system

Abstract

We provide the system. [Solution] Means for analyzing biological information and image information received from the device, A means for evaluating the user's health status based on the analysis results and generating appropriate health guidelines, A means of notifying users of health guidelines on their information terminals, A means of monitoring the user's health status within the home and providing suggestions, A means of analyzing food data and identifying nutritional information, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, there is a problem that it is difficult to perform appropriate health management due to the labor and lack of expertise in individual health management. In particular, it is difficult to grasp health data, maintain a nutritional balance in diet, carry out regular exercise, and use medical services at appropriate times. In addition, since means for quickly obtaining refined advice based on an individual's health condition are limited, there is a demand for providing individually customized health guidelines.

Means for Solving the Problems

[0005] The system according to the present invention includes means for analyzing biological information and image information received from a device, means for evaluating the user's health status based on the analysis results and generating appropriate health guidelines, and means for notifying the user of the health guidelines at their terminal. This enables the system to automatically analyze the user's health data and provide appropriate health guidelines tailored to their individual health status in real time. Furthermore, by including means for receiving feedback information from the user and updating the analysis means to improve the generated health guidelines, the system can continuously improve accuracy and provide customized health management services to the user. In addition, it includes means for proposing remote diagnosis by medical professionals, and by making such a proposal to the user if an abnormality is found in the evaluation results, it is possible to encourage prompt medical response.

[0006] "Device" refers to any device that inputs or outputs information, and is a type of equipment used to acquire biometric or image information.

[0007] "Received biometric information" refers to data on the user's health status, including data such as heart rate, body temperature, and blood pressure.

[0008] "Image information" refers to photographs and visual data provided by users, which are used to analyze the content of their diet and their living environment.

[0009] "Analyzing" refers to a series of processes that involve processing received information and deriving meaningful results.

[0010] "Assessing health status" refers to quantifying or visualizing a user's physical health status based on analyzed data.

[0011] "Generating health guidelines" refers to the act of formulating specific advice for improvement or maintenance of an assessed health condition.

[0012] "User's device" refers to the digital device that the user uses on a daily basis, and it is here that analysis results and health guidelines are notified.

[0013] "Feedback information" refers to data on responses or reactions provided by users to the system, and is information that can be used to improve the system.

[0014] "Updating analytical methods" refers to optimizing or improving the system's analytical models or criteria based on new information and feedback.

[0015] "Suggesting remote diagnosis" refers to the process of encouraging consultation with a medical professional from a physically distant location, and is carried out based on evaluation results as part of a rapid medical response. [Brief explanation of the drawing]

[0016] [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] Shows an emotion map where multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 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

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

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

[0019] In the following embodiments, a processor with a reference numeral (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

[0021] In the following embodiments, the 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.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

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

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] This invention is a system for supporting personal health management, and is implemented by a program. Specific examples are shown below.

[0038] System Overview

[0039] This system is designed to receive biometric information and food images sent from the user's device. Users are required to periodically measure their vital data and take pictures of their meals. The terminal then collects this data and sends it to the server.

[0040] The server runs an AI model equipped with the ability to analyze received data. Based on biometric information, the server numerically evaluates the user's health status and identifies patterns and trends. It also calculates the types of food consumed and calorie intake from image information and analyzes the user's dietary balance. Based on the health status evaluation and dietary analysis, the server uses AI to formulate optimal health guidelines for each user.

[0041] The established health guidelines are notified to the user's device in real time from the server. This allows users to take immediate action in their daily lives to improve or maintain their health. For example, if the server detects an upward trend in heart rate, it may suggest simple exercises for stress relief. It can also identify vitamin deficiencies from dietary analysis and recommend foods to supplement them.

[0042] After implementing the provided guidelines, users send feedback to the system via their terminal regarding the effectiveness and feasibility of those guidelines. The server collects this feedback and retrains or adjusts the AI ​​model to improve the accuracy of future analyses and the quality of the guidelines.

[0043] Furthermore, if abnormalities are detected in the health assessment results, the server can suggest that the user utilize an online medical consultation service. This gives the user the opportunity to receive prompt and appropriate medical support.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] Users collect vital data using the device and upload photos of their meals to their terminal to monitor their health. This makes daily health information available in digital format.

[0047] Step 2:

[0048] The device sends collected vital data and photos of meals to a server. This transmission occurs periodically based on a set time schedule or can be triggered manually by the user.

[0049] Step 3:

[0050] The server prepares the received data for analysis. Specifically, it inputs vital data into the analysis module and passes meal photo data to the image analysis unit.

[0051] Step 4:

[0052] The server's AI model analyzes vital data, examining trends in heart rate and body temperature to assess for any abnormalities. This forms a basic profile of the user's health status.

[0053] Step 5:

[0054] The server analyzes photos of meals using image recognition technology to estimate the types of food, their nutrients, and calories. This information is used to evaluate whether the meal is healthy.

[0055] Step 6:

[0056] The server integrates the results of vital data analysis and dietary analysis to generate health guidelines tailored to the user's current health status. These guidelines include points for dietary improvement and recommended exercise plans.

[0057] Step 7:

[0058] The server generates health guidelines and sends them to the device, immediately notifying the user. The user checks the notification and takes action according to the guidelines to practice health management.

[0059] Step 8:

[0060] After implementing the guidelines, users send the results and feedback from their device to the server. This feedback is stored on the server to be used for future analyses.

[0061] Step 9:

[0062] The server uses the collected feedback data to retrain or adjust the AI ​​model. This continuously improves the accuracy of analysis and health guidance, providing users with a better service.

[0063] Step 10:

[0064] The server will suggest that the user undergo an online medical consultation if it detects an anomaly or deems it necessary. This suggestion aims to understand the user's health status and provide prompt medical support.

[0065] (Example 1)

[0066] 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."

[0067] In modern society, personal health management is a crucial issue, but busy daily lives often make it difficult to dedicate sufficient time to it. Traditional health management methods are time-consuming and cumbersome, and it is particularly difficult to obtain appropriate advice tailored to individual circumstances. Furthermore, opportunities to detect abnormalities early and receive appropriate medical support are often missed. A system is needed to solve these problems and provide effective health guidelines optimized for each individual's health condition in real time.

[0068] 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.

[0069] In this invention, the server includes means for transmitting personal biometric data measured by a terminal and image data of food captured by the terminal to the server; means for the server to evaluate the received biometric data using an AI model and numerically analyze the health status; and means for the server to process the received image data using an AI model and identify the type of food and its nutritional value. This enables rapid and personalized health support by providing health guidelines tailored to each individual user in real time and proposing telemedicine services when an anomaly is detected.

[0070] A "terminal" is a portable device used by an individual to measure and record data, and is equipped with the function to transmit biometric data and image data to a server.

[0071] A "server" is a computer system that has the central function of receiving data from terminals via a network, analyzing it, and generating health guidelines.

[0072] "Biometric data" refers to data that indicates an individual's physical condition, such as heart rate and blood pressure, and is used to evaluate their health status.

[0073] "Image data" refers to visual data that shows the contents of a user's meals, and is used to identify the type of food and its nutritional value.

[0074] An "AI model" is an algorithm based on machine learning technology that analyzes biometric and image data to evaluate health status or identify food products.

[0075] "Health guidelines" are suggestions for improving the health of individual users, generated by the server based on analysis results, and include specific advice on diet, exercise, and other topics.

[0076] "Feedback information" refers to information that users send to the server regarding their implementation status and the effectiveness of the health guidelines provided, and this data is used to improve the AI ​​model.

[0077] "Telemedicine services" are services that provide online diagnoses and advice from medical professionals as needed, based on the user's health condition.

[0078] This invention is a system for supporting individual health management, implemented through the cooperation of a server, terminal, and user. By integrating multiple technological elements, this system provides users with customized health guidelines, promoting the improvement and maintenance of their health status.

[0079] The terminals are wearable devices worn by users or personal digital assistants (PDAs) used to measure biometric data such as heart rate and blood pressure, and to capture image data of meals. This data is collected by smartphones and other devices and transmitted to a server via wireless communication technology. The terminals used are devices equipped with iOS or Android® operating systems.

[0080] The server stores and manages the received data and analyzes it using a generative AI model. The AI ​​model, built using machine learning libraries such as TENSORFLOW® and PyTorch, evaluates the user's health status based on biometric data and performs nutritional analysis of meals from image data. Based on the analysis results, the server develops personalized health guidelines for each user and transmits this information to the terminal in real time.

[0081] Users can utilize health guidelines displayed on their smartphones or tablets. For example, they may be recommended to consume foods that supplement nutrient deficiencies identified by the AI. Users can also provide feedback on the effectiveness and feasibility of the guidelines they followed, and the server uses this feedback to retrain the AI ​​model, improving the accuracy of future analysis results.

[0082] As a concrete example, consider a scenario where a user takes a photo of bread and fruit for breakfast, measures their heart rate, and sends the photo to a server. The server recognizes the type of food from the image and evaluates its nutritional value. Simultaneously, it analyzes the heart rate and, if it determines that the user is under high stress, can suggest exercises for relaxation.

[0083] An example of a prompt for a generative AI model is, "Evaluate the types and calorie content of the foods consumed for breakfast, and suggest exercises recommended when your heart rate is elevated." This allows users to incorporate specific actions based on their own health status into their daily lives.

[0084] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0085] Step 1:

[0086] The terminal's role is to measure biometric data and collect images of food. The main devices used are wearable devices capable of measuring heart rate and blood pressure, and smartphones for taking pictures of food. As input, it collects various vital data measured by the user and images of the food taken. As output, the terminal prepares to send this data to a server for later analysis.

[0087] Step 2:

[0088] The device converts the collected data into a specific format (e.g., JSON) and sends it to the server. Data transmission is performed using Wi-Fi or mobile data communication. The input consists of biometric and image data stored on the device, and the output is the transmission of this data as an organized data package to the server.

[0089] Step 3:

[0090] The server receives data sent from the terminal and stores each piece of data in an analysis preparation queue. The input is the received data package, which is then formatted into an appropriate analysis format and output. Specifically, the data is structured using a Python script and transformed to facilitate processing by the AI ​​model.

[0091] Step 4:

[0092] The server begins analyzing biometric data using a generative AI model. Structured biometric information is used as input data, and the AI ​​model analyzes it to numerically evaluate the user's health status. The output includes deviations from normal ranges for heart rate and blood pressure. TensorFlow software is used for advanced numerical processing.

[0093] Step 5:

[0094] The server then analyzes the image data of the food. The input for this step is structured photographic data. The generative AI model uses image recognition techniques to identify the types of food and then calculates their nutritional components and calories. The output provides a list of foods, their calorie counts, and detailed nutritional information.

[0095] Step 6:

[0096] The server develops personalized health guidelines for each user based on biometric information and image analysis results. Input consists of biometric evaluation results and image analysis results, which an AI model combines to output optimal health guidelines. These guidelines include suggestions for dietary improvements and necessary exercises.

[0097] Step 7:

[0098] The server notifies the device in real time of the established health guidelines. The input here is the generated health guidelines, which are then pushed to the device as output. Information is instantly sent to the user's device using Firebase Cloud Messaging (FCM), etc.

[0099] Step 8:

[0100] Users implement the provided health guidelines and send feedback on their results and effectiveness from their device to the server. The input here is the user feedback data, and the output is this data which is then fed back into the AI ​​model to improve the accuracy of the analysis.

[0101] (Application Example 1)

[0102] 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."

[0103] In personal health management, it is crucial to comprehensively analyze daily biological information and dietary content to provide appropriate health guidance in real time. However, current health management systems often make it difficult for users to fully understand their own health status and take prompt action. Furthermore, there is a lack of systems that allow users to grasp detailed individual nutritional information and receive daily applicable guidance for health management at home. As a result, many users are unable to immediately improve their health in their daily lives and are likely to miss opportunities to address health deterioration early.

[0104] 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.

[0105] In this invention, the server includes means for analyzing biometric and image information received from the device, means for evaluating the user's health status based on the analysis results and generating appropriate health guidelines, and means for monitoring the user's health status within the home and providing suggestions. This enables the user to instantly understand their own health status and take concrete and effective actions in their daily life based on health guidelines.

[0106] "Device" refers to a terminal used to acquire user biometric information and food image information and transmit it to the server.

[0107] "Receiving" refers to the process where the server acquires data sent by the user and begins internal processing.

[0108] "Biometric information" refers to information that includes data such as heart rate, blood pressure, and body temperature, which indicate the user's health status.

[0109] "Image information" refers to image data of meals consumed by the user, and is used for analyzing the contents of the meal.

[0110] "Analysis" refers to evaluating the user's health and nutritional status based on received biometric and image data.

[0111] "Assessing health status and generating health guidelines" refers to the procedure for analyzing the user's health status based on the analysis results and suggesting desirable lifestyle habits.

[0112] "Notifying the device" means sending the generated health guidelines to the user's individual device in real time, enabling the user to take immediate action.

[0113] "Monitoring the user's health status within the home and providing suggestions" means continuously collecting health information in the user's daily life and providing advice for appropriate health management.

[0114] "Analyzing food data and identifying nutritional information" refers to analyzing image information of meals to identify the types of food and the nutrients they contain, and then evaluating the user's dietary balance.

[0115] To implement this invention, a user terminal device, a server, and an AI model are used. First, the user device acquires biometric information and image information. The biometric information includes heart rate, blood pressure, and body temperature, and the image information includes photos of meals. This data is transmitted to the server via the terminal.

[0116] The server analyzes the received data using an AI model. Biometric information is used to assess health status, with the AI ​​model performing a numerical evaluation. Image information is used for food classification and extraction of nutritional information, and calorie intake and nutritional balance are calculated. Computer vision technology is used for this analysis. For example, images are preprocessed using OpenCV, and classification is performed using a pre-trained AI model with Keras.

[0117] Based on the analysis results, the server generates optimal health guidelines for the user. These guidelines include, for example, recommended foods to improve dietary content and suggestions for light exercise to manage stress. This information is notified to the user's device in real time, allowing the user to take immediate action in their daily life.

