system

Abstract

We provide the system. [Solution] A means of receiving vital data from a device that acquires medical data, A means of evaluating health risks by using a generative model to analyze received vital data, A means of generating personalized lifestyle improvement plans based on assessed health risks, A means of notifying an output device of personalized lifestyle improvement suggestions, 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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, the increase in the number of diabetes patients and those at risk is remarkable, and the health damage caused by its complications has become a social problem. However, there is a current situation where systems for individuals to grasp their own health status on a daily basis and appropriately improve their lifestyle habits have not been sufficiently popularized. Conventional health management systems mainly focus on an approach based on fragmented health data and it is difficult to provide solutions for long-term health maintenance. Therefore, it is required to realize the prevention and management of diabetes and related lifestyle diseases by providing individualized lifestyle improvement measures based on the analysis of continuous vital data.

Means for Solving the Problems

[0005] The present invention provides a system comprising means for receiving vital data from a device that acquires medical data; means for using a generative model to analyze the received vital data and evaluate health risks; means for generating personalized lifestyle improvement suggestions based on the evaluated health risks; and means for notifying an output device of the personalized lifestyle improvement suggestions. Furthermore, this system uses a model that predicts future vital trends by referring to past data when evaluating health risks, and generates specific improvement suggestions regarding diet, exercise, and sleep based on user data, thereby enabling users to efficiently and effectively prevent and manage lifestyle-related diseases, including diabetes.

[0006] A "device for acquiring medical data" is a device that measures the body's vital signs and health indicators and transmits that data to an external system.

[0007] "Vital data" refers to data on important physiological indicators related to maintaining life, such as heart rate, blood pressure, and blood glucose levels.

[0008] A "generative model" is an algorithm that uses machine learning or artificial intelligence techniques to learn patterns from past data and generate or predict new data.

[0009] "Assessing health risks" means determining the degree or likelihood of an individual's health condition based on certain criteria or algorithms.

[0010] A "lifestyle improvement plan" is a proposal that outlines specific behavioral guidelines, such as diet, exercise, and sleep, necessary to improve the user's health.

[0011] An "output device" is an interface device such as a display or speaker that effectively conveys processed data or information to the user.

[0012] "Referring to past data" means retrieving data that has already been acquired and stored, and using it for current or future decisions or analyses.

[0013] A "model for predicting future vital trends" is a mathematical or statistical model that uses existing data to predict future trends in changes in vital signs. [Brief explanation of the drawing]

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

Embodiments for Carrying Out the Invention

[0015] 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.

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

[0017] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

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

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

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

[0022] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0035] This invention comprises a system consisting of a device for acquiring medical data, a server, a terminal, and a user, thereby enabling the prevention and management of lifestyle-related diseases, including diabetes.

[0036] The user wears a device that acquires medical data, and daily vital data is collected. This device measures indicators such as heart rate and blood glucose levels and transmits the data to a terminal. The terminal receives this data and transmits it to a server.

[0037] The server stores and analyzes the received vital data. Using a generative model, it assesses current health status by considering past data and calculates health risks, including diabetes risk. This model is designed to predict future vital trends.

[0038] Based on the analysis results, the server creates personalized lifestyle improvement plans tailored to each user. For example, if an analysis of a user detects a tendency for blood sugar levels to rise, the server can suggest dietary restrictions and exercise programs.

[0039] The device receives lifestyle improvement suggestions sent from the server and notifies the user. The user can then review these suggestions on the device and aim to improve their health by implementing them on a daily basis.

[0040] This system continuously monitors and analyzes individual user vital data to support the prevention and management of lifestyle-related diseases, thereby reducing the management burden on users while achieving highly accurate health maintenance.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The user uses a medical data acquisition device to measure vital data such as heart rate and blood glucose levels. The device transmits this data to the terminal.

[0044] Step 2:

[0045] The terminal prepares to send the received vital data to the server. Here, the data format is adjusted to a format that the server can easily parse.

[0046] Step 3:

[0047] The server receives vital data transmitted from the terminal and records it in a database. This data is stored for analysis.

[0048] Step 4:

[0049] The server uses a generative model to analyze the received vital data and evaluate the current situation in conjunction with historical data. This analysis calculates health risks and predicts future vital trends.

[0050] Step 5:

[0051] Based on the analysis results, the server generates personalized lifestyle improvement plans for each user. These plans include specific suggestions regarding diet, exercise, sleep, and other factors.

[0052] Step 6:

[0053] The device receives personalized lifestyle improvement suggestions sent from the server.

[0054] Step 7:

[0055] Users can view lifestyle improvement suggestions through their device. Based on the suggestions they receive, they can adjust their daily action plans.

[0056] (Example 1)

[0057] 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."

[0058] Currently, many people suffer from lifestyle-related diseases, and their prevention and management are crucial. However, there is a lack of systems that can efficiently collect individuals' daily health data and provide appropriate and individually optimized lifestyle improvement plans based on that data. This makes it difficult to control the risk of lifestyle-related diseases, and therefore, a system that can more effectively and efficiently assess risks and propose improvements is needed.

[0059] 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.

[0060] In this invention, the server includes means for receiving information from a device that acquires biometric information, means for using a generation algorithm model to analyze the received information data and evaluate health risks, and means for generating personalized lifestyle improvement plans based on the evaluated health risks. This makes it possible to provide highly accurate health risk assessments based on individual vital data and personalized lifestyle improvement plans.

[0061] "Biometric information" refers to physical indicators such as heart rate and blood sugar levels that show an individual's health status.

[0062] A "device" is an instrument or apparatus worn by an individual to acquire biometric information.

[0063] "Information data" refers to digital data, including biometric information, collected by a device.

[0064] A "generative algorithm model" is a mathematical model used to assess health risks based on received informational data.

[0065] "Health risk" refers to an assessment indicator that shows the likelihood of developing lifestyle-related diseases or other health problems.

[0066] "Personalization" refers to providing tailored support based on the data and needs of a specific individual.

[0067] A "lifestyle improvement plan" is a set of specific guidelines regarding diet, exercise, and sleep proposed to improve an individual's health.

[0068] An "output device" is a digital device used to notify users of personalized lifestyle improvement suggestions.

[0069] This system begins with the user wearing a device that acquires medical data. The device, worn on the user's body, measures biometric information such as heart rate and blood glucose levels in real time. The device transmits this data to the user's terminal using communication technologies such as Bluetooth or Wi-Fi. The terminal receives the data and transmits the information to a server using a secure communication protocol.

[0070] The server stores the received information data and analyzes it using a generative algorithm model. This analysis takes into account the user's past health information and assesses health risks based on current biometric data. The generative algorithm model is optimized to generate personalized recommendations by comprehensively analyzing each user's health data.

[0071] Based on the analysis results, the server creates lifestyle improvement plans. These plans specifically include dietary restrictions, exercise programs, and sleep improvement strategies. These suggestions are individually optimized based on each user's data. The improvement plans generated by the server are then sent back to the terminal, which notifies the user.

[0072] This allows users to improve their health by reviewing and implementing suggested improvements in their daily lives. For example, a prompt such as "Analyze user A's heart rate and blood glucose data to generate future health risks and improvement suggestions" can be used. This prompt allows the system to provide personalized suggestions based on the specified user's health data.

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] The user wears a device that acquires medical data. The device collects biometric information such as heart rate and blood glucose levels in real time. The input is biometric information obtained from the user's body, and the output is this raw data. The measured data is transmitted to a terminal via Bluetooth or Wi-Fi.

[0076] Step 2:

[0077] The terminal receives biometric information transmitted from the device. The input is raw data from the device, and the output is data stored on the terminal in the received format. The terminal organizes this data and prepares it for secure transmission to the server. During this process, the data is encrypted.

[0078] Step 3:

[0079] The terminal sends the received data to the server. The input here is encrypted vital data, and the output is the data received on the server side. The terminal transfers the data to the server via a secure communication protocol. This function is necessary to maintain the confidentiality and integrity of the data.

[0080] Step 4:

[0081] The server stores the received data in storage and prepares it for analysis. The input is data acquired from the terminal, and the output is information stored in the database in a format suitable for analysis. The data is organized by user, and specific actions are taken to create indexes for future reference and analysis.

[0082] Step 5:

[0083] The server uses a generative AI model to analyze data. The input is biometric information prepared for analysis, and the output is the result of an assessment of health risks. The server uses historical data as a reference and compares it with current data to identify risks and perform calculations to predict future health trends. This model is designed to learn data patterns based on the user's health status.

[0084] Step 6:

[0085] The server generates lifestyle improvement plans based on the analysis results. The input is the results of the health risk assessment, and the output is a personalized lifestyle improvement plan. This includes dietary revisions, exercise guidelines, and sleep advice, generating specific suggestions tailored to the user's personal information.

[0086] Step 7:

[0087] The server sends improvement suggestions to the terminal. The input is the generated improvement suggestions, and the output is the specific instructions that arrive at the user's terminal. The server sends this to the terminal using a standard communication protocol, making it immediately accessible to the user.

[0088] Step 8:

[0089] The device notifies the user of improvement suggestions. The input is personalized improvement suggestions received from the server, and the output is notification information received by the user on the device. This allows the user to incorporate the suggested improvements into their daily life and improve their health. Specifically, the device utilizes notification pop-ups and reminder functions to allow the user to easily access the improvement suggestions.

[0090] (Application Example 1)

[0091] 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."

[0092] In modern healthcare systems, personalized health management is essential for the prevention and control of lifestyle-related diseases. However, conventional methods involve uniform analysis of vital data, making it difficult to provide highly accurate, personalized recommendations. Furthermore, delays in notifying users of the results after data analysis often prevented prompt action. As a result, users were unable to intervene at the appropriate time, making it difficult to prevent the deterioration of their health.

[0093] 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.

[0094] In this invention, the server includes means for receiving biometric data from a device that acquires medical data, means for using a generative model to analyze the received biometric data and assess health risks, and means for sending immediate notifications when risks are detected. This enables personalized and rapid health management, and allows for the efficient prevention and improvement of lifestyle-related diseases.

[0095] "Medical data" refers to information related to an individual's health status and bodily functions.

[0096] "Biometric data" refers to numerical information that indicates an individual's physical condition, such as heart rate and blood sugar levels.

[0097] A "generative model" is an algorithm or program used to predict future data trends based on past data.

[0098] "Health risk" refers to an indicator that assesses the likelihood of developing illness or health deterioration.

[0099] A "habit improvement plan" refers to actions or guidelines proposed to improve a user's lifestyle and maintain or improve their health.

[0100] An "output mechanism" refers to the means or devices used by a system to provide information to a user.

[0101] "Immediate notification" refers to a message or warning that immediately conveys relevant information to the user when a specific event is detected.

[0102] "User data" refers to various vital data and behavioral information collected from individual users.

[0103] "Physical activity" refers to physical exercise and activities related to fitness.

[0104] "Rest" refers to physical and mental relaxation and a break.

[0105] This invention is a system that supports personalized prevention and management of lifestyle-related diseases based on medical data. The server receives biometric data measured by a device that acquires medical data from the user. The entire system consists of a smartphone, a generative model, a server, and a terminal including an output mechanism.

[0106] The server analyzes the received biometric data in real time using TENSORFLOW® and assesses health risks using a generative model. Based on this assessment, it creates a list of lifestyle improvement suggestions for the user and immediately notifies the device of the details. The device then presents this information to the user through a dashboard.

[0107] For example, if a user's heart rate suddenly increases while running, this system immediately detects the risk and sends a message prompting them to pause their exercise and take an appropriate rest. In this process, the server and terminal rapidly communicate with various hardware components using React Native, Flask, and TensorFlow to deliver the best possible advice to the user.

[0108] An example of a prompt message would be a specific instruction such as, "Detect anomalies from the user's vital data and generate details and countermeasures." By utilizing this generative AI model, continuous personalized health management becomes possible.

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] The user wears a device that acquires medical data, recording biometric data on a daily basis. The device measures heart rate, blood glucose levels, etc., and transmits this data to a terminal. The input is biometric data from the device, and the output is the transmission of biometric data to the terminal. Data collection is carried out through this process.

[0112] Step 2:

[0113] The terminal transfers the received biometric data to the server. The server receives this data and prepares it for analysis. The input is the biometric data from the terminal, and the output is the data transfer to the server. Here, data transfer and storage take place.

