system

Abstract

We provide the system. [Solution] Means for collecting and storing personal health data, A means for analyzing health status based on the aforementioned health data, A means for predicting future health status based on the aforementioned analysis results, A means for presenting health risks to the user based on the aforementioned prediction results, A means of proposing specific improvement actions for the aforementioned health risks, 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 persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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] Modern healthcare services mainly remain at the visualization of health data, and there is a problem that it is difficult for users to specifically understand their own health conditions and link them to appropriate health improvement actions. In addition, since there is a lack of means to predict future health conditions and promote specific improvement actions according to risks, users cannot effectively manage their own health over a long period of time.

Means for Solving the Problems

[0005] This invention relates to a system that includes means for collecting and storing personal health data, analyzes health status based on this data, and predicts future health status from the analysis results. This system has the function of presenting predicted health risks to the user and proposing specific improvement actions corresponding to those risks. Furthermore, by using wearable devices to collect health data, highly accurate data can be obtained, and by calculating and presenting the economic impact based on the prediction results, the system promotes behavioral change in the user.

[0006] "Personal health data" refers to information about a person's physical condition and daily activities, including heart rate, exercise levels, sleep patterns, and diet.

[0007] "Means of collection and storage" refers to methods and devices for acquiring and storing data, such as wearable devices and smartphone apps.

[0008] "Methods for analyzing health status" refer to the process of using collected data to evaluate the current health status, detect abnormal values, and analyze health trends.

[0009] "Methods for predicting future health status" refer to technologies and algorithms that estimate future health risks based on current data and past history.

[0010] "Means of presenting health risks to users" refers to methods and interfaces for clearly showing users information about predicted health risks.

[0011] "Means of proposing improvement actions" refers to processes and systems that provide users with specific action plans to improve their health in response to the risks presented.

[0012] "Wearable devices" refer to electronic devices that are worn and used to collect health-related data.

[0013] "Means for calculating and presenting economic impacts" refers to processes and systems for evaluating the potential economic burden resulting from health predictions and communicating that information to users. [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 a sentiment engine is combined.

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

[0018] In the following embodiments, a numbered 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 numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[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 is a system that provides advanced support for individual health management, carrying out a series of processes from collecting and analyzing health data to predicting the future and proposing specific improvement actions.

[0036] In this system, users use wearable devices in their daily lives to automatically record their health data. The wearable devices collect data such as heart rate, steps taken, calories burned, and type and intensity of exercise, and transmit it to a smartphone. The smartphone periodically sends this data to a server to store the latest information in the cloud.

[0037] The server analyzes the user's current health status based on the received health data. The analysis uses machine learning algorithms to detect data anomalies and identify health trends. The analysis results are visualized to allow users to intuitively understand their health status and are provided through a dedicated user interface.

[0038] Next, the server uses digital twin technology to predict future health conditions based on the collected data and analysis results. Specifically, it takes into account current lifestyle, medical history, and genetic information to calculate future health risks and potential disease signs, and then notifies the user.

[0039] Furthermore, based on the prediction results, the server suggests specific actions necessary to reduce the user's health risks. These suggestions include improving exercise habits, revising meal plans, and undergoing regular health checkups. These are customized to the extent that the user can realistically implement them.

[0040] As a concrete example, consider how User B uses the system. User B uses a wearable device and a smartphone to record their daily activities. Based on this data, the server detects that User B tends to be sedentary and predicts that this may increase their risk of heart disease in the future. Based on these results, the server suggests that User B add 30 minutes of walking to their daily routine five days a week and recommends increasing their intake of fruits and vegetables in their diet.

[0041] Thus, this invention transforms health information into a concrete and actionable form, supporting users in their daily decision-making toward maintaining their health and preventing disease.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] Users wear wearable devices to collect health data such as heart rate, steps taken, calories burned, and activity level. This data is transmitted to a smartphone in real time.

[0045] Step 2:

[0046] The device temporarily stores data received from wearable devices. The data is automatically transferred to a server via Bluetooth or Wi-Fi. Users can manually enter additional information, such as meal details and weight, as needed.

[0047] Step 3:

[0048] The server receives health data transmitted from the terminal and stores it in a database. The stored data is preprocessed and prepared for analysis.

[0049] Step 4:

[0050] The server uses machine learning algorithms to analyze health data. It detects anomalies, identifies trends, analyzes patterns, and evaluates the user's current health status.

[0051] Step 5:

[0052] Based on the analysis results, the server utilizes digital twin technology to simulate the user's future health status. It calculates predicted health risks and potential medical expenses, and reflects this in the user profile.

[0053] Step 6:

[0054] Based on predictions and analysis results, the server proposes specific health improvement actions for the user. These suggestions are customized to suit the user's lifestyle.

[0055] Step 7:

[0056] Users access health information, risks, and improvement actions provided by the server through a smartphone app. This prepares them to translate this information into concrete health behaviors in their daily lives.

[0057] Step 8:

[0058] The user performs the suggested action and feeds the results back to the server via the wearable device and app. This loop allows the system to continuously monitor the user's health and make new suggestions as needed.

[0059] (Example 1)

[0060] 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."

[0061] In modern times, it is difficult for individuals to continuously and accurately manage their own health, predict potential disease risks, and take preventive measures. To address this situation, a sophisticated system is needed that effectively collects and analyzes individual health information, predicts future health risks based on that information, and presents concrete improvement actions in an easy-to-understand manner.

[0062] 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.

[0063] In this invention, the server includes means for acquiring and recording information about an individual's health, means for using machine learning algorithms to analyze the health status based on the health information, means for predicting future health status using digital twin technology based on the analysis results, and means for generating prompt sentences to enhance predictions and suggestions using a generative AI model. This enables concrete decision-making toward continuous health management and disease prevention for individuals.

[0064] "Health-related information" refers to data related to an individual's physical condition, including data such as heart rate, steps taken, calories burned, and type and intensity of exercise.

[0065] A "machine learning algorithm" refers to mathematical methods and models that allow computers to learn patterns and regularities from data and use that knowledge to perform analysis and predictions.

[0066] "Digital twin technology" is a technique that utilizes virtual models that accurately mimic real-world physical objects or processes to simulate and analyze their actions and behaviors.

[0067] "Health risk" refers to factors or conditions that could potentially harm an individual's health in the future, including the possibility of developing a specific disease.

[0068] A "generative AI model" is a computational model that uses artificial intelligence technology to generate new data and information, and in particular operates based on prompts to improve the accuracy of predictions and suggestions.

[0069] A "prompt statement" is a document used to give specific instructions or questions to a generative AI model. It plays a role in controlling the AI's behavior and defining its output.

[0070] This invention provides a system that analyzes a user's health status, predicts future health risks, and proposes specific improvement actions.

[0071] By wearing a wearable device daily, users automatically collect health-related information such as heart rate, steps taken, calories burned, and type and intensity of exercise. This information is transferred to a smartphone via Bluetooth or Wi-Fi and stored in an application. The application on the device then transmits this data to a server via the internet.

[0072] The server uses machine learning algorithms to analyze health information collected in the cloud. Specifically, it uses anomaly detection algorithms to detect unusual health patterns. The analysis results are visualized and provided to the user through a dedicated user interface.

[0073] Furthermore, the server uses digital twin technology to predict future health conditions. This process takes into account the user's past data, lifestyle, medical history, and genetic information. The predicted risk information is communicated to the user, and specific improvement actions are suggested.

[0074] The generative AI model enhances analysis and prediction, operating based on appropriate prompts. For example, a prompt might be, "Use the current heart rate data to detect anomalies and assess the risk of future heart disease."

[0075] As a concrete example, consider a scenario where a user utilizes this system. The user's daily activities are recorded by a wearable device, and this data is analyzed on a server. The analysis reveals an abnormality in the user's heart rate pattern, predicting a potential increased risk of heart disease in the future. Based on these results, the server suggests that the user increase their daily walking to 30 minutes, five times a week, to improve their health. This allows the user to obtain a concrete action plan.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] Users wear wearable devices to collect health-related information such as heart rate, steps taken, calories burned, and type and intensity of exercise. The wearable devices acquire data from their respective sensors and transmit it to a smartphone via Bluetooth or Wi-Fi. The input is raw data from the wearable device sensors, while the output is aggregated data sent to the smartphone. A specific example of this operation is a heart rate sensor measuring heart rate every minute.

[0079] Step 2:

[0080] The device temporarily stores received health data in an application and periodically sends it to a server via an internet connection. The input is aggregated data sent from the wearable device, and the output is data transferred to the server. During this process, data format conversion and verification are performed to prevent data loss or duplication. Specifically, the device adds data transmission tasks to a queue at regular intervals.

[0081] Step 3:

[0082] The server stores data in cloud storage and updates the database. The input is health data sent from the terminal, and the output is a dataset stored in cloud storage. The server first verifies data integrity and then writes it to the database. Specifically, it performs actions such as deleting duplicate data and standardizing the format.

[0083] Step 4:

[0084] The server executes machine learning algorithms to analyze stored health data. The input is health data from cloud storage, and the output is anomaly detection and trend analysis results. The server uses machine learning models to identify abnormal heart rates, trends in sedentary lifestyles, and other issues. Specific operations include model initialization and parameter tuning.

[0085] Step 5:

[0086] The server uses digital twin technology to predict future health conditions based on the analysis results. The input is the analysis results, and the output is a prediction of the user's future health risks and potential diseases. The server integrates current lifestyle and medical history and calculates health risks through simulation. Specific operations include trend analysis of historical data.

[0087] Step 6:

[0088] The server uses a generative AI model to propose specific improvement actions based on the prediction results. The input is a prediction of future health risks, and the output is a proposal for improvement actions that will be notified to the user. The proposals generated using prompts include exercise improvement plans and dietary guidance. Specific actions include creating notification messages for the user.

[0089] (Application Example 1)

[0090] 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."

[0091] There is a growing need to integrate individual health information into daily life and seamlessly facilitate more specific and personalized health promotion decisions. However, currently, health data is merely recorded, and there are few opportunities to utilize this data in concrete aspects of daily life. This problem leads to underutilization of health information, making it difficult for individuals to find their optimal lifestyle. In particular, there is a lack of systems that utilize health data in shopping and selection at physical stores to provide specific advice and benefits.

[0092] 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.

[0093] In this invention, the server includes means for collecting and storing personal health data, means for analyzing the health status based on the health data, and means for predicting future health status based on the analysis results. This enables the provision of product information and special offer information linked to the user's health data in a physical store environment, allowing for personal health-related decision-making.

[0094] "Personal health data" refers to information about an individual's physical condition and activity, such as heart rate, steps taken, calories burned, and type and intensity of exercise.

[0095] "Analyzing health status" refers to the process of analyzing an individual's current physical condition and activity trends based on collected health data.

[0096] "Predicting future health status" means estimating future health risks and signs of disease by taking into account current lifestyle, medical history, genetic information, etc.

[0097] "Presenting health risks to users" means notifying users of potential health risks that may affect them, based on analysis and prediction results.

[0098] "Proposing specific improvement actions" means recommending concrete measures to users, such as exercise or dietary changes, in order to reduce their personal health risks.

[0099] A "physical store" is a physical commercial facility where goods and services are directly provided to end users.

[0100] "Providing product information and special offer information" means informing users of available products, as well as related special discounts and services, based on their health data.

[0101] This invention is a system that provides advanced support for personal health management and will be used as part of services in physical stores. The server stores and analyzes personal health data collected using wearable devices and smartphone terminals. This analysis uses machine learning algorithms to understand the individual's health status and make future predictions, and the results are presented to the user.

[0102] Based on these results, the server suggests specific improvement actions tailored to the user's health status and predicted health risks. These include suggestions for exercise and diet, customized to be easily implemented by the user.

[0103] In addition, to enhance the in-store shopping experience, the server provides product and special offer information linked to health data. This information is displayed in real time on user devices such as smartphones and smart glasses. This allows users to receive health advice and select suitable products while shopping in-store.

[0104] The terminal connects with the store system based on the user's current health data and notifies the user of product information and special offers. As a concrete example of product selection based on health data, the user may receive a discount coupon when purchasing fruit rich in vitamin C.

[0105] It is also possible to optimize promotional strategies based on user health data using generative AI models. An example of such a prompt message would be: "Based on the user's health data, a vitamin C deficiency is detected. Product information in the store is referenced, and a coupon for a suitable fruit is issued. Notifications are sent in real time."

