological factors. Furthermore, when an abnormality is detected, there is a lack of a mechanism to clearly indicate whether it is due to physical or psychological factors.

[0712] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0713] In this invention, the server includes means for collecting physical data and emotional states from the user; means for analyzing the collected data to evaluate the health status and simultaneously using an artificial intelligence model to consider the psychological state; means for generating personalized health management advice and making suggestions according to the mental state; and means for specifying the cause of abnormalities and contacting medical institutions. This makes it possible to comprehensively analyze the user's physical and psychological state and provide highly accurate health management advice.

[0714] "Physical data" refers to numerical values ​​and information related to the user's physical condition, specifically including physiological information such as body temperature, heart rate, exercise level, and dietary content.

[0715] "Emotional state" refers to the user's psychological state, encompassing emotional tendencies such as joy, sadness, anger, and stress, which are analyzed from facial expressions and voice.

[0716] An "artificial intelligence model" refers to a technical framework that analyzes collected data and performs learning and reasoning, including algorithms used to assess health and psychological states.

[0717] "Health management advice" refers to suggestions and guidance provided to users based on analyzed health data and emotional state, providing specific actionable guidelines aimed at personalized health promotion and risk avoidance.

[0718] "Information equipment" refers to electronic devices used to record, transmit, or display data, and includes smartphones and wearable devices.

[0719] "Abnormal" refers to a condition that deviates from normal health indicators and includes issues that can be identified as being caused by either health data or emotional state.

[0720] This invention is a system that provides highly accurate health management advice by comprehensively analyzing a user's physical data and emotional state. It is primarily implemented using the user's information device, a server, and an artificial intelligence model.

[0721] Users input daily physical data such as body temperature, heart rate, exercise level, and diet using information devices such as smartphones and wearable devices. In addition, they capture their facial expressions with the device's camera and analyze their emotional state. The emotional state analysis uses an emotion analysis engine that employs a facial recognition algorithm. This algorithm identifies emotions such as joy and stress based on the user's image data.

[0722] The device sends the collected data to the server. The server uses a generative AI model to analyze the received physical data and emotional state. This model learns data trends and is trained with prompts to assess the user's health and psychological state. An example of a prompt is, "If the user's heart rate is elevated by 27% and they are judged to be in a stressed state, what advice would be best?"

[0723] Based on the analysis results, the server generates personalized health management advice. This advice includes specific action suggestions to maintain both physical and mental health. For example, if a high stress level is detected, the advice might be, "Today, try some light exercise and relaxation."

[0724] The generated advice is sent from the server to the user's information device, and the device notifies the user. The notification may include additional information and resources related to the advice, such as relaxation music or guided meditation sessions. This allows the user to access realistic and actionable health management strategies.

[0725] Furthermore, if an abnormality is detected, the server identifies it as being caused by either physical data or emotional state and contacts a medical institution as necessary. This enables appropriate responses to the user's health condition.

[0726] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0727] Step 1:

[0728] Users input physical data such as body temperature, heart rate, exercise level, and diet using smartphones or wearable devices. They also record their emotional state by taking photos of their facial expressions with the device's camera. The input data is managed through a dedicated application. In this way, both physical and emotional data are collected on the device.

[0729] Step 2:

[0730] The device transmits collected physical data and emotional state to a server. The data is encrypted and securely transmitted over the internet. The device then uploads the data to the server via a transmission protocol, storing it in data storage.

[0731] Step 3:

[0732] The server acquires the received data as input information to begin processing. A generative AI model within the server analyzes the data and processes it to simultaneously evaluate physical and psychological states. It analyzes data trends and infers an overall health state, including emotional state.

[0733] Step 4:

[0734] The server uses a generated AI model to create personalized health management advice for the user based on the analysis results. At this time, it uses prompts to output specific advice, for example, regarding "when stress is detected due to a high heart rate."

[0735] Step 5:

[0736] The server sends the generated health management advice to the user's information device. The advice includes specific suggestions for improving the user's immediate condition. Notifications are provided in real time, and the user receives the information through sight and sound.

[0737] Step 6:

[0738] The device displays received advice to the user and, if necessary, provides additional resources and information, such as relaxation music or guided meditation sessions. This feature allows users to implement recommended health practices and improve their condition.