[0114] Step 3:

[0115] The server starts analyzing biometric data using TensorFlow. Based on a generative model, it assesses health risks by referencing historical data. The input is accumulated biometric data, and the output is the result of the risk assessment. Health risks are calculated through data computation.

[0116] Step 4:

[0117] Based on the evaluation results, the server uses a generative AI model to create prompt messages and generate suggestions for habit improvement for the user. The input is the result of the risk assessment, and the output is the prompt message for the improvement suggestion. This step generates specific improvement suggestions.

[0118] Step 5:

[0119] The server sends the generated habit improvement suggestions to the device. The device displays a notification to the user, allowing them to review the risk assessment results and suggestions via a dashboard. The input is the improvement suggestions from the server, and the output is the notification to the user. This is where the notification is generated and sent.

[0120] Step 6:

[0121] Based on the information received from the device, users implement suggested measures in their daily lives. The input is suggested information from the device, and the output is the user's behavioral changes. Ultimately, this promotes data-driven behavioral improvement.

[0122] 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.

[0123] This invention provides a new approach to user health management, enabling personalized lifestyle improvements by constructing a system consisting of a device for acquiring medical data, an emotion engine, a server, a terminal, and the user.

[0124] The user uses a medical data acquisition device to measure vital data, such as heart rate and blood glucose levels. This data is sent to a terminal, which then sends the data to a server. The terminal is also equipped with a camera and microphone, and an emotion engine runs to recognize emotions from the user's facial expressions and voice.

[0125] The server receives vital data and emotional state data transmitted from the terminal and stores them in a database. The generative model within the server assesses the user's health risks based on the vital data and predicts future vital trends by referencing past data. It also uses emotional data provided by the emotion engine to perform analysis that takes the user's emotional state into account.

[0126] This allows the server to generate personalized lifestyle improvement plans. In particular, by incorporating suggestions based on emotional state, the server provides improvement plans that are easy for the user to implement. For example, it may recommend activities that promote relaxation during periods of high stress, providing instructions that are appropriate to the user's current psychological state.

[0127] The device receives personalized lifestyle improvement suggestions sent from the server and notifies the user visually or audibly. The user can review the suggestions through the device's interface and adjust their daily life to improve their health.

[0128] In this way, this system enables more precise and personalized health management by linking the user's vital data with their emotional state. It is an important tool that supports effective health maintenance while reducing the burden on the user.

[0129] The following describes the processing flow.

[0130] Step 1:

[0131] The user wears a medical data acquisition device to collect vital data such as blood glucose levels and heart rate. This device transmits the measured data to a terminal.

[0132] Step 2:

[0133] The terminal formats the received vital data and sends it to the server. During this process, the emotion engine installed in the terminal analyzes the user's facial expressions and voice, and also acquires emotional state data.

[0134] Step 3:

[0135] The server receives vital data and emotional state data transmitted from the terminal. This data is stored in a database.

[0136] Step 4:

[0137] The server inputs stored data into a generative model to assess the user's health risks. This assessment includes a process of referencing historical data to predict future vital trends.

[0138] Step 5:

[0139] The server generates personalized lifestyle improvement suggestions based on assessed health risk and emotional state data. These suggestions include recommendations tailored to the user's psychological state and are designed with feasibility in mind.

[0140] Step 6:

[0141] The device receives personalized lifestyle improvement suggestions sent from the server and displays them to the user. These suggestions are notified visually or audibly.

[0142] Step 7:

[0143] Users can review lifestyle improvement suggestions through their devices and apply them to their own lives. This process aims to reduce health risks and improve their daily routines.

[0144] (Example 2)

[0145] 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".

[0146] In modern society, individual health management is crucial, but traditional methods have made it difficult to improve lifestyle habits appropriately while considering an individual's psychological state. Furthermore, in the analysis of health information, individual data has not been fully utilized, resulting in a lack of prediction of future health risks and corresponding countermeasures.

[0147] 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.

[0148] In this invention, the server includes means for receiving health information from a device that acquires biometric information, means for analyzing the received health information from multiple perspectives and evaluating health risks based on psychological state, means for generating personalized lifestyle improvement plans based on the evaluated health risks and psychological state, and means for notifying an output device of the personalized lifestyle improvement plans. This makes it possible to take into account the individual's psychological state and to make effective and personalized lifestyle improvements in response to future health risks.

[0149] A "device that acquires biometric information" refers to a device that measures and collects health-related data such as a user's heart rate and blood glucose levels.

[0150] "Receiving health information" refers to the process of receiving the user's physiological indicators transmitted from a medical data acquisition device in digital format.

[0151] "Multifaceted analysis" refers to the process of analyzing received data in detail from different perspectives to identify correlations and trends.

[0152] "Psychological state" refers to information that represents the user's mental and emotional state, such as their feelings and stress levels.

[0153] "Assessing health risks" refers to estimating potential dangers and problems in a user's current health condition based on their health information.

[0154] "Generating personalized lifestyle improvement plans" refers to creating specific lifestyle modification suggestions tailored to each user, based on their health assessment results.

[0155] "Output device" refers to an electronic device or application used to notify the user of the generated improvement suggestions.

[0156] "Health information" refers to all health-related data, such as the user's heart rate, blood sugar levels, and emotional state.

[0157] "Future health risks" refer to potential health problems or challenges that can be predicted based on current health data.

[0158] This invention comprises a system that continuously monitors a user's health information and provides personalized lifestyle improvement suggestions based on that information. Specifically, it uses a medical data acquisition device for acquiring biometric information, a terminal for analyzing the user's psychological state, and a server for processing and analyzing the data.

[0159] Users measure vital data using wearable devices and home medical equipment. This includes indicators such as heart rate and blood glucose levels. The devices (smartphones, tablets, etc.) are equipped with cameras and microphones, and an emotion engine operates to analyze the user's psychological state by capturing their facial expressions and recording their voice.

[0160] This data is transmitted to the server via the terminal and stored in a database on the server. The server uses a generative AI model to analyze the data from multiple perspectives, assess the user's health risks, and predict future health trends by referring to past data. Furthermore, by inputting prompts into the generative AI model, a process is initiated to generate personalized lifestyle improvement suggestions. These suggestions may include improvements tailored to the user's psychological state, such as suggesting activities to promote relaxation based on their stress levels.

[0161] The device notifies the user of lifestyle improvement suggestions received from the server. These notifications are sent via on-screen messages and voice instructions, and the user reviews them and selects improvements that can be implemented in their daily life.

[0162] For example, if a user's heart rate is higher than normal and the emotional analysis indicates a high-stress state, the generative AI model will generate a suggestion to "recommend doing yoga twice a week to promote relaxation." This suggestion will be communicated to the user via text or voice.

[0163] An example of a prompt message is, "Based on heart rate and emotional data, please output personalized lifestyle improvement suggestions to promote relaxation." This ensures that appropriate suggestions are provided that reflect the user's psychological state.

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

[0165] Step 1:

[0166] The user wears a medical data acquisition device to collect vital data such as heart rate and blood glucose levels. The input is data measured by biosensors. The output is the acquired vital data. This data is transmitted to the terminal using Bluetooth or Wi-Fi. Specifically, the user puts on the device and presses a button to start measurement.

[0167] Step 2:

[0168] The device uses its built-in camera and microphone to capture the user's facial expressions and voice, and analyzes their psychological state using an emotion engine. Input consists of image data from the camera and audio data from the microphone. Output is the analyzed psychological state data. Specifically, the camera captures the user's face for a few seconds and records audio, allowing for real-time analysis.

[0169] Step 3:

[0170] The terminal transmits acquired vital data and psychological state data to the server. Input consists of various data acquired by the terminal. Output consists of data packets sent to the server. In its specific operation, network synchronization is performed for data transmission.

[0171] Step 4:

[0172] The server stores data received from terminals in a database and performs multi-dimensional analysis of the data using a generative AI model. Inputs are vital data and psychological state data transmitted from the terminals. Outputs are assessed health risks and predicted health trends. Data processing includes data cleansing and normalization, followed by adaptive model analysis.

[0173] Step 5:

[0174] The server generates personalized lifestyle improvement suggestions from a generating AI model using prompt statements. The input is the evaluated health risk and the prompt statement. The output is personalized lifestyle improvement suggestions. Specifically, the AI ​​model generates analysis results and creates improvement suggestions according to the prompts.

[0175] Step 6:

[0176] The device notifies the user of lifestyle improvement suggestions received from the server. The input is the improvement suggestions sent from the server. The output is the lifestyle improvement suggestions notified to the user. Specifically, a pop-up notification or voice guidance is displayed on the device to allow the user to confirm the information.

[0177] (Application Example 2)

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

[0179] In health management, personalized advice is needed that takes into account not only the user's biometric information but also their emotional state. However, conventional systems have struggled to comprehensively analyze a user's biometric information and emotional state in real time and provide personalized training advice. In particular, the lack of methods to provide immediate feedback during sustained exercise has resulted in a problem where users cannot improve their health at an optimal pace.

[0180] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0181] In this invention, the server includes means for receiving biometric information from a device that acquires medical data, means for evaluating health risks using a generative model to analyze the received biometric information, and means for estimating emotional state with an image processing function for analyzing the user's facial images. This makes it possible to provide training advice in real time based on physical information and emotional state.

[0182] A "device for acquiring medical data" is a device that measures biometric information such as the user's heart rate and blood glucose levels.

[0183] "Biometric information" refers to data that quantitatively indicates the user's physical condition, such as heart rate and blood glucose levels.

[0184] A "generative model" is an algorithm that uses past biometric data to predict and analyze a user's health risks.

[0185] "Assessing health risks" means predicting potential health problems that may occur in the future based on the user's biometric information.

[0186] A "lifestyle improvement plan" is a set of specific action suggestions to reduce health risks, and is adjusted according to the user's health condition.

[0187] "Personalization" means providing optimal improvement suggestions by taking into account each user's biometric information and emotional state.

[0188] An "output device" is a device that presents the generated lifestyle improvement suggestions to the user.

[0189] "Analyzing user facial images" is the process of evaluating a user's emotional state from images of their face.

[0190] "Inferring emotional state" means analyzing the user's psychological state at a given time using their facial expression data.

[0191] "Image processing functionality" refers to technology that analyzes a user's facial expressions and processes their content as digital data.

[0192] "Providing training advice in real time" means providing immediate feedback based on the user's biometric information and emotional state during exercise.

[0193] The system realized by this invention acquires biometric information from the user and provides personalized training advice based on that information. The biometric information is acquired through a device that acquires medical data and transmitted as a signal to a terminal. The terminal transfers this data to a server, where a generative AI model evaluates health risks. Part of the generative AI model uses the user's past biometric data to predict future biometric trends. The results of this analysis form the basis for generating specific advice.

[0194] The device is equipped with a camera that captures images of the user's face and sends them to the server. The server uses image processing capabilities to estimate the user's emotional state based on this image data. Tools such as OpenCV are used for specific emotion analysis to obtain emotional data from facial expressions. By combining this emotional data with biometric information, it becomes possible to generate optimal training advice for the user in real time.

[0195] This advice is provided in real time via the display device of the smart glasses worn by the user. For example, if the user records a high heart rate and shows a tense expression, the system will immediately instruct the user to "switch to a walking pace, take deep breaths, and relax." This kind of immediate feedback based on biometric information and emotional state improves the user's efficiency and safety during exercise.

[0196] An example of a prompt message might be, "If the heart rate exceeds 120 and the user shows signs of tension, generate advice to encourage relaxation." Such prompt messages improve the overall operational accuracy of the system.

[0197] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0198] Step 1:

[0199] The terminal receives biometric information transmitted from a device that acquires medical data. This input data includes heart rate, blood glucose levels, and other parameters. The terminal then transfers this information to a server via Bluetooth communication.

[0200] Step 2:

[0201] The server stores the received biometric information and uses a generated AI model to assess the user's health risks. This process involves data calculations that refer to past biometric data to predict future vital trends. A health risk assessment report is generated as output.

[0202] Step 3:

[0203] The device captures an image of the user's face through its camera. This image data becomes the input. The device uses its image processing capabilities to perform facial expression analysis using OpenCV and estimate the emotional state. The output is the estimated emotional data.