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] Users collect health data such as heart rate, steps taken, and calorie consumption through wearable devices. This data is transferred to their smartphones via Bluetooth or Wi-Fi.

[0109] Step 2:

[0110] The device sends the received health data to a cloud server. Here, the data's integrity is verified, and it is stored on the server. The input data consists of various biometric information; by verifying and storing this information, accurate health information is obtained as output.

[0111] Step 3:

[0112] The server uses stored health data to analyze health status using machine learning algorithms. This analysis process includes detecting outliers and performing trend analysis, and the results are used as input for the next step.

[0113] Step 4:

[0114] The server uses digital twin technology to predict future health status based on analysis results and historical health data. This prediction involves risk assessment using statistical models and historical data. The output is the predicted health risk and the need for improvement.

[0115] Step 5:

[0116] Based on the prediction results, the server proposes specific improvement actions tailored to each individual user. This includes exercise and diet improvement plans. These suggestions are notified to the user's device, allowing the user to receive real-time feedback.

[0117] Step 6:

[0118] In physical stores, the terminal connects with the store system based on the user's health data to retrieve product information and special offers. Inputs include the user's health data and real-time product information from the store, while outputs include health-conscious product recommendations and special offers.

[0119] Step 7:

[0120] Based on information from their devices, users receive health advice and benefits that help them make informed purchasing decisions in stores. This process enables health-conscious consumer behavior.

[0121] Step 8:

[0122] Using a generative AI model, we dynamically optimize in-store promotions based on this health-related data to improve the user experience. This is achieved by generating effective prompt messages using user purchase data and health data.

[0123] 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.

[0124] This invention is a system that integrates and manages a user's health data and emotional state, and promotes health improvement based on this data. This system combines the user's physical data acquired using wearable devices and terminals with emotional data recognized using an emotion engine.

[0125] First, users utilize wearable devices to acquire health data such as heart rate and activity levels. This data is transmitted to a smartphone and aggregated by the device. Sensor technology is also used to recognize the user's movements, voice, and facial expressions, and this data is analyzed through an emotion engine.

[0126] The server stores health and emotional data received from the terminal in a database and analyzes the user's health status. First, it applies machine learning algorithms to the health data to identify outliers and health trends. Next, it evaluates the emotional state to understand how the user is feeling on a daily basis.

[0127] Based on these analysis results, the server uses digital twin technology to predict future health and emotional states. For example, it might suggest that prolonged stressful daily life could increase the risk of heart disease or high blood pressure. By combining this prediction with emotional data, more personalized health advice can be provided.

[0128] The suggested health improvement actions are tailored to the user's emotional state and designed to increase motivation for daily life activities. For example, if a user is feeling stressed, mindfulness meditation or relaxation activities may be suggested, while a more challenging fitness program may be introduced if the user is feeling positive.

[0129] As a concrete example, consider the case where User C uses this system. The emotion engine recognizes that User C frequently experiences anxiety in daily life. Based on this, the server suggests guided meditation to maintain a stable mental state and breathing exercises to promote normalization of heart rate for User C. It also indicates the expected positive health impacts of implementing these and a specific timeframe, supporting User C's continued efforts.

[0130] This invention aims to achieve better health outcomes by pursuing not only the user's physical health but also their overall well-being, including their emotional well-being, and by encouraging their active participation.

[0131] The following describes the processing flow.

[0132] Step 1:

[0133] Users wear wearable devices to periodically collect health data such as heart rate, steps taken, and calories burned. Furthermore, the devices utilize sensors to capture voice and facial expression data.

[0134] Step 2:

[0135] The device collects physiological data from wearable devices and simultaneously gathers information for analyzing emotional data from sensors. This data is transmitted to a server in real time.

[0136] Step 3:

[0137] The server stores the received health data in a database. Simultaneously, it utilizes an emotion engine to analyze the user's emotional state and identify emotional trends based on the data.

[0138] Step 4:

[0139] The server applies machine learning algorithms to detect anomalies in physical data and analyze patterns in health status. It also identifies factors such as stress and anxiety based on recognized emotional data.

[0140] Step 5:

[0141] The server integrates the analysis results of health and emotional data and uses digital twin technology to simulate the user's future health and emotional state. It analyzes how predicted health risks and emotional changes interact with each other.

[0142] Step 6:

[0143] Based on the analysis and prediction results, the server proposes specific actions to mitigate health risks and improve emotional state. These suggestions are adjusted and optimized according to the user's current emotional state.

[0144] Step 7:

[0145] Users use a smartphone app to receive feedback from the server and review the proposed action plan. Based on this, users can then translate their actions into daily health behaviors while maintaining their motivation.

[0146] Step 8:

[0147] By sharing user behavior data and feedback on improvement actions, the server updates the data and continuously optimizes analysis and recommendations. This loop strengthens support for each individual user, addressing both their physical and emotional needs.

[0148] (Example 2)

[0149] 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".

[0150] In personal health management, a comprehensive approach that considers not only physical information but also emotional states is necessary, but current systems do not adequately achieve this. Therefore, there is a need for a method that integrates health and emotional data, predicts future states, and proposes personalized health improvement measures.

[0151] 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.

[0152] In this invention, the server includes means for collecting and storing an individual's biometric and emotional information, means for analyzing the biometric and emotional information independently or integrally, and means for predicting future health and emotional states based on the analysis results. This enables comprehensive management of health and emotions, and allows for the suggestion of personalized preventive and corrective measures.

[0153] "Biometric information" refers to data that indicates an individual's health status, including physical measurements such as heart rate and exercise level.

[0154] "Emotional information" refers to data that indicates an individual's psychological state, and includes information about their emotional state obtained from facial expressions and tone of voice.

[0155] "Means for collection and storage" refers to functions or devices for performing the process of acquiring data and recording that data so that it can be used later.

[0156] "Means of analysis" refer to functions or devices that perform a process of reviewing collected data and extracting meaningful information.

[0157] "Means for predicting future health and emotional states" refers to functions and methods that predict future changes in an individual's health and emotions based on analysis results.

[0158] "Means of presenting factors that influence health risks and emotional states" refer to processes and functions that highlight and draw attention to factors that may influence predicted risks and emotions.

[0159] "Means of proposing specific improvement actions" refer to processes and methods for providing specific action guidelines and plans aimed at improving an individual's health and emotional well-being.

[0160] A "wearable device" is a device that a user can wear and carry with them, and which incorporates sensors and other components for collecting data.

[0161] A "terminal device" is an electronic device used for collecting, storing, analyzing, and presenting data, and is a device that the user directly operates.

[0162] A "learning model" is an algorithm and environment that learns patterns based on collected data and generates personalized advice.

[0163] This invention provides a system for comprehensively managing an individual's health and emotions. Specifically, the user uses a wearable device to measure biometric information in real time. This device incorporates a heart rate sensor and accelerometer, enabling the acquisition of health data such as heart rate and activity level. The acquired data is transmitted to terminal devices such as smartphones and tablets via Bluetooth or Wi-Fi.

[0164] The device receives this information and uses a dedicated application to collect and store the data. Furthermore, it uses the device's camera and microphone to capture the user's facial expressions and voice, and acquire emotional information. This emotional information is analyzed by an emotion engine that utilizes natural language processing and speech recognition technologies.

[0165] The server receives biometric and emotional information transmitted from the terminal and stores it in a cloud-based database. This stored data is then analyzed using machine learning frameworks such as Python. Based on the analysis results, future health and emotional states are predicted. A virtual model of the user is created using digital twin technology, and appropriate health improvement measures are proposed based on the risks and trends predicted by this model.

[0166] For example, by entering a prompt such as "Generate health advice based on the user's health data and sentiment analysis" into the system, the generating AI model creates personalized advice. By acting on this advice, the user can expect to maintain better health and emotional stability.

[0167] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0168] Step 1:

[0169] The user wears a wearable device to measure biometric information. This device uses a heart rate sensor and accelerometer to acquire data such as heart rate and activity level. The input is the user's physical state, and the output is biometric data derived from it. Accurate acquisition of this data enables processing in the next step.

[0170] Step 2:

[0171] The device receives biometric information from wearable devices via Bluetooth or Wi-Fi. This data is collected and stored by an application on the device. The device also acquires emotional information by capturing the user's facial expressions and voice using its built-in camera and microphone. The input consists of biometric information acquired from the device and emotional information from the device's sensors, and the output is a combined version of this data.

[0172] Step 3:

[0173] The server receives biometric and emotional information transmitted from the terminal and stores it in a cloud database. This data is then analyzed using a Python machine learning library to detect anomalies and analyze trends. The input is data transmitted from the terminal, and the output is the identification of anomalies and health trends through analysis. This allows for a detailed understanding of individual health conditions, which is then used in the next prediction step.

[0174] Step 4:

[0175] The server uses digital twin technology to predict future health and emotional states based on the analysis results. It then utilizes a generative AI model to create personalized advice. The input is the analysis results from the previous step, and the output is predictions of future health risks and emotional changes, as well as specific improvement measures. This process provides health advice optimized for the user.

[0176] Step 5:

[0177] The user performs health improvement actions displayed on the device. Specifically, they incorporate suggested mindfulness meditation and appropriate exercise programs into their daily life. The input is the improvement action suggested in the previous step, and the output is the user's practice and feedback. This feedback is sent back to the server via the device and used for continuous health management.

[0178] (Application Example 2)

[0179] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0180] In modern society, an individual's physical and emotional state are closely intertwined, and there is a need to manage them comprehensively. However, conventional systems only manage physical data, making it difficult to propose health improvement actions that take emotional states into account. Furthermore, because the feedback does not specifically indicate what improvements can be expected, there is a problem in that it does not lead to practical and sustainable behavioral change for users.

[0181] 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.

[0182] In this invention, the server includes means for collecting and storing personal health data and emotional data, means for analyzing health and emotional states based on the health and emotional data, and means for predicting future health and emotional states based on the analysis results. This makes it possible to propose personalized health improvement actions that correspond to the user's emotional state. Furthermore, by providing dietary suggestions that correspond to emotional changes along with health risks, more practical and sustainable health management can be achieved.

[0183] "Health data" refers to information that expresses an individual's physical condition as numerical values ​​or indicators, including heart rate, exercise level, and blood pressure.

[0184] "Emotional data" refers to information that expresses an individual's psychological state and emotional changes, and is obtained from facial expressions, voice patterns, behavioral indicators, and other sources.

[0185] "Means of analysis" refers to the process of applying appropriate algorithms to collected data in order to clarify the characteristics and trends of that data.

[0186] "Means for predicting future health and emotional states" refers to technologies that use collected and analyzed data to anticipate future changes in an individual's health and emotions, and to predict the risks and conditions associated with those changes.

[0187] "Specific improvement actions" refer to recommended behaviors and activities aimed at improvement, such as exercise programs or meal plans, based on the user's health and emotional state.

[0188] To implement this invention, it is necessary to construct a system that efficiently collects and analyzes individual health and emotional data and provides appropriate health improvement actions and dietary suggestions. This system is implemented using the following hardware and software.

[0189] hardware

[0190] The system primarily utilizes wearable devices and mobile terminals. Wearable devices collect health data such as the user's heart rate and activity level. Simultaneously, sensors are incorporated to analyze the user's facial expressions and voice, collecting emotional data.

[0191] software

[0192] An application is installed on the mobile device, and the acquired health and emotional data are aggregated. This data is sent to a cloud server, where detailed analysis is performed. Generative AI models such as Google Cloud AI and OpenAI are applied to the server, and AI-powered data analysis and prompt generation are performed.

[0193] Data processing and calculations

[0194] The server applies machine learning algorithms based on the received data to analyze an individual's health and emotional state. This predicts future health and emotional states, and creates a personalized health improvement plan for each user. Based on the analysis results, prompts are input into a generating AI model to generate specific health actions and dietary suggestions.

[0195] Specific example

[0196] For example, if the system analyzes that the user is in a stressed state, it will generate a menu suggestion for a meal that has a relaxing effect. An example of a prompt message would be input to the generating AI in the format of, "Based on the user's health and emotional data, please create a recommended menu for tonight's dinner. The user's emotional state has been found to be slightly stressed."

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

[0198] Step 1:

[0199] The user wears a wearable device to collect health data such as heart rate and activity level. This health data is transmitted in real time to a mobile device via sensors. The input is biometric data from the wearable device, and the output is health data stored on the mobile device.