[0118] For example, if heart rate data rises above expectations, the server will send a message such as, "You might want to try yoga to relax." Also, if it determines that calorie intake is too high, it will suggest, "We recommend eating more vegetables at your next meal." An example of a prompt message would be, "Your heart rate is higher than average today. Try yoga to promote relaxation."

[0119] This system makes it easier for users to receive personalized advice based on their individual health data and to work towards maintaining and improving their health on a daily basis.

[0120] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0121] Step 1:

[0122] The user's device acquires biometric and image information. Input includes heart rate, blood pressure, body temperature, and photos of meals. This data is stored on the device and prepared as data packets. Specifically, data is collected using a smartwatch or digital camera.

[0123] Step 2:

[0124] The terminal sends the prepared data packets to the server. The input consists of data packets containing the user's biometric information and image information. These are sent to the server via the internet. Specifically, the terminal's communication module is used to transfer the data using a secure protocol (e.g., HTTPS).

[0125] Step 3:

[0126] The server runs an AI model to analyze the received data. It receives user biometric and image data as input. The biometric data is used to assess health status and is quantified by the AI ​​model. Image data is preprocessed using computer vision technology to identify food types and nutritional information. Specifically, an AI model built with Keras is used to generate the analysis results.

[0127] Step 4:

[0128] The server generates health guidelines based on the analysis results. The input is analyzed biological and dietary information. As output, it creates personalized health guidelines for each user. Specifically, it queries a database that references relaxation methods based on heart rate variability and dietary advice to improve nutritional balance, and generates prompt statements using a generation AI model.

[0129] Step 5:

[0130] The server notifies the user's device of the generated health guidelines. The output is real-time health guideline information. This is sent to the user's mobile device via push notification or email. Specifically, it uses a notification API to provide real-time feedback.

[0131] Step 6:

[0132] The user takes specific actions based on the health guidelines they receive. The input is the health guidelines sent from the server. The output is the user's actions, such as health improvement activities. Specifically, this could include seeing the application's notification and trying the recommended exercise or adding the suggested ingredients to the shopping list.

[0133] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0134] This invention is a system for providing highly personalized care that takes into account the emotional state of the user in managing their health. Specific embodiments are described below.

[0135] System Overview

[0136] In addition to its basic function of analyzing biometric and image information transmitted from the device, this system incorporates an emotion engine to comprehensively assess the user's physical and mental health. Users routinely use the device to acquire biometric information such as heart rate and body temperature, and provide image information related to their diet and lifestyle via a terminal.

[0137] Upon receiving this information, the server activates an emotion engine and analyzes data representing the user's emotional state. This quantifies emotions such as stress, happiness, and fatigue, adding a new perspective to health assessments. This analyzed data, combined with the user's biological state, forms the basis for an overall health assessment.

[0138] The server has the ability to generate health guidelines that reflect the user's emotional state and notify the user's device in real time. For example, if the server assesses the user's emotional state as "high stress," health guidelines will be provided, including guidance on relaxation-promoting exercises, music therapy, or meditation. Furthermore, if changes in emotional state show a certain pattern, fundamental lifestyle improvements may also be recommended.

[0139] Furthermore, user feedback is used as learning material for the emotion engine, creating a loop that improves the accuracy and personalization of future health guidelines. This allows the server to continuously evolve the system.

[0140] In the event that an abnormality is detected in the user's emotional state or biometric information, the server can suggest the use of an online medical consultation service, facilitating the user's rapid access to expert advice. Thus, this system incorporates not only traditional physiological health information but also an emotional health perspective, enabling comprehensive health management.

[0141] The following describes the processing flow.

[0142] Step 1:

[0143] Users use the device to measure vital data, collecting heart rate and body temperature. They also take photos of their meals and environment, and input emotional diaries and feedback through the device.

[0144] Step 2:

[0145] The device sends biometric information, image data, and emotion entries collected from the user to the server. Transfers can be performed periodically or manually by the user.

[0146] Step 3:

[0147] The server receives the transmitted information and first passes the biometric data to an analysis module to check for abnormalities in heart rate and body temperature.

[0148] Step 4:

[0149] The server uses image information to identify the type of food and performs image analysis to estimate its nutritional value and calories.

[0150] Step 5:

[0151] The server's emotion engine analyzes emotion diaries and feedback, and evaluates the user's emotional state through language and emotion cues. Based on this evaluation, the user's stress level and happiness are quantified.

[0152] Step 6:

[0153] The server integrates biometric information, dietary information, and emotional assessments to comprehensively evaluate the user's health status. Based on this, it generates health guidelines.

[0154] Step 7:

[0155] The server sends generated health guidelines to the device, notifying the user in real time. These guidelines include dietary improvements, appropriate exercise, and stress reduction strategies.

[0156] Step 8:

[0157] Users follow the notified health guidelines and take specific actions in their daily lives. Subsequent feedback is sent from the device to the server.

[0158] Step 9:

[0159] The server receives user feedback and uses it as training data for the emotion engine and analysis algorithm to improve accuracy in subsequent sessions.

[0160] Step 10:

[0161] If an anomaly is detected, the server will suggest that the user use the online medical consultation service and prompt them to contact a specialist according to the prescribed procedures.

[0162] (Example 2)

[0163] 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".

[0164] Traditional health management systems focus on assessments based on physiological indicators and do not adequately consider the impact of an individual's emotional state on their health. This makes comprehensive health management difficult, and there is a challenge in taking appropriate measures, particularly for health problems caused by stress and changes in emotional state.

[0165] 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.

[0166] In this invention, the server includes means for analyzing physiological indicators and image data received from the device, means for analyzing emotional states using a generative AI model to add a new perspective to health assessment, and means for notifying the individual's device of health guidelines in real time. This makes it possible to provide a comprehensive health assessment including emotional states and personalized health guidelines.

[0167] "Device" refers to hardware that measures an individual's physiological indicators and records them as data.

[0168] "Physiological indicators" refer to data that indicates an individual's health status, such as heart rate and body temperature.

[0169] "Image data" refers to data that visually records an individual's daily activities and diet.

[0170] "Analysis" refers to the process of performing pattern recognition and evaluation based on physiological indicators and image data.

[0171] A "generative AI model" refers to an artificial intelligence method that analyzes and predicts human emotions and health conditions from multiple perspectives.

[0172] "Emotional state" refers to psychological factors such as an individual's stress level, happiness level, and fatigue level.

[0173] "Health guidelines" refer to specific health behaviors and improvement measures recommended to individuals based on analysis results.

[0174] "Notifying the device in real time" means that the generated health guidelines are immediately transmitted to the user's personal mobile device or other device.

[0175] "Feedback data" refers to information provided by individuals, such as their reactions and requests regarding guidelines.

[0176] "Remote diagnosis by medical professionals" refers to a situation where, when an abnormality is detected, a medical professional uses communication technology to perform a diagnosis.

[0177] This invention is a system that comprehensively approaches user health management from both physiological indicators and emotional states. This enables personalized care that considers the user's psychological health in addition to conventional health management based on physiological indicators.

[0178] Hardware and software to be used:

[0179] Users wear wearable devices to measure their heart rate and body temperature, and transfer this data to their smartphones. Furthermore, they use their smartphone cameras to record image data of their meals and living environment.

[0180] The terminal is the user's smartphone, which uses Bluetooth communication to receive physiological indicators obtained from wearable devices and sends them to the server as a single data package along with image data.

[0181] The server analyzes the received physiological indicators and image data using machine learning algorithms. Here, a generative AI model is used to quantify the user's emotional state, generating new evaluation perspectives. The analysis results generate health guidelines as a basis for health assessment.

[0182] The generative AI model utilizes natural language processing and image recognition technologies to determine the user's psychological health status and reflect this in health guidelines. This model uses user feedback as learning material to improve the accuracy of future guidelines.

[0183] Specific example:

[0184] For example, if a user wants to manage their daily stress, they can use a device to measure their heart rate and provide images of their meals and work environment from their terminal. Based on this, the server can perform an analysis and suggest specific relaxation techniques tailored to their stress level.

[0185] An example of a prompt message for a generative AI model would be: "Based on the user's heart rate and image data, assess their current stress level and suggest appropriate relaxation methods."

[0186] In this way, the present invention aims to provide users with personalized health management and to achieve continuous system evolution.

[0187] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0188] Step 1:

[0189] The user wears a wearable device to measure their heart rate and body temperature. At the same time, they use their smartphone camera to take pictures of their food and living environment, and save this data to the device.

[0190] Input: User's physiological indicators (heart rate, body temperature), image data (meal intake, environment)

[0191] Output: Physiological data and image data stored on the device.

[0192] Step 2:

[0193] The terminal receives physiological data acquired from wearable devices using Bluetooth communication, integrates it with image data to build a data package, and transmits it to the server via a secure protocol.

[0194] Input: User's physiological data, image data

[0195] Output: Data package sent to the server

[0196] Step 3:

[0197] The server analyzes the received data package. First, it analyzes physiological data to assess the user's basic health status. Next, it uses image data to activate a generative AI model, creating prompts that quantify emotional states and performing further analysis.

[0198] Input: Data package (physiological data, image data)

[0199] Output: Analysis results (health assessment, emotional state)

[0200] Step 4:

[0201] Based on the analysis results, the server uses an AI model to generate personalized health guidelines for the user. These guidelines include specific lifestyle improvements and stress reduction suggestions tailored to the user's emotional state.

[0202] Input: Analysis results (health assessment, emotional state)

[0203] Output: Personalized health guidelines

[0204] Step 5:

[0205] The device notifies the user in real time of health guidelines transmitted from the server and displays them on the user's screen. The user can then practice health management based on the presented guidelines. The user can also input feedback into the device.

[0206] Input: Health indicators from the server, user feedback

[0207] Output: Display of health guidelines on the screen, saving of feedback

[0208] Step 6:

[0209] The server receives feedback and uses it as training material for the generated AI model. This improves the accuracy of subsequent analyses and guideline generation, enabling the system to evolve.

[0210] Input: User feedback

[0211] Output: Updated generative AI model, improved guideline generation accuracy.

[0212] (Application Example 2)

[0213] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0214] In modern society, individual health management is extremely important, but conventional technologies generally rely solely on biometric data for health assessments, resulting in a lack of comprehensive evaluations that consider the user's emotional state. Furthermore, there is a need for effective personalized care by comprehensively understanding the health status of citizens, but this has not yet been achieved.

[0215] 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.

[0216] In this invention, the server includes means for analyzing biometric and image information received from the device, means for evaluating the user's health status based on the analysis results and generating appropriate health guidelines, means for performing emotion analysis and quantifying the emotional state, means for adjusting the health guidelines in real time based on the quantified emotional state, and means for notifying the health guidelines to the user's terminal. This makes it possible to comprehensively evaluate the user's physical and mental state and provide health guidelines tailored to each individual.

[0217] "Biometric information received from a device" refers to data indicating the user's physical condition, such as heart rate and body temperature, and is acquired from wearable devices, etc.

[0218] "Image information" refers to image data that captures a user's facial expressions and lifestyle, and is acquired using an image sensor such as a camera.

[0219] "Means of analysis" refers to software and algorithms that process biometric and image information to analyze the user's health status.

[0220] "Means of evaluating health status" refers to a process of comprehensively assessing a user's physical and emotional health status based on analyzed data.

[0221] "Emotion analysis" is a technology that analyzes facial expressions and other characteristics from a user's image information, quantifies their emotional state, and handles it as data.

[0222] "Methods for quantifying emotional states" refer to methods for converting analyzed emotional data into mathematical values ​​that can be used in health guidelines.

[0223] A "means for adjusting health guidelines in real time" refers to a mechanism that instantly modifies health advice in response to changing biological information and emotional states, providing optimal care.

[0224] "Means of notifying users of health guidelines on their devices" refers to methods of sending the generated health guidelines to the user's smartphone or other digital devices.

[0225] This invention is a system for providing personalized health guidance in real time by utilizing a user's biometric and image information. The server receives biometric information such as heart rate and body temperature transmitted from wearable devices and smartphones, as well as image information including the user's facial expressions captured by a camera. This data is stored in a database and processed comprehensively by an analysis engine.

[0226] The server uses software libraries such as TensorFlow and OpenCV to analyze the received biometric and image information. This analysis assesses the user's health status and quantifies emotional states such as stress and happiness. By quantifying emotional states, the emotion engine can adjust health guidelines based on the user's emotions.

[0227] The generated health guidelines are notified to the user's device in real time. Using a smartphone or head-mounted display, the user receives the health guidelines and uses them as behavioral guidelines in their daily life. These health guidelines are automatically updated as needed and continuously improved based on user feedback and new data.

[0228] As a concrete example, the system enhances the experience of event participants by suggesting different relaxation exercises and activities based on the emotional state of users attending a community festival. Furthermore, if an abnormality is detected in the evaluation results, the system can suggest a remote diagnosis by a medical professional. This suggestion enables prompt action and supports the maintenance of health.

[0229] Examples of prompts to input into a generative AI model:

[0230] "We want to provide emotion-based health guidance at community events. Please propose a feedback system based on emotion analysis results."

[0231] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0232] Step 1:

[0233] Users collect biometric and image information using wearable devices and smartphones. This includes data such as heart rate and body temperature, and facial expressions are also recorded via camera. Input is data from sensors, and this information is sent to the cloud as output.

[0234] Step 2:

[0235] The server receives biometric and image data sent to the cloud. The received data is stored in a database, temporarily holding the necessary information. The input is data sent by the user, and the output is ready for analysis.

[0236] Step 3:

[0237] The server analyzes the received data using TensorFlow and OpenCV. Specifically, it evaluates the user's health status based on biometric information and quantifies their emotional state by analyzing facial expressions from image information. The input is data stored on the server, and the output is a health status evaluation and an emotional score.

[0238] Step 4:

[0239] The server generates health guidelines in real time based on quantified emotional states. Using the analysis results, it creates appropriate health advice and behavioral guidelines. The input is the emotional score and health assessment obtained in the previous step, and the output is the generated health guidelines.

[0240] Step 5:

[0241] The device receives health guidelines transmitted from the server and notifies the user. The notified guidelines are displayed on the user's smartphone or head-mounted display and provided as actionable advice. The input is health guidelines from the server, and the output is the notification to the user.