[0204] Step 4:

[0205] The server integrates biometric and emotional data and utilizes a generative AI model to generate training advice in real time. Based on the instructions provided by these prompts, optimal action suggestions are output.

[0206] Step 5:

[0207] The user receives training advice transmitted from the server via smart glasses. This output is displayed as a visual notification, guiding the user on exercise and rest.

[0208] Step 6:

[0209] Based on the feedback, users adjust their training pace and methods. The results are then captured again as biometric data by the device and incorporated into a real-time feedback loop across the entire system.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] [Second Embodiment]

[0214] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0215] 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.

[0216] 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).

[0217] 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.

[0218] 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.

[0219] 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).

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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".

[0226] This invention comprises a system consisting of a device for acquiring medical data, a server, a terminal, and a user, thereby enabling the prevention and management of lifestyle-related diseases, including diabetes.

[0227] The user wears a device that acquires medical data, and daily vital data is collected. This device measures indicators such as heart rate and blood glucose levels and transmits the data to a terminal. The terminal receives this data and transmits it to a server.

[0228] The server stores and analyzes the received vital data. Using a generative model, it assesses current health status by considering past data and calculates health risks, including diabetes risk. This model is designed to predict future vital trends.

[0229] Based on the analysis results, the server creates personalized lifestyle improvement plans tailored to each user. For example, if an analysis of a user detects a tendency for blood sugar levels to rise, the server can suggest dietary restrictions and exercise programs.

[0230] The device receives lifestyle improvement suggestions sent from the server and notifies the user. The user can then review these suggestions on the device and aim to improve their health by implementing them on a daily basis.

[0231] This system continuously monitors and analyzes individual user vital data to support the prevention and management of lifestyle-related diseases, thereby reducing the management burden on users while achieving highly accurate health maintenance.

[0232] The following describes the processing flow.

[0233] Step 1:

[0234] The user uses a medical data acquisition device to measure vital data such as heart rate and blood glucose levels. The device transmits this data to the terminal.

[0235] Step 2:

[0236] The terminal prepares to send the received vital data to the server. Here, the data format is adjusted to a format that the server can easily parse.

[0237] Step 3:

[0238] The server receives vital data transmitted from the terminal and records it in a database. This data is stored for analysis.

[0239] Step 4:

[0240] The server uses a generative model to analyze the received vital data and evaluate the current situation in conjunction with historical data. This analysis calculates health risks and predicts future vital trends.

[0241] Step 5:

[0242] Based on the analysis results, the server generates personalized lifestyle improvement plans for each user. These plans include specific suggestions regarding diet, exercise, sleep, and other factors.

[0243] Step 6:

[0244] The device receives personalized lifestyle improvement suggestions sent from the server.

[0245] Step 7:

[0246] Users can view lifestyle improvement suggestions through their device. Based on the suggestions they receive, they can adjust their daily action plans.

[0247] (Example 1)

[0248] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0249] Currently, many people suffer from lifestyle-related diseases, and their prevention and management are crucial. However, there is a lack of systems that can efficiently collect individuals' daily health data and provide appropriate and individually optimized lifestyle improvement plans based on that data. This makes it difficult to control the risk of lifestyle-related diseases, and therefore, a system that can more effectively and efficiently assess risks and propose improvements is needed.

[0250] 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.

[0251] In this invention, the server includes means for receiving information from a device that acquires biometric information, means for using a generation algorithm model to analyze the received information data and evaluate health risks, and means for generating personalized lifestyle improvement plans based on the evaluated health risks. This makes it possible to provide highly accurate health risk assessments based on individual vital data and personalized lifestyle improvement plans.

[0252] "Biometric information" refers to physical indicators such as heart rate and blood sugar levels that show an individual's health status.

[0253] A "device" is an instrument or apparatus worn by an individual to acquire biometric information.

[0254] "Information data" refers to digital data, including biometric information, collected by a device.

[0255] A "generative algorithm model" is a mathematical model used to assess health risks based on received informational data.

[0256] "Health risk" refers to an assessment indicator that shows the likelihood of developing lifestyle-related diseases or other health problems.

[0257] "Personalization" refers to providing tailored support based on the data and needs of a specific individual.

[0258] A "lifestyle improvement plan" is a set of specific guidelines regarding diet, exercise, and sleep proposed to improve an individual's health.

[0259] An "output device" is a digital device used to notify users of personalized lifestyle improvement suggestions.

[0260] This system begins with the user wearing a device that acquires medical data. The device, worn on the user's body, measures biometric information such as heart rate and blood glucose levels in real time. The device transmits this data to the user's terminal using communication technologies such as Bluetooth or Wi-Fi. The terminal receives the data and transmits the information to a server using a secure communication protocol.

[0261] The server stores the received information data and analyzes it using a generative algorithm model. This analysis takes into account the user's past health information and assesses health risks based on current biometric data. The generative algorithm model is optimized to generate personalized recommendations by comprehensively analyzing each user's health data.

[0262] Based on the analysis results, the server creates lifestyle improvement plans. These plans specifically include dietary restrictions, exercise programs, and sleep improvement strategies. These suggestions are individually optimized based on each user's data. The improvement plans generated by the server are then sent back to the terminal, which notifies the user.

[0263] This allows users to improve their health by reviewing and implementing suggested improvements in their daily lives. For example, a prompt such as "Analyze user A's heart rate and blood glucose data to generate future health risks and improvement suggestions" can be used. This prompt allows the system to provide personalized suggestions based on the specified user's health data.

[0264] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0265] Step 1:

[0266] The user wears a device that acquires medical data. The device collects biometric information such as heart rate and blood glucose levels in real time. The input is biometric information obtained from the user's body, and the output is this raw data. The measured data is transmitted to a terminal via Bluetooth or Wi-Fi.

[0267] Step 2:

[0268] The terminal receives biometric information transmitted from the device. The input is raw data from the device, and the output is data stored on the terminal in the received format. The terminal organizes this data and prepares it for secure transmission to the server. During this process, the data is encrypted.

[0269] Step 3:

[0270] The terminal sends the received data to the server. The input here is encrypted vital data, and the output is the data received on the server side. The terminal transfers the data to the server via a secure communication protocol. This function is necessary to maintain the confidentiality and integrity of the data.

[0271] Step 4:

[0272] The server stores the received data in storage and prepares it for analysis. The input is data acquired from the terminal, and the output is information stored in the database in a format suitable for analysis. The data is organized by user, and specific actions are taken to create indexes for future reference and analysis.

[0273] Step 5:

[0274] The server uses a generative AI model to analyze data. The input is biometric information prepared for analysis, and the output is the result of an assessment of health risks. The server uses historical data as a reference and compares it with current data to identify risks and perform calculations to predict future health trends. This model is designed to learn data patterns based on the user's health status.

[0275] Step 6:

[0276] The server generates lifestyle improvement plans based on the analysis results. The input is the results of the health risk assessment, and the output is a personalized lifestyle improvement plan. This includes dietary revisions, exercise guidelines, and sleep advice, generating specific suggestions tailored to the user's personal information.

[0277] Step 7:

[0278] The server sends improvement suggestions to the terminal. The input is the generated improvement suggestions, and the output is the specific instructions that arrive at the user's terminal. The server sends this to the terminal using a standard communication protocol, making it immediately accessible to the user.

[0279] Step 8:

[0280] The device notifies the user of improvement suggestions. The input is personalized improvement suggestions received from the server, and the output is notification information received by the user on the device. This allows the user to incorporate the suggested improvements into their daily life and improve their health. Specifically, the device utilizes notification pop-ups and reminder functions to allow the user to easily access the improvement suggestions.

[0281] (Application Example 1)

[0282] 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."

[0283] In modern medical systems, individualized health management is required for the prevention and management of lifestyle diseases. However, with conventional methods, the analysis of vital data is uniform, making it difficult to provide highly accurate individualized proposals. Also, notifications of results after data analysis are often delayed, and prompt responses are often not possible. As a result, users were unable to intervene at appropriate times and it was difficult to prevent deterioration of their health status.

[0284] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0285] In this invention, the server includes means for receiving biological data from a device that acquires medical data, means for using a generation model to analyze the received biological data and evaluate the risk regarding the health state, and means for transmitting an immediate notification when a risk is detected. Thereby, individualized and prompt health management becomes possible, and it becomes possible to efficiently prevent and improve lifestyle diseases.

[0286] "Medical data" refers to information related to an individual's health status and body functions.

[0287] "Biological data" is numerical information indicating an individual's physical state such as heart rate and blood glucose level.

[0288] "Generation model" is an algorithm or program for predicting the trend of future data based on past data.

[0289] "Risk regarding the health state" is an index for evaluating the possibility of leading to illness or deterioration of health.

[0290] "Habits improvement plan" is an action or action guideline proposed to improve the user's lifestyle and maintain or improve health.

[0291] "Output mechanism" refers to means or devices for providing information from the system to the user.

[0292] "Immediate notification" refers to a message or warning that immediately conveys relevant information to the user when a specific event is detected.

[0293] "User data" refers to various vital data and behavioral information collected from individual users.

[0294] "Physical activity" refers to physical exercise and activities related to fitness.

[0295] "Rest" refers to physical and mental relaxation and a break.

[0296] This invention is a system that supports personalized prevention and management of lifestyle-related diseases based on medical data. The server receives biometric data measured by a device that acquires medical data from the user. The entire system consists of a smartphone, a generative model, a server, and a terminal including an output mechanism.

[0297] The server analyzes received biometric data in real time using TensorFlow and assesses health risks using a generative model. Based on this assessment, it creates suggestions for habit improvement for the user and immediately notifies the device of the details. The device then presents this information to the user through a dashboard.

[0298] For example, if a user's heart rate suddenly increases while running, this system immediately detects the risk and sends a message prompting them to pause their exercise and take an appropriate rest. In this process, the server and terminal rapidly communicate with various hardware components using React Native, Flask, and TensorFlow to deliver the best possible advice to the user.

[0299] Examples of prompt texts include specific instructions such as "Detect abnormalities from the user's vital data and generate details and countermeasures." By utilizing this generative AI model, continuously individualized health management becomes possible.

[0300] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0301] Step 1:

[0302] The user wears a device that acquires medical data and routinely records biometric data. The device measures items such as heart rate and blood glucose level and transmits the data to the terminal. The input is the biometric data from the device, and the output is the transmission of the biometric data to the terminal. Data collection is performed in this process.

[0303] Step 2:

[0304] The terminal transfers the received biometric data to the server. The server receives this data and prepares for analysis. The input is the biometric data from the terminal, and the output is the data transfer to the server. Here, data transfer and storage are performed.

[0305] Step 3:

[0306] The server starts analyzing the biometric data using TensorFlow. Based on the generative model, it evaluates the risk regarding the health condition while referring to past data. The input is the accumulated biometric data, and the output is the result of the risk assessment. The health risk is calculated through data operations.

[0307] Step 4:

[0308] Based on the evaluation result, the server uses the generative AI model to create a prompt text and generate improvement suggestions for the user. The input is the result of the risk assessment, and the output is the prompt text for the improvement suggestions. Specific improvement proposals are generated in this step.

[0309] Step 5:

[0310] The server sends the generated habit improvement suggestions to the device. The device displays a notification to the user, allowing them to review the risk assessment results and suggestions via a dashboard. The input is the improvement suggestions from the server, and the output is the notification to the user. This is where the notification is generated and sent.

[0311] Step 6:

[0312] Based on the information received from the device, users implement suggested measures in their daily lives. The input is suggested information from the device, and the output is the user's behavioral changes. Ultimately, this promotes data-driven behavioral improvement.

[0313] 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.

[0314] This invention provides a new approach to user health management, enabling personalized lifestyle improvements by constructing a system consisting of a device for acquiring medical data, an emotion engine, a server, a terminal, and the user.

[0315] The user uses a medical data acquisition device to measure vital data, such as heart rate and blood glucose levels. This data is sent to a terminal, which then sends the data to a server. The terminal is also equipped with a camera and microphone, and an emotion engine runs to recognize emotions from the user's facial expressions and voice.

[0316] The server receives vital data and emotional state data transmitted from the terminal and stores them in a database. The generative model within the server assesses the user's health risks based on the vital data and predicts future vital trends by referencing past data. It also uses emotional data provided by the emotion engine to perform analysis that takes the user's emotional state into account.