[0200] Step 2:

[0201] The device collects emotional data using voice and facial expression sensors. This information is also analyzed in real time and aggregated on the mobile device. The input is the user's voice and facial expression data, and the output is the emotional data analyzed by the device.

[0202] Step 3:

[0203] The device sends health and emotional data to the server. This allows the server to centrally manage both types of data. The input is the data sent from the device, and the output is higher-level data stored on the server.

[0204] Step 4:

[0205] The server applies machine learning algorithms to the received health data to analyze the user's health status. If abnormalities are detected or health trends are identified, a detailed report is generated. The input is raw data, and the output is the result of the health status analysis.

[0206] Step 5:

[0207] The server analyzes the emotional state based on the collected emotional data. It evaluates what emotions the user is experiencing and generates an emotional state report based on that evaluation. The input is emotional data, and the output is the evaluation result of the emotional state.

[0208] Step 6:

[0209] The server uses a generative AI model based on the analysis results to form prompt sentences for generating personalized health improvement actions and meal suggestions. By inputting prompts into the generative AI, it outputs specific suggestions. The input is the analysis results of health and emotional states, and the output is personalized health improvement suggestions and meal menus.

[0210] Step 7:

[0211] Users view health improvement actions and dietary suggestions sent from the server on their devices and incorporate them into their daily lives. Because the suggestions are specific, users can easily incorporate them into their daily habits. The input is the suggestions from the server, and the output is the change in the user's behavior.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] [Second Embodiment]

[0216] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0217] 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.

[0218] 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).

[0219] 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.

[0220] 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.

[0221] 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).

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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".

[0228] This invention is a system that provides advanced support for individual health management, carrying out a series of processes from collecting and analyzing health data to predicting the future and proposing specific improvement actions.

[0229] In this system, users use wearable devices in their daily lives to automatically record their health data. The wearable devices collect data such as heart rate, steps taken, calories burned, and type and intensity of exercise, and transmit it to a smartphone. The smartphone periodically sends this data to a server to store the latest information in the cloud.

[0230] The server analyzes the user's current health status based on the received health data. The analysis uses machine learning algorithms to detect data anomalies and identify health trends. The analysis results are visualized to allow users to intuitively understand their health status and are provided through a dedicated user interface.

[0231] Next, the server uses digital twin technology to predict future health conditions based on the collected data and analysis results. Specifically, it takes into account current lifestyle, medical history, and genetic information to calculate future health risks and potential disease signs, and then notifies the user.

[0232] Furthermore, based on the prediction results, the server suggests specific actions necessary to reduce the user's health risks. These suggestions include improving exercise habits, revising meal plans, and undergoing regular health checkups. These are customized to the extent that the user can realistically implement them.

[0233] As a concrete example, consider how User B uses the system. User B uses a wearable device and a smartphone to record their daily activities. Based on this data, the server detects that User B tends to be sedentary and predicts that this may increase their risk of heart disease in the future. Based on these results, the server suggests that User B add 30 minutes of walking to their daily routine five days a week and recommends increasing their intake of fruits and vegetables in their diet.

[0234] Thus, this invention transforms health information into a concrete and actionable form, supporting users in their daily decision-making toward maintaining their health and preventing disease.

[0235] The following describes the processing flow.

[0236] Step 1:

[0237] Users wear wearable devices to collect health data such as heart rate, steps taken, calories burned, and activity level. This data is transmitted to a smartphone in real time.

[0238] Step 2:

[0239] The device temporarily stores data received from wearable devices. The data is automatically transferred to a server via Bluetooth or Wi-Fi. Users can manually enter additional information, such as meal details and weight, as needed.

[0240] Step 3:

[0241] The server receives health data transmitted from the terminal and stores it in a database. The stored data is preprocessed and prepared for analysis.

[0242] Step 4:

[0243] The server uses machine learning algorithms to analyze health data. It detects anomalies, identifies trends, analyzes patterns, and evaluates the user's current health status.

[0244] Step 5:

[0245] Based on the analysis results, the server utilizes digital twin technology to simulate the user's future health status. It calculates predicted health risks and potential medical expenses, and reflects this in the user profile.

[0246] Step 6:

[0247] Based on predictions and analysis results, the server proposes specific health improvement actions for the user. These suggestions are customized to suit the user's lifestyle.

[0248] Step 7:

[0249] Users access health information, risks, and improvement actions provided by the server through a smartphone app. This prepares them to translate this information into concrete health behaviors in their daily lives.

[0250] Step 8:

[0251] The user performs the suggested action and feeds the results back to the server via the wearable device and app. This loop allows the system to continuously monitor the user's health and make new suggestions as needed.

[0252] (Example 1)

[0253] 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."

[0254] In modern times, it is difficult for individuals to continuously and accurately manage their own health, predict potential disease risks, and take preventive measures. To address this situation, a sophisticated system is needed that effectively collects and analyzes individual health information, predicts future health risks based on that information, and presents concrete improvement actions in an easy-to-understand manner.

[0255] 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.

[0256] In this invention, the server includes means for acquiring and recording information about an individual's health, means for using machine learning algorithms to analyze the health status based on the health information, means for predicting future health status using digital twin technology based on the analysis results, and means for generating prompt sentences to enhance predictions and suggestions using a generative AI model. This enables concrete decision-making toward continuous health management and disease prevention for individuals.

[0257] "Health-related information" refers to data related to an individual's physical condition, including data such as heart rate, steps taken, calories burned, and type and intensity of exercise.

[0258] A "machine learning algorithm" refers to mathematical methods and models that allow computers to learn patterns and regularities from data and use that knowledge to perform analysis and predictions.

[0259] "Digital twin technology" is a technique that utilizes virtual models that accurately mimic real-world physical objects or processes to simulate and analyze their actions and behaviors.

[0260] "Health risk" refers to factors or conditions that could potentially harm an individual's health in the future, including the possibility of developing a specific disease.

[0261] A "generative AI model" is a computational model that uses artificial intelligence technology to generate new data and information, and in particular operates based on prompts to improve the accuracy of predictions and suggestions.

[0262] A "prompt statement" is a document used to give specific instructions or questions to a generative AI model. It plays a role in controlling the AI's behavior and defining its output.

[0263] This invention provides a system that analyzes a user's health status, predicts future health risks, and proposes specific improvement actions.

[0264] By wearing a wearable device daily, users automatically collect health-related information such as heart rate, steps taken, calories burned, and type and intensity of exercise. This information is transferred to a smartphone via Bluetooth or Wi-Fi and stored in an application. The application on the device then transmits this data to a server via the internet.

[0265] The server uses machine learning algorithms to analyze health information collected in the cloud. Specifically, it uses anomaly detection algorithms to detect unusual health patterns. The analysis results are visualized and provided to the user through a dedicated user interface.

[0266] Furthermore, the server uses digital twin technology to predict future health conditions. This process takes into account the user's past data, lifestyle, medical history, and genetic information. The predicted risk information is communicated to the user, and specific improvement actions are suggested.

[0267] The generative AI model enhances analysis and prediction, operating based on appropriate prompts. For example, a prompt might be, "Use the current heart rate data to detect anomalies and assess the risk of future heart disease."

[0268] As a concrete example, consider a scenario where a user utilizes this system. The user's daily activities are recorded by a wearable device, and this data is analyzed on a server. The analysis reveals an abnormality in the user's heart rate pattern, predicting a potential increased risk of heart disease in the future. Based on these results, the server suggests that the user increase their daily walking to 30 minutes, five times a week, to improve their health. This allows the user to obtain a concrete action plan.

[0269] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0270] Step 1:

[0271] Users wear wearable devices to collect health-related information such as heart rate, steps taken, calories burned, and type and intensity of exercise. The wearable devices acquire data from their respective sensors and transmit it to a smartphone via Bluetooth or Wi-Fi. The input is raw data from the wearable device sensors, while the output is aggregated data sent to the smartphone. A specific example of this operation is a heart rate sensor measuring heart rate every minute.

[0272] Step 2:

[0273] The device temporarily stores received health data in an application and periodically sends it to a server via an internet connection. The input is aggregated data sent from the wearable device, and the output is data transferred to the server. During this process, data format conversion and verification are performed to prevent data loss or duplication. Specifically, the device adds data transmission tasks to a queue at regular intervals.

[0274] Step 3:

[0275] The server stores data in cloud storage and updates the database. The input is health data sent from the terminal, and the output is a dataset stored in cloud storage. The server first verifies data integrity and then writes it to the database. Specifically, it performs actions such as deleting duplicate data and standardizing the format.

[0276] Step 4:

[0277] The server executes machine learning algorithms to analyze stored health data. The input is health data from cloud storage, and the output is anomaly detection and trend analysis results. The server uses machine learning models to identify abnormal heart rates, trends in sedentary lifestyles, and other issues. Specific operations include model initialization and parameter tuning.

[0278] Step 5:

[0279] The server uses digital twin technology to predict the future health status based on the analysis results. The input is the analysis result, and the output is the prediction of the user's future health risks and possible diseases. The server integrates the current lifestyle and past history and calculates the health risks through simulation. Specific operations include trend analysis of past data.

[0280] Step 6:

[0281] The server uses a generative AI model to propose specific improvement actions based on the prediction results. The input is the future health risk prediction, and the output is the proposal of improvement actions to be notified to the user. The proposals generated by leveraging the prompt text include improvement plans for exercise habits and dietary guidance. Specific operations include creating notification messages for the user.

[0282] (Application Example 1)

[0283] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0284] There is a need to integrate an individual's health status into daily life and seamlessly make more specific and individualized decisions for health promotion However, currently, health data is merely recorded, and there are few opportunities for these data to be utilized in specific scenarios of daily life. Due to this problem, health-related information is not utilized, making it difficult for individuals to find the optimal lifestyle. In particular, there is a lack of a system that utilizes health data in shopping and selection at physical stores and provides specific advice and benefits.

[0285] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0286] In this invention, the server includes means for collecting and storing personal health data, means for analyzing the health status based on the health data, and means for predicting the future health status based on the analysis result. Thereby, it is possible to provide product information and privilege information linked to the user's health data in a physical store environment, enabling personal health-related decision-making.

[0287] "Personal health data" refers to information related to an individual's physical condition and activities, such as heart rate, number of steps, calories burned, type and intensity of exercise, etc.

[0288] "Analyzing the health status" is a process of analyzing an individual's current physical condition and activity trends based on the collected health data.

[0289] "Predicting the future health status" means estimating future health risks and disease symptoms by taking into account the current lifestyle, medical history, genetic information, etc.

[0290] "Presenting health risks to the user" means notifying the user of health risk factors that may affect the individual based on the analysis and prediction results.

[0291] "Proposing specific improvement actions" is an act of recommending specific measures such as exercise or diet review to the user in order to reduce the individual's health risks.

[0292] "Physical store" is a physical commercial facility and a place that directly provides products and services to end-users.

[0293] "Providing product information and privilege information" means informing the user of information about purchasable products and related special discounts or services based on the user's health data.

[0294] This invention is a system that provides advanced support for personal health management and will be used as part of services in physical stores. The server stores and analyzes personal health data collected using wearable devices and smartphone terminals. This analysis uses machine learning algorithms to understand the individual's health status and make future predictions, and the results are presented to the user.

[0295] Based on these results, the server suggests specific improvement actions tailored to the user's health status and predicted health risks. These include suggestions for exercise and diet, customized to be easily implemented by the user.

[0296] In addition, to enhance the in-store shopping experience, the server provides product and special offer information linked to health data. This information is displayed in real time on user devices such as smartphones and smart glasses. This allows users to receive health advice and select suitable products while shopping in-store.

[0297] The terminal connects with the store system based on the user's current health data and notifies the user of product information and special offers. As a concrete example of product selection based on health data, the user may receive a discount coupon when purchasing fruit rich in vitamin C.

[0298] It is also possible to optimize promotional strategies based on user health data using generative AI models. An example of such a prompt message would be: "Based on the user's health data, a vitamin C deficiency is detected. Product information in the store is referenced, and a coupon for a suitable fruit is issued. Notifications are sent in real time."

[0299] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0300] Step 1:

[0301] The user collects health data such as heart rate, number of steps, and calorie consumption through a wearable device. This data is transferred to a smartphone via Bluetooth or Wi-Fi.