[0242] Step 6:

[0243] Users provide feedback based on the health guidelines provided. This feedback is used to generate future guidelines for the system, leading to continuous improvement. The input is user feedback, and the output is data updates to the system.

[0244] Step 7:

[0245] The server activates a function that suggests remote diagnosis by a medical professional if an anomaly is detected. Based on the anomaly data, it notifies the user of the importance of prompt action and presents options for connecting with a professional. The input is the analyzed anomaly data, and the output is a suggested notification.

[0246] 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.

[0247] 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.

[0248] 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.

[0249] [Second Embodiment]

[0250] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0251] 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.

[0252] 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).

[0253] 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.

[0254] 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.

[0255] 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).

[0256] 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.

[0257] 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.

[0258] 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.

[0259] 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.

[0260] 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.

[0261] 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".

[0262] This invention is a system for supporting personal health management, and is implemented by a program. Specific examples are shown below.

[0263] System Overview

[0264] This system is designed to receive biometric information and food images sent from the user's device. Users are required to periodically measure their vital data and take pictures of their meals. The terminal then collects this data and sends it to the server.

[0265] The server runs an AI model equipped with the ability to analyze received data. Based on biometric information, the server numerically evaluates the user's health status and identifies patterns and trends. It also calculates the types of food consumed and calorie intake from image information and analyzes the user's dietary balance. Based on the health status evaluation and dietary analysis, the server uses AI to formulate optimal health guidelines for each user.

[0266] The established health guidelines are notified to the user's device in real time from the server. This allows users to take immediate action in their daily lives to improve or maintain their health. For example, if the server detects an upward trend in heart rate, it may suggest simple exercises for stress relief. It can also identify vitamin deficiencies from dietary analysis and recommend foods to supplement them.

[0267] After implementing the provided guidelines, users send feedback to the system via their terminal regarding the effectiveness and feasibility of those guidelines. The server collects this feedback and retrains or adjusts the AI ​​model to improve the accuracy of future analyses and the quality of the guidelines.

[0268] Furthermore, if abnormalities are detected in the health assessment results, the server can suggest that the user utilize an online medical consultation service. This gives the user the opportunity to receive prompt and appropriate medical support.

[0269] The following describes the processing flow.

[0270] Step 1:

[0271] Users collect vital data using the device and upload photos of their meals to their terminal to monitor their health. This makes daily health information available in digital format.

[0272] Step 2:

[0273] The terminal sends the collected vital data and photos of meals to the server. This transmission is performed periodically based on a set time schedule or by the user manually triggering it.

[0274] Step 3:

[0275] The server prepares the received data for analysis. Specifically, it inputs the vital data into an analysis module and passes the meal photo data to an image analysis unit.

[0276] Step 4:

[0277] The server's AI model analyzes the vital data to analyze trends in heart rate and body temperature and evaluates whether there are any abnormalities. This forms a basic profile of the health status.

[0278] Step 5:

[0279] The server analyzes the meal photos using image recognition technology and estimates the types of foods and their nutrients and calories. This information is used to evaluate whether the meal is healthy.

[0280] Step 6:

[0281] The server integrates the analysis results of the vital data and the meal analysis results and generates health guidelines according to the user's current health status. These guidelines include points for improving the diet and recommended exercise plans.

[0282] Step 7:

[0283] The server sends the health guidelines generated to the terminal and immediately notifies the user. The user confirms the notification and practices health management by taking actions according to the guidelines.

[0284] Step 8:

[0285] After the implementation of the guidelines, the user sends the results and feedback from the terminal to the server. This feedback is stored on the server for use in subsequent analyses.

[0286] Step 9:

[0287] The server uses the accumulated feedback data to retrain or adjust the AI model. This continuously improves the accuracy of the analysis and health guidelines, providing better services to users.

[0288] Step 10:

[0289] When an anomaly is detected or when it is determined necessary, the server proposes that the user seek an online medical consultation. This proposal aims to understand the user's health condition and provide prompt medical assistance.

[0290] (Example 1)

[0291] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0292] In modern society, personal health management is an important issue, but it is difficult to allocate sufficient time due to the busyness of daily life. Conventional health management methods are time-consuming and laborious, and it is particularly difficult to obtain appropriate advice according to individual situations. Also, there are many opportunities to miss the chance to detect anomalies early and receive appropriate medical assistance. There is a need for a system that solves such problems and provides effective health guidelines optimized for individual health conditions in real time.

[0293] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0294] In this invention, the server includes means for transmitting personal biometric data measured by a terminal and image data of food captured by the terminal to the server; means for the server to evaluate the received biometric data using an AI model and numerically analyze the health status; and means for the server to process the received image data using an AI model and identify the type of food and its nutritional value. This enables rapid and personalized health support by providing health guidelines tailored to each individual user in real time and proposing telemedicine services when an anomaly is detected.

[0295] A "terminal" is a portable device used by an individual to measure and record data, and is equipped with the function to transmit biometric data and image data to a server.

[0296] A "server" is a computer system that has the central function of receiving data from terminals via a network, analyzing it, and generating health guidelines.

[0297] "Biometric data" refers to data that indicates an individual's physical condition, such as heart rate and blood pressure, and is used to evaluate their health status.

[0298] "Image data" refers to visual data that shows the contents of a user's meals, and is used to identify the type of food and its nutritional value.

[0299] An "AI model" is an algorithm based on machine learning technology that analyzes biometric and image data to evaluate health status or identify food products.

[0300] "Health guidelines" are suggestions for improving the health of individual users, generated by the server based on analysis results, and include specific advice on diet, exercise, and other topics.

[0301] "Feedback information" refers to information that users send to the server regarding their implementation status and the effectiveness of the health guidelines provided, and this data is used to improve the AI ​​model.

[0302] "Telemedicine services" are services that provide online diagnoses and advice from medical professionals as needed, based on the user's health condition.

[0303] This invention is a system for supporting individual health management, implemented through the cooperation of a server, terminal, and user. By integrating multiple technological elements, this system provides users with customized health guidelines, promoting the improvement and maintenance of their health status.

[0304] The terminals are wearable devices or personal digital assistants worn by users, used to measure biometric data such as heart rate and blood pressure, and to capture image data of meals. This data is collected by smartphones and other devices and transmitted to a server via wireless communication technology. The terminals used are devices equipped with iOS or Android operating systems.

[0305] The server stores and manages the received data and analyzes it using a generative AI model. The AI ​​model, built using machine learning libraries such as TensorFlow and PyTorch, evaluates the user's health status based on biometric data and performs nutritional analysis of their diet from image data. Based on the analysis results, the server develops personalized health guidelines for each user and transmits this information to the terminal in real time.

[0306] Users can utilize health guidelines displayed on their smartphones or tablets. For example, they may be recommended to consume foods that supplement nutrient deficiencies identified by the AI. Users can also provide feedback on the effectiveness and feasibility of the guidelines they followed, and the server uses this feedback to retrain the AI ​​model, improving the accuracy of future analysis results.

[0307] As a specific example, consider the case where a user takes a photo of bread and fruit consumed for breakfast, measures the heart rate, and sends it to the server. The server recognizes the type of food from the image and evaluates the nutritional value. At the same time, by analyzing the heart rate, if it is determined that the stress is high, it can propose exercises for relaxation.

[0308] As an example of the prompt text for the generative AI model, "Please evaluate the type of food consumed for breakfast and the calorie amount, and propose exercises recommended when the heart rate increases." can be cited. This enables the user to incorporate specific actions based on their health condition into their daily life.

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

[0310] Step 1:

[0311] The terminal plays the role of collecting biometric data measurements and images of meals. The main devices used here are wearable devices that can measure heart rate, blood pressure, etc., and smartphones for taking photos of meals. As input, it collects various vital data measured by the user and image data of the food taken. As output, the terminal prepares to send these data to the server for later analysis.

[0312] Step 2:

[0313] The terminal converts the collected data into a certain format (e.g., JSON format) and sends it to the server. The data transmission is carried out using Wi-Fi or mobile data communication. The input is the biometric information and image data stored in the terminal, and the output is to send this as an organized data package to the server.

[0314] Step 3:

[0315] The server receives data sent from the terminal and stores each piece of data in an analysis preparation queue. The input is the received data package, which is then formatted into an appropriate analysis format and output. Specifically, the data is structured using a Python script and transformed to facilitate processing by the AI ​​model.

[0316] Step 4:

[0317] The server begins analyzing biometric data using a generative AI model. Structured biometric information is used as input data, and the AI ​​model analyzes it to numerically evaluate the user's health status. The output includes deviations from normal ranges for heart rate and blood pressure. TensorFlow software is used for advanced numerical processing.

[0318] Step 5:

[0319] The server then analyzes the image data of the food. The input for this step is structured photographic data. The generative AI model uses image recognition techniques to identify the types of food and then calculates their nutritional components and calories. The output provides a list of foods, their calorie counts, and detailed nutritional information.

[0320] Step 6:

[0321] The server develops personalized health guidelines for each user based on biometric information and image analysis results. Input consists of biometric evaluation results and image analysis results, which an AI model combines to output optimal health guidelines. These guidelines include suggestions for dietary improvements and necessary exercises.

[0322] Step 7:

[0323] The server notifies the device in real time of the established health guidelines. The input here is the generated health guidelines, which are then pushed to the device as output. Information is instantly sent to the user's device using Firebase Cloud Messaging (FCM), etc.

[0324] Step 8:

[0325] Users implement the provided health guidelines and send feedback on their results and effectiveness from their device to the server. The input here is the user feedback data, and the output is this data which is then fed back into the AI ​​model to improve the accuracy of the analysis.

[0326] (Application Example 1)

[0327] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0328] In personal health management, it is crucial to comprehensively analyze daily biological information and dietary content to provide appropriate health guidance in real time. However, current health management systems often make it difficult for users to fully understand their own health status and take prompt action. Furthermore, there is a lack of systems that allow users to grasp detailed individual nutritional information and receive daily applicable guidance for health management at home. As a result, many users are unable to immediately improve their health in their daily lives and are likely to miss opportunities to address health deterioration early.

[0329] 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.

[0330] In this invention, the server includes means for analyzing biometric and image information received from the device, means for evaluating the user's health status based on the analysis results and generating appropriate health guidelines, and means for monitoring the user's health status within the home and providing suggestions. This enables the user to instantly understand their own health status and take concrete and effective actions in their daily life based on health guidelines.

[0331] "Device" refers to a terminal used to acquire user biometric information and food image information and transmit it to the server.

[0332] "Receiving" refers to the process where the server acquires data sent by the user and begins internal processing.

[0333] "Biometric information" refers to information that includes data such as heart rate, blood pressure, and body temperature, which indicate the user's health status.

[0334] "Image information" refers to image data of meals consumed by the user, and is used for analyzing the contents of the meal.

[0335] "Analysis" refers to evaluating the user's health and nutritional status based on received biometric and image data.

[0336] "Assessing health status and generating health guidelines" refers to the procedure for analyzing the user's health status based on the analysis results and suggesting desirable lifestyle habits.

[0337] "Notifying the device" means sending the generated health guidelines to the user's individual device in real time, enabling the user to take immediate action.

[0338] "Monitoring the user's health status within the home and providing suggestions" means continuously collecting health information in the user's daily life and providing advice for appropriate health management.

[0339] "Analyzing food data and identifying nutritional information" refers to analyzing image information of meals to identify the types of food and the nutrients they contain, and then evaluating the user's dietary balance.

[0340] To implement this invention, a user terminal device, a server, and an AI model are used. First, the user device acquires biometric information and image information. The biometric information includes heart rate, blood pressure, and body temperature, and the image information includes photos of meals. This data is transmitted to the server via the terminal.

[0341] The server analyzes the received data using an AI model. Biometric information is used to assess health status, with the AI ​​model performing a numerical evaluation. Image information is used for food classification and extraction of nutritional information, and calorie intake and nutritional balance are calculated. Computer vision technology is used for this analysis. For example, images are preprocessed using OpenCV, and classification is performed using a pre-trained AI model with Keras.

[0342] Based on the analysis results, the server generates optimal health guidelines for the user. These guidelines include, for example, recommended foods to improve dietary content and suggestions for light exercise to manage stress. This information is notified to the user's device in real time, allowing the user to take immediate action in their daily life.

[0343] For example, if heart rate data rises above expectations, the server will send a message such as, "You might want to try yoga to relax." Also, if it determines that calorie intake is too high, it will suggest, "We recommend eating more vegetables at your next meal." An example of a prompt message would be, "Your heart rate is higher than average today. Try yoga to promote relaxation."

[0344] This system makes it easier for users to receive personalized advice based on their individual health data and to work towards maintaining and improving their health on a daily basis.

[0345] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0346] Step 1:

[0347] The user's device acquires biometric and image information. Input includes heart rate, blood pressure, body temperature, and photos of meals. This data is stored on the device and prepared as data packets. Specifically, data is collected using a smartwatch or digital camera.

[0348] Step 2:

[0349] The terminal sends the prepared data packets to the server. The input consists of data packets containing the user's biometric information and image information. These are sent to the server via the internet. Specifically, the terminal's communication module is used to transfer the data using a secure protocol (e.g., HTTPS).

[0350] Step 3:

[0351] The server runs an AI model to analyze the received data. It receives user biometric and image data as input. The biometric data is used to assess health status and is quantified by the AI ​​model. Image data is preprocessed using computer vision technology to identify food types and nutritional information. Specifically, an AI model built with Keras is used to generate the analysis results.

[0352] Step 4:

[0353] The server generates health guidelines based on the analysis results. The input is analyzed biological and dietary information. As output, it creates personalized health guidelines for each user. Specifically, it queries a database that references relaxation methods based on heart rate variability and dietary advice to improve nutritional balance, and generates prompt statements using a generation AI model.

[0354] Step 5:

[0355] The server notifies the user's device of the generated health guidelines. The output is real-time health guideline information. This is sent to the user's mobile device via push notification or email. Specifically, it uses a notification API to provide real-time feedback.