[0317] This allows the server to generate personalized lifestyle improvement plans. In particular, by incorporating suggestions based on emotional state, the server provides improvement plans that are easy for the user to implement. For example, it may recommend activities that promote relaxation during periods of high stress, providing instructions that are appropriate to the user's current psychological state.

[0318] The device receives personalized lifestyle improvement suggestions sent from the server and notifies the user visually or audibly. The user can review the suggestions through the device's interface and adjust their daily life to improve their health.

[0319] In this way, this system enables more precise and personalized health management by linking the user's vital data with their emotional state. It is an important tool that supports effective health maintenance while reducing the burden on the user.

[0320] The following describes the processing flow.

[0321] Step 1:

[0322] The user wears a medical data acquisition device to collect vital data such as blood glucose levels and heart rate. This device transmits the measured data to a terminal.

[0323] Step 2:

[0324] The terminal formats the received vital data and sends it to the server. During this process, the emotion engine installed in the terminal analyzes the user's facial expressions and voice, and also acquires emotional state data.

[0325] Step 3:

[0326] The server receives vital data and emotional state data transmitted from the terminal. This data is stored in a database.

[0327] Step 4:

[0328] The server inputs stored data into a generative model to assess the user's health risks. This assessment includes a process of referencing historical data to predict future vital trends.

[0329] Step 5:

[0330] The server generates personalized lifestyle improvement suggestions based on assessed health risk and emotional state data. These suggestions include recommendations tailored to the user's psychological state and are designed with feasibility in mind.

[0331] Step 6:

[0332] The device receives personalized lifestyle improvement suggestions sent from the server and displays them to the user. These suggestions are notified visually or audibly.

[0333] Step 7:

[0334] Users can review lifestyle improvement suggestions through their devices and apply them to their own lives. This process aims to reduce health risks and improve their daily routines.

[0335] (Example 2)

[0336] 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".

[0337] In modern society, individual health management is crucial, but traditional methods have made it difficult to improve lifestyle habits appropriately while considering an individual's psychological state. Furthermore, in the analysis of health information, individual data has not been fully utilized, resulting in a lack of prediction of future health risks and corresponding countermeasures.

[0338] 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.

[0339] In this invention, the server includes means for receiving health information from a device that acquires biometric information, means for analyzing the received health information from multiple perspectives and evaluating health risks based on psychological state, means for generating personalized lifestyle improvement plans based on the evaluated health risks and psychological state, and means for notifying an output device of the personalized lifestyle improvement plans. This makes it possible to take into account the individual's psychological state and to make effective and personalized lifestyle improvements in response to future health risks.

[0340] A "device that acquires biometric information" refers to a device that measures and collects health-related data such as a user's heart rate and blood glucose levels.

[0341] "Receiving health information" refers to the process of receiving the user's physiological indicators transmitted from a medical data acquisition device in digital format.

[0342] "Multifaceted analysis" refers to the process of analyzing received data in detail from different perspectives to identify correlations and trends.

[0343] "Psychological state" refers to information that represents the user's mental and emotional state, such as their feelings and stress levels.

[0344] "Assessing health risks" refers to estimating potential dangers and problems in a user's current health condition based on their health information.

[0345] "Generating personalized lifestyle improvement plans" refers to creating specific lifestyle modification suggestions tailored to each user, based on their health assessment results.

[0346] "Output device" refers to an electronic device or application used to notify the user of the generated improvement suggestions.

[0347] "Health information" refers to all health-related data, such as the user's heart rate, blood sugar levels, and emotional state.

[0348] "Future health risks" refer to potential health problems or challenges that can be predicted based on current health data.

[0349] This invention comprises a system that continuously monitors a user's health information and provides personalized lifestyle improvement suggestions based on that information. Specifically, it uses a medical data acquisition device for acquiring biometric information, a terminal for analyzing the user's psychological state, and a server for processing and analyzing the data.

[0350] Users measure vital data using wearable devices and home medical equipment. This includes indicators such as heart rate and blood glucose levels. The devices (smartphones, tablets, etc.) are equipped with cameras and microphones, and an emotion engine operates to analyze the user's psychological state by capturing their facial expressions and recording their voice.

[0351] This data is transmitted to the server via the terminal and stored in a database on the server. The server uses a generative AI model to analyze the data from multiple perspectives, assess the user's health risks, and predict future health trends by referring to past data. Furthermore, by inputting prompts into the generative AI model, a process is initiated to generate personalized lifestyle improvement suggestions. These suggestions may include improvements tailored to the user's psychological state, such as suggesting activities to promote relaxation based on their stress levels.

[0352] The device notifies the user of lifestyle improvement suggestions received from the server. These notifications are sent via on-screen messages and voice instructions, and the user reviews them and selects improvements that can be implemented in their daily life.

[0353] For example, if a user's heart rate is higher than normal and the emotional analysis indicates a high-stress state, the generative AI model will generate a suggestion to "recommend doing yoga twice a week to promote relaxation." This suggestion will be communicated to the user via text or voice.

[0354] An example of a prompt message is, "Based on heart rate and emotional data, please output personalized lifestyle improvement suggestions to promote relaxation." This ensures that appropriate suggestions are provided that reflect the user's psychological state.

[0355] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0356] Step 1:

[0357] The user wears a medical data acquisition device to collect vital data such as heart rate and blood glucose levels. The input is data measured by biosensors. The output is the acquired vital data. This data is transmitted to the terminal using Bluetooth or Wi-Fi. Specifically, the user puts on the device and presses a button to start measurement.

[0358] Step 2:

[0359] The device uses its built-in camera and microphone to capture the user's facial expressions and voice, and analyzes their psychological state using an emotion engine. Input consists of image data from the camera and audio data from the microphone. Output is the analyzed psychological state data. Specifically, the camera captures the user's face for a few seconds and records audio, allowing for real-time analysis.

[0360] Step 3:

[0361] The terminal transmits acquired vital data and psychological state data to the server. Input consists of various data acquired by the terminal. Output consists of data packets sent to the server. In its specific operation, network synchronization is performed for data transmission.

[0362] Step 4:

[0363] The server stores data received from terminals in a database and performs multi-dimensional analysis of the data using a generative AI model. Inputs are vital data and psychological state data transmitted from the terminals. Outputs are assessed health risks and predicted health trends. Data processing includes data cleansing and normalization, followed by adaptive model analysis.

[0364] Step 5:

[0365] The server generates personalized lifestyle improvement suggestions from a generating AI model using prompt statements. The input is the evaluated health risk and the prompt statement. The output is personalized lifestyle improvement suggestions. Specifically, the AI ​​model generates analysis results and creates improvement suggestions according to the prompts.

[0366] Step 6:

[0367] The device notifies the user of lifestyle improvement suggestions received from the server. The input is the improvement suggestions sent from the server. The output is the lifestyle improvement suggestions notified to the user. Specifically, a pop-up notification or voice guidance is displayed on the device to allow the user to confirm the information.

[0368] (Application Example 2)

[0369] 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."

[0370] In health management, personalized advice is needed that takes into account not only the user's biometric information but also their emotional state. However, conventional systems have struggled to comprehensively analyze a user's biometric information and emotional state in real time and provide personalized training advice. In particular, the lack of methods to provide immediate feedback during sustained exercise has resulted in a problem where users cannot improve their health at an optimal pace.

[0371] 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.

[0372] In this invention, the server includes means for receiving biometric information from a device that acquires medical data, means for evaluating health risks using a generative model to analyze the received biometric information, and means for estimating emotional state with an image processing function for analyzing the user's facial images. This makes it possible to provide training advice in real time based on physical information and emotional state.

[0373] A "device for acquiring medical data" is a device that measures biometric information such as the user's heart rate and blood glucose levels.

[0374] "Biometric information" refers to data that quantitatively indicates the user's physical condition, such as heart rate and blood glucose levels.

[0375] A "generative model" is an algorithm that uses past biometric data to predict and analyze a user's health risks.

[0376] "Assessing health risks" means predicting potential health problems that may occur in the future based on the user's biometric information.

[0377] A "lifestyle improvement plan" is a set of specific action suggestions to reduce health risks, and is adjusted according to the user's health condition.

[0378] "Personalization" means providing optimal improvement suggestions by taking into account each user's biometric information and emotional state.

[0379] An "output device" is a device that presents the generated lifestyle improvement suggestions to the user.

[0380] "Analyzing user facial images" is the process of evaluating a user's emotional state from images of their face.

[0381] "Inferring emotional state" means analyzing the user's psychological state at a given time using their facial expression data.

[0382] "Image processing functionality" refers to technology that analyzes a user's facial expressions and processes their content as digital data.

[0383] "Providing training advice in real time" means providing immediate feedback based on the user's biometric information and emotional state during exercise.

[0384] The system realized by this invention acquires biometric information from the user and provides personalized training advice based on that information. The biometric information is acquired through a device that acquires medical data and transmitted as a signal to a terminal. The terminal transfers this data to a server, where a generative AI model evaluates health risks. Part of the generative AI model uses the user's past biometric data to predict future biometric trends. The results of this analysis form the basis for generating specific advice.

[0385] The device is equipped with a camera that captures images of the user's face and sends them to the server. The server uses image processing capabilities to estimate the user's emotional state based on this image data. Tools such as OpenCV are used for specific emotion analysis to obtain emotional data from facial expressions. By combining this emotional data with biometric information, it becomes possible to generate optimal training advice for the user in real time.

[0386] This advice is provided in real time via the display device of the smart glasses worn by the user. For example, if the user records a high heart rate and shows a tense expression, the system will immediately instruct the user to "switch to a walking pace, take deep breaths, and relax." This kind of immediate feedback based on biometric information and emotional state improves the user's efficiency and safety during exercise.

[0387] An example of a prompt message might be, "If the heart rate exceeds 120 and the user shows signs of tension, generate advice to encourage relaxation." Such prompt messages improve the overall operational accuracy of the system.

[0388] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0389] Step 1:

[0390] The terminal receives biometric information transmitted from a device that acquires medical data. This input data includes heart rate, blood glucose levels, and other parameters. The terminal then transfers this information to a server via Bluetooth communication.

[0391] Step 2:

[0392] The server stores the received biometric information and uses a generated AI model to assess the user's health risks. This process involves data calculations that refer to past biometric data to predict future vital trends. A health risk assessment report is generated as output.

[0393] Step 3:

[0394] The device captures an image of the user's face through its camera. This image data becomes the input. The device uses its image processing capabilities to perform facial expression analysis using OpenCV and estimate the emotional state. The output is the estimated emotional data.

[0395] Step 4:

[0396] The server integrates biometric and emotional data and utilizes a generative AI model to generate training advice in real time. Based on the instructions provided by these prompts, optimal action suggestions are output.

[0397] Step 5:

[0398] The user receives training advice transmitted from the server via smart glasses. This output is displayed as a visual notification, guiding the user on exercise and rest.

[0399] Step 6:

[0400] Based on the feedback, users adjust their training pace and methods. The results are then captured again as biometric data by the device and incorporated into a real-time feedback loop across the entire system.

[0401] 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.

[0402] 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.

[0403] 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.

[0404] [Third Embodiment]

[0405] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0406] 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.

[0407] 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).

[0408] 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.

[0409] 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.

[0410] 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).

[0411] 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.

[0412] 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.

[0413] 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.

[0414] 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.

[0415] 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.

[0416] 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".

[0417] This invention comprises a system consisting of a device for acquiring medical data, a server, a terminal, and a user, thereby enabling the prevention and management of lifestyle-related diseases, including diabetes.

[0418] The user wears a device that acquires medical data, and daily vital data is collected. This device measures indicators such as heart rate and blood glucose levels and transmits the data to a terminal. The terminal receives this data and transmits it to a server.

[0419] The server stores and analyzes the received vital data. Using a generative model, it assesses current health status by considering past data and calculates health risks, including diabetes risk. This model is designed to predict future vital trends.

[0420] Based on the analysis results, the server creates personalized lifestyle improvement plans tailored to each user. For example, if an analysis of a user detects a tendency for blood sugar levels to rise, the server can suggest dietary restrictions and exercise programs.

[0421] The device receives lifestyle improvement suggestions sent from the server and notifies the user. The user can then review these suggestions on the device and aim to improve their health by implementing them on a daily basis.