[0302] Step 2:

[0303] The terminal sends the received health data to a cloud server. Here, the data integrity is verified and stored in the server. The input data is composed of various biometric information, and by verifying and storing its content, accurate health information can be obtained as output.

[0304] Step 3:

[0305] The server uses the stored health data and applies a machine learning algorithm to analyze the health status. In this analysis process, detection of outliers and trend analysis are performed, and the results are used as input for the next step.

[0306] Step 4:

[0307] The server predicts the future health status using digital twin technology based on the analysis results and past health data. In this prediction, statistical models and risk assessments using past data are performed. The output is the predicted health risk and the need for improvement.

[0308] Step 5:

[0309] Based on the prediction results, the server proposes specific improvement actions suitable for individual users. This includes exercise and diet improvement plans. The proposed content is notified to the user's terminal, and the user can receive real-time feedback.

[0310] Step 6:

[0311] In physical stores, the terminal connects with the store system based on the user's health data to retrieve product information and special offers. Inputs include the user's health data and real-time product information from the store, while outputs include health-conscious product recommendations and special offers.

[0312] Step 7:

[0313] Based on information from their devices, users receive health advice and benefits that help them make informed purchasing decisions in stores. This process enables health-conscious consumer behavior.

[0314] Step 8:

[0315] Using a generative AI model, we dynamically optimize in-store promotions based on this health-related data to improve the user experience. This is achieved by generating effective prompt messages using user purchase data and health data.

[0316] 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.

[0317] This invention is a system that integrates and manages a user's health data and emotional state, and promotes health improvement based on this data. This system combines the user's physical data acquired using wearable devices and terminals with emotional data recognized using an emotion engine.

[0318] First, users utilize wearable devices to acquire health data such as heart rate and activity levels. This data is transmitted to a smartphone and aggregated by the device. Sensor technology is also used to recognize the user's movements, voice, and facial expressions, and this data is analyzed through an emotion engine.

[0319] The server stores health and emotional data received from the terminal in a database and analyzes the user's health status. First, it applies machine learning algorithms to the health data to identify outliers and health trends. Next, it evaluates the emotional state to understand how the user is feeling on a daily basis.

[0320] Based on these analysis results, the server uses digital twin technology to predict future health and emotional states. For example, it might suggest that prolonged stressful daily life could increase the risk of heart disease or high blood pressure. By combining this prediction with emotional data, more personalized health advice can be provided.

[0321] The suggested health improvement actions are tailored to the user's emotional state and designed to increase motivation for daily life activities. For example, if a user is feeling stressed, mindfulness meditation or relaxation activities may be suggested, while a more challenging fitness program may be introduced if the user is feeling positive.

[0322] As a concrete example, consider the case where User C uses this system. The emotion engine recognizes that User C frequently experiences anxiety in daily life. Based on this, the server suggests guided meditation to maintain a stable mental state and breathing exercises to promote normalization of heart rate for User C. It also indicates the expected positive health impacts of implementing these and a specific timeframe, supporting User C's continued efforts.

[0323] This invention aims to achieve better health outcomes by pursuing not only the user's physical health but also their overall well-being, including their emotional well-being, and by encouraging their active participation.

[0324] The following describes the processing flow.

[0325] Step 1:

[0326] Users wear wearable devices to periodically collect health data such as heart rate, steps taken, and calories burned. Furthermore, the devices utilize sensors to capture voice and facial expression data.

[0327] Step 2:

[0328] The device collects physiological data from wearable devices and simultaneously gathers information for analyzing emotional data from sensors. This data is transmitted to a server in real time.

[0329] Step 3:

[0330] The server stores the received health data in a database. Simultaneously, it utilizes an emotion engine to analyze the user's emotional state and identify emotional trends based on the data.

[0331] Step 4:

[0332] The server applies machine learning algorithms to detect anomalies in physical data and analyze patterns in health status. It also identifies factors such as stress and anxiety based on recognized emotional data.

[0333] Step 5:

[0334] The server integrates the analysis results of health and emotional data and uses digital twin technology to simulate the user's future health and emotional state. It analyzes how predicted health risks and emotional changes interact with each other.

[0335] Step 6:

[0336] Based on the analysis and prediction results, the server proposes specific actions to mitigate health risks and improve emotional state. These suggestions are adjusted and optimized according to the user's current emotional state.

[0337] Step 7:

[0338] Users use a smartphone app to receive feedback from the server and review the proposed action plan. Based on this, users can then translate their actions into daily health behaviors while maintaining their motivation.

[0339] Step 8:

[0340] By sharing user behavior data and feedback on improvement actions, the server updates the data and continuously optimizes analysis and recommendations. This loop strengthens support for each individual user, addressing both their physical and emotional needs.

[0341] (Example 2)

[0342] 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".

[0343] In personal health management, a comprehensive approach that considers not only physical information but also emotional states is necessary, but current systems do not adequately achieve this. Therefore, there is a need for a method that integrates health and emotional data, predicts future states, and proposes personalized health improvement measures.

[0344] 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.

[0345] In this invention, the server includes means for collecting and storing an individual's biometric and emotional information, means for analyzing the biometric and emotional information independently or integrally, and means for predicting future health and emotional states based on the analysis results. This enables comprehensive management of health and emotions, and allows for the suggestion of personalized preventive and corrective measures.

[0346] "Biometric information" refers to data that indicates an individual's health status, including physical measurements such as heart rate and exercise level.

[0347] "Emotional information" refers to data that indicates an individual's psychological state, and includes information about their emotional state obtained from facial expressions and tone of voice.

[0348] "Means for collection and storage" refers to functions or devices for performing the process of acquiring data and recording that data so that it can be used later.

[0349] "Means of analysis" refer to functions or devices that perform a process of reviewing collected data and extracting meaningful information.

[0350] "Means for predicting future health and emotional states" refers to functions and methods that predict future changes in an individual's health and emotions based on analysis results.

[0351] "Means of presenting factors that influence health risks and emotional states" refer to processes and functions that highlight and draw attention to factors that may influence predicted risks and emotions.

[0352] "Means of proposing specific improvement actions" refer to processes and methods for providing specific action guidelines and plans aimed at improving an individual's health and emotional well-being.

[0353] A "wearable device" is a device that a user can wear and carry with them, and which incorporates sensors and other components for collecting data.

[0354] A "terminal device" is an electronic device used for collecting, storing, analyzing, and presenting data, and is a device that the user directly operates.

[0355] A "learning model" is an algorithm and environment that learns patterns based on collected data and generates personalized advice.

[0356] This invention provides a system for comprehensively managing an individual's health and emotions. Specifically, the user uses a wearable device to measure biometric information in real time. This device incorporates a heart rate sensor and accelerometer, enabling the acquisition of health data such as heart rate and activity level. The acquired data is transmitted to terminal devices such as smartphones and tablets via Bluetooth or Wi-Fi.

[0357] The device receives this information and uses a dedicated application to collect and store the data. Furthermore, it uses the device's camera and microphone to capture the user's facial expressions and voice, and acquire emotional information. This emotional information is analyzed by an emotion engine that utilizes natural language processing and speech recognition technologies.

[0358] The server receives biometric and emotional information transmitted from the terminal and stores it in a cloud-based database. This stored data is then analyzed using machine learning frameworks such as Python. Based on the analysis results, future health and emotional states are predicted. A virtual model of the user is created using digital twin technology, and appropriate health improvement measures are proposed based on the risks and trends predicted by this model.

[0359] For example, by entering a prompt such as "Generate health advice based on the user's health data and sentiment analysis" into the system, the generating AI model creates personalized advice. By acting on this advice, the user can expect to maintain better health and emotional stability.

[0360] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0361] Step 1:

[0362] The user wears a wearable device to measure biometric information. This device uses a heart rate sensor and accelerometer to acquire data such as heart rate and activity level. The input is the user's physical state, and the output is biometric data derived from it. Accurate acquisition of this data enables processing in the next step.

[0363] Step 2:

[0364] The device receives biometric information from wearable devices via Bluetooth or Wi-Fi. This data is collected and stored by an application on the device. The device also acquires emotional information by capturing the user's facial expressions and voice using its built-in camera and microphone. The input consists of biometric information acquired from the device and emotional information from the device's sensors, and the output is a combined version of this data.

[0365] Step 3:

[0366] The server receives biometric and emotional information transmitted from the terminal and stores it in a cloud database. This data is then analyzed using a Python machine learning library to detect anomalies and analyze trends. The input is data transmitted from the terminal, and the output is the identification of anomalies and health trends through analysis. This allows for a detailed understanding of individual health conditions, which is then used in the next prediction step.

[0367] Step 4:

[0368] The server uses digital twin technology to predict future health and emotional states based on the analysis results. It then utilizes a generative AI model to create personalized advice. The input is the analysis results from the previous step, and the output is predictions of future health risks and emotional changes, as well as specific improvement measures. This process provides health advice optimized for the user.

[0369] Step 5:

[0370] The user performs health improvement actions displayed on the device. Specifically, they incorporate suggested mindfulness meditation and appropriate exercise programs into their daily life. The input is the improvement action suggested in the previous step, and the output is the user's practice and feedback. This feedback is sent back to the server via the device and used for continuous health management.

[0371] (Application Example 2)

[0372] 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."

[0373] In modern society, an individual's physical and emotional state are closely intertwined, and there is a need to manage them comprehensively. However, conventional systems only manage physical data, making it difficult to propose health improvement actions that take emotional states into account. Furthermore, because the feedback does not specifically indicate what improvements can be expected, there is a problem in that it does not lead to practical and sustainable behavioral change for users.

[0374] 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.

[0375] In this invention, the server includes means for collecting and storing personal health data and emotional data, means for analyzing health and emotional states based on the health and emotional data, and means for predicting future health and emotional states based on the analysis results. This makes it possible to propose personalized health improvement actions that correspond to the user's emotional state. Furthermore, by providing dietary suggestions that correspond to emotional changes along with health risks, more practical and sustainable health management can be achieved.

[0376] "Health data" refers to information that expresses an individual's physical condition as numerical values ​​or indicators, including heart rate, exercise level, and blood pressure.

[0377] "Emotional data" refers to information that expresses an individual's psychological state and emotional changes, and is obtained from facial expressions, voice patterns, behavioral indicators, and other sources.

[0378] "Means of analysis" refers to the process of applying appropriate algorithms to collected data in order to clarify the characteristics and trends of that data.

[0379] "Means for predicting future health and emotional states" refers to technologies that use collected and analyzed data to anticipate future changes in an individual's health and emotions, and to predict the risks and conditions associated with those changes.

[0380] "Specific improvement actions" refer to recommended behaviors and activities aimed at improvement, such as exercise programs or meal plans, based on the user's health and emotional state.

[0381] To implement this invention, it is necessary to construct a system that efficiently collects and analyzes individual health and emotional data and provides appropriate health improvement actions and dietary suggestions. This system is implemented using the following hardware and software.

[0382] hardware

[0383] The system primarily utilizes wearable devices and mobile terminals. Wearable devices collect health data such as the user's heart rate and activity level. Simultaneously, sensors are incorporated to analyze the user's facial expressions and voice, collecting emotional data.

[0384] software

[0385] An application is installed on the mobile device, and the acquired health and emotional data are aggregated. This data is sent to a cloud server, where detailed analysis is performed. Generative AI models such as Google Cloud AI and OpenAI are applied to the server, and AI-powered data analysis and prompt generation are performed.

[0386] Data processing and calculations

[0387] The server applies machine learning algorithms based on the received data to analyze an individual's health and emotional state. This predicts future health and emotional states, and creates a personalized health improvement plan for each user. Based on the analysis results, prompts are input into a generating AI model to generate specific health actions and dietary suggestions.

[0388] Specific example

[0389] For example, if the system analyzes that the user is in a stressed state, it will generate a menu suggestion for a meal that has a relaxing effect. An example of a prompt message would be input to the generating AI in the format of, "Based on the user's health and emotional data, please create a recommended menu for tonight's dinner. The user's emotional state has been found to be slightly stressed."

[0390] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0391] Step 1:

[0392] The user wears a wearable device to collect health data such as heart rate and activity level. This health data is transmitted in real time to a mobile device via sensors. The input is biometric data from the wearable device, and the output is health data stored on the mobile device.