[0356] Step 6:

[0357] The user takes specific actions based on the health guidelines they receive. The input is the health guidelines sent from the server. The output is the user's actions, such as health improvement activities. Specifically, this could include seeing the application's notification and trying the recommended exercise or adding the suggested ingredients to the shopping list.

[0358] 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.

[0359] This invention is a system for providing highly personalized care that takes into account the emotional state of the user in managing their health. Specific embodiments are described below.

[0360] System Overview

[0361] In addition to its basic function of analyzing biometric and image information transmitted from the device, this system incorporates an emotion engine to comprehensively assess the user's physical and mental health. Users routinely use the device to acquire biometric information such as heart rate and body temperature, and provide image information related to their diet and lifestyle via a terminal.

[0362] Upon receiving this information, the server activates an emotion engine and analyzes data representing the user's emotional state. This quantifies emotions such as stress, happiness, and fatigue, adding a new perspective to health assessments. This analyzed data, combined with the user's biological state, forms the basis for an overall health assessment.

[0363] The server has the ability to generate health guidelines that reflect the user's emotional state and notify the user's device in real time. For example, if the server assesses the user's emotional state as "high stress," health guidelines will be provided, including guidance on relaxation-promoting exercises, music therapy, or meditation. Furthermore, if changes in emotional state show a certain pattern, fundamental lifestyle improvements may also be recommended.

[0364] Furthermore, user feedback is used as learning material for the emotion engine, creating a loop that improves the accuracy and personalization of future health guidelines. This allows the server to continuously evolve the system.

[0365] In the event that an abnormality is detected in the user's emotional state or biometric information, the server can suggest the use of an online medical consultation service, facilitating the user's rapid access to expert advice. Thus, this system incorporates not only traditional physiological health information but also an emotional health perspective, enabling comprehensive health management.

[0366] The following describes the processing flow.

[0367] Step 1:

[0368] Users use the device to measure vital data, collecting heart rate and body temperature. They also take photos of their meals and environment, and input emotional diaries and feedback through the device.

[0369] Step 2:

[0370] The device sends biometric information, image data, and emotion entries collected from the user to the server. Transfers can be performed periodically or manually by the user.

[0371] Step 3:

[0372] The server receives the transmitted information and first passes the biometric data to an analysis module to check for abnormalities in heart rate and body temperature.

[0373] Step 4:

[0374] The server uses image information to identify the type of food and performs image analysis to estimate its nutritional value and calories.

[0375] Step 5:

[0376] The server's emotion engine analyzes emotion diaries and feedback, and evaluates the user's emotional state through language and emotion cues. Based on this evaluation, the user's stress level and happiness are quantified.

[0377] Step 6:

[0378] The server integrates biometric information, dietary information, and emotional assessments to comprehensively evaluate the user's health status. Based on this, it generates health guidelines.

[0379] Step 7:

[0380] The server sends generated health guidelines to the device, notifying the user in real time. These guidelines include dietary improvements, appropriate exercise, and stress reduction strategies.

[0381] Step 8:

[0382] Users follow the notified health guidelines and take specific actions in their daily lives. Subsequent feedback is sent from the device to the server.

[0383] Step 9:

[0384] The server receives user feedback and uses it as training data for the emotion engine and analysis algorithm to improve accuracy in subsequent sessions.

[0385] Step 10:

[0386] If an anomaly is detected, the server will suggest that the user use the online medical consultation service and prompt them to contact a specialist according to the prescribed procedures.

[0387] (Example 2)

[0388] 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".

[0389] Traditional health management systems focus on assessments based on physiological indicators and do not adequately consider the impact of an individual's emotional state on their health. This makes comprehensive health management difficult, and there is a challenge in taking appropriate measures, particularly for health problems caused by stress and changes in emotional state.

[0390] 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.

[0391] In this invention, the server includes means for analyzing physiological indicators and image data received from the device, means for analyzing emotional states using a generative AI model to add a new perspective to health assessment, and means for notifying the individual's device of health guidelines in real time. This makes it possible to provide a comprehensive health assessment including emotional states and personalized health guidelines.

[0392] "Device" refers to hardware that measures an individual's physiological indicators and records them as data.

[0393] "Physiological indicators" refer to data that indicates an individual's health status, such as heart rate and body temperature.

[0394] "Image data" refers to data that visually records an individual's daily activities and diet.

[0395] "Analysis" refers to the process of performing pattern recognition and evaluation based on physiological indicators and image data.

[0396] A "generative AI model" refers to an artificial intelligence method that analyzes and predicts human emotions and health conditions from multiple perspectives.

[0397] "Emotional state" refers to psychological factors such as an individual's stress level, happiness level, and fatigue level.

[0398] "Health guidelines" refer to specific health behaviors and improvement measures recommended to individuals based on analysis results.

[0399] "Notifying the device in real time" means that the generated health guidelines are immediately transmitted to the user's personal mobile device or other device.

[0400] "Feedback data" refers to information provided by individuals, such as their reactions and requests regarding guidelines.

[0401] "Remote diagnosis by medical professionals" refers to a situation where, when an abnormality is detected, a medical professional uses communication technology to perform a diagnosis.

[0402] This invention is a system that comprehensively approaches user health management from both physiological indicators and emotional states. This enables personalized care that considers the user's psychological health in addition to conventional health management based on physiological indicators.

[0403] Hardware and software to be used:

[0404] Users wear wearable devices to measure their heart rate and body temperature, and transfer this data to their smartphones. Furthermore, they use their smartphone cameras to record image data of their meals and living environment.

[0405] The terminal is the user's smartphone, which uses Bluetooth communication to receive physiological indicators obtained from wearable devices and sends them to the server as a single data package along with image data.

[0406] The server analyzes the received physiological indicators and image data using machine learning algorithms. Here, a generative AI model is used to quantify the user's emotional state, generating new evaluation perspectives. The analysis results generate health guidelines as a basis for health assessment.

[0407] The generative AI model utilizes natural language processing and image recognition technologies to determine the user's psychological health status and reflect this in health guidelines. This model uses user feedback as learning material to improve the accuracy of future guidelines.

[0408] Specific example:

[0409] For example, if a user wants to manage their daily stress, they can use a device to measure their heart rate and provide images of their meals and work environment from their terminal. Based on this, the server can perform an analysis and suggest specific relaxation techniques tailored to their stress level.

[0410] An example of a prompt message for a generative AI model would be: "Based on the user's heart rate and image data, assess their current stress level and suggest appropriate relaxation methods."

[0411] In this way, the present invention aims to provide users with personalized health management and to achieve continuous system evolution.

[0412] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0413] Step 1:

[0414] The user wears a wearable device to measure their heart rate and body temperature. At the same time, they use their smartphone camera to take pictures of their food and living environment, and save this data to the device.

[0415] Input: User's physiological indicators (heart rate, body temperature), image data (meal intake, environment)

[0416] Output: Physiological data and image data stored on the device.

[0417] Step 2:

[0418] The terminal receives physiological data acquired from wearable devices using Bluetooth communication, integrates it with image data to build a data package, and transmits it to the server via a secure protocol.

[0419] Input: User's physiological data, image data

[0420] Output: Data package sent to the server

[0421] Step 3:

[0422] The server analyzes the received data package. First, it analyzes physiological data to assess the user's basic health status. Next, it uses image data to activate a generative AI model, creating prompts that quantify emotional states and performing further analysis.

[0423] Input: Data package (physiological data, image data)

[0424] Output: Analysis results (health assessment, emotional state)

[0425] Step 4:

[0426] Based on the analysis results, the server uses an AI model to generate personalized health guidelines for the user. These guidelines include specific lifestyle improvements and stress reduction suggestions tailored to the user's emotional state.

[0427] Input: Analysis results (health assessment, emotional state)

[0428] Output: Personalized health guidelines

[0429] Step 5:

[0430] The device notifies the user in real time of health guidelines transmitted from the server and displays them on the user's screen. The user can then practice health management based on the presented guidelines. The user can also input feedback into the device.

[0431] Input: Health indicators from the server, user feedback

[0432] Output: Display of health guidelines on the screen, saving of feedback

[0433] Step 6:

[0434] The server receives feedback and uses it as training material for the generated AI model. This improves the accuracy of subsequent analyses and guideline generation, enabling the system to evolve.

[0435] Input: User feedback

[0436] Output: Updated generative AI model, improved guideline generation accuracy.

[0437] (Application Example 2)

[0438] 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."

[0439] In modern society, individual health management is extremely important, but conventional technologies generally rely solely on biometric data for health assessments, resulting in a lack of comprehensive evaluations that consider the user's emotional state. Furthermore, there is a need for effective personalized care by comprehensively understanding the health status of citizens, but this has not yet been achieved.

[0440] 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.

[0441] In this invention, the server includes means for analyzing biometric and image information received from the device, means for evaluating the user's health status based on the analysis results and generating appropriate health guidelines, means for performing emotion analysis and quantifying the emotional state, means for adjusting the health guidelines in real time based on the quantified emotional state, and means for notifying the health guidelines to the user's terminal. This makes it possible to comprehensively evaluate the user's physical and mental state and provide health guidelines tailored to each individual.

[0442] "Biometric information received from a device" refers to data indicating the user's physical condition, such as heart rate and body temperature, and is acquired from wearable devices, etc.

[0443] "Image information" refers to image data that captures a user's facial expressions and lifestyle, and is acquired using an image sensor such as a camera.

[0444] "Means of analysis" refers to software and algorithms that process biometric and image information to analyze the user's health status.

[0445] "Means of evaluating health status" refers to a process of comprehensively assessing a user's physical and emotional health status based on analyzed data.

[0446] "Emotion analysis" is a technology that analyzes facial expressions and other characteristics from a user's image information, quantifies their emotional state, and handles it as data.

[0447] "Methods for quantifying emotional states" refer to methods for converting analyzed emotional data into mathematical values ​​that can be used in health guidelines.

[0448] A "means for adjusting health guidelines in real time" refers to a mechanism that instantly modifies health advice in response to changing biological information and emotional states, providing optimal care.

[0449] "Means of notifying users of health guidelines on their devices" refers to methods of sending the generated health guidelines to the user's smartphone or other digital devices.

[0450] This invention is a system for providing personalized health guidance in real time by utilizing a user's biometric and image information. The server receives biometric information such as heart rate and body temperature transmitted from wearable devices and smartphones, as well as image information including the user's facial expressions captured by a camera. This data is stored in a database and processed comprehensively by an analysis engine.

[0451] The server uses software libraries such as TensorFlow and OpenCV to analyze the received biometric and image information. This analysis assesses the user's health status and quantifies emotional states such as stress and happiness. By quantifying emotional states, the emotion engine can adjust health guidelines based on the user's emotions.

[0452] The generated health guidelines are notified to the user's device in real time. Using a smartphone or head-mounted display, the user receives the health guidelines and uses them as behavioral guidelines in their daily life. These health guidelines are automatically updated as needed and continuously improved based on user feedback and new data.

[0453] As a concrete example, the system enhances the experience of event participants by suggesting different relaxation exercises and activities based on the emotional state of users attending a community festival. Furthermore, if an abnormality is detected in the evaluation results, the system can suggest a remote diagnosis by a medical professional. This suggestion enables prompt action and supports the maintenance of health.

[0454] Examples of prompts to input into a generative AI model:

[0455] "We want to provide emotion-based health guidance at community events. Please propose a feedback system based on emotion analysis results."

[0456] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0457] Step 1:

[0458] Users collect biometric and image information using wearable devices and smartphones. This includes data such as heart rate and body temperature, and facial expressions are also recorded via camera. Input is data from sensors, and this information is sent to the cloud as output.

[0459] Step 2:

[0460] The server receives biometric and image data sent to the cloud. The received data is stored in a database, temporarily holding the necessary information. The input is data sent by the user, and the output is ready for analysis.

[0461] Step 3:

[0462] The server analyzes the received data using TensorFlow and OpenCV. Specifically, it evaluates the user's health status based on biometric information and quantifies their emotional state by analyzing facial expressions from image information. The input is data stored on the server, and the output is a health status evaluation and an emotional score.

[0463] Step 4:

[0464] The server generates health guidelines in real time based on quantified emotional states. Using the analysis results, it creates appropriate health advice and behavioral guidelines. The input is the emotional score and health assessment obtained in the previous step, and the output is the generated health guidelines.

[0465] Step 5:

[0466] The device receives health guidelines transmitted from the server and notifies the user. The notified guidelines are displayed on the user's smartphone or head-mounted display and provided as actionable advice. The input is health guidelines from the server, and the output is the notification to the user.

[0467] Step 6:

[0468] Users provide feedback based on the health guidelines provided. This feedback is used to generate future guidelines for the system, leading to continuous improvement. The input is user feedback, and the output is data updates to the system.

[0469] Step 7:

[0470] The server activates a function that suggests remote diagnosis by a medical professional if an anomaly is detected. Based on the anomaly data, it notifies the user of the importance of prompt action and presents options for connecting with a professional. The input is the analyzed anomaly data, and the output is a suggested notification.

[0471] 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.

[0472] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0473] 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.

[0474] [Third Embodiment]

[0475] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0476] 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.

[0477] 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).

[0478] 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.

[0479] 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.

[0480] 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).

[0481] 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.

[0482] 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.

[0483] 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.

[0484] 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.

[0485] 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.

[0486] 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".

[0487] This invention is a system for supporting personal health management, and is implemented by a program. Specific examples are shown below.

[0488] System Overview

[0489] This system is designed to receive biometric information and food images sent from the user's device. Users are required to periodically measure their vital data and take pictures of their meals. The terminal then collects this data and sends it to the server.

[0490] The server runs an AI model equipped with the ability to analyze received data. Based on biometric information, the server numerically evaluates the user's health status and identifies patterns and trends. It also calculates the types of food consumed and calorie intake from image information and analyzes the user's dietary balance. Based on the health status evaluation and dietary analysis, the server uses AI to formulate optimal health guidelines for each user.

[0491] The established health guidelines are notified to the user's device in real time from the server. This allows users to take immediate action in their daily lives to improve or maintain their health. For example, if the server detects an upward trend in heart rate, it may suggest simple exercises for stress relief. It can also identify vitamin deficiencies from dietary analysis and recommend foods to supplement them.