[0422] This system continuously monitors and analyzes individual user vital data to support the prevention and management of lifestyle-related diseases, thereby reducing the management burden on users while achieving highly accurate health maintenance.

[0423] The following describes the processing flow.

[0424] Step 1:

[0425] The user uses a medical data acquisition device to measure vital data such as heart rate and blood glucose levels. The device transmits this data to the terminal.

[0426] Step 2:

[0427] The terminal prepares to send the received vital data to the server. Here, the data format is adjusted to a format that the server can easily parse.

[0428] Step 3:

[0429] The server receives vital data transmitted from the terminal and records it in a database. This data is stored for analysis.

[0430] Step 4:

[0431] The server uses a generative model to analyze the received vital data and evaluate the current situation in conjunction with historical data. This analysis calculates health risks and predicts future vital trends.

[0432] Step 5:

[0433] Based on the analysis results, the server generates personalized lifestyle improvement plans for each user. These plans include specific suggestions regarding diet, exercise, sleep, and other factors.

[0434] Step 6:

[0435] The device receives personalized lifestyle improvement suggestions sent from the server.

[0436] Step 7:

[0437] Users can view lifestyle improvement suggestions through their device. Based on the suggestions they receive, they can adjust their daily action plans.

[0438] (Example 1)

[0439] 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."

[0440] Currently, many people suffer from lifestyle-related diseases, and their prevention and management are crucial. However, there is a lack of systems that can efficiently collect individuals' daily health data and provide appropriate and individually optimized lifestyle improvement plans based on that data. This makes it difficult to control the risk of lifestyle-related diseases, and therefore, a system that can more effectively and efficiently assess risks and propose improvements is needed.

[0441] 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.

[0442] In this invention, the server includes means for receiving information from a device that acquires biometric information, means for using a generation algorithm model to analyze the received information data and evaluate health risks, and means for generating personalized lifestyle improvement plans based on the evaluated health risks. This makes it possible to provide highly accurate health risk assessments based on individual vital data and personalized lifestyle improvement plans.

[0443] "Biometric information" refers to physical indicators such as heart rate and blood sugar levels that show an individual's health status.

[0444] A "device" is an instrument or apparatus worn by an individual to acquire biometric information.

[0445] "Information data" refers to digital data, including biometric information, collected by a device.

[0446] A "generative algorithm model" is a mathematical model used to assess health risks based on received informational data.

[0447] "Health risk" refers to an assessment indicator that shows the likelihood of developing lifestyle-related diseases or other health problems.

[0448] "Personalization" refers to providing tailored support based on the data and needs of a specific individual.

[0449] A "lifestyle improvement plan" is a set of specific guidelines regarding diet, exercise, and sleep proposed to improve an individual's health.

[0450] An "output device" is a digital device used to notify users of personalized lifestyle improvement suggestions.

[0451] This system begins with the user wearing a device that acquires medical data. The device, worn on the user's body, measures biometric information such as heart rate and blood glucose levels in real time. The device transmits this data to the user's terminal using communication technologies such as Bluetooth or Wi-Fi. The terminal receives the data and transmits the information to a server using a secure communication protocol.

[0452] The server stores the received information data and analyzes it using a generative algorithm model. This analysis takes into account the user's past health information and assesses health risks based on current biometric data. The generative algorithm model is optimized to generate personalized recommendations by comprehensively analyzing each user's health data.

[0453] Based on the analysis results, the server creates lifestyle improvement plans. These plans specifically include dietary restrictions, exercise programs, and sleep improvement strategies. These suggestions are individually optimized based on each user's data. The improvement plans generated by the server are then sent back to the terminal, which notifies the user.

[0454] This allows users to improve their health by reviewing and implementing suggested improvements in their daily lives. For example, a prompt such as "Analyze user A's heart rate and blood glucose data to generate future health risks and improvement suggestions" can be used. This prompt allows the system to provide personalized suggestions based on the specified user's health data.

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

[0456] Step 1:

[0457] The user wears a device that acquires medical data. The device collects biometric information such as heart rate and blood glucose levels in real time. The input is biometric information obtained from the user's body, and the output is this raw data. The measured data is transmitted to a terminal via Bluetooth or Wi-Fi.

[0458] Step 2:

[0459] The terminal receives biometric information transmitted from the device. The input is raw data from the device, and the output is data stored on the terminal in the received format. The terminal organizes this data and prepares it for secure transmission to the server. During this process, the data is encrypted.

[0460] Step 3:

[0461] The terminal sends the received data to the server. The input here is encrypted vital data, and the output is the data received on the server side. The terminal transfers the data to the server via a secure communication protocol. This function is necessary to maintain the confidentiality and integrity of the data.

[0462] Step 4:

[0463] The server stores the received data in storage and prepares it for analysis. The input is data acquired from the terminal, and the output is information stored in the database in a format suitable for analysis. The data is organized by user, and specific actions are taken to create indexes for future reference and analysis.

[0464] Step 5:

[0465] The server uses a generative AI model to analyze data. The input is biometric information prepared for analysis, and the output is the result of an assessment of health risks. The server uses historical data as a reference and compares it with current data to identify risks and perform calculations to predict future health trends. This model is designed to learn data patterns based on the user's health status.

[0466] Step 6:

[0467] The server generates lifestyle improvement plans based on the analysis results. The input is the results of the health risk assessment, and the output is a personalized lifestyle improvement plan. This includes dietary revisions, exercise guidelines, and sleep advice, generating specific suggestions tailored to the user's personal information.

[0468] Step 7:

[0469] The server sends improvement suggestions to the terminal. The input is the generated improvement suggestions, and the output is the specific instructions that arrive at the user's terminal. The server sends this to the terminal using a standard communication protocol, making it immediately accessible to the user.

[0470] Step 8:

[0471] The device notifies the user of improvement suggestions. The input is personalized improvement suggestions received from the server, and the output is notification information received by the user on the device. This allows the user to incorporate the suggested improvements into their daily life and improve their health. Specifically, the device utilizes notification pop-ups and reminder functions to allow the user to easily access the improvement suggestions.

[0472] (Application Example 1)

[0473] 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."

[0474] In modern healthcare systems, personalized health management is essential for the prevention and control of lifestyle-related diseases. However, conventional methods involve uniform analysis of vital data, making it difficult to provide highly accurate, personalized recommendations. Furthermore, delays in notifying users of the results after data analysis often prevented prompt action. As a result, users were unable to intervene at the appropriate time, making it difficult to prevent the deterioration of their health.

[0475] 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.

[0476] In this invention, the server includes means for receiving biometric data from a device that acquires medical data, means for using a generative model to analyze the received biometric data and assess health risks, and means for sending immediate notifications when risks are detected. This enables personalized and rapid health management, and allows for the efficient prevention and improvement of lifestyle-related diseases.

[0477] "Medical data" refers to information related to an individual's health status and bodily functions.

[0478] "Biometric data" refers to numerical information that indicates an individual's physical condition, such as heart rate and blood sugar levels.

[0479] A "generative model" is an algorithm or program used to predict future data trends based on past data.

[0480] "Health risk" refers to an indicator that assesses the likelihood of developing illness or health deterioration.

[0481] A "habit improvement plan" refers to actions or guidelines proposed to improve a user's lifestyle and maintain or improve their health.

[0482] An "output mechanism" refers to the means or devices used by a system to provide information to a user.

[0483] "Immediate notification" refers to a message or warning that immediately conveys relevant information to the user when a specific event is detected.

[0484] "User data" refers to various vital data and behavioral information collected from individual users.

[0485] "Physical activity" refers to physical exercise and activities related to fitness.

[0486] "Rest" refers to physical and mental relaxation and a break.

[0487] This invention is a system that supports personalized prevention and management of lifestyle-related diseases based on medical data. The server receives biometric data measured by a device that acquires medical data from the user. The entire system consists of a smartphone, a generative model, a server, and a terminal including an output mechanism.

[0488] The server analyzes received biometric data in real time using TensorFlow and assesses health risks using a generative model. Based on this assessment, it creates suggestions for habit improvement for the user and immediately notifies the device of the details. The device then presents this information to the user through a dashboard.

[0489] For example, if a user's heart rate suddenly increases while running, this system immediately detects the risk and sends a message prompting them to pause their exercise and take an appropriate rest. In this process, the server and terminal rapidly communicate with various hardware components using React Native, Flask, and TensorFlow to deliver the best possible advice to the user.

[0490] An example of a prompt message would be a specific instruction such as, "Detect anomalies from the user's vital data and generate details and countermeasures." By utilizing this generative AI model, continuous personalized health management becomes possible.

[0491] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0492] Step 1:

[0493] The user wears a device that acquires medical data, recording biometric data on a daily basis. The device measures heart rate, blood glucose levels, etc., and transmits this data to a terminal. The input is biometric data from the device, and the output is the transmission of biometric data to the terminal. Data collection is carried out through this process.

[0494] Step 2:

[0495] The terminal transfers the received biometric data to the server. The server receives this data and prepares it for analysis. The input is the biometric data from the terminal, and the output is the data transfer to the server. Here, data transfer and storage take place.

[0496] Step 3:

[0497] The server starts analyzing biometric data using TensorFlow. Based on a generative model, it assesses health risks by referencing historical data. The input is accumulated biometric data, and the output is the result of the risk assessment. Health risks are calculated through data computation.

[0498] Step 4:

[0499] Based on the evaluation results, the server uses a generative AI model to create prompt messages and generate suggestions for habit improvement for the user. The input is the result of the risk assessment, and the output is the prompt message for the improvement suggestion. This step generates specific improvement suggestions.

[0500] Step 5:

[0501] The server sends the generated habit improvement suggestions to the device. The device displays a notification to the user, allowing them to review the risk assessment results and suggestions via a dashboard. The input is the improvement suggestions from the server, and the output is the notification to the user. This is where the notification is generated and sent.

[0502] Step 6:

[0503] Based on the information received from the device, users implement suggested measures in their daily lives. The input is suggested information from the device, and the output is the user's behavioral changes. Ultimately, this promotes data-driven behavioral improvement.

[0504] 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.

[0505] This invention provides a new approach to user health management, enabling personalized lifestyle improvements by constructing a system consisting of a device for acquiring medical data, an emotion engine, a server, a terminal, and the user.

[0506] The user uses a medical data acquisition device to measure vital data, such as heart rate and blood glucose levels. This data is sent to a terminal, which then sends the data to a server. The terminal is also equipped with a camera and microphone, and an emotion engine runs to recognize emotions from the user's facial expressions and voice.

[0507] The server receives vital data and emotional state data transmitted from the terminal and stores them in a database. The generative model within the server assesses the user's health risks based on the vital data and predicts future vital trends by referencing past data. It also uses emotional data provided by the emotion engine to perform analysis that takes the user's emotional state into account.

[0508] This allows the server to generate personalized lifestyle improvement plans. In particular, by incorporating suggestions based on emotional state, the server provides improvement plans that are easy for the user to implement. For example, it may recommend activities that promote relaxation during periods of high stress, providing instructions that are appropriate to the user's current psychological state.

[0509] The device receives personalized lifestyle improvement suggestions sent from the server and notifies the user visually or audibly. The user can review the suggestions through the device's interface and adjust their daily life to improve their health.

[0510] In this way, this system enables more precise and personalized health management by linking the user's vital data with their emotional state. It is an important tool that supports effective health maintenance while reducing the burden on the user.

[0511] The following describes the processing flow.

[0512] Step 1:

[0513] The user wears a medical data acquisition device to collect vital data such as blood glucose levels and heart rate. This device transmits the measured data to a terminal.

[0514] Step 2:

[0515] The terminal formats the received vital data and sends it to the server. During this process, the emotion engine installed in the terminal analyzes the user's facial expressions and voice, and also acquires emotional state data.

[0516] Step 3:

[0517] The server receives vital data and emotional state data transmitted from the terminal. This data is stored in a database.

[0518] Step 4:

[0519] The server inputs stored data into a generative model to assess the user's health risks. This assessment includes a process of referencing historical data to predict future vital trends.

[0520] Step 5:

[0521] The server generates personalized lifestyle improvement suggestions based on assessed health risk and emotional state data. These suggestions include recommendations tailored to the user's psychological state and are designed with feasibility in mind.