[0393] Step 2:

[0394] The device collects emotional data using voice and facial expression sensors. This information is also analyzed in real time and aggregated on the mobile device. The input is the user's voice and facial expression data, and the output is the emotional data analyzed by the device.

[0395] Step 3:

[0396] The device sends health and emotional data to the server. This allows the server to centrally manage both types of data. The input is the data sent from the device, and the output is higher-level data stored on the server.

[0397] Step 4:

[0398] The server applies machine learning algorithms to the received health data to analyze the user's health status. If abnormalities are detected or health trends are identified, a detailed report is generated. The input is raw data, and the output is the result of the health status analysis.

[0399] Step 5:

[0400] The server analyzes the emotional state based on the collected emotional data. It evaluates what emotions the user is experiencing and generates an emotional state report based on that evaluation. The input is emotional data, and the output is the evaluation result of the emotional state.

[0401] Step 6:

[0402] The server uses a generative AI model based on the analysis results to form prompt sentences for generating personalized health improvement actions and meal suggestions. By inputting prompts into the generative AI, it outputs specific suggestions. The input is the analysis results of health and emotional states, and the output is personalized health improvement suggestions and meal menus.

[0403] Step 7:

[0404] Users view health improvement actions and dietary suggestions sent from the server on their devices and incorporate them into their daily lives. Because the suggestions are specific, users can easily incorporate them into their daily habits. The input is the suggestions from the server, and the output is the change in the user's behavior.

[0405] 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.

[0406] 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.

[0407] 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.

[0408] [Third Embodiment]

[0409] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0410] 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.

[0411] 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).

[0412] 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.

[0413] 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.

[0414] 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).

[0415] 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.

[0416] 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.

[0417] 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.

[0418] 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.

[0419] 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.

[0420] 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".

[0421] This invention is a system that provides advanced support for individual health management, carrying out a series of processes from collecting and analyzing health data to predicting the future and proposing specific improvement actions.

[0422] In this system, users use wearable devices in their daily lives to automatically record their health data. The wearable devices collect data such as heart rate, steps taken, calories burned, and type and intensity of exercise, and transmit it to a smartphone. The smartphone periodically sends this data to a server to store the latest information in the cloud.

[0423] The server analyzes the user's current health status based on the received health data. The analysis uses machine learning algorithms to detect data anomalies and identify health trends. The analysis results are visualized to allow users to intuitively understand their health status and are provided through a dedicated user interface.

[0424] Next, the server uses digital twin technology to predict future health conditions based on the collected data and analysis results. Specifically, it takes into account current lifestyle, medical history, and genetic information to calculate future health risks and potential disease signs, and then notifies the user.

[0425] Furthermore, based on the prediction results, the server suggests specific actions necessary to reduce the user's health risks. These suggestions include improving exercise habits, revising meal plans, and undergoing regular health checkups. These are customized to the extent that the user can realistically implement them.

[0426] As a concrete example, consider how User B uses the system. User B uses a wearable device and a smartphone to record their daily activities. Based on this data, the server detects that User B tends to be sedentary and predicts that this may increase their risk of heart disease in the future. Based on these results, the server suggests that User B add 30 minutes of walking to their daily routine five days a week and recommends increasing their intake of fruits and vegetables in their diet.

[0427] Thus, this invention transforms health information into a concrete and actionable form, supporting users in their daily decision-making toward maintaining their health and preventing disease.

[0428] The following describes the processing flow.

[0429] Step 1:

[0430] Users wear wearable devices to collect health data such as heart rate, steps taken, calories burned, and activity level. This data is transmitted to a smartphone in real time.

[0431] Step 2:

[0432] The device temporarily stores data received from wearable devices. The data is automatically transferred to a server via Bluetooth or Wi-Fi. Users can manually enter additional information, such as meal details and weight, as needed.

[0433] Step 3:

[0434] The server receives health data transmitted from the terminal and stores it in a database. The stored data is preprocessed and prepared for analysis.

[0435] Step 4:

[0436] The server uses machine learning algorithms to analyze health data. It detects anomalies, identifies trends, analyzes patterns, and evaluates the user's current health status.

[0437] Step 5:

[0438] Based on the analysis results, the server utilizes digital twin technology to simulate the user's future health status. It calculates predicted health risks and potential medical expenses, and reflects this in the user profile.

[0439] Step 6:

[0440] Based on predictions and analysis results, the server proposes specific health improvement actions for the user. These suggestions are customized to suit the user's lifestyle.

[0441] Step 7:

[0442] Users access health information, risks, and improvement actions provided by the server through a smartphone app. This prepares them to translate this information into concrete health behaviors in their daily lives.

[0443] Step 8:

[0444] The user performs the suggested action and feeds the results back to the server via the wearable device and app. This loop allows the system to continuously monitor the user's health and make new suggestions as needed.

[0445] (Example 1)

[0446] 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."

[0447] In modern times, it is difficult for individuals to continuously and accurately manage their own health, predict potential disease risks, and take preventive measures. To address this situation, a sophisticated system is needed that effectively collects and analyzes individual health information, predicts future health risks based on that information, and presents concrete improvement actions in an easy-to-understand manner.

[0448] 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.

[0449] In this invention, the server includes means for acquiring and recording information about an individual's health, means for using machine learning algorithms to analyze the health status based on the health information, means for predicting future health status using digital twin technology based on the analysis results, and means for generating prompt sentences to enhance predictions and suggestions using a generative AI model. This enables concrete decision-making toward continuous health management and disease prevention for individuals.

[0450] "Health-related information" refers to data related to an individual's physical condition, including data such as heart rate, steps taken, calories burned, and type and intensity of exercise.

[0451] A "machine learning algorithm" refers to mathematical methods and models that allow computers to learn patterns and regularities from data and use that knowledge to perform analysis and predictions.

[0452] "Digital twin technology" is a technique that utilizes virtual models that accurately mimic real-world physical objects or processes to simulate and analyze their actions and behaviors.

[0453] "Health risk" refers to factors or conditions that could potentially harm an individual's health in the future, including the possibility of developing a specific disease.

[0454] A "generative AI model" is a computational model that uses artificial intelligence technology to generate new data and information, and in particular operates based on prompts to improve the accuracy of predictions and suggestions.

[0455] A "prompt statement" is a document used to give specific instructions or questions to a generative AI model. It plays a role in controlling the AI's behavior and defining its output.

[0456] This invention provides a system that analyzes a user's health status, predicts future health risks, and proposes specific improvement actions.

[0457] By wearing a wearable device daily, users automatically collect health-related information such as heart rate, steps taken, calories burned, and type and intensity of exercise. This information is transferred to a smartphone via Bluetooth or Wi-Fi and stored in an application. The application on the device then transmits this data to a server via the internet.

[0458] The server uses machine learning algorithms to analyze health information collected in the cloud. Specifically, it uses anomaly detection algorithms to detect unusual health patterns. The analysis results are visualized and provided to the user through a dedicated user interface.

[0459] Furthermore, the server uses digital twin technology to predict future health conditions. This process takes into account the user's past data, lifestyle, medical history, and genetic information. The predicted risk information is communicated to the user, and specific improvement actions are suggested.

[0460] The generative AI model enhances analysis and prediction, operating based on appropriate prompts. For example, a prompt might be, "Use the current heart rate data to detect anomalies and assess the risk of future heart disease."

[0461] As a concrete example, consider a scenario where a user utilizes this system. The user's daily activities are recorded by a wearable device, and this data is analyzed on a server. The analysis reveals an abnormality in the user's heart rate pattern, predicting a potential increased risk of heart disease in the future. Based on these results, the server suggests that the user increase their daily walking to 30 minutes, five times a week, to improve their health. This allows the user to obtain a concrete action plan.

[0462] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0463] Step 1:

[0464] Users wear wearable devices to collect health-related information such as heart rate, steps taken, calories burned, and type and intensity of exercise. The wearable devices acquire data from their respective sensors and transmit it to a smartphone via Bluetooth or Wi-Fi. The input is raw data from the wearable device sensors, while the output is aggregated data sent to the smartphone. A specific example of this operation is a heart rate sensor measuring heart rate every minute.

[0465] Step 2:

[0466] The device temporarily stores received health data in an application and periodically sends it to a server via an internet connection. The input is aggregated data sent from the wearable device, and the output is data transferred to the server. During this process, data format conversion and verification are performed to prevent data loss or duplication. Specifically, the device adds data transmission tasks to a queue at regular intervals.

[0467] Step 3:

[0468] The server stores data in cloud storage and updates the database. The input is health data sent from the terminal, and the output is a dataset stored in cloud storage. The server first verifies data integrity and then writes it to the database. Specifically, it performs actions such as deleting duplicate data and standardizing the format.

[0469] Step 4:

[0470] The server executes machine learning algorithms to analyze stored health data. The input is health data from cloud storage, and the output is anomaly detection and trend analysis results. The server uses machine learning models to identify abnormal heart rates, trends in sedentary lifestyles, and other issues. Specific operations include model initialization and parameter tuning.

[0471] Step 5:

[0472] The server uses digital twin technology to predict future health conditions based on the analysis results. The input is the analysis results, and the output is a prediction of the user's future health risks and potential diseases. The server integrates current lifestyle and medical history and calculates health risks through simulation. Specific operations include trend analysis of historical data.

[0473] Step 6:

[0474] The server uses a generative AI model to propose specific improvement actions based on the prediction results. The input is a prediction of future health risks, and the output is a proposal for improvement actions that will be notified to the user. The proposals generated using prompts include exercise improvement plans and dietary guidance. Specific actions include creating notification messages for the user.

[0475] (Application Example 1)

[0476] 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."

[0477] There is a growing need to integrate individual health information into daily life and seamlessly facilitate more specific and personalized health promotion decisions. However, currently, health data is merely recorded, and there are few opportunities to utilize this data in concrete aspects of daily life. This problem leads to underutilization of health information, making it difficult for individuals to find their optimal lifestyle. In particular, there is a lack of systems that utilize health data in shopping and selection at physical stores to provide specific advice and benefits.

[0478] 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.

[0479] In this invention, the server includes means for collecting and storing personal health data, means for analyzing the health status based on the health data, and means for predicting future health status based on the analysis results. This enables the provision of product information and special offer information linked to the user's health data in a physical store environment, allowing for personal health-related decision-making.

[0480] "Personal health data" refers to information about an individual's physical condition and activity, such as heart rate, steps taken, calories burned, and type and intensity of exercise.

[0481] "Analyzing health status" refers to the process of analyzing an individual's current physical condition and activity trends based on collected health data.

[0482] "Predicting future health status" means estimating future health risks and signs of disease by taking into account current lifestyle, medical history, genetic information, etc.

[0483] "Presenting health risks to users" means notifying users of potential health risks that may affect them, based on analysis and prediction results.

[0484] "Proposing specific improvement actions" means recommending concrete measures to users, such as exercise or dietary changes, in order to reduce their personal health risks.

[0485] A "physical store" is a physical commercial facility where goods and services are directly provided to end users.

[0486] "Providing product information and special offer information" means informing users of available products, as well as related special discounts and services, based on their health data.

[0487] This invention is a system that provides advanced support for personal health management and will be used as part of services in physical stores. The server stores and analyzes personal health data collected using wearable devices and smartphone terminals. This analysis uses machine learning algorithms to understand the individual's health status and make future predictions, and the results are presented to the user.

[0488] Based on these results, the server suggests specific improvement actions tailored to the user's health status and predicted health risks. These include suggestions for exercise and diet, customized to be easily implemented by the user.

[0489] In addition, to enhance the in-store shopping experience, the server provides product and special offer information linked to health data. This information is displayed in real time on user devices such as smartphones and smart glasses. This allows users to receive health advice and select suitable products while shopping in-store.

[0490] The terminal connects with the store system based on the user's current health data and notifies the user of product information and special offers. As a concrete example of product selection based on health data, the user may receive a discount coupon when purchasing fruit rich in vitamin C.

[0491] It is also possible to optimize promotional strategies based on user health data using generative AI models. An example of such a prompt message would be: "Based on the user's health data, a vitamin C deficiency is detected. Product information in the store is referenced, and a coupon for a suitable fruit is issued. Notifications are sent in real time."

[0492] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0493] Step 1:

[0494] Users collect health data such as heart rate, steps taken, and calorie consumption through wearable devices. This data is transferred to their smartphones via Bluetooth or Wi-Fi.