[0492] After implementing the provided guidelines, users send feedback to the system via their terminal regarding the effectiveness and feasibility of those guidelines. The server collects this feedback and retrains or adjusts the AI ​​model to improve the accuracy of future analyses and the quality of the guidelines.

[0493] Furthermore, if abnormalities are detected in the health assessment results, the server can suggest that the user utilize an online medical consultation service. This gives the user the opportunity to receive prompt and appropriate medical support.

[0494] The following describes the processing flow.

[0495] Step 1:

[0496] Users collect vital data using the device and upload photos of their meals to their terminal to monitor their health. This makes daily health information available in digital format.

[0497] Step 2:

[0498] The device sends collected vital data and photos of meals to a server. This transmission occurs periodically based on a set time schedule or can be triggered manually by the user.

[0499] Step 3:

[0500] The server prepares the received data for analysis. Specifically, it inputs vital data into the analysis module and passes meal photo data to the image analysis unit.

[0501] Step 4:

[0502] The server's AI model analyzes vital data, examining trends in heart rate and body temperature to assess for any abnormalities. This forms a basic profile of the user's health status.

[0503] Step 5:

[0504] The server analyzes photos of meals using image recognition technology to estimate the types of food, their nutrients, and calories. This information is used to evaluate whether the meal is healthy.

[0505] Step 6:

[0506] The server integrates the results of vital data analysis and dietary analysis to generate health guidelines tailored to the user's current health status. These guidelines include points for dietary improvement and recommended exercise plans.

[0507] Step 7:

[0508] The server generates health guidelines and sends them to the device, immediately notifying the user. The user checks the notification and takes action according to the guidelines to practice health management.

[0509] Step 8:

[0510] After implementing the guidelines, users send the results and feedback from their device to the server. This feedback is stored on the server to be used for future analyses.

[0511] Step 9:

[0512] The server uses the collected feedback data to retrain or adjust the AI ​​model. This continuously improves the accuracy of analysis and health guidance, providing users with a better service.

[0513] Step 10:

[0514] The server will suggest that the user undergo an online medical consultation if it detects an anomaly or deems it necessary. This suggestion aims to understand the user's health status and provide prompt medical support.

[0515] (Example 1)

[0516] 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."

[0517] In modern society, personal health management is a crucial issue, but busy daily lives often make it difficult to dedicate sufficient time to it. Traditional health management methods are time-consuming and cumbersome, and it is particularly difficult to obtain appropriate advice tailored to individual circumstances. Furthermore, opportunities to detect abnormalities early and receive appropriate medical support are often missed. A system is needed to solve these problems and provide effective health guidelines optimized for each individual's health condition in real time.

[0518] 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.

[0519] In this invention, the server includes means for transmitting personal biometric data measured by a terminal and image data of food captured by the terminal to the server; means for the server to evaluate the received biometric data using an AI model and numerically analyze the health status; and means for the server to process the received image data using an AI model and identify the type of food and its nutritional value. This enables rapid and personalized health support by providing health guidelines tailored to each individual user in real time and proposing telemedicine services when an anomaly is detected.

[0520] A "terminal" is a portable device used by an individual to measure and record data, and is equipped with the function to transmit biometric data and image data to a server.

[0521] A "server" is a computer system that has the central function of receiving data from terminals via a network, analyzing it, and generating health guidelines.

[0522] "Biometric data" refers to data that indicates an individual's physical condition, such as heart rate and blood pressure, and is used to evaluate their health status.

[0523] "Image data" refers to visual data that shows the contents of a user's meals, and is used to identify the type of food and its nutritional value.

[0524] An "AI model" is an algorithm based on machine learning technology that analyzes biometric and image data to evaluate health status or identify food products.

[0525] "Health guidelines" are suggestions for improving the health of individual users, generated by the server based on analysis results, and include specific advice on diet, exercise, and other topics.

[0526] "Feedback information" refers to information that users send to the server regarding their implementation status and the effectiveness of the health guidelines provided, and this data is used to improve the AI ​​model.

[0527] "Telemedicine services" are services that provide online diagnoses and advice from medical professionals as needed, based on the user's health condition.

[0528] This invention is a system for supporting individual health management, implemented through the cooperation of a server, terminal, and user. By integrating multiple technological elements, this system provides users with customized health guidelines, promoting the improvement and maintenance of their health status.

[0529] The terminals are wearable devices or personal digital assistants worn by users, used to measure biometric data such as heart rate and blood pressure, and to capture image data of meals. This data is collected by smartphones and other devices and transmitted to a server via wireless communication technology. The terminals used are devices equipped with iOS or Android operating systems.

[0530] The server stores and manages the received data and analyzes it using a generative AI model. The AI ​​model, built using machine learning libraries such as TensorFlow and PyTorch, evaluates the user's health status based on biometric data and performs nutritional analysis of their diet from image data. Based on the analysis results, the server develops personalized health guidelines for each user and transmits this information to the terminal in real time.

[0531] Users can utilize health guidelines displayed on their smartphones or tablets. For example, they may be recommended to consume foods that supplement nutrient deficiencies identified by the AI. Users can also provide feedback on the effectiveness and feasibility of the guidelines they followed, and the server uses this feedback to retrain the AI ​​model, improving the accuracy of future analysis results.

[0532] As a concrete example, consider a scenario where a user takes a photo of bread and fruit for breakfast, measures their heart rate, and sends the photo to a server. The server recognizes the type of food from the image and evaluates its nutritional value. Simultaneously, it analyzes the heart rate and, if it determines that the user is under high stress, can suggest exercises for relaxation.

[0533] An example of a prompt for a generative AI model is, "Evaluate the types and calorie content of the foods consumed for breakfast, and suggest exercises recommended when your heart rate is elevated." This allows users to incorporate specific actions based on their own health status into their daily lives.

[0534] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0535] Step 1:

[0536] The terminal's role is to measure biometric data and collect images of food. The main devices used are wearable devices capable of measuring heart rate and blood pressure, and smartphones for taking pictures of food. As input, it collects various vital data measured by the user and images of the food taken. As output, the terminal prepares to send this data to a server for later analysis.

[0537] Step 2:

[0538] The device converts the collected data into a specific format (e.g., JSON) and sends it to the server. Data transmission is performed using Wi-Fi or mobile data communication. The input consists of biometric and image data stored on the device, and the output is the transmission of this data as an organized data package to the server.

[0539] Step 3:

[0540] The server receives data sent from the terminal and stores each piece of data in an analysis preparation queue. The input is the received data package, which is then formatted into an appropriate analysis format and output. Specifically, the data is structured using a Python script and transformed to facilitate processing by the AI ​​model.

[0541] Step 4:

[0542] The server begins analyzing biometric data using a generative AI model. Structured biometric information is used as input data, and the AI ​​model analyzes it to numerically evaluate the user's health status. The output includes deviations from normal ranges for heart rate and blood pressure. TensorFlow software is used for advanced numerical processing.

[0543] Step 5:

[0544] The server then analyzes the image data of the food. The input for this step is structured photographic data. The generative AI model uses image recognition techniques to identify the types of food and then calculates their nutritional components and calories. The output provides a list of foods, their calorie counts, and detailed nutritional information.

[0545] Step 6:

[0546] The server develops personalized health guidelines for each user based on biometric information and image analysis results. Input consists of biometric evaluation results and image analysis results, which an AI model combines to output optimal health guidelines. These guidelines include suggestions for dietary improvements and necessary exercises.

[0547] Step 7:

[0548] The server notifies the device in real time of the established health guidelines. The input here is the generated health guidelines, which are then pushed to the device as output. Information is instantly sent to the user's device using Firebase Cloud Messaging (FCM), etc.

[0549] Step 8:

[0550] Users implement the provided health guidelines and send feedback on their results and effectiveness from their device to the server. The input here is the user feedback data, and the output is this data which is then fed back into the AI ​​model to improve the accuracy of the analysis.

[0551] (Application Example 1)

[0552] 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."

[0553] In personal health management, it is crucial to comprehensively analyze daily biological information and dietary content to provide appropriate health guidance in real time. However, current health management systems often make it difficult for users to fully understand their own health status and take prompt action. Furthermore, there is a lack of systems that allow users to grasp detailed individual nutritional information and receive daily applicable guidance for health management at home. As a result, many users are unable to immediately improve their health in their daily lives and are likely to miss opportunities to address health deterioration early.

[0554] 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.

[0555] In this invention, the server includes means for analyzing biometric and image information received from the device, means for evaluating the user's health status based on the analysis results and generating appropriate health guidelines, and means for monitoring the user's health status within the home and providing suggestions. This enables the user to instantly understand their own health status and take concrete and effective actions in their daily life based on health guidelines.

[0556] "Device" refers to a terminal used to acquire user biometric information and food image information and transmit it to the server.

[0557] "Receiving" refers to the process where the server acquires data sent by the user and begins internal processing.

[0558] "Biometric information" refers to information that includes data such as heart rate, blood pressure, and body temperature, which indicate the user's health status.

[0559] "Image information" refers to image data of meals consumed by the user, and is used for analyzing the contents of the meal.

[0560] "Analysis" refers to evaluating the user's health and nutritional status based on received biometric and image data.

[0561] "Assessing health status and generating health guidelines" refers to the procedure for analyzing the user's health status based on the analysis results and suggesting desirable lifestyle habits.

[0562] "Notifying the device" means sending the generated health guidelines to the user's individual device in real time, enabling the user to take immediate action.

[0563] "Monitoring the user's health status within the home and providing suggestions" means continuously collecting health information in the user's daily life and providing advice for appropriate health management.

[0564] "Analyzing food data and identifying nutritional information" refers to analyzing image information of meals to identify the types of food and the nutrients they contain, and then evaluating the user's dietary balance.

[0565] To implement this invention, a user terminal device, a server, and an AI model are used. First, the user device acquires biometric information and image information. The biometric information includes heart rate, blood pressure, and body temperature, and the image information includes photos of meals. This data is transmitted to the server via the terminal.

[0566] The server analyzes the received data using an AI model. Biometric information is used to assess health status, with the AI ​​model performing a numerical evaluation. Image information is used for food classification and extraction of nutritional information, and calorie intake and nutritional balance are calculated. Computer vision technology is used for this analysis. For example, images are preprocessed using OpenCV, and classification is performed using a pre-trained AI model with Keras.

[0567] Based on the analysis results, the server generates optimal health guidelines for the user. These guidelines include, for example, recommended foods to improve dietary content and suggestions for light exercise to manage stress. This information is notified to the user's device in real time, allowing the user to take immediate action in their daily life.

[0568] For example, if heart rate data rises above expectations, the server will send a message such as, "You might want to try yoga to relax." Also, if it determines that calorie intake is too high, it will suggest, "We recommend eating more vegetables at your next meal." An example of a prompt message would be, "Your heart rate is higher than average today. Try yoga to promote relaxation."

[0569] This system makes it easier for users to receive personalized advice based on their individual health data and to work towards maintaining and improving their health on a daily basis.

[0570] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0571] Step 1:

[0572] The user's device acquires biometric and image information. Input includes heart rate, blood pressure, body temperature, and photos of meals. This data is stored on the device and prepared as data packets. Specifically, data is collected using a smartwatch or digital camera.

[0573] Step 2:

[0574] The terminal sends the prepared data packets to the server. The input consists of data packets containing the user's biometric information and image information. These are sent to the server via the internet. Specifically, the terminal's communication module is used to transfer the data using a secure protocol (e.g., HTTPS).

[0575] Step 3:

[0576] The server runs an AI model to analyze the received data. It receives user biometric and image data as input. The biometric data is used to assess health status and is quantified by the AI ​​model. Image data is preprocessed using computer vision technology to identify food types and nutritional information. Specifically, an AI model built with Keras is used to generate the analysis results.

[0577] Step 4:

[0578] The server generates health guidelines based on the analysis results. The input is analyzed biological and dietary information. As output, it creates personalized health guidelines for each user. Specifically, it queries a database that references relaxation methods based on heart rate variability and dietary advice to improve nutritional balance, and generates prompt statements using a generation AI model.

[0579] Step 5:

[0580] The server notifies the user's device of the generated health guidelines. The output is real-time health guideline information. This is sent to the user's mobile device via push notification or email. Specifically, it uses a notification API to provide real-time feedback.

[0581] Step 6:

[0582] The user takes specific actions based on the health guidelines they receive. The input is the health guidelines sent from the server. The output is the user's actions, such as health improvement activities. Specifically, this could include seeing the application's notification and trying the recommended exercise or adding the suggested ingredients to the shopping list.

[0583] 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.

[0584] This invention is a system for providing highly personalized care that takes into account the emotional state of the user in managing their health. Specific embodiments are described below.

[0585] System Overview

[0586] In addition to its basic function of analyzing biometric and image information transmitted from the device, this system incorporates an emotion engine to comprehensively assess the user's physical and mental health. Users routinely use the device to acquire biometric information such as heart rate and body temperature, and provide image information related to their diet and lifestyle via a terminal.

[0587] Upon receiving this information, the server activates an emotion engine and analyzes data representing the user's emotional state. This quantifies emotions such as stress, happiness, and fatigue, adding a new perspective to health assessments. This analyzed data, combined with the user's biological state, forms the basis for an overall health assessment.

[0588] The server has the ability to generate health guidelines that reflect the user's emotional state and notify the user's device in real time. For example, if the server assesses the user's emotional state as "high stress," health guidelines will be provided, including guidance on relaxation-promoting exercises, music therapy, or meditation. Furthermore, if changes in emotional state show a certain pattern, fundamental lifestyle improvements may also be recommended.

[0589] Furthermore, user feedback is used as learning material for the emotion engine, creating a loop that improves the accuracy and personalization of future health guidelines. This allows the server to continuously evolve the system.

[0590] In the event that an abnormality is detected in the user's emotional state or biometric information, the server can suggest the use of an online medical consultation service, facilitating the user's rapid access to expert advice. Thus, this system incorporates not only traditional physiological health information but also an emotional health perspective, enabling comprehensive health management.

[0591] The following describes the processing flow.

[0592] Step 1:

[0593] Users use the device to measure vital data, collecting heart rate and body temperature. They also take photos of their meals and environment, and input emotional diaries and feedback through the device.