[0522] Step 6:

[0523] The device receives personalized lifestyle improvement suggestions sent from the server and displays them to the user. These suggestions are notified visually or audibly.

[0524] Step 7:

[0525] Users can review lifestyle improvement suggestions through their devices and apply them to their own lives. This process aims to reduce health risks and improve their daily routines.

[0526] (Example 2)

[0527] 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."

[0528] In modern society, individual health management is crucial, but traditional methods have made it difficult to improve lifestyle habits appropriately while considering an individual's psychological state. Furthermore, in the analysis of health information, individual data has not been fully utilized, resulting in a lack of prediction of future health risks and corresponding countermeasures.

[0529] 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.

[0530] In this invention, the server includes means for receiving health information from a device that acquires biometric information, means for analyzing the received health information from multiple perspectives and evaluating health risks based on psychological state, means for generating personalized lifestyle improvement plans based on the evaluated health risks and psychological state, and means for notifying an output device of the personalized lifestyle improvement plans. This makes it possible to take into account the individual's psychological state and to make effective and personalized lifestyle improvements in response to future health risks.

[0531] A "device that acquires biometric information" refers to a device that measures and collects health-related data such as a user's heart rate and blood glucose levels.

[0532] "Receiving health information" refers to the process of receiving the user's physiological indicators transmitted from a medical data acquisition device in digital format.

[0533] "Multifaceted analysis" refers to the process of analyzing received data in detail from different perspectives to identify correlations and trends.

[0534] "Psychological state" refers to information that represents the user's mental and emotional state, such as their feelings and stress levels.

[0535] "Assessing health risks" refers to estimating potential dangers and problems in a user's current health condition based on their health information.

[0536] "Generating personalized lifestyle improvement plans" refers to creating specific lifestyle modification suggestions tailored to each user, based on their health assessment results.

[0537] "Output device" refers to an electronic device or application used to notify the user of the generated improvement suggestions.

[0538] "Health information" refers to all health-related data, such as the user's heart rate, blood sugar levels, and emotional state.

[0539] "Future health risks" refer to potential health problems or challenges that can be predicted based on current health data.

[0540] This invention comprises a system that continuously monitors a user's health information and provides personalized lifestyle improvement suggestions based on that information. Specifically, it uses a medical data acquisition device for acquiring biometric information, a terminal for analyzing the user's psychological state, and a server for processing and analyzing the data.

[0541] Users measure vital data using wearable devices and home medical equipment. This includes indicators such as heart rate and blood glucose levels. The devices (smartphones, tablets, etc.) are equipped with cameras and microphones, and an emotion engine operates to analyze the user's psychological state by capturing their facial expressions and recording their voice.

[0542] This data is transmitted to the server via the terminal and stored in a database on the server. The server uses a generative AI model to analyze the data from multiple perspectives, assess the user's health risks, and predict future health trends by referring to past data. Furthermore, by inputting prompts into the generative AI model, a process is initiated to generate personalized lifestyle improvement suggestions. These suggestions may include improvements tailored to the user's psychological state, such as suggesting activities to promote relaxation based on their stress levels.

[0543] The device notifies the user of lifestyle improvement suggestions received from the server. These notifications are sent via on-screen messages and voice instructions, and the user reviews them and selects improvements that can be implemented in their daily life.

[0544] For example, if a user's heart rate is higher than normal and the emotional analysis indicates a high-stress state, the generative AI model will generate a suggestion to "recommend doing yoga twice a week to promote relaxation." This suggestion will be communicated to the user via text or voice.

[0545] An example of a prompt message is, "Based on heart rate and emotional data, please output personalized lifestyle improvement suggestions to promote relaxation." This ensures that appropriate suggestions are provided that reflect the user's psychological state.

[0546] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0547] Step 1:

[0548] The user wears a medical data acquisition device to collect vital data such as heart rate and blood glucose levels. The input is data measured by biosensors. The output is the acquired vital data. This data is transmitted to the terminal using Bluetooth or Wi-Fi. Specifically, the user puts on the device and presses a button to start measurement.

[0549] Step 2:

[0550] The device uses its built-in camera and microphone to capture the user's facial expressions and voice, and analyzes their psychological state using an emotion engine. Input consists of image data from the camera and audio data from the microphone. Output is the analyzed psychological state data. Specifically, the camera captures the user's face for a few seconds and records audio, allowing for real-time analysis.

[0551] Step 3:

[0552] The terminal transmits acquired vital data and psychological state data to the server. Input consists of various data acquired by the terminal. Output consists of data packets sent to the server. In its specific operation, network synchronization is performed for data transmission.

[0553] Step 4:

[0554] The server stores data received from terminals in a database and performs multi-dimensional analysis of the data using a generative AI model. Inputs are vital data and psychological state data transmitted from the terminals. Outputs are assessed health risks and predicted health trends. Data processing includes data cleansing and normalization, followed by adaptive model analysis.

[0555] Step 5:

[0556] The server generates personalized lifestyle improvement suggestions from a generating AI model using prompt statements. The input is the evaluated health risk and the prompt statement. The output is personalized lifestyle improvement suggestions. Specifically, the AI ​​model generates analysis results and creates improvement suggestions according to the prompts.

[0557] Step 6:

[0558] The device notifies the user of lifestyle improvement suggestions received from the server. The input is the improvement suggestions sent from the server. The output is the lifestyle improvement suggestions notified to the user. Specifically, a pop-up notification or voice guidance is displayed on the device to allow the user to confirm the information.

[0559] (Application Example 2)

[0560] 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."

[0561] In health management, personalized advice is needed that takes into account not only the user's biometric information but also their emotional state. However, conventional systems have struggled to comprehensively analyze a user's biometric information and emotional state in real time and provide personalized training advice. In particular, the lack of methods to provide immediate feedback during sustained exercise has resulted in a problem where users cannot improve their health at an optimal pace.

[0562] 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.

[0563] In this invention, the server includes means for receiving biometric information from a device that acquires medical data, means for evaluating health risks using a generative model to analyze the received biometric information, and means for estimating emotional state with an image processing function for analyzing the user's facial images. This makes it possible to provide training advice in real time based on physical information and emotional state.

[0564] A "device for acquiring medical data" is a device that measures biometric information such as the user's heart rate and blood glucose levels.

[0565] "Biometric information" refers to data that quantitatively indicates the user's physical condition, such as heart rate and blood glucose levels.

[0566] A "generative model" is an algorithm that uses past biometric data to predict and analyze a user's health risks.

[0567] "Assessing health risks" means predicting potential health problems that may occur in the future based on the user's biometric information.

[0568] A "lifestyle improvement plan" is a set of specific action suggestions to reduce health risks, and is adjusted according to the user's health condition.

[0569] "Personalization" means providing optimal improvement suggestions by taking into account each user's biometric information and emotional state.

[0570] An "output device" is a device that presents the generated lifestyle improvement suggestions to the user.

[0571] "Analyzing user facial images" is the process of evaluating a user's emotional state from images of their face.

[0572] "Inferring emotional state" means analyzing the user's psychological state at a given time using their facial expression data.

[0573] "Image processing functionality" refers to technology that analyzes a user's facial expressions and processes their content as digital data.

[0574] "Providing training advice in real time" means providing immediate feedback based on the user's biometric information and emotional state during exercise.

[0575] The system realized by this invention acquires biometric information from the user and provides personalized training advice based on that information. The biometric information is acquired through a device that acquires medical data and transmitted as a signal to a terminal. The terminal transfers this data to a server, where a generative AI model evaluates health risks. Part of the generative AI model uses the user's past biometric data to predict future biometric trends. The results of this analysis form the basis for generating specific advice.

[0576] The device is equipped with a camera that captures images of the user's face and sends them to the server. The server uses image processing capabilities to estimate the user's emotional state based on this image data. Tools such as OpenCV are used for specific emotion analysis to obtain emotional data from facial expressions. By combining this emotional data with biometric information, it becomes possible to generate optimal training advice for the user in real time.

[0577] This advice is provided in real time via the display device of the smart glasses worn by the user. For example, if the user records a high heart rate and shows a tense expression, the system will immediately instruct the user to "switch to a walking pace, take deep breaths, and relax." This kind of immediate feedback based on biometric information and emotional state improves the user's efficiency and safety during exercise.

[0578] An example of a prompt message might be, "If the heart rate exceeds 120 and the user shows signs of tension, generate advice to encourage relaxation." Such prompt messages improve the overall operational accuracy of the system.

[0579] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0580] Step 1:

[0581] The terminal receives biometric information transmitted from a device that acquires medical data. This input data includes heart rate, blood glucose levels, and other parameters. The terminal then transfers this information to a server via Bluetooth communication.

[0582] Step 2:

[0583] The server stores the received biometric information and uses a generated AI model to assess the user's health risks. This process involves data calculations that refer to past biometric data to predict future vital trends. A health risk assessment report is generated as output.

[0584] Step 3:

[0585] The device captures an image of the user's face through its camera. This image data becomes the input. The device uses its image processing capabilities to perform facial expression analysis using OpenCV and estimate the emotional state. The output is the estimated emotional data.

[0586] Step 4:

[0587] The server integrates biometric and emotional data and utilizes a generative AI model to generate training advice in real time. Based on the instructions provided by these prompts, optimal action suggestions are output.

[0588] Step 5:

[0589] The user receives training advice transmitted from the server via smart glasses. This output is displayed as a visual notification, guiding the user on exercise and rest.

[0590] Step 6:

[0591] Based on the feedback, users adjust their training pace and methods. The results are then captured again as biometric data by the device and incorporated into a real-time feedback loop across the entire system.

[0592] 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.

[0593] 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.

[0594] 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.

[0595] [Fourth Embodiment]

[0596] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0597] 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.

[0598] 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).

[0599] 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.

[0600] 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.

[0601] 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).

[0602] 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.

[0603] 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.

[0604] 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.

[0605] 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.

[0606] 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.

[0607] 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.

[0608] 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".

[0609] This invention comprises a system consisting of a device for acquiring medical data, a server, a terminal, and a user, thereby enabling the prevention and management of lifestyle-related diseases, including diabetes.

[0610] The user wears a device that acquires medical data, and daily vital data is collected. This device measures indicators such as heart rate and blood glucose levels and transmits the data to a terminal. The terminal receives this data and transmits it to a server.

[0611] The server stores and analyzes the received vital data. Using a generative model, it assesses current health status by considering past data and calculates health risks, including diabetes risk. This model is designed to predict future vital trends.

[0612] Based on the analysis results, the server creates personalized lifestyle improvement plans tailored to each user. For example, if an analysis of a user detects a tendency for blood sugar levels to rise, the server can suggest dietary restrictions and exercise programs.

[0613] The device receives lifestyle improvement suggestions sent from the server and notifies the user. The user can then review these suggestions on the device and aim to improve their health by implementing them on a daily basis.

[0614] This system continuously monitors and analyzes individual user vital data to support the prevention and management of lifestyle-related diseases, thereby reducing the management burden on users while achieving highly accurate health maintenance.

[0615] The following describes the processing flow.

[0616] Step 1:

[0617] The user uses a medical data acquisition device to measure vital data such as heart rate and blood glucose levels. The device transmits this data to the terminal.

[0618] Step 2:

[0619] The terminal prepares to send the received vital data to the server. Here, the data format is adjusted to a format that the server can easily parse.

[0620] Step 3:

[0621] The server receives vital data transmitted from the terminal and records it in a database. This data is stored for analysis.

[0622] Step 4:

[0623] The server uses a generative model to analyze the received vital data and evaluate the current situation in conjunction with historical data. This analysis calculates health risks and predicts future vital trends.

[0624] Step 5:

[0625] Based on the analysis results, the server generates personalized lifestyle improvement plans for each user. These plans include specific suggestions regarding diet, exercise, sleep, and other factors.

[0626] Step 6:

[0627] The device receives personalized lifestyle improvement suggestions sent from the server.

[0628] Step 7:

[0629] Users can view lifestyle improvement suggestions through their device. Based on the suggestions they receive, they can adjust their daily action plans.

[0630] (Example 1)

[0631] 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".

[0632] Currently, many people suffer from lifestyle-related diseases, and their prevention and management are crucial. However, there is a lack of systems that can efficiently collect individuals' daily health data and provide appropriate and individually optimized lifestyle improvement plans based on that data. This makes it difficult to control the risk of lifestyle-related diseases, and therefore, a system that can more effectively and efficiently assess risks and propose improvements is needed.