[0495] Step 2:

[0496] The device sends the received health data to a cloud server. Here, the data's integrity is verified, and it is stored on the server. The input data consists of various biometric information; by verifying and storing this information, accurate health information is obtained as output.

[0497] Step 3:

[0498] The server uses stored health data to analyze health status using machine learning algorithms. This analysis process includes detecting outliers and performing trend analysis, and the results are used as input for the next step.

[0499] Step 4:

[0500] The server uses digital twin technology to predict future health status based on analysis results and historical health data. This prediction involves risk assessment using statistical models and historical data. The output is the predicted health risk and the need for improvement.

[0501] Step 5:

[0502] Based on the prediction results, the server proposes specific improvement actions tailored to each individual user. This includes exercise and diet improvement plans. These suggestions are notified to the user's device, allowing the user to receive real-time feedback.

[0503] Step 6:

[0504] In physical stores, the terminal connects with the store system based on the user's health data to retrieve product information and special offers. Inputs include the user's health data and real-time product information from the store, while outputs include health-conscious product recommendations and special offers.

[0505] Step 7:

[0506] Based on information from their devices, users receive health advice and benefits that help them make informed purchasing decisions in stores. This process enables health-conscious consumer behavior.

[0507] Step 8:

[0508] Using a generative AI model, we dynamically optimize in-store promotions based on this health-related data to improve the user experience. This is achieved by generating effective prompt messages using user purchase data and health data.

[0509] 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.

[0510] This invention is a system that integrates and manages a user's health data and emotional state, and promotes health improvement based on this data. This system combines the user's physical data acquired using wearable devices and terminals with emotional data recognized using an emotion engine.

[0511] First, users utilize wearable devices to acquire health data such as heart rate and activity levels. This data is transmitted to a smartphone and aggregated by the device. Sensor technology is also used to recognize the user's movements, voice, and facial expressions, and this data is analyzed through an emotion engine.

[0512] The server stores health and emotional data received from the terminal in a database and analyzes the user's health status. First, it applies machine learning algorithms to the health data to identify outliers and health trends. Next, it evaluates the emotional state to understand how the user is feeling on a daily basis.

[0513] Based on these analysis results, the server uses digital twin technology to predict future health and emotional states. For example, it might suggest that prolonged stressful daily life could increase the risk of heart disease or high blood pressure. By combining this prediction with emotional data, more personalized health advice can be provided.

[0514] The suggested health improvement actions are tailored to the user's emotional state and designed to increase motivation for daily life activities. For example, if a user is feeling stressed, mindfulness meditation or relaxation activities may be suggested, while a more challenging fitness program may be introduced if the user is feeling positive.

[0515] As a concrete example, consider the case where User C uses this system. The emotion engine recognizes that User C frequently experiences anxiety in daily life. Based on this, the server suggests guided meditation to maintain a stable mental state and breathing exercises to promote normalization of heart rate for User C. It also indicates the expected positive health impacts of implementing these and a specific timeframe, supporting User C's continued efforts.

[0516] This invention aims to achieve better health outcomes by pursuing not only the user's physical health but also their overall well-being, including their emotional well-being, and by encouraging their active participation.

[0517] The following describes the processing flow.

[0518] Step 1:

[0519] Users wear wearable devices to periodically collect health data such as heart rate, steps taken, and calories burned. Furthermore, the devices utilize sensors to capture voice and facial expression data.

[0520] Step 2:

[0521] The device collects physiological data from wearable devices and simultaneously gathers information for analyzing emotional data from sensors. This data is transmitted to a server in real time.

[0522] Step 3:

[0523] The server stores the received health data in a database. Simultaneously, it utilizes an emotion engine to analyze the user's emotional state and identify emotional trends based on the data.

[0524] Step 4:

[0525] The server applies machine learning algorithms to detect anomalies in physical data and analyze patterns in health status. It also identifies factors such as stress and anxiety based on recognized emotional data.

[0526] Step 5:

[0527] The server integrates the analysis results of health and emotional data and uses digital twin technology to simulate the user's future health and emotional state. It analyzes how predicted health risks and emotional changes interact with each other.

[0528] Step 6:

[0529] Based on the analysis and prediction results, the server proposes specific actions to mitigate health risks and improve emotional state. These suggestions are adjusted and optimized according to the user's current emotional state.

[0530] Step 7:

[0531] Users use a smartphone app to receive feedback from the server and review the proposed action plan. Based on this, users can then translate their actions into daily health behaviors while maintaining their motivation.

[0532] Step 8:

[0533] By sharing user behavior data and feedback on improvement actions, the server updates the data and continuously optimizes analysis and recommendations. This loop strengthens support for each individual user, addressing both their physical and emotional needs.

[0534] (Example 2)

[0535] 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."

[0536] In personal health management, a comprehensive approach that considers not only physical information but also emotional states is necessary, but current systems do not adequately achieve this. Therefore, there is a need for a method that integrates health and emotional data, predicts future states, and proposes personalized health improvement measures.

[0537] 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.

[0538] In this invention, the server includes means for collecting and storing an individual's biometric and emotional information, means for analyzing the biometric and emotional information independently or integrally, and means for predicting future health and emotional states based on the analysis results. This enables comprehensive management of health and emotions, and allows for the suggestion of personalized preventive and corrective measures.

[0539] "Biometric information" refers to data that indicates an individual's health status, including physical measurements such as heart rate and exercise level.

[0540] "Emotional information" refers to data that indicates an individual's psychological state, and includes information about their emotional state obtained from facial expressions and tone of voice.

[0541] "Means for collection and storage" refers to functions or devices for performing the process of acquiring data and recording that data so that it can be used later.

[0542] "Means of analysis" refer to functions or devices that perform a process of reviewing collected data and extracting meaningful information.

[0543] "Means for predicting future health and emotional states" refers to functions and methods that predict future changes in an individual's health and emotions based on analysis results.

[0544] "Means of presenting factors that influence health risks and emotional states" refer to processes and functions that highlight and draw attention to factors that may influence predicted risks and emotions.

[0545] "Means of proposing specific improvement actions" refer to processes and methods for providing specific action guidelines and plans aimed at improving an individual's health and emotional well-being.

[0546] A "wearable device" is a device that a user can wear and carry with them, and which incorporates sensors and other components for collecting data.

[0547] A "terminal device" is an electronic device used for collecting, storing, analyzing, and presenting data, and is a device that the user directly operates.

[0548] A "learning model" is an algorithm and environment that learns patterns based on collected data and generates personalized advice.

[0549] This invention provides a system for comprehensively managing an individual's health and emotions. Specifically, the user uses a wearable device to measure biometric information in real time. This device incorporates a heart rate sensor and accelerometer, enabling the acquisition of health data such as heart rate and activity level. The acquired data is transmitted to terminal devices such as smartphones and tablets via Bluetooth or Wi-Fi.

[0550] The device receives this information and uses a dedicated application to collect and store the data. Furthermore, it uses the device's camera and microphone to capture the user's facial expressions and voice, and acquire emotional information. This emotional information is analyzed by an emotion engine that utilizes natural language processing and speech recognition technologies.

[0551] The server receives biometric and emotional information transmitted from the terminal and stores it in a cloud-based database. This stored data is then analyzed using machine learning frameworks such as Python. Based on the analysis results, future health and emotional states are predicted. A virtual model of the user is created using digital twin technology, and appropriate health improvement measures are proposed based on the risks and trends predicted by this model.

[0552] For example, by entering a prompt such as "Generate health advice based on the user's health data and sentiment analysis" into the system, the generating AI model creates personalized advice. By acting on this advice, the user can expect to maintain better health and emotional stability.

[0553] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0554] Step 1:

[0555] The user wears a wearable device to measure biometric information. This device uses a heart rate sensor and accelerometer to acquire data such as heart rate and activity level. The input is the user's physical state, and the output is biometric data derived from it. Accurate acquisition of this data enables processing in the next step.

[0556] Step 2:

[0557] The device receives biometric information from wearable devices via Bluetooth or Wi-Fi. This data is collected and stored by an application on the device. The device also acquires emotional information by capturing the user's facial expressions and voice using its built-in camera and microphone. The input consists of biometric information acquired from the device and emotional information from the device's sensors, and the output is a combined version of this data.

[0558] Step 3:

[0559] The server receives biometric and emotional information transmitted from the terminal and stores it in a cloud database. This data is then analyzed using a Python machine learning library to detect anomalies and analyze trends. The input is data transmitted from the terminal, and the output is the identification of anomalies and health trends through analysis. This allows for a detailed understanding of individual health conditions, which is then used in the next prediction step.

[0560] Step 4:

[0561] The server uses digital twin technology to predict future health and emotional states based on the analysis results. It then utilizes a generative AI model to create personalized advice. The input is the analysis results from the previous step, and the output is predictions of future health risks and emotional changes, as well as specific improvement measures. This process provides health advice optimized for the user.

[0562] Step 5:

[0563] The user performs health improvement actions displayed on the device. Specifically, they incorporate suggested mindfulness meditation and appropriate exercise programs into their daily life. The input is the improvement action suggested in the previous step, and the output is the user's practice and feedback. This feedback is sent back to the server via the device and used for continuous health management.

[0564] (Application Example 2)

[0565] 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."

[0566] In modern society, an individual's physical and emotional state are closely intertwined, and there is a need to manage them comprehensively. However, conventional systems only manage physical data, making it difficult to propose health improvement actions that take emotional states into account. Furthermore, because the feedback does not specifically indicate what improvements can be expected, there is a problem in that it does not lead to practical and sustainable behavioral change for users.

[0567] 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.

[0568] In this invention, the server includes means for collecting and storing personal health data and emotional data, means for analyzing health and emotional states based on the health and emotional data, and means for predicting future health and emotional states based on the analysis results. This makes it possible to propose personalized health improvement actions that correspond to the user's emotional state. Furthermore, by providing dietary suggestions that correspond to emotional changes along with health risks, more practical and sustainable health management can be achieved.

[0569] "Health data" refers to information that expresses an individual's physical condition as numerical values ​​or indicators, including heart rate, exercise level, and blood pressure.

[0570] "Emotional data" refers to information that expresses an individual's psychological state and emotional changes, and is obtained from facial expressions, voice patterns, behavioral indicators, and other sources.

[0571] "Means of analysis" refers to the process of applying appropriate algorithms to collected data in order to clarify the characteristics and trends of that data.

[0572] "Means for predicting future health and emotional states" refers to technologies that use collected and analyzed data to anticipate future changes in an individual's health and emotions, and to predict the risks and conditions associated with those changes.

[0573] "Specific improvement actions" refer to recommended behaviors and activities aimed at improvement, such as exercise programs or meal plans, based on the user's health and emotional state.

[0574] To implement this invention, it is necessary to construct a system that efficiently collects and analyzes individual health and emotional data and provides appropriate health improvement actions and dietary suggestions. This system is implemented using the following hardware and software.

[0575] hardware

[0576] The system primarily utilizes wearable devices and mobile terminals. Wearable devices collect health data such as the user's heart rate and activity level. Simultaneously, sensors are incorporated to analyze the user's facial expressions and voice, collecting emotional data.

[0577] software

[0578] An application is installed on the mobile device, and the acquired health and emotional data are aggregated. This data is sent to a cloud server, where detailed analysis is performed. Generative AI models such as Google Cloud AI and OpenAI are applied to the server, and AI-powered data analysis and prompt generation are performed.

[0579] Data processing and calculations

[0580] The server applies machine learning algorithms based on the received data to analyze an individual's health and emotional state. This predicts future health and emotional states, and creates a personalized health improvement plan for each user. Based on the analysis results, prompts are input into a generating AI model to generate specific health actions and dietary suggestions.

[0581] Specific example

[0582] For example, if the system analyzes that the user is in a stressed state, it will generate a menu suggestion for a meal that has a relaxing effect. An example of a prompt message would be input to the generating AI in the format of, "Based on the user's health and emotional data, please create a recommended menu for tonight's dinner. The user's emotional state has been found to be slightly stressed."

[0583] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0584] Step 1:

[0585] The user wears a wearable device to collect health data such as heart rate and activity level. This health data is transmitted in real time to a mobile device via sensors. The input is biometric data from the wearable device, and the output is health data stored on the mobile device.

[0586] Step 2:

[0587] The device collects emotional data using voice and facial expression sensors. This information is also analyzed in real time and aggregated on the mobile device. The input is the user's voice and facial expression data, and the output is the emotional data analyzed by the device.