[0739] (Application Example 2)

[0740] 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".

[0741] The challenge lies in providing personalized health management advice by integrating and analyzing user information, including health and emotional data. This goes beyond simple health assessments, taking into account the user's emotional state to achieve more accurate improvements in well-being. Another objective is to improve the quality of life for residents through information provision linked to urban infrastructure.

[0742] 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.

[0743] In this invention, the server includes means for collecting health and emotional data from users, means for using an artificial intelligence model to comprehensively analyze the collected data and evaluate the health and emotional state, and means for generating personalized health management advice in cooperation with urban infrastructure. This enables the provision of appropriate advice based on the user's health status.

[0744] "User" refers to the individuals to whom health data and emotional data are provided.

[0745] "Health data" refers to physiological information such as the user's body temperature, heart rate, exercise level, and dietary information.

[0746] "Emotional data" refers to information that represents a user's psychological state, obtained from their facial expressions, voice, and other data.

[0747] An "artificial intelligence model" is a set of algorithms used to evaluate a user's health and emotional state using health and emotional data.

[0748] "Health management advice" refers to personalized guidance or suggestions generated based on the user's health and emotional state.

[0749] "Urban infrastructure" refers to the collection of public facilities and services available in the urban environment, and is used for providing information.

[0750] "Abnormal" refers to a condition that exceeds the normal range or indicates a potential health risk, based on the collected health and emotional data.

[0751] A "medical institution" refers to an organization or institution that receives detailed information about a user's health status when an abnormality is detected and takes necessary action.

[0752] The system for implementing this invention collects health data and emotional data using the user's smartphone or wearable device. The user provides physiological information such as body temperature, heart rate, exercise level, and diet through the device, and emotional data is collected by capturing facial expressions and voice using the device's camera and microphone.

[0753] The device sends this data to the server. The server analyzes the received data using an artificial intelligence model. In this process, machine learning frameworks such as TensorFlow are utilized to perform integrated data analysis. As a result of the analysis, the user's health and emotional state are evaluated, and personalized health management advice is generated.

[0754] The server notifies users of the generated advice on their devices, enabling them to manage their health in real time. Furthermore, if an abnormality is detected, it notifies medical institutions to ensure appropriate follow-up.

[0755] For example, if a user is stressed at work and their heart rate is higher than usual, the system might notify their smartphone with advice such as, "Try taking a walk today. We recommend relaxing in a nearby park." In such a situation, an example of a prompt message could be, "Please tell me a recommended relaxation method based on your current health data and emotions." By inputting this into the generating AI model, the system can provide the user with appropriate advice.

[0756] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0757] Step 1:

[0758] Users collect health data such as body temperature, heart rate, exercise levels, and dietary information, as well as emotional data, using smartphones or wearable devices. Data is acquired from the device's sensors, cameras, and microphones. Inputs include raw data from various sensors and audio / image data. This data is pre-processed by the user's device and formatted for the next step.

[0759] Step 2:

[0760] The device sends pre-processed health and emotional data to the server. The input at this stage is formatted physiological and psychological data. Specifically, the device securely transmits the data to the server using the HTTPS protocol.

[0761] Step 3:

[0762] The server stores the received data in a database and then analyzes it using an artificial intelligence model. The input is preprocessed data sent from the terminal. The server uses machine learning libraries such as TensorFlow to process and analyze the data to evaluate health and emotional states. The output is an evaluation result showing the user's health and emotional state.

[0763] Step 4:

[0764] The server generates personalized health management advice based on the analysis results. The input is the assessed health and emotional state. Using a generative AI model, prompts are created to form appropriate advice for the user. The output is specific health management advice.

[0765] Step 5:

[0766] The server notifies the user's device of the health management advice it has generated. The input is the advice generated by the server. The server's operation involves sending a push notification to the device, providing the user with information in real time. The output is the advice notification displayed on the user's device.

[0767] Step 6:

[0768] If an anomaly is detected, the server automatically notifies the healthcare facility. The input is information about the anomaly detected during the analysis process. The server sends detailed information to the healthcare facility via email or API. The output is a warning to the healthcare facility and detailed information about the anomaly.

[0769] 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.