[0633] 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.

[0634] In this invention, the server includes means for receiving information from a device that acquires biometric information, means for using a generation algorithm model to analyze the received information data and evaluate health risks, and means for generating personalized lifestyle improvement plans based on the evaluated health risks. This makes it possible to provide highly accurate health risk assessments based on individual vital data and personalized lifestyle improvement plans.

[0635] "Biometric information" refers to physical indicators such as heart rate and blood sugar levels that show an individual's health status.

[0636] A "device" is an instrument or apparatus worn by an individual to acquire biometric information.

[0637] "Information data" refers to digital data, including biometric information, collected by a device.

[0638] A "generative algorithm model" is a mathematical model used to assess health risks based on received informational data.

[0639] "Health risk" refers to an assessment indicator that shows the likelihood of developing lifestyle-related diseases or other health problems.

[0640] "Personalization" refers to providing tailored support based on the data and needs of a specific individual.

[0641] A "lifestyle improvement plan" is a set of specific guidelines regarding diet, exercise, and sleep proposed to improve an individual's health.

[0642] An "output device" is a digital device used to notify users of personalized lifestyle improvement suggestions.

[0643] This system begins with the user wearing a device that acquires medical data. The device, worn on the user's body, measures biometric information such as heart rate and blood glucose levels in real time. The device transmits this data to the user's terminal using communication technologies such as Bluetooth or Wi-Fi. The terminal receives the data and transmits the information to a server using a secure communication protocol.

[0644] The server stores the received information data and analyzes it using a generative algorithm model. This analysis takes into account the user's past health information and assesses health risks based on current biometric data. The generative algorithm model is optimized to generate personalized recommendations by comprehensively analyzing each user's health data.

[0645] Based on the analysis results, the server creates lifestyle improvement plans. These plans specifically include dietary restrictions, exercise programs, and sleep improvement strategies. These suggestions are individually optimized based on each user's data. The improvement plans generated by the server are then sent back to the terminal, which notifies the user.

[0646] This allows users to improve their health by reviewing and implementing suggested improvements in their daily lives. For example, a prompt such as "Analyze user A's heart rate and blood glucose data to generate future health risks and improvement suggestions" can be used. This prompt allows the system to provide personalized suggestions based on the specified user's health data.

[0647] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0648] Step 1:

[0649] The user wears a device that acquires medical data. The device collects biometric information such as heart rate and blood glucose levels in real time. The input is biometric information obtained from the user's body, and the output is this raw data. The measured data is transmitted to a terminal via Bluetooth or Wi-Fi.

[0650] Step 2:

[0651] The terminal receives biometric information transmitted from the device. The input is raw data from the device, and the output is data stored on the terminal in the received format. The terminal organizes this data and prepares it for secure transmission to the server. During this process, the data is encrypted.

[0652] Step 3:

[0653] The terminal sends the received data to the server. The input here is encrypted vital data, and the output is the data received on the server side. The terminal transfers the data to the server via a secure communication protocol. This function is necessary to maintain the confidentiality and integrity of the data.

[0654] Step 4:

[0655] The server stores the received data in storage and prepares it for analysis. The input is data acquired from the terminal, and the output is information stored in the database in a format suitable for analysis. The data is organized by user, and specific actions are taken to create indexes for future reference and analysis.

[0656] Step 5:

[0657] The server uses a generative AI model to analyze data. The input is biometric information prepared for analysis, and the output is the result of an assessment of health risks. The server uses historical data as a reference and compares it with current data to identify risks and perform calculations to predict future health trends. This model is designed to learn data patterns based on the user's health status.

[0658] Step 6:

[0659] The server generates lifestyle improvement plans based on the analysis results. The input is the results of the health risk assessment, and the output is a personalized lifestyle improvement plan. This includes dietary revisions, exercise guidelines, and sleep advice, generating specific suggestions tailored to the user's personal information.

[0660] Step 7:

[0661] The server sends improvement suggestions to the terminal. The input is the generated improvement suggestions, and the output is the specific instructions that arrive at the user's terminal. The server sends this to the terminal using a standard communication protocol, making it immediately accessible to the user.

[0662] Step 8:

[0663] The device notifies the user of improvement suggestions. The input is personalized improvement suggestions received from the server, and the output is notification information received by the user on the device. This allows the user to incorporate the suggested improvements into their daily life and improve their health. Specifically, the device utilizes notification pop-ups and reminder functions to allow the user to easily access the improvement suggestions.

[0664] (Application Example 1)

[0665] 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".

[0666] In modern healthcare systems, personalized health management is essential for the prevention and control of lifestyle-related diseases. However, conventional methods involve uniform analysis of vital data, making it difficult to provide highly accurate, personalized recommendations. Furthermore, delays in notifying users of the results after data analysis often prevented prompt action. As a result, users were unable to intervene at the appropriate time, making it difficult to prevent the deterioration of their health.

[0667] 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.

[0668] In this invention, the server includes means for receiving biometric data from a device that acquires medical data, means for using a generative model to analyze the received biometric data and assess health risks, and means for sending immediate notifications when risks are detected. This enables personalized and rapid health management, and allows for the efficient prevention and improvement of lifestyle-related diseases.

[0669] "Medical data" refers to information related to an individual's health status and bodily functions.

[0670] "Biometric data" refers to numerical information that indicates an individual's physical condition, such as heart rate and blood sugar levels.

[0671] A "generative model" is an algorithm or program used to predict future data trends based on past data.

[0672] "Health risk" refers to an indicator that assesses the likelihood of developing illness or health deterioration.

[0673] A "habit improvement plan" refers to actions or guidelines proposed to improve a user's lifestyle and maintain or improve their health.

[0674] An "output mechanism" refers to the means or devices used by a system to provide information to a user.

[0675] "Immediate notification" refers to a message or warning that immediately conveys relevant information to the user when a specific event is detected.

[0676] "User data" refers to various vital data and behavioral information collected from individual users.

[0677] "Physical activity" refers to physical exercise and activities related to fitness.

[0678] "Rest" refers to physical and mental relaxation and a break.

[0679] This invention is a system that supports personalized prevention and management of lifestyle-related diseases based on medical data. The server receives biometric data measured by a device that acquires medical data from the user. The entire system consists of a smartphone, a generative model, a server, and a terminal including an output mechanism.

[0680] The server analyzes received biometric data in real time using TensorFlow and assesses health risks using a generative model. Based on this assessment, it creates suggestions for habit improvement for the user and immediately notifies the device of the details. The device then presents this information to the user through a dashboard.

[0681] For example, if a user's heart rate suddenly increases while running, this system immediately detects the risk and sends a message prompting them to pause their exercise and take an appropriate rest. In this process, the server and terminal rapidly communicate with various hardware components using React Native, Flask, and TensorFlow to deliver the best possible advice to the user.

[0682] An example of a prompt message would be a specific instruction such as, "Detect anomalies from the user's vital data and generate details and countermeasures." By utilizing this generative AI model, continuous personalized health management becomes possible.

[0683] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0684] Step 1:

[0685] The user wears a device that acquires medical data, recording biometric data on a daily basis. The device measures heart rate, blood glucose levels, etc., and transmits this data to a terminal. The input is biometric data from the device, and the output is the transmission of biometric data to the terminal. Data collection is carried out through this process.

[0686] Step 2:

[0687] The terminal transfers the received biometric data to the server. The server receives this data and prepares it for analysis. The input is the biometric data from the terminal, and the output is the data transfer to the server. Here, data transfer and storage take place.

[0688] Step 3:

[0689] The server starts analyzing biometric data using TensorFlow. Based on a generative model, it assesses health risks by referencing historical data. The input is accumulated biometric data, and the output is the result of the risk assessment. Health risks are calculated through data computation.

[0690] Step 4:

[0691] Based on the evaluation results, the server uses a generative AI model to create prompt messages and generate suggestions for habit improvement for the user. The input is the result of the risk assessment, and the output is the prompt message for the improvement suggestion. This step generates specific improvement suggestions.

[0692] Step 5:

[0693] The server sends the generated habit improvement suggestions to the device. The device displays a notification to the user, allowing them to review the risk assessment results and suggestions via a dashboard. The input is the improvement suggestions from the server, and the output is the notification to the user. This is where the notification is generated and sent.

[0694] Step 6:

[0695] Based on the information received from the device, users implement suggested measures in their daily lives. The input is suggested information from the device, and the output is the user's behavioral changes. Ultimately, this promotes data-driven behavioral improvement.

[0696] 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.

[0697] This invention provides a new approach to user health management, enabling personalized lifestyle improvements by constructing a system consisting of a device for acquiring medical data, an emotion engine, a server, a terminal, and the user.

[0698] The user uses a medical data acquisition device to measure vital data, such as heart rate and blood glucose levels. This data is sent to a terminal, which then sends the data to a server. The terminal is also equipped with a camera and microphone, and an emotion engine runs to recognize emotions from the user's facial expressions and voice.

[0699] The server receives vital data and emotional state data transmitted from the terminal and stores them in a database. The generative model within the server assesses the user's health risks based on the vital data and predicts future vital trends by referencing past data. It also uses emotional data provided by the emotion engine to perform analysis that takes the user's emotional state into account.

[0700] This allows the server to generate personalized lifestyle improvement plans. In particular, by incorporating suggestions based on emotional state, the server provides improvement plans that are easy for the user to implement. For example, it may recommend activities that promote relaxation during periods of high stress, providing instructions that are appropriate to the user's current psychological state.

[0701] The device receives personalized lifestyle improvement suggestions sent from the server and notifies the user visually or audibly. The user can review the suggestions through the device's interface and adjust their daily life to improve their health.

[0702] In this way, this system enables more precise and personalized health management by linking the user's vital data with their emotional state. It is an important tool that supports effective health maintenance while reducing the burden on the user.

[0703] The following describes the processing flow.

[0704] Step 1:

[0705] The user wears a medical data acquisition device to collect vital data such as blood glucose levels and heart rate. This device transmits the measured data to a terminal.

[0706] Step 2:

[0707] The terminal formats the received vital data and sends it to the server. During this process, the emotion engine installed in the terminal analyzes the user's facial expressions and voice, and also acquires emotional state data.

[0708] Step 3:

[0709] The server receives vital data and emotional state data transmitted from the terminal. This data is stored in a database.

[0710] Step 4:

[0711] The server inputs stored data into a generative model to assess the user's health risks. This assessment includes a process of referencing historical data to predict future vital trends.

[0712] Step 5:

[0713] The server generates personalized lifestyle improvement suggestions based on assessed health risk and emotional state data. These suggestions include recommendations tailored to the user's psychological state and are designed with feasibility in mind.

[0714] Step 6:

[0715] The device receives personalized lifestyle improvement suggestions sent from the server and displays them to the user. These suggestions are notified visually or audibly.

[0716] Step 7:

[0717] Users can review lifestyle improvement suggestions through their devices and apply them to their own lives. This process aims to reduce health risks and improve their daily routines.

[0718] (Example 2)

[0719] 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".

[0720] In modern society, individual health management is crucial, but traditional methods have made it difficult to improve lifestyle habits appropriately while considering an individual's psychological state. Furthermore, in the analysis of health information, individual data has not been fully utilized, resulting in a lack of prediction of future health risks and corresponding countermeasures.

[0721] 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.

[0722] In this invention, the server includes means for receiving health information from a device that acquires biometric information, means for analyzing the received health information from multiple perspectives and evaluating health risks based on psychological state, means for generating personalized lifestyle improvement plans based on the evaluated health risks and psychological state, and means for notifying an output device of the personalized lifestyle improvement plans. This makes it possible to take into account the individual's psychological state and to make effective and personalized lifestyle improvements in response to future health risks.

[0723] A "device that acquires biometric information" refers to a device that measures and collects health-related data such as a user's heart rate and blood glucose levels.

[0724] "Receiving health information" refers to the process of receiving the user's physiological indicators transmitted from a medical data acquisition device in digital format.

[0725] "Multifaceted analysis" refers to the process of analyzing received data in detail from different perspectives to identify correlations and trends.

[0726] "Psychological state" refers to information that represents the user's mental and emotional state, such as their feelings and stress levels.