[0588] Step 3:

[0589] The device sends health and emotional data to the server. This allows the server to centrally manage both types of data. The input is the data sent from the device, and the output is higher-level data stored on the server.

[0590] Step 4:

[0591] The server applies machine learning algorithms to the received health data to analyze the user's health status. If abnormalities are detected or health trends are identified, a detailed report is generated. The input is raw data, and the output is the result of the health status analysis.

[0592] Step 5:

[0593] The server analyzes the emotional state based on the collected emotional data. It evaluates what emotions the user is experiencing and generates an emotional state report based on that evaluation. The input is emotional data, and the output is the evaluation result of the emotional state.

[0594] Step 6:

[0595] The server uses a generative AI model based on the analysis results to form prompt sentences for generating personalized health improvement actions and meal suggestions. By inputting prompts into the generative AI, it outputs specific suggestions. The input is the analysis results of health and emotional states, and the output is personalized health improvement suggestions and meal menus.

[0596] Step 7:

[0597] Users view health improvement actions and dietary suggestions sent from the server on their devices and incorporate them into their daily lives. Because the suggestions are specific, users can easily incorporate them into their daily habits. The input is the suggestions from the server, and the output is the change in the user's behavior.

[0598] 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.

[0599] 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.

[0600] 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.

[0601] [Fourth Embodiment]

[0602] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0603] 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.

[0604] 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).

[0605] 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.

[0606] 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.

[0607] 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).

[0608] 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.

[0609] 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.

[0610] 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.

[0611] 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.

[0612] 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.

[0613] 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.

[0614] 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".

[0615] This invention is a system that provides advanced support for individual health management, carrying out a series of processes from collecting and analyzing health data to predicting the future and proposing specific improvement actions.

[0616] In this system, users use wearable devices in their daily lives to automatically record their health data. The wearable devices collect data such as heart rate, steps taken, calories burned, and type and intensity of exercise, and transmit it to a smartphone. The smartphone periodically sends this data to a server to store the latest information in the cloud.

[0617] The server analyzes the user's current health status based on the received health data. The analysis uses machine learning algorithms to detect data anomalies and identify health trends. The analysis results are visualized to allow users to intuitively understand their health status and are provided through a dedicated user interface.

[0618] Next, the server uses digital twin technology to predict future health conditions based on the collected data and analysis results. Specifically, it takes into account current lifestyle, medical history, and genetic information to calculate future health risks and potential disease signs, and then notifies the user.

[0619] Furthermore, based on the prediction results, the server suggests specific actions necessary to reduce the user's health risks. These suggestions include improving exercise habits, revising meal plans, and undergoing regular health checkups. These are customized to the extent that the user can realistically implement them.

[0620] As a concrete example, consider how User B uses the system. User B uses a wearable device and a smartphone to record their daily activities. Based on this data, the server detects that User B tends to be sedentary and predicts that this may increase their risk of heart disease in the future. Based on these results, the server suggests that User B add 30 minutes of walking to their daily routine five days a week and recommends increasing their intake of fruits and vegetables in their diet.

[0621] Thus, this invention transforms health information into a concrete and actionable form, supporting users in their daily decision-making toward maintaining their health and preventing disease.

[0622] The following describes the processing flow.

[0623] Step 1:

[0624] Users wear wearable devices to collect health data such as heart rate, steps taken, calories burned, and activity level. This data is transmitted to a smartphone in real time.

[0625] Step 2:

[0626] The device temporarily stores data received from wearable devices. The data is automatically transferred to a server via Bluetooth or Wi-Fi. Users can manually enter additional information, such as meal details and weight, as needed.

[0627] Step 3:

[0628] The server receives health data transmitted from the terminal and stores it in a database. The stored data is preprocessed and prepared for analysis.

[0629] Step 4:

[0630] The server uses machine learning algorithms to analyze health data. It detects anomalies, identifies trends, analyzes patterns, and evaluates the user's current health status.

[0631] Step 5:

[0632] Based on the analysis results, the server utilizes digital twin technology to simulate the user's future health status. It calculates predicted health risks and potential medical expenses, and reflects this in the user profile.

[0633] Step 6:

[0634] Based on predictions and analysis results, the server proposes specific health improvement actions for the user. These suggestions are customized to suit the user's lifestyle.

[0635] Step 7:

[0636] Users access health information, risks, and improvement actions provided by the server through a smartphone app. This prepares them to translate this information into concrete health behaviors in their daily lives.

[0637] Step 8:

[0638] The user performs the suggested action and feeds the results back to the server via the wearable device and app. This loop allows the system to continuously monitor the user's health and make new suggestions as needed.

[0639] (Example 1)

[0640] 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".

[0641] In modern times, it is difficult for individuals to continuously and accurately manage their own health, predict potential disease risks, and take preventive measures. To address this situation, a sophisticated system is needed that effectively collects and analyzes individual health information, predicts future health risks based on that information, and presents concrete improvement actions in an easy-to-understand manner.

[0642] 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.

[0643] In this invention, the server includes means for acquiring and recording information about an individual's health, means for using machine learning algorithms to analyze the health status based on the health information, means for predicting future health status using digital twin technology based on the analysis results, and means for generating prompt sentences to enhance predictions and suggestions using a generative AI model. This enables concrete decision-making toward continuous health management and disease prevention for individuals.

[0644] "Health-related information" refers to data related to an individual's physical condition, including data such as heart rate, steps taken, calories burned, and type and intensity of exercise.

[0645] A "machine learning algorithm" refers to mathematical methods and models that allow computers to learn patterns and regularities from data and use that knowledge to perform analysis and predictions.

[0646] "Digital twin technology" is a technique that utilizes virtual models that accurately mimic real-world physical objects or processes to simulate and analyze their actions and behaviors.

[0647] "Health risk" refers to factors or conditions that could potentially harm an individual's health in the future, including the possibility of developing a specific disease.

[0648] A "generative AI model" is a computational model that uses artificial intelligence technology to generate new data and information, and in particular operates based on prompts to improve the accuracy of predictions and suggestions.

[0649] A "prompt statement" is a document used to give specific instructions or questions to a generative AI model. It plays a role in controlling the AI's behavior and defining its output.

[0650] This invention provides a system that analyzes a user's health status, predicts future health risks, and proposes specific improvement actions.

[0651] By wearing a wearable device daily, users automatically collect health-related information such as heart rate, steps taken, calories burned, and type and intensity of exercise. This information is transferred to a smartphone via Bluetooth or Wi-Fi and stored in an application. The application on the device then transmits this data to a server via the internet.

[0652] The server uses machine learning algorithms to analyze health information collected in the cloud. Specifically, it uses anomaly detection algorithms to detect unusual health patterns. The analysis results are visualized and provided to the user through a dedicated user interface.

[0653] Furthermore, the server uses digital twin technology to predict future health conditions. This process takes into account the user's past data, lifestyle, medical history, and genetic information. The predicted risk information is communicated to the user, and specific improvement actions are suggested.

[0654] The generative AI model enhances analysis and prediction, operating based on appropriate prompts. For example, a prompt might be, "Use the current heart rate data to detect anomalies and assess the risk of future heart disease."

[0655] As a concrete example, consider a scenario where a user utilizes this system. The user's daily activities are recorded by a wearable device, and this data is analyzed on a server. The analysis reveals an abnormality in the user's heart rate pattern, predicting a potential increased risk of heart disease in the future. Based on these results, the server suggests that the user increase their daily walking to 30 minutes, five times a week, to improve their health. This allows the user to obtain a concrete action plan.

[0656] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0657] Step 1:

[0658] Users wear wearable devices to collect health-related information such as heart rate, steps taken, calories burned, and type and intensity of exercise. The wearable devices acquire data from their respective sensors and transmit it to a smartphone via Bluetooth or Wi-Fi. The input is raw data from the wearable device sensors, while the output is aggregated data sent to the smartphone. A specific example of this operation is a heart rate sensor measuring heart rate every minute.

[0659] Step 2:

[0660] The device temporarily stores received health data in an application and periodically sends it to a server via an internet connection. The input is aggregated data sent from the wearable device, and the output is data transferred to the server. During this process, data format conversion and verification are performed to prevent data loss or duplication. Specifically, the device adds data transmission tasks to a queue at regular intervals.

[0661] Step 3:

[0662] The server stores data in cloud storage and updates the database. The input is health data sent from the terminal, and the output is a dataset stored in cloud storage. The server first verifies data integrity and then writes it to the database. Specifically, it performs actions such as deleting duplicate data and standardizing the format.

[0663] Step 4:

[0664] The server executes machine learning algorithms to analyze stored health data. The input is health data from cloud storage, and the output is anomaly detection and trend analysis results. The server uses machine learning models to identify abnormal heart rates, trends in sedentary lifestyles, and other issues. Specific operations include model initialization and parameter tuning.

[0665] Step 5:

[0666] The server uses digital twin technology to predict future health conditions based on the analysis results. The input is the analysis results, and the output is a prediction of the user's future health risks and potential diseases. The server integrates current lifestyle and medical history and calculates health risks through simulation. Specific operations include trend analysis of historical data.

[0667] Step 6:

[0668] The server uses a generative AI model to propose specific improvement actions based on the prediction results. The input is a prediction of future health risks, and the output is a proposal for improvement actions that will be notified to the user. The proposals generated using prompts include exercise improvement plans and dietary guidance. Specific actions include creating notification messages for the user.

[0669] (Application Example 1)

[0670] 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".

[0671] There is a growing need to integrate individual health information into daily life and seamlessly facilitate more specific and personalized health promotion decisions. However, currently, health data is merely recorded, and there are few opportunities to utilize this data in concrete aspects of daily life. This problem leads to underutilization of health information, making it difficult for individuals to find their optimal lifestyle. In particular, there is a lack of systems that utilize health data in shopping and selection at physical stores to provide specific advice and benefits.

[0672] 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.

[0673] In this invention, the server includes means for collecting and storing personal health data, means for analyzing the health status based on the health data, and means for predicting future health status based on the analysis results. This enables the provision of product information and special offer information linked to the user's health data in a physical store environment, allowing for personal health-related decision-making.

[0674] "Personal health data" refers to information about an individual's physical condition and activity, such as heart rate, steps taken, calories burned, and type and intensity of exercise.

[0675] "Analyzing health status" refers to the process of analyzing an individual's current physical condition and activity trends based on collected health data.

[0676] "Predicting future health status" means estimating future health risks and signs of disease by taking into account current lifestyle, medical history, genetic information, etc.

[0677] "Presenting health risks to users" means notifying users of potential health risks that may affect them, based on analysis and prediction results.

[0678] "Proposing specific improvement actions" means recommending concrete measures to users, such as exercise or dietary changes, in order to reduce their personal health risks.

[0679] A "physical store" is a physical commercial facility where goods and services are directly provided to end users.

[0680] "Providing product information and special offer information" means informing users of available products, as well as related special discounts and services, based on their health data.

[0681] This invention is a system that provides advanced support for personal health management and will be used as part of services in physical stores. The server stores and analyzes personal health data collected using wearable devices and smartphone terminals. This analysis uses machine learning algorithms to understand the individual's health status and make future predictions, and the results are presented to the user.

[0682] Based on these results, the server suggests specific improvement actions tailored to the user's health status and predicted health risks. These include suggestions for exercise and diet, customized to be easily implemented by the user.

[0683] In addition, to enhance the in-store shopping experience, the server provides product and special offer information linked to health data. This information is displayed in real time on user devices such as smartphones and smart glasses. This allows users to receive health advice and select suitable products while shopping in-store.

[0684] The terminal connects with the store system based on the user's current health data and notifies the user of product information and special offers. As a concrete example of product selection based on health data, the user may receive a discount coupon when purchasing fruit rich in vitamin C.

[0685] It is also possible to optimize promotional strategies based on user health data using generative AI models. An example of such a prompt message would be: "Based on the user's health data, a vitamin C deficiency is detected. Product information in the store is referenced, and a coupon for a suitable fruit is issued. Notifications are sent in real time."

[0686] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0687] Step 1:

[0688] Users collect health data such as heart rate, steps taken, and calorie consumption through wearable devices. This data is transferred to their smartphones via Bluetooth or Wi-Fi.