[0594] Step 2:

[0595] The device sends biometric information, image data, and emotion entries collected from the user to the server. Transfers can be performed periodically or manually by the user.

[0596] Step 3:

[0597] The server receives the transmitted information and first passes the biometric data to an analysis module to check for abnormalities in heart rate and body temperature.

[0598] Step 4:

[0599] The server uses image information to identify the type of food and performs image analysis to estimate its nutritional value and calories.

[0600] Step 5:

[0601] The server's emotion engine analyzes emotion diaries and feedback, and evaluates the user's emotional state through language and emotion cues. Based on this evaluation, the user's stress level and happiness are quantified.

[0602] Step 6:

[0603] The server integrates biometric information, dietary information, and emotional assessments to comprehensively evaluate the user's health status. Based on this, it generates health guidelines.

[0604] Step 7:

[0605] The server sends generated health guidelines to the device, notifying the user in real time. These guidelines include dietary improvements, appropriate exercise, and stress reduction strategies.

[0606] Step 8:

[0607] Users follow the notified health guidelines and take specific actions in their daily lives. Subsequent feedback is sent from the device to the server.

[0608] Step 9:

[0609] The server receives user feedback and uses it as training data for the emotion engine and analysis algorithm to improve accuracy in subsequent sessions.

[0610] Step 10:

[0611] If an anomaly is detected, the server will suggest that the user use the online medical consultation service and prompt them to contact a specialist according to the prescribed procedures.

[0612] (Example 2)

[0613] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0614] Traditional health management systems focus on assessments based on physiological indicators and do not adequately consider the impact of an individual's emotional state on their health. This makes comprehensive health management difficult, and there is a challenge in taking appropriate measures, particularly for health problems caused by stress and changes in emotional state.

[0615] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0616] In this invention, the server includes means for analyzing physiological indicators and image data received from the device, means for analyzing emotional states using a generative AI model to add a new perspective to health assessment, and means for notifying the individual's device of health guidelines in real time. This makes it possible to provide a comprehensive health assessment including emotional states and personalized health guidelines.

[0617] "Device" refers to hardware that measures an individual's physiological indicators and records them as data.

[0618] "Physiological indicators" refer to data that indicates an individual's health status, such as heart rate and body temperature.

[0619] "Image data" refers to data that visually records an individual's daily activities and diet.

[0620] "Analysis" refers to the process of performing pattern recognition and evaluation based on physiological indicators and image data.

[0621] A "generative AI model" refers to an artificial intelligence method that analyzes and predicts human emotions and health conditions from multiple perspectives.

[0622] "Emotional state" refers to psychological factors such as an individual's stress level, happiness level, and fatigue level.

[0623] "Health guidelines" refer to specific health behaviors and improvement measures recommended to individuals based on analysis results.

[0624] "Notifying the device in real time" means that the generated health guidelines are immediately transmitted to the user's personal mobile device or other device.

[0625] "Feedback data" refers to information provided by individuals, such as their reactions and requests regarding guidelines.

[0626] "Remote diagnosis by medical professionals" refers to a situation where, when an abnormality is detected, a medical professional uses communication technology to perform a diagnosis.

[0627] This invention is a system that comprehensively approaches user health management from both physiological indicators and emotional states. This enables personalized care that considers the user's psychological health in addition to conventional health management based on physiological indicators.

[0628] Hardware and software to be used:

[0629] Users wear wearable devices to measure their heart rate and body temperature, and transfer this data to their smartphones. Furthermore, they use their smartphone cameras to record image data of their meals and living environment.

[0630] The terminal is the user's smartphone, which uses Bluetooth communication to receive physiological indicators obtained from wearable devices and sends them to the server as a single data package along with image data.

[0631] The server analyzes the received physiological indicators and image data using machine learning algorithms. Here, a generative AI model is used to quantify the user's emotional state, generating new evaluation perspectives. The analysis results generate health guidelines as a basis for health assessment.

[0632] The generative AI model utilizes natural language processing and image recognition technologies to determine the user's psychological health status and reflect this in health guidelines. This model uses user feedback as learning material to improve the accuracy of future guidelines.

[0633] Specific example:

[0634] For example, if a user wants to manage their daily stress, they can use a device to measure their heart rate and provide images of their meals and work environment from their terminal. Based on this, the server can perform an analysis and suggest specific relaxation techniques tailored to their stress level.

[0635] An example of a prompt message for a generative AI model would be: "Based on the user's heart rate and image data, assess their current stress level and suggest appropriate relaxation methods."

[0636] In this way, the present invention aims to provide users with personalized health management and to achieve continuous system evolution.

[0637] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0638] Step 1:

[0639] The user wears a wearable device to measure their heart rate and body temperature. At the same time, they use their smartphone camera to take pictures of their food and living environment, and save this data to the device.

[0640] Input: User's physiological indicators (heart rate, body temperature), image data (meal intake, environment)

[0641] Output: Physiological data and image data stored on the device.

[0642] Step 2:

[0643] The terminal receives physiological data acquired from wearable devices using Bluetooth communication, integrates it with image data to build a data package, and transmits it to the server via a secure protocol.

[0644] Input: User's physiological data, image data

[0645] Output: Data package sent to the server

[0646] Step 3:

[0647] The server analyzes the received data package. First, it analyzes physiological data to assess the user's basic health status. Next, it uses image data to activate a generative AI model, creating prompts that quantify emotional states and performing further analysis.

[0648] Input: Data package (physiological data, image data)

[0649] Output: Analysis results (health assessment, emotional state)

[0650] Step 4:

[0651] Based on the analysis results, the server uses an AI model to generate personalized health guidelines for the user. These guidelines include specific lifestyle improvements and stress reduction suggestions tailored to the user's emotional state.

[0652] Input: Analysis results (health assessment, emotional state)

[0653] Output: Personalized health guidelines

[0654] Step 5:

[0655] The device notifies the user in real time of health guidelines transmitted from the server and displays them on the user's screen. The user can then practice health management based on the presented guidelines. The user can also input feedback into the device.

[0656] Input: Health indicators from the server, user feedback

[0657] Output: Display of health guidelines on the screen, saving of feedback

[0658] Step 6:

[0659] The server receives feedback and uses it as training material for the generated AI model. This improves the accuracy of subsequent analyses and guideline generation, enabling the system to evolve.

[0660] Input: User feedback

[0661] Output: Updated generative AI model, improved guideline generation accuracy.

[0662] (Application Example 2)

[0663] 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."

[0664] In modern society, individual health management is extremely important, but conventional technologies generally rely solely on biometric data for health assessments, resulting in a lack of comprehensive evaluations that consider the user's emotional state. Furthermore, there is a need for effective personalized care by comprehensively understanding the health status of citizens, but this has not yet been achieved.

[0665] 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.

[0666] In this invention, the server includes means for analyzing biometric and image information received from the device, means for evaluating the user's health status based on the analysis results and generating appropriate health guidelines, means for performing emotion analysis and quantifying the emotional state, means for adjusting the health guidelines in real time based on the quantified emotional state, and means for notifying the health guidelines to the user's terminal. This makes it possible to comprehensively evaluate the user's physical and mental state and provide health guidelines tailored to each individual.

[0667] "Biometric information received from a device" refers to data indicating the user's physical condition, such as heart rate and body temperature, and is acquired from wearable devices, etc.

[0668] "Image information" refers to image data that captures a user's facial expressions and lifestyle, and is acquired using an image sensor such as a camera.

[0669] "Means of analysis" refers to software and algorithms that process biometric and image information to analyze the user's health status.

[0670] "Means of evaluating health status" refers to a process of comprehensively assessing a user's physical and emotional health status based on analyzed data.

[0671] "Emotion analysis" is a technology that analyzes facial expressions and other characteristics from a user's image information, quantifies their emotional state, and handles it as data.

[0672] "Methods for quantifying emotional states" refer to methods for converting analyzed emotional data into mathematical values ​​that can be used in health guidelines.

[0673] A "means for adjusting health guidelines in real time" refers to a mechanism that instantly modifies health advice in response to changing biological information and emotional states, providing optimal care.

[0674] "Means of notifying users of health guidelines on their devices" refers to methods of sending the generated health guidelines to the user's smartphone or other digital devices.

[0675] This invention is a system for providing personalized health guidance in real time by utilizing a user's biometric and image information. The server receives biometric information such as heart rate and body temperature transmitted from wearable devices and smartphones, as well as image information including the user's facial expressions captured by a camera. This data is stored in a database and processed comprehensively by an analysis engine.

[0676] The server uses software libraries such as TensorFlow and OpenCV to analyze the received biometric and image information. This analysis assesses the user's health status and quantifies emotional states such as stress and happiness. By quantifying emotional states, the emotion engine can adjust health guidelines based on the user's emotions.

[0677] The generated health guidelines are notified to the user's device in real time. Using a smartphone or head-mounted display, the user receives the health guidelines and uses them as behavioral guidelines in their daily life. These health guidelines are automatically updated as needed and continuously improved based on user feedback and new data.

[0678] As a concrete example, the system enhances the experience of event participants by suggesting different relaxation exercises and activities based on the emotional state of users attending a community festival. Furthermore, if an abnormality is detected in the evaluation results, the system can suggest a remote diagnosis by a medical professional. This suggestion enables prompt action and supports the maintenance of health.

[0679] Examples of prompts to input into a generative AI model:

[0680] "We want to provide emotion-based health guidance at community events. Please propose a feedback system based on emotion analysis results."

[0681] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0682] Step 1:

[0683] Users collect biometric and image information using wearable devices and smartphones. This includes data such as heart rate and body temperature, and facial expressions are also recorded via camera. Input is data from sensors, and this information is sent to the cloud as output.

[0684] Step 2:

[0685] The server receives biometric and image data sent to the cloud. The received data is stored in a database, temporarily holding the necessary information. The input is data sent by the user, and the output is ready for analysis.

[0686] Step 3:

[0687] The server analyzes the received data using TensorFlow and OpenCV. Specifically, it evaluates the user's health status based on biometric information and quantifies their emotional state by analyzing facial expressions from image information. The input is data stored on the server, and the output is a health status evaluation and an emotional score.

[0688] Step 4:

[0689] The server generates health guidelines in real time based on quantified emotional states. Using the analysis results, it creates appropriate health advice and behavioral guidelines. The input is the emotional score and health assessment obtained in the previous step, and the output is the generated health guidelines.

[0690] Step 5:

[0691] The device receives health guidelines transmitted from the server and notifies the user. The notified guidelines are displayed on the user's smartphone or head-mounted display and provided as actionable advice. The input is health guidelines from the server, and the output is the notification to the user.

[0692] Step 6:

[0693] Users provide feedback based on the health guidelines provided. This feedback is used to generate future guidelines for the system, leading to continuous improvement. The input is user feedback, and the output is data updates to the system.

[0694] Step 7:

[0695] The server activates a function that suggests remote diagnosis by a medical professional if an anomaly is detected. Based on the anomaly data, it notifies the user of the importance of prompt action and presents options for connecting with a professional. The input is the analyzed anomaly data, and the output is a suggested notification.

[0696] 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.

[0697] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0698] 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.

[0699] [Fourth Embodiment]

[0700] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0701] 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.

[0702] 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).

[0703] 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.

[0704] 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.

[0705] 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).

[0706] 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.

[0707] 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.

[0708] 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.

[0709] 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.

[0710] 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.

[0711] 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.

[0712] 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".

[0713] This invention is a system for supporting personal health management, and is implemented by a program. Specific examples are shown below.

[0714] System Overview

[0715] This system is designed to receive biometric information and food images sent from the user's device. Users are required to periodically measure their vital data and take pictures of their meals. The terminal then collects this data and sends it to the server.

[0716] The server runs an AI model equipped with the ability to analyze received data. Based on biometric information, the server numerically evaluates the user's health status and identifies patterns and trends. It also calculates the types of food consumed and calorie intake from image information and analyzes the user's dietary balance. Based on the health status evaluation and dietary analysis, the server uses AI to formulate optimal health guidelines for each user.

[0717] The established health guidelines are notified to the user's device in real time from the server. This allows users to take immediate action in their daily lives to improve or maintain their health. For example, if the server detects an upward trend in heart rate, it may suggest simple exercises for stress relief. It can also identify vitamin deficiencies from dietary analysis and recommend foods to supplement them.

[0718] After implementing the provided guidelines, users send feedback to the system via their terminal regarding the effectiveness and feasibility of those guidelines. The server collects this feedback and retrains or adjusts the AI ​​model to improve the accuracy of future analyses and the quality of the guidelines.

[0719] Furthermore, if abnormalities are detected in the health assessment results, the server can suggest that the user utilize an online medical consultation service. This gives the user the opportunity to receive prompt and appropriate medical support.

[0720] The following describes the processing flow.

[0721] Step 1:

[0722] Users collect vital data using the device and upload photos of their meals to their terminal to monitor their health. This makes daily health information available in digital format.

[0723] Step 2:

[0724] The device sends collected vital data and photos of meals to a server. This transmission occurs periodically based on a set time schedule or can be triggered manually by the user.

[0725] Step 3:

[0726] The server prepares the received data for analysis. Specifically, it inputs vital data into the analysis module and passes meal photo data to the image analysis unit.

[0727] Step 4:

[0728] The server's AI model analyzes vital data, examining trends in heart rate and body temperature to assess for any abnormalities. This forms a basic profile of the user's health status.

[0729] Step 5:

[0730] The server analyzes photos of meals using image recognition technology to estimate the types of food, their nutrients, and calories. This information is used to evaluate whether the meal is healthy.

[0731] Step 6:

[0732] The server integrates the results of vital data analysis and dietary analysis to generate health guidelines tailored to the user's current health status. These guidelines include points for dietary improvement and recommended exercise plans.

[0733] Step 7:

[0734] The server generates health guidelines and sends them to the device, immediately notifying the user. The user checks the notification and takes action according to the guidelines to practice health management.

[0735] Step 8:

[0736] After implementing the guidelines, users send the results and feedback from their device to the server. This feedback is stored on the server to be used for future analyses.