[0727] "Assessing health risks" refers to estimating potential dangers and problems in a user's current health condition based on their health information.

[0728] "Generating personalized lifestyle improvement plans" refers to creating specific lifestyle modification suggestions tailored to each user, based on their health assessment results.

[0729] "Output device" refers to an electronic device or application used to notify the user of the generated improvement suggestions.

[0730] "Health information" refers to all health-related data, such as the user's heart rate, blood sugar levels, and emotional state.

[0731] "Future health risks" refer to potential health problems or challenges that can be predicted based on current health data.

[0732] This invention comprises a system that continuously monitors a user's health information and provides personalized lifestyle improvement suggestions based on that information. Specifically, it uses a medical data acquisition device for acquiring biometric information, a terminal for analyzing the user's psychological state, and a server for processing and analyzing the data.

[0733] Users measure vital data using wearable devices and home medical equipment. This includes indicators such as heart rate and blood glucose levels. The devices (smartphones, tablets, etc.) are equipped with cameras and microphones, and an emotion engine operates to analyze the user's psychological state by capturing their facial expressions and recording their voice.

[0734] This data is transmitted to the server via the terminal and stored in a database on the server. The server uses a generative AI model to analyze the data from multiple perspectives, assess the user's health risks, and predict future health trends by referring to past data. Furthermore, by inputting prompts into the generative AI model, a process is initiated to generate personalized lifestyle improvement suggestions. These suggestions may include improvements tailored to the user's psychological state, such as suggesting activities to promote relaxation based on their stress levels.

[0735] The device notifies the user of lifestyle improvement suggestions received from the server. These notifications are sent via on-screen messages and voice instructions, and the user reviews them and selects improvements that can be implemented in their daily life.

[0736] For example, if a user's heart rate is higher than normal and the emotional analysis indicates a high-stress state, the generative AI model will generate a suggestion to "recommend doing yoga twice a week to promote relaxation." This suggestion will be communicated to the user via text or voice.

[0737] An example of a prompt message is, "Based on heart rate and emotional data, please output personalized lifestyle improvement suggestions to promote relaxation." This ensures that appropriate suggestions are provided that reflect the user's psychological state.

[0738] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0739] Step 1:

[0740] The user wears a medical data acquisition device to collect vital data such as heart rate and blood glucose levels. The input is data measured by biosensors. The output is the acquired vital data. This data is transmitted to the terminal using Bluetooth or Wi-Fi. Specifically, the user puts on the device and presses a button to start measurement.

[0741] Step 2:

[0742] The device uses its built-in camera and microphone to capture the user's facial expressions and voice, and analyzes their psychological state using an emotion engine. Input consists of image data from the camera and audio data from the microphone. Output is the analyzed psychological state data. Specifically, the camera captures the user's face for a few seconds and records audio, allowing for real-time analysis.

[0743] Step 3:

[0744] The terminal transmits acquired vital data and psychological state data to the server. Input consists of various data acquired by the terminal. Output consists of data packets sent to the server. In its specific operation, network synchronization is performed for data transmission.

[0745] Step 4:

[0746] The server stores data received from terminals in a database and performs multi-dimensional analysis of the data using a generative AI model. Inputs are vital data and psychological state data transmitted from the terminals. Outputs are assessed health risks and predicted health trends. Data processing includes data cleansing and normalization, followed by adaptive model analysis.

[0747] Step 5:

[0748] The server generates personalized lifestyle improvement suggestions from a generating AI model using prompt statements. The input is the evaluated health risk and the prompt statement. The output is personalized lifestyle improvement suggestions. Specifically, the AI ​​model generates analysis results and creates improvement suggestions according to the prompts.

[0749] Step 6:

[0750] The device notifies the user of lifestyle improvement suggestions received from the server. The input is the improvement suggestions sent from the server. The output is the lifestyle improvement suggestions notified to the user. Specifically, a pop-up notification or voice guidance is displayed on the device to allow the user to confirm the information.

[0751] (Application Example 2)

[0752] 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".

[0753] In health management, personalized advice is needed that takes into account not only the user's biometric information but also their emotional state. However, conventional systems have struggled to comprehensively analyze a user's biometric information and emotional state in real time and provide personalized training advice. In particular, the lack of methods to provide immediate feedback during sustained exercise has resulted in a problem where users cannot improve their health at an optimal pace.

[0754] 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.

[0755] In this invention, the server includes means for receiving biometric information from a device that acquires medical data, means for evaluating health risks using a generative model to analyze the received biometric information, and means for estimating emotional state with an image processing function for analyzing the user's facial images. This makes it possible to provide training advice in real time based on physical information and emotional state.

[0756] A "device for acquiring medical data" is a device that measures biometric information such as the user's heart rate and blood glucose levels.

[0757] "Biometric information" refers to data that quantitatively indicates the user's physical condition, such as heart rate and blood glucose levels.

[0758] A "generative model" is an algorithm that uses past biometric data to predict and analyze a user's health risks.

[0759] "Assessing health risks" means predicting potential health problems that may occur in the future based on the user's biometric information.

[0760] A "lifestyle improvement plan" is a set of specific action suggestions to reduce health risks, and is adjusted according to the user's health condition.

[0761] "Personalization" means providing optimal improvement suggestions by taking into account each user's biometric information and emotional state.

[0762] An "output device" is a device that presents the generated lifestyle improvement suggestions to the user.

[0763] "Analyzing user facial images" is the process of evaluating a user's emotional state from images of their face.

[0764] "Inferring emotional state" means analyzing the user's psychological state at a given time using their facial expression data.

[0765] "Image processing functionality" refers to technology that analyzes a user's facial expressions and processes their content as digital data.

[0766] "Providing training advice in real time" means providing immediate feedback based on the user's biometric information and emotional state during exercise.

[0767] The system realized by this invention acquires biometric information from the user and provides personalized training advice based on that information. The biometric information is acquired through a device that acquires medical data and transmitted as a signal to a terminal. The terminal transfers this data to a server, where a generative AI model evaluates health risks. Part of the generative AI model uses the user's past biometric data to predict future biometric trends. The results of this analysis form the basis for generating specific advice.

[0768] The device is equipped with a camera that captures images of the user's face and sends them to the server. The server uses image processing capabilities to estimate the user's emotional state based on this image data. Tools such as OpenCV are used for specific emotion analysis to obtain emotional data from facial expressions. By combining this emotional data with biometric information, it becomes possible to generate optimal training advice for the user in real time.

[0769] This advice is provided in real time via the display device of the smart glasses worn by the user. For example, if the user records a high heart rate and shows a tense expression, the system will immediately instruct the user to "switch to a walking pace, take deep breaths, and relax." This kind of immediate feedback based on biometric information and emotional state improves the user's efficiency and safety during exercise.

[0770] An example of a prompt message might be, "If the heart rate exceeds 120 and the user shows signs of tension, generate advice to encourage relaxation." Such prompt messages improve the overall operational accuracy of the system.

[0771] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0772] Step 1:

[0773] The terminal receives biometric information transmitted from a device that acquires medical data. This input data includes heart rate, blood glucose levels, and other parameters. The terminal then transfers this information to a server via Bluetooth communication.

[0774] Step 2:

[0775] The server stores the received biometric information and uses a generated AI model to assess the user's health risks. This process involves data calculations that refer to past biometric data to predict future vital trends. A health risk assessment report is generated as output.

[0776] Step 3:

[0777] The device captures an image of the user's face through its camera. This image data becomes the input. The device uses its image processing capabilities to perform facial expression analysis using OpenCV and estimate the emotional state. The output is the estimated emotional data.

[0778] Step 4:

[0779] The server integrates biometric and emotional data and utilizes a generative AI model to generate training advice in real time. Based on the instructions provided by these prompts, optimal action suggestions are output.

[0780] Step 5:

[0781] The user receives training advice transmitted from the server via smart glasses. This output is displayed as a visual notification, guiding the user on exercise and rest.

[0782] Step 6:

[0783] Based on the feedback, users adjust their training pace and methods. The results are then captured again as biometric data by the device and incorporated into a real-time feedback loop across the entire system.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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."

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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 as being incorporated by reference.

[0805] The following is further disclosed regarding the embodiments described above.

[0806] (Claim 1)

[0807] A means of receiving vital data from a device that acquires medical data,

[0808] A means of evaluating health risks by using a generative model to analyze received vital data,

[0809] A means of generating personalized lifestyle improvement plans based on assessed health risks,

[0810] A means of notifying an output device of personalized lifestyle improvement suggestions,

[0811] A system that includes this.

[0812] (Claim 2)

[0813] The system according to claim 1, comprising means of using a model that predicts future vital trends by referring to past data when assessing health risks.

[0814] (Claim 3)

[0815] The system according to claim 1, comprising means for generating specific improvement suggestions regarding diet, exercise, and sleep based on user data.

[0816] "Example 1"

[0817] (Claim 1)

[0818] A means for receiving information from a device that acquires biometric information,

[0819] A means of evaluating health risks by using a generative algorithm model to analyze the received information data,

[0820] A means of generating personalized lifestyle improvement plans based on assessed health risks,

[0821] A means of notifying an output device of personalized lifestyle improvement suggestions,

[0822] A means of making suggestions regarding diet and exercise based on the analyzed information,

[0823] A system that includes this.

[0824] (Claim 2)

[0825] The system according to claim 1, which uses a model that predicts future biometric information trends by referring to historical data when assessing health risks.

[0826] (Claim 3)

[0827] The system according to claim 1, which generates specific improvement suggestions related to diet, exercise, and sleep based on individual information.

[0828] "Application Example 1"

[0829] (Claim 1)

[0830] A means of receiving biometric data from a device that acquires medical data,

[0831] A means of using a generative model to analyze received biometric data and assess the risk related to health status,

[0832] A means of generating personalized habit improvement plans based on the assessed health risks,

[0833] A means of notifying the output mechanism of personalized habit improvement suggestions,

[0834] A means of sending immediate notification when a risk is detected,

[0835] A system that includes this.

[0836] (Claim 2)

[0837] The system according to claim 1, which uses a model that predicts future trends in biometric data by referring to past data when assessing health risks.

[0838] (Claim 3)

[0839] The system according to claim 1, which generates specific improvement suggestions regarding diet, physical activity, and rest based on user data.

[0840] "Example 2 of combining an emotion engine"

[0841] (Claim 1)

[0842] A means of receiving health information from a device that acquires biometric information,

[0843] A means of analyzing received health information from multiple perspectives and evaluating health risks based on psychological state,

[0844] A means of generating personalized lifestyle improvement plans based on assessed health risks and psychological state,

[0845] A means of notifying an output device of personalized lifestyle improvement suggestions,

[0846] A system that includes this.

[0847] (Claim 2)

[0848] The system according to claim 1, which uses a model to predict future health trends by referring to past information when assessing health risks.

[0849] (Claim 3)

[0850] The system according to claim 1, which generates specific improvement suggestions to enhance the quality of life based on an individual's health information and psychological state.

[0851] "Application example 2 when combining with an emotional engine"

[0852] (Claim 1)

[0853] A means of receiving biometric information from a device that acquires medical data,

[0854] A means of evaluating health risks by using a generative model to analyze received biometric information,

[0855] A means of generating personalized lifestyle improvement plans based on assessed health risks,

[0856] A means of notifying an output device of personalized lifestyle improvement suggestions,

[0857] It has image processing capabilities for analyzing user facial images and means for inferring emotional states,

[0858] A means of providing training advice in real time based on physical information and emotional state,

[0859] A system that includes this.

[0860] (Claim 2)

[0861] The system according to claim 1, which uses a model that predicts future biometric trends by referring to past data when assessing health risks.

[0862] (Claim 3)

[0863] The system according to claim 1, which generates specific improvement suggestions regarding diet, exercise, and sleep based on user data, and also provides real-time advice during training. [Explanation of symbols]

[0864] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving vital data from a device that acquires medical data, A means of evaluating health risks by using a generative model to analyze received vital data, A means of generating personalized lifestyle improvement plans based on assessed health risks, A means of notifying an output device of personalized lifestyle improvement suggestions, A system that includes this.

2. The system according to claim 1, comprising means of using a model that predicts future vital trends by referring to past data when assessing health risks.

3. The system according to claim 1, comprising means for generating specific improvement suggestions regarding diet, exercise, and sleep based on user data.

Images