[0689] Step 2:

[0690] The device sends the received health data to a cloud server. Here, the data's integrity is verified, and it is stored on the server. The input data consists of various biometric information; by verifying and storing this information, accurate health information is obtained as output.

[0691] Step 3:

[0692] The server uses stored health data to analyze health status using machine learning algorithms. This analysis process includes detecting outliers and performing trend analysis, and the results are used as input for the next step.

[0693] Step 4:

[0694] The server uses digital twin technology to predict future health status based on analysis results and historical health data. This prediction involves risk assessment using statistical models and historical data. The output is the predicted health risk and the need for improvement.

[0695] Step 5:

[0696] Based on the prediction results, the server proposes specific improvement actions tailored to each individual user. This includes exercise and diet improvement plans. These suggestions are notified to the user's device, allowing the user to receive real-time feedback.

[0697] Step 6:

[0698] In physical stores, the terminal connects with the store system based on the user's health data to retrieve product information and special offers. Inputs include the user's health data and real-time product information from the store, while outputs include health-conscious product recommendations and special offers.

[0699] Step 7:

[0700] Based on information from their devices, users receive health advice and benefits that help them make informed purchasing decisions in stores. This process enables health-conscious consumer behavior.

[0701] Step 8:

[0702] Using a generative AI model, we dynamically optimize in-store promotions based on this health-related data to improve the user experience. This is achieved by generating effective prompt messages using user purchase data and health data.

[0703] 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.

[0704] This invention is a system that integrates and manages a user's health data and emotional state, and promotes health improvement based on this data. This system combines the user's physical data acquired using wearable devices and terminals with emotional data recognized using an emotion engine.

[0705] First, users utilize wearable devices to acquire health data such as heart rate and activity levels. This data is transmitted to a smartphone and aggregated by the device. Sensor technology is also used to recognize the user's movements, voice, and facial expressions, and this data is analyzed through an emotion engine.

[0706] The server stores health and emotional data received from the terminal in a database and analyzes the user's health status. First, it applies machine learning algorithms to the health data to identify outliers and health trends. Next, it evaluates the emotional state to understand how the user is feeling on a daily basis.

[0707] Based on these analysis results, the server uses digital twin technology to predict future health and emotional states. For example, it might suggest that prolonged stressful daily life could increase the risk of heart disease or high blood pressure. By combining this prediction with emotional data, more personalized health advice can be provided.

[0708] The suggested health improvement actions are tailored to the user's emotional state and designed to increase motivation for daily life activities. For example, if a user is feeling stressed, mindfulness meditation or relaxation activities may be suggested, while a more challenging fitness program may be introduced if the user is feeling positive.

[0709] As a concrete example, consider the case where User C uses this system. The emotion engine recognizes that User C frequently experiences anxiety in daily life. Based on this, the server suggests guided meditation to maintain a stable mental state and breathing exercises to promote normalization of heart rate for User C. It also indicates the expected positive health impacts of implementing these and a specific timeframe, supporting User C's continued efforts.

[0710] This invention aims to achieve better health outcomes by pursuing not only the user's physical health but also their overall well-being, including their emotional well-being, and by encouraging their active participation.

[0711] The following describes the processing flow.

[0712] Step 1:

[0713] Users wear wearable devices to periodically collect health data such as heart rate, steps taken, and calories burned. Furthermore, the devices utilize sensors to capture voice and facial expression data.

[0714] Step 2:

[0715] The device collects physiological data from wearable devices and simultaneously gathers information for analyzing emotional data from sensors. This data is transmitted to a server in real time.

[0716] Step 3:

[0717] The server stores the received health data in a database. Simultaneously, it utilizes an emotion engine to analyze the user's emotional state and identify emotional trends based on the data.

[0718] Step 4:

[0719] The server applies machine learning algorithms to detect anomalies in physical data and analyze patterns in health status. It also identifies factors such as stress and anxiety based on recognized emotional data.

[0720] Step 5:

[0721] The server integrates the analysis results of health and emotional data and uses digital twin technology to simulate the user's future health and emotional state. It analyzes how predicted health risks and emotional changes interact with each other.

[0722] Step 6:

[0723] Based on the analysis and prediction results, the server proposes specific actions to mitigate health risks and improve emotional state. These suggestions are adjusted and optimized according to the user's current emotional state.

[0724] Step 7:

[0725] Users use a smartphone app to receive feedback from the server and review the proposed action plan. Based on this, users can then translate their actions into daily health behaviors while maintaining their motivation.

[0726] Step 8:

[0727] By sharing user behavior data and feedback on improvement actions, the server updates the data and continuously optimizes analysis and recommendations. This loop strengthens support for each individual user, addressing both their physical and emotional needs.

[0728] (Example 2)

[0729] 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".

[0730] In personal health management, a comprehensive approach that considers not only physical information but also emotional states is necessary, but current systems do not adequately achieve this. Therefore, there is a need for a method that integrates health and emotional data, predicts future states, and proposes personalized health improvement measures.

[0731] 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.

[0732] In this invention, the server includes means for collecting and storing an individual's biometric and emotional information, means for analyzing the biometric and emotional information independently or integrally, and means for predicting future health and emotional states based on the analysis results. This enables comprehensive management of health and emotions, and allows for the suggestion of personalized preventive and corrective measures.

[0733] "Biometric information" refers to data that indicates an individual's health status, including physical measurements such as heart rate and exercise level.

[0734] "Emotional information" refers to data that indicates an individual's psychological state, and includes information about their emotional state obtained from facial expressions and tone of voice.

[0735] "Means for collection and storage" refers to functions or devices for performing the process of acquiring data and recording that data so that it can be used later.

[0736] "Means of analysis" refer to functions or devices that perform a process of reviewing collected data and extracting meaningful information.

[0737] "Means for predicting future health and emotional states" refers to functions and methods that predict future changes in an individual's health and emotions based on analysis results.

[0738] "Means of presenting factors that influence health risks and emotional states" refer to processes and functions that highlight and draw attention to factors that may influence predicted risks and emotions.

[0739] "Means of proposing specific improvement actions" refer to processes and methods for providing specific action guidelines and plans aimed at improving an individual's health and emotional well-being.

[0740] A "wearable device" is a device that a user can wear and carry with them, and which incorporates sensors and other components for collecting data.

[0741] A "terminal device" is an electronic device used for collecting, storing, analyzing, and presenting data, and is a device that the user directly operates.

[0742] A "learning model" is an algorithm and environment that learns patterns based on collected data and generates personalized advice.

[0743] This invention provides a system for comprehensively managing an individual's health and emotions. Specifically, the user uses a wearable device to measure biometric information in real time. This device incorporates a heart rate sensor and accelerometer, enabling the acquisition of health data such as heart rate and activity level. The acquired data is transmitted to terminal devices such as smartphones and tablets via Bluetooth or Wi-Fi.

[0744] The device receives this information and uses a dedicated application to collect and store the data. Furthermore, it uses the device's camera and microphone to capture the user's facial expressions and voice, and acquire emotional information. This emotional information is analyzed by an emotion engine that utilizes natural language processing and speech recognition technologies.

[0745] The server receives biometric and emotional information transmitted from the terminal and stores it in a cloud-based database. This stored data is then analyzed using machine learning frameworks such as Python. Based on the analysis results, future health and emotional states are predicted. A virtual model of the user is created using digital twin technology, and appropriate health improvement measures are proposed based on the risks and trends predicted by this model.

[0746] For example, by entering a prompt such as "Generate health advice based on the user's health data and sentiment analysis" into the system, the generating AI model creates personalized advice. By acting on this advice, the user can expect to maintain better health and emotional stability.

[0747] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0748] Step 1:

[0749] The user wears a wearable device to measure biometric information. This device uses a heart rate sensor and accelerometer to acquire data such as heart rate and activity level. The input is the user's physical state, and the output is biometric data derived from it. Accurate acquisition of this data enables processing in the next step.

[0750] Step 2:

[0751] The device receives biometric information from wearable devices via Bluetooth or Wi-Fi. This data is collected and stored by an application on the device. The device also acquires emotional information by capturing the user's facial expressions and voice using its built-in camera and microphone. The input consists of biometric information acquired from the device and emotional information from the device's sensors, and the output is a combined version of this data.

[0752] Step 3:

[0753] The server receives biometric and emotional information transmitted from the terminal and stores it in a cloud database. This data is then analyzed using a Python machine learning library to detect anomalies and analyze trends. The input is data transmitted from the terminal, and the output is the identification of anomalies and health trends through analysis. This allows for a detailed understanding of individual health conditions, which is then used in the next prediction step.

[0754] Step 4:

[0755] The server uses digital twin technology to predict future health and emotional states based on the analysis results. It then utilizes a generative AI model to create personalized advice. The input is the analysis results from the previous step, and the output is predictions of future health risks and emotional changes, as well as specific improvement measures. This process provides health advice optimized for the user.

[0756] Step 5:

[0757] The user performs health improvement actions displayed on the device. Specifically, they incorporate suggested mindfulness meditation and appropriate exercise programs into their daily life. The input is the improvement action suggested in the previous step, and the output is the user's practice and feedback. This feedback is sent back to the server via the device and used for continuous health management.

[0758] (Application Example 2)

[0759] 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".

[0760] In modern society, an individual's physical and emotional state are closely intertwined, and there is a need to manage them comprehensively. However, conventional systems only manage physical data, making it difficult to propose health improvement actions that take emotional states into account. Furthermore, because the feedback does not specifically indicate what improvements can be expected, there is a problem in that it does not lead to practical and sustainable behavioral change for users.

[0761] 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.

[0762] In this invention, the server includes means for collecting and storing personal health data and emotional data, means for analyzing health and emotional states based on the health and emotional data, and means for predicting future health and emotional states based on the analysis results. This makes it possible to propose personalized health improvement actions that correspond to the user's emotional state. Furthermore, by providing dietary suggestions that correspond to emotional changes along with health risks, more practical and sustainable health management can be achieved.

[0763] "Health data" refers to information that expresses an individual's physical condition as numerical values ​​or indicators, including heart rate, exercise level, and blood pressure.

[0764] "Emotional data" refers to information that expresses an individual's psychological state and emotional changes, and is obtained from facial expressions, voice patterns, behavioral indicators, and other sources.

[0765] "Means of analysis" refers to the process of applying appropriate algorithms to collected data in order to clarify the characteristics and trends of that data.

[0766] "Means for predicting future health and emotional states" refers to technologies that use collected and analyzed data to anticipate future changes in an individual's health and emotions, and to predict the risks and conditions associated with those changes.

[0767] "Specific improvement actions" refer to recommended behaviors and activities aimed at improvement, such as exercise programs or meal plans, based on the user's health and emotional state.

[0768] To implement this invention, it is necessary to construct a system that efficiently collects and analyzes individual health and emotional data and provides appropriate health improvement actions and dietary suggestions. This system is implemented using the following hardware and software.

[0769] hardware

[0770] The system primarily utilizes wearable devices and mobile terminals. Wearable devices collect health data such as the user's heart rate and activity level. Simultaneously, sensors are incorporated to analyze the user's facial expressions and voice, collecting emotional data.

[0771] software

[0772] An application is installed on the mobile device, and the acquired health and emotional data are aggregated. This data is sent to a cloud server, where detailed analysis is performed. Generative AI models such as Google Cloud AI and OpenAI are applied to the server, and AI-powered data analysis and prompt generation are performed.

[0773] Data processing and calculations

[0774] The server applies machine learning algorithms based on the received data to analyze an individual's health and emotional state. This predicts future health and emotional states, and creates a personalized health improvement plan for each user. Based on the analysis results, prompts are input into a generating AI model to generate specific health actions and dietary suggestions.

[0775] Specific example

[0776] For example, if the system analyzes that the user is in a stressed state, it will generate a menu suggestion for a meal that has a relaxing effect. An example of a prompt message would be input to the generating AI in the format of, "Based on the user's health and emotional data, please create a recommended menu for tonight's dinner. The user's emotional state has been found to be slightly stressed."

[0777] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0778] Step 1:

[0779] The user wears a wearable device to collect health data such as heart rate and activity level. This health data is transmitted in real time to a mobile device via sensors. The input is biometric data from the wearable device, and the output is health data stored on the mobile device.

[0780] Step 2:

[0781] The device collects emotional data using voice and facial expression sensors. This information is also analyzed in real time and aggregated on the mobile device. The input is the user's voice and facial expression data, and the output is the emotional data analyzed