[0737] Step 9:

[0738] The server uses the collected feedback data to retrain or adjust the AI ​​model. This continuously improves the accuracy of analysis and health guidance, providing users with a better service.

[0739] Step 10:

[0740] The server will suggest that the user undergo an online medical consultation if it detects an anomaly or deems it necessary. This suggestion aims to understand the user's health status and provide prompt medical support.

[0741] (Example 1)

[0742] 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".

[0743] In modern society, personal health management is a crucial issue, but busy daily lives often make it difficult to dedicate sufficient time to it. Traditional health management methods are time-consuming and cumbersome, and it is particularly difficult to obtain appropriate advice tailored to individual circumstances. Furthermore, opportunities to detect abnormalities early and receive appropriate medical support are often missed. A system is needed to solve these problems and provide effective health guidelines optimized for each individual's health condition in real time.

[0744] 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.

[0745] In this invention, the server includes means for transmitting personal biometric data measured by a terminal and image data of food captured by the terminal to the server; means for the server to evaluate the received biometric data using an AI model and numerically analyze the health status; and means for the server to process the received image data using an AI model and identify the type of food and its nutritional value. This enables rapid and personalized health support by providing health guidelines tailored to each individual user in real time and proposing telemedicine services when an anomaly is detected.

[0746] A "terminal" is a portable device used by an individual to measure and record data, and is equipped with the function to transmit biometric data and image data to a server.

[0747] A "server" is a computer system that has the central function of receiving data from terminals via a network, analyzing it, and generating health guidelines.

[0748] "Biometric data" refers to data that indicates an individual's physical condition, such as heart rate and blood pressure, and is used to evaluate their health status.

[0749] "Image data" refers to visual data that shows the contents of a user's meals, and is used to identify the type of food and its nutritional value.

[0750] An "AI model" is an algorithm based on machine learning technology that analyzes biometric and image data to evaluate health status or identify food products.

[0751] "Health guidelines" are suggestions for improving the health of individual users, generated by the server based on analysis results, and include specific advice on diet, exercise, and other topics.

[0752] "Feedback information" refers to information that users send to the server regarding their implementation status and the effectiveness of the health guidelines provided, and this data is used to improve the AI ​​model.

[0753] "Telemedicine services" are services that provide online diagnoses and advice from medical professionals as needed, based on the user's health condition.

[0754] This invention is a system for supporting individual health management, implemented through the cooperation of a server, terminal, and user. By integrating multiple technological elements, this system provides users with customized health guidelines, promoting the improvement and maintenance of their health status.

[0755] The terminals are wearable devices or personal digital assistants worn by users, used to measure biometric data such as heart rate and blood pressure, and to capture image data of meals. This data is collected by smartphones and other devices and transmitted to a server via wireless communication technology. The terminals used are devices equipped with iOS or Android operating systems.

[0756] The server stores and manages the received data and analyzes it using a generative AI model. The AI ​​model, built using machine learning libraries such as TensorFlow and PyTorch, evaluates the user's health status based on biometric data and performs nutritional analysis of their diet from image data. Based on the analysis results, the server develops personalized health guidelines for each user and transmits this information to the terminal in real time.

[0757] Users can utilize health guidelines displayed on their smartphones or tablets. For example, they may be recommended to consume foods that supplement nutrient deficiencies identified by the AI. Users can also provide feedback on the effectiveness and feasibility of the guidelines they followed, and the server uses this feedback to retrain the AI ​​model, improving the accuracy of future analysis results.

[0758] As a concrete example, consider a scenario where a user takes a photo of bread and fruit for breakfast, measures their heart rate, and sends the photo to a server. The server recognizes the type of food from the image and evaluates its nutritional value. Simultaneously, it analyzes the heart rate and, if it determines that the user is under high stress, can suggest exercises for relaxation.

[0759] An example of a prompt for a generative AI model is, "Evaluate the types and calorie content of the foods consumed for breakfast, and suggest exercises recommended when your heart rate is elevated." This allows users to incorporate specific actions based on their own health status into their daily lives.

[0760] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0761] Step 1:

[0762] The terminal's role is to measure biometric data and collect images of food. The main devices used are wearable devices capable of measuring heart rate and blood pressure, and smartphones for taking pictures of food. As input, it collects various vital data measured by the user and images of the food taken. As output, the terminal prepares to send this data to a server for later analysis.

[0763] Step 2:

[0764] The device converts the collected data into a specific format (e.g., JSON) and sends it to the server. Data transmission is performed using Wi-Fi or mobile data communication. The input consists of biometric and image data stored on the device, and the output is the transmission of this data as an organized data package to the server.

[0765] Step 3:

[0766] The server receives data sent from the terminal and stores each piece of data in an analysis preparation queue. The input is the received data package, which is then formatted into an appropriate analysis format and output. Specifically, the data is structured using a Python script and transformed to facilitate processing by the AI ​​model.

[0767] Step 4:

[0768] The server begins analyzing biometric data using a generative AI model. Structured biometric information is used as input data, and the AI ​​model analyzes it to numerically evaluate the user's health status. The output includes deviations from normal ranges for heart rate and blood pressure. TensorFlow software is used for advanced numerical processing.

[0769] Step 5:

[0770] The server then analyzes the image data of the food. The input for this step is structured photographic data. The generative AI model uses image recognition techniques to identify the types of food and then calculates their nutritional components and calories. The output provides a list of foods, their calorie counts, and detailed nutritional information.

[0771] Step 6:

[0772] The server develops personalized health guidelines for each user based on biometric information and image analysis results. Input consists of biometric evaluation results and image analysis results, which an AI model combines to output optimal health guidelines. These guidelines include suggestions for dietary improvements and necessary exercises.

[0773] Step 7:

[0774] The server notifies the device in real time of the established health guidelines. The input here is the generated health guidelines, which are then pushed to the device as output. Information is instantly sent to the user's device using Firebase Cloud Messaging (FCM), etc.

[0775] Step 8:

[0776] Users implement the provided health guidelines and send feedback on their results and effectiveness from their device to the server. The input here is the user feedback data, and the output is this data which is then fed back into the AI ​​model to improve the accuracy of the analysis.

[0777] (Application Example 1)

[0778] 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".

[0779] In personal health management, it is crucial to comprehensively analyze daily biological information and dietary content to provide appropriate health guidance in real time. However, current health management systems often make it difficult for users to fully understand their own health status and take prompt action. Furthermore, there is a lack of systems that allow users to grasp detailed individual nutritional information and receive daily applicable guidance for health management at home. As a result, many users are unable to immediately improve their health in their daily lives and are likely to miss opportunities to address health deterioration early.

[0780] 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.

[0781] In this invention, the server includes means for analyzing biometric and image information received from the device, means for evaluating the user's health status based on the analysis results and generating appropriate health guidelines, and means for monitoring the user's health status within the home and providing suggestions. This enables the user to instantly understand their own health status and take concrete and effective actions in their daily life based on health guidelines.

[0782] "Device" refers to a terminal used to acquire user biometric information and food image information and transmit it to the server.

[0783] "Receiving" refers to the process where the server acquires data sent by the user and begins internal processing.

[0784] "Biometric information" refers to information that includes data such as heart rate, blood pressure, and body temperature, which indicate the user's health status.

[0785] "Image information" refers to image data of meals consumed by the user, and is used for analyzing the contents of the meal.

[0786] "Analysis" refers to evaluating the user's health and nutritional status based on received biometric and image data.

[0787] "Assessing health status and generating health guidelines" refers to the procedure for analyzing the user's health status based on the analysis results and suggesting desirable lifestyle habits.

[0788] "Notifying the device" means sending the generated health guidelines to the user's individual device in real time, enabling the user to take immediate action.

[0789] "Monitoring the user's health status within the home and providing suggestions" means continuously collecting health information in the user's daily life and providing advice for appropriate health management.

[0790] "Analyzing food data and identifying nutritional information" refers to analyzing image information of meals to identify the types of food and the nutrients they contain, and then evaluating the user's dietary balance.

[0791] To implement this invention, a user terminal device, a server, and an AI model are used. First, the user device acquires biometric information and image information. The biometric information includes heart rate, blood pressure, and body temperature, and the image information includes photos of meals. This data is transmitted to the server via the terminal.

[0792] The server analyzes the received data using an AI model. Biometric information is used to assess health status, with the AI ​​model performing a numerical evaluation. Image information is used for food classification and extraction of nutritional information, and calorie intake and nutritional balance are calculated. Computer vision technology is used for this analysis. For example, images are preprocessed using OpenCV, and classification is performed using a pre-trained AI model with Keras.

[0793] Based on the analysis results, the server generates optimal health guidelines for the user. These guidelines include, for example, recommended foods to improve dietary content and suggestions for light exercise to manage stress. This information is notified to the user's device in real time, allowing the user to take immediate action in their daily life.

[0794] For example, if heart rate data rises above expectations, the server will send a message such as, "You might want to try yoga to relax." Also, if it determines that calorie intake is too high, it will suggest, "We recommend eating more vegetables at your next meal." An example of a prompt message would be, "Your heart rate is higher than average today. Try yoga to promote relaxation."

[0795] This system makes it easier for users to receive personalized advice based on their individual health data and to work towards maintaining and improving their health on a daily basis.

[0796] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0797] Step 1:

[0798] The user's device acquires biometric and image information. Input includes heart rate, blood pressure, body temperature, and photos of meals. This data is stored on the device and prepared as data packets. Specifically, data is collected using a smartwatch or digital camera.

[0799] Step 2:

[0800] The terminal sends the prepared data packets to the server. The input consists of data packets containing the user's biometric information and image information. These are sent to the server via the internet. Specifically, the terminal's communication module is used to transfer the data using a secure protocol (e.g., HTTPS).

[0801] Step 3:

[0802] The server runs an AI model to analyze the received data. It receives user biometric and image data as input. The biometric data is used to assess health status and is quantified by the AI ​​model. Image data is preprocessed using computer vision technology to identify food types and nutritional information. Specifically, an AI model built with Keras is used to generate the analysis results.

[0803] Step 4:

[0804] The server generates health guidelines based on the analysis results. The input is analyzed biological and dietary information. As output, it creates personalized health guidelines for each user. Specifically, it queries a database that references relaxation methods based on heart rate variability and dietary advice to improve nutritional balance, and generates prompt statements using a generation AI model.

[0805] Step 5:

[0806] The server notifies the user's device of the generated health guidelines. The output is real-time health guideline information. This is sent to the user's mobile device via push notification or email. Specifically, it uses a notification API to provide real-time feedback.

[0807] Step 6:

[0808] The user takes specific actions based on the health guidelines they receive. The input is the health guidelines sent from the server. The output is the user's actions, such as health improvement activities. Specifically, this could include seeing the application's notification and trying the recommended exercise or adding the suggested ingredients to the shopping list.

[0809] 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.

[0810] This invention is a system for providing highly personalized care that takes into account the emotional state of the user in managing their health. Specific embodiments are described below.

[0811] System Overview

[0812] In addition to its basic function of analyzing biometric and image information transmitted from the device, this system incorporates an emotion engine to comprehensively assess the user's physical and mental health. Users routinely use the device to acquire biometric information such as heart rate and body temperature, and provide image information related to their diet and lifestyle via a terminal.

[0813] Upon receiving this information, the server activates an emotion engine and analyzes data representing the user's emotional state. This quantifies emotions such as stress, happiness, and fatigue, adding a new perspective to health assessments. This analyzed data, combined with the user's biological state, forms the basis for an overall health assessment.

[0814] The server has the ability to generate health guidelines that reflect the user's emotional state and notify the user's device in real time. For example, if the server assesses the user's emotional state as "high stress," health guidelines will be provided, including guidance on relaxation-promoting exercises, music therapy, or meditation. Furthermore, if changes in emotional state show a certain pattern, fundamental lifestyle improvements may also be recommended.

[0815] Furthermore, user feedback is used as learning material for the emotion engine, creating a loop that improves the accuracy and personalization of future health guidelines. This allows the server to continuously evolve the system.

[0816] In the event that an abnormality is detected in the user's emotional state or biometric information, the server can suggest the use of an online medical consultation service, facilitating the user's rapid access to expert advice. Thus, this system incorporates not only traditional physiological health information but also an emotional health perspective, enabling comprehensive health management.

[0817] The following describes the processing flow.

[0818] Step 1:

[0819] Users use the device to measure vital data, collecting heart rate and body temperature. They also take photos of their meals and environment, and input emotional diaries and feedback through the device.

[0820] Step 2:

[0821] The device sends biometric information, image data, and emotion entries collected from the user to the server. Transfers can be performed periodically or manually by the user.

[0822] Step 3:

[0823] The server receives the transmitted information and first passes the biometric data to an analysis module to check for abnormalities in heart rate and body temperature.

[0824] Step 4:

[0825] The server uses image information to identify the type of food and performs image analysis to estimate its nutritional value and calories.

[0826] Step 5:

[0827] The server's emotion engine analyzes emotion diaries and feedback, and evaluates the user's emotional state through language and emotion cues. Based on this evaluation, the user's stress level and happiness are quantified.

[0828] Step 6:

[0829] The server integrates biometric information, dietary information, and emotional assessments to comprehensively evaluate the user's health status. Based on this, it generates health guidelines.

[0830] Step 7:

[0831] The server sends generated health guidelines to the device, notifying the user in real time. These guidelines include dietary improvements, appropriate exercise, and stress reduction strategies.

[0832] Step 8:

[0833] Users follow the notified health guidelines and take specific actions in their daily lives. Subsequent feedback is sent from the device to the server.

[0834] Step 9:

[0835] The server receives user feedback and uses it as training data for the emotion engine and analysis algorithm to improve accuracy in subsequent sessions.

[0836] Step 10:

[0837] If an anomaly is detected, the server will suggest that the user use the online medical consultation service and prompt them to contact a specialist according to the prescribed procedures.

[0838] (Example 2)

[0839] 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".

[0840] Traditional health management systems focus on assessments based on physiological indicators and do not adequately consider the impact of an individual's emotional state on their health. This makes comprehensive health management difficult, and there is a challenge in taking appropriate measures, particularly for health problems caused by stress and changes in emotional state.

[0841] 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.