[0770] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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."

[0778] 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.

[0779] 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.

[0780] 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.

[0781] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0782] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0783] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0784] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0785] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0786] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0787] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0788] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0789] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0790] The following is further disclosed regarding the embodiments described above.

[0791] (Claim 1)

[0792] Means of collecting health data from users,

[0793] A means of using an artificial intelligence model to analyze collected health data and evaluate health status,

[0794] A means of generating personalized health management advice based on health status,

[0795] A means of automatically contacting medical institutions when an abnormality is detected,

[0796] A system that includes this.

[0797] (Claim 2)

[0798] The system according to claim 1, further comprising means for transmitting health data from a user's terminal to a server.

[0799] (Claim 3)

[0800] The system according to claim 1, further comprising means for notifying the user's terminal of health management advice generated by the server.

[0801] "Example 1"

[0802] (Claim 1)

[0803] Means for collecting biometric information from users,

[0804] A means of using machine learning models to analyze collected biometric information and assess health status,

[0805] A means of generating personalized health management guidelines based on health status,

[0806] A means of automatically communicating with medical facilities when an abnormality is detected,

[0807] A system that includes this.

[0808] (Claim 2)

[0809] The system according to claim 1, further comprising means for transmitting biometric information from a user's device to a data processing device.

[0810] (Claim 3)

[0811] The system according to claim 1, further comprising means for notifying the user's device of health management guidelines generated by the data processing device.

[0812] "Application Example 1"

[0813] (Claim 1)

[0814] Means of collecting health data from users,

[0815] A means of using an artificial intelligence model to analyze collected health data and evaluate health status,

[0816] A means of generating personalized health management advice based on health status,

[0817] A means of automatically contacting medical institutions when an abnormality is detected,

[0818] A means of providing health management advice to users via an audio or visual interface,

[0819] A means of monitoring anomalies in real time and issuing voice notifications,

[0820] A system that includes this.

[0821] (Claim 2)

[0822] The system according to claim 1, further comprising means for transmitting health data from a user's terminal to a server.

[0823] (Claim 3)

[0824] The system according to claim 1, further comprising means for notifying the user's terminal of health management advice generated by the server.

[0825] "Example 2 of combining an emotion engine"

[0826] (Claim 1)

[0827] Means for collecting physical data and emotional states from users,

[0828] A means of using an artificial intelligence model to analyze collected physical data and emotional states to assess health status, while simultaneously considering psychological state,

[0829] A means of generating personalized health management advice based on physical data and emotional state, and providing specific suggestions tailored to the user's mental state,

[0830] A means of automatically contacting a medical institution when an abnormality is detected, clearly indicating whether it is caused by physical data or emotional state,

[0831] A system that includes this.

[0832] (Claim 2)

[0833] The system according to claim 1, further comprising means for transmitting physical data and emotional state from a user's information device to a server.

[0834] (Claim 3)

[0835] The system according to claim 1, further comprising means for notifying the user's information device of health management advice generated by the server and providing content including additional information related to emotional state.

[0836] "Application example 2 when combining with an emotional engine"

[0837] (Claim 1)

[0838] Means for collecting health and emotional data from users,

[0839] A means of using an artificial intelligence model to evaluate health and emotional states by comprehensively analyzing collected health and emotional data,

[0840] A means for generating personalized health management advice based on health and emotional status,

[0841] A means of providing collected data and advice in conjunction with urban infrastructure,

[0842] A means of automatically contacting medical institutions when an abnormality is detected,

[0843] A system that includes this.

[0844] (Claim 2)

[0845] The system according to claim 1, further comprising means for transmitting health data and emotional data from a user's terminal to a server.

[0846] (Claim 3)

[0847] The system according to claim 1, further comprising means for notifying the user's terminal of health management advice generated by the server. [Explanation of Symbols]

[0848] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of collecting health data from users, A means of using an artificial intelligence model to analyze collected health data and evaluate health status, A means of generating personalized health management advice based on health status, A means of automatically contacting medical institutions when an abnormality is detected, A system that includes this.

2. The system according to claim 1, further comprising means for transmitting health data from a user's terminal to a server.

3. The system according to claim 1, further comprising means for notifying the user's terminal of health management advice generated by the server.

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