[0842] In this invention, the server includes means for analyzing physiological indicators and image data received from the device, means for analyzing emotional states using a generative AI model to add a new perspective to health assessment, and means for notifying the individual's device of health guidelines in real time. This makes it possible to provide a comprehensive health assessment including emotional states and personalized health guidelines.

[0843] "Device" refers to hardware that measures an individual's physiological indicators and records them as data.

[0844] "Physiological indicators" refer to data that indicates an individual's health status, such as heart rate and body temperature.

[0845] "Image data" refers to data that visually records an individual's daily activities and diet.

[0846] "Analysis" refers to the process of performing pattern recognition and evaluation based on physiological indicators and image data.

[0847] A "generative AI model" refers to an artificial intelligence method that analyzes and predicts human emotions and health conditions from multiple perspectives.

[0848] "Emotional state" refers to psychological factors such as an individual's stress level, happiness level, and fatigue level.

[0849] "Health guidelines" refer to specific health behaviors and improvement measures recommended to individuals based on analysis results.

[0850] "Notifying the device in real time" means that the generated health guidelines are immediately transmitted to the user's personal mobile device or other device.

[0851] "Feedback data" refers to information provided by individuals, such as their reactions and requests regarding guidelines.

[0852] "Remote diagnosis by medical professionals" refers to a situation where, when an abnormality is detected, a medical professional uses communication technology to perform a diagnosis.

[0853] This invention is a system that comprehensively approaches user health management from both physiological indicators and emotional states. This enables personalized care that considers the user's psychological health in addition to conventional health management based on physiological indicators.

[0854] Hardware and software to be used:

[0855] Users wear wearable devices to measure their heart rate and body temperature, and transfer this data to their smartphones. Furthermore, they use their smartphone cameras to record image data of their meals and living environment.

[0856] The terminal is the user's smartphone, which uses Bluetooth communication to receive physiological indicators obtained from wearable devices and sends them to the server as a single data package along with image data.

[0857] The server analyzes the received physiological indicators and image data using machine learning algorithms. Here, a generative AI model is used to quantify the user's emotional state, generating new evaluation perspectives. The analysis results generate health guidelines as a basis for health assessment.

[0858] The generative AI model utilizes natural language processing and image recognition technologies to determine the user's psychological health status and reflect this in health guidelines. This model uses user feedback as learning material to improve the accuracy of future guidelines.

[0859] Specific example:

[0860] For example, if a user wants to manage their daily stress, they can use a device to measure their heart rate and provide images of their meals and work environment from their terminal. Based on this, the server can perform an analysis and suggest specific relaxation techniques tailored to their stress level.

[0861] An example of a prompt message for a generative AI model would be: "Based on the user's heart rate and image data, assess their current stress level and suggest appropriate relaxation methods."

[0862] In this way, the present invention aims to provide users with personalized health management and to achieve continuous system evolution.

[0863] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0864] Step 1:

[0865] The user wears a wearable device to measure their heart rate and body temperature. At the same time, they use their smartphone camera to take pictures of their food and living environment, and save this data to the device.

[0866] Input: User's physiological indicators (heart rate, body temperature), image data (meal intake, environment)

[0867] Output: Physiological data and image data stored on the device.

[0868] Step 2:

[0869] The terminal receives physiological data acquired from wearable devices using Bluetooth communication, integrates it with image data to build a data package, and transmits it to the server via a secure protocol.

[0870] Input: User's physiological data, image data

[0871] Output: Data package sent to the server

[0872] Step 3:

[0873] The server analyzes the received data package. First, it analyzes physiological data to assess the user's basic health status. Next, it uses image data to activate a generative AI model, creating prompts that quantify emotional states and performing further analysis.

[0874] Input: Data package (physiological data, image data)

[0875] Output: Analysis results (health assessment, emotional state)

[0876] Step 4:

[0877] Based on the analysis results, the server uses an AI model to generate personalized health guidelines for the user. These guidelines include specific lifestyle improvements and stress reduction suggestions tailored to the user's emotional state.

[0878] Input: Analysis results (health assessment, emotional state)

[0879] Output: Personalized health guidelines

[0880] Step 5:

[0881] The device notifies the user in real time of health guidelines transmitted from the server and displays them on the user's screen. The user can then practice health management based on the presented guidelines. The user can also input feedback into the device.

[0882] Input: Health indicators from the server, user feedback

[0883] Output: Display of health guidelines on the screen, saving of feedback

[0884] Step 6:

[0885] The server receives feedback and uses it as training material for the generated AI model. This improves the accuracy of subsequent analyses and guideline generation, enabling the system to evolve.

[0886] Input: User feedback

[0887] Output: Updated generative AI model, improved guideline generation accuracy.

[0888] (Application Example 2)

[0889] 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".

[0890] In modern society, individual health management is extremely important, but conventional technologies generally rely solely on biometric data for health assessments, resulting in a lack of comprehensive evaluations that consider the user's emotional state. Furthermore, there is a need for effective personalized care by comprehensively understanding the health status of citizens, but this has not yet been achieved.

[0891] 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.

[0892] In this invention, the server includes means for analyzing biometric and image information received from the device, means for evaluating the user's health status based on the analysis results and generating appropriate health guidelines, means for performing emotion analysis and quantifying the emotional state, means for adjusting the health guidelines in real time based on the quantified emotional state, and means for notifying the health guidelines to the user's terminal. This makes it possible to comprehensively evaluate the user's physical and mental state and provide health guidelines tailored to each individual.

[0893] "Biometric information received from a device" refers to data indicating the user's physical condition, such as heart rate and body temperature, and is acquired from wearable devices, etc.

[0894] "Image information" refers to image data that captures a user's facial expressions and lifestyle, and is acquired using an image sensor such as a camera.

[0895] "Means of analysis" refers to software and algorithms that process biometric and image information to analyze the user's health status.

[0896] "Means of evaluating health status" refers to a process of comprehensively assessing a user's physical and emotional health status based on analyzed data.

[0897] "Emotion analysis" is a technology that analyzes facial expressions and other characteristics from a user's image information, quantifies their emotional state, and handles it as data.

[0898] "Methods for quantifying emotional states" refer to methods for converting analyzed emotional data into mathematical values ​​that can be used in health guidelines.

[0899] A "means for adjusting health guidelines in real time" refers to a mechanism that instantly modifies health advice in response to changing biological information and emotional states, providing optimal care.

[0900] "Means of notifying users of health guidelines on their devices" refers to methods of sending the generated health guidelines to the user's smartphone or other digital devices.

[0901] This invention is a system for providing personalized health guidance in real time by utilizing a user's biometric and image information. The server receives biometric information such as heart rate and body temperature transmitted from wearable devices and smartphones, as well as image information including the user's facial expressions captured by a camera. This data is stored in a database and processed comprehensively by an analysis engine.

[0902] The server uses software libraries such as TensorFlow and OpenCV to analyze the received biometric and image information. This analysis assesses the user's health status and quantifies emotional states such as stress and happiness. By quantifying emotional states, the emotion engine can adjust health guidelines based on the user's emotions.

[0903] The generated health guidelines are notified to the user's device in real time. Using a smartphone or head-mounted display, the user receives the health guidelines and uses them as behavioral guidelines in their daily life. These health guidelines are automatically updated as needed and continuously improved based on user feedback and new data.

[0904] As a concrete example, the system enhances the experience of event participants by suggesting different relaxation exercises and activities based on the emotional state of users attending a community festival. Furthermore, if an abnormality is detected in the evaluation results, the system can suggest a remote diagnosis by a medical professional. This suggestion enables prompt action and supports the maintenance of health.

[0905] Examples of prompts to input into a generative AI model:

[0906] "We want to provide emotion-based health guidance at community events. Please propose a feedback system based on emotion analysis results."

[0907] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0908] Step 1:

[0909] Users collect biometric and image information using wearable devices and smartphones. This includes data such as heart rate and body temperature, and facial expressions are also recorded via camera. Input is data from sensors, and this information is sent to the cloud as output.

[0910] Step 2:

[0911] The server receives biometric and image data sent to the cloud. The received data is stored in a database, temporarily holding the necessary information. The input is data sent by the user, and the output is ready for analysis.

[0912] Step 3:

[0913] The server analyzes the received data using TensorFlow and OpenCV. Specifically, it evaluates the user's health status based on biometric information and quantifies their emotional state by analyzing facial expressions from image information. The input is data stored on the server, and the output is a health status evaluation and an emotional score.

[0914] Step 4:

[0915] The server generates health guidelines in real time based on quantified emotional states. Using the analysis results, it creates appropriate health advice and behavioral guidelines. The input is the emotional score and health assessment obtained in the previous step, and the output is the generated health guidelines.

[0916] Step 5:

[0917] The device receives health guidelines transmitted from the server and notifies the user. The notified guidelines are displayed on the user's smartphone or head-mounted display and provided as actionable advice. The input is health guidelines from the server, and the output is the notification to the user.

[0918] Step 6:

[0919] Users provide feedback based on the health guidelines provided. This feedback is used to generate future guidelines for the system, leading to continuous improvement. The input is user feedback, and the output is data updates to the system.

[0920] Step 7:

[0921] The server activates a function that suggests remote diagnosis by a medical professional if an anomaly is detected. Based on the anomaly data, it notifies the user of the importance of prompt action and presents options for connecting with a professional. The input is the analyzed anomaly data, and the output is a suggested notification.

[0922] 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.

[0923] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0924] 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.

[0925] 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.

[0926] 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.

[0927] 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.

[0928] 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.

[0929] 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.

[0930] 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."

[0931] 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.

[0932] 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.

[0933] 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.

[0934] 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.

[0935] 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.

[0936] 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.

[0937] 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.

[0938] 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.

[0939] 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.

[0940] 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.

[0941] 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.

[0942] 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.

[0943] The following is further disclosed regarding the embodiments described above.

[0944] (Claim 1)

[0945] Means for analyzing biological information and image information received from the device,

[0946] A means for evaluating the user's health status based on the analysis results and generating appropriate health guidelines,

[0947] A means of notifying users of health guidelines on their devices,

[0948] A system that includes this.

[0949] (Claim 2)

[0950] The system according to claim 1, further comprising means for receiving feedback information from users and updating the analysis means in order to improve the generated health guidelines.

[0951] (Claim 3)

[0952] The system according to claim 1, further comprising means for proposing remote diagnosis by a medical professional, and making such proposal to the user if an abnormality is found in the evaluation results.

[0953] "Example 1"

[0954] (Claim 1)

[0955] A means for transmitting personal biometric data measured by the device and image data of the food taken to a server,

[0956] A means of numerically analyzing health status by evaluating biometric data received by a server using an AI model,

[0957] A means of processing image data received by a server using an AI model to identify the type and nutritional value of food,

[0958] A means by which the server creates customized health guidelines for individual users based on the analyzed data,

[0959] A means of transmitting the created health guidelines to the user's device in real time,

[0960] A system that includes this.

[0961] (Claim 2)

[0962] The system according to claim 1, which receives user feedback information and updates the AI ​​model based on the received feedback to improve the accuracy of health guidelines.

[0963] (Claim 3)

[0964] The system according to claim 1, wherein the server detects an anomaly in the analysis results and proposes to the user the use of a telemedicine service.

[0965] "Application Example 1"

[0966] (Claim 1)

[0967] Means for analyzing biological information and image information received from the device,

[0968] A means for evaluating the user's health status based on the analysis results and generating appropriate health guidelines,

[0969] A means of notifying users of health guidelines on their information terminals,

[0970] A means of monitoring the user's health status within the home and providing suggestions,

[0971] A means of analyzing food data and identifying nutritional information,

[0972] A system that includes this.

[0973] (Claim 2)

[0974] The system according to claim 1, further comprising means for receiving feedback information from users and updating the analysis means in order to improve the generated health guidelines.

[0975] (Claim 3)

[0976] The system according to claim 1, further comprising means for proposing remote diagnosis by a medical professional, and making such proposal to the user if an abnormality is found in the evaluation results.

[0977] "Example 2 of combining an emotion engine"

[0978] (Claim 1)

[0979] Means for analyzing physiological indicators and image data received from the device,

[0980] A means for evaluating an individual's health status based on analysis results and generating appropriate health guidelines,

[0981] A method to analyze emotional states using generative AI models and add a new perspective to health assessment,

[0982] A means of notifying individuals of health guidelines in real time on their devices,

[0983] A system that includes this.

[0984] (Claim 2)

[0985] The system according to claim 1, further comprising means for receiving feedback data from individuals, updating the analysis means to improve the generated health guidelines, and realizing continuous system evolution.

[0986] (Claim 3)

[0987] The system according to claim 1, which proposes remote diagnosis by a medical professional and makes the proposal to the individual if an abnormality is found in the evaluation results.

[0988] "Application example 2 when combining with an emotional engine"

[0989] (Claim 1)

[0990] Means for analyzing biological information and image information received from the device,

[0991] A means for evaluating the user's health status based on the analysis results and generating appropriate health guidelines,

[0992] A means of performing emotion analysis and quantifying emotional states,

[0993] A means of adjusting health guidelines in real time based on quantified emotional states,

[0994] A means of notifying users of health guidelines on their devices,

[0995] A system that includes this.

[0996] (Claim 2)

[0997] The system according to claim 1, which receives feedback information from users and updates the analysis means and emotion analysis means in order to improve the generated health guidelines.

[0998] (Claim 3)

[0999] The system according to claim 1, further comprising means for proposing a remote diagnosis by a medical professional if an abnormality is detected, and making this proposal to the user based on the evaluation results. [Explanation of symbols]

[1000] 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 for analyzing biological information and image information received from the device, A means for evaluating the user's health status based on the analysis results and generating appropriate health guidelines, A means of notifying users of health guidelines on their information terminals, A means of monitoring the user's health status within the home and providing suggestions, A means of analyzing food data and identifying nutritional information, A system that includes this.

2. The system according to claim 1, further comprising means for receiving feedback information from users and updating the analysis means in order to improve the generated health guidelines.

3. The system according to claim 1, further comprising means for proposing remote diagnosis by a medical professional, and making such proposal to the user if an abnormality is found in the evaluation results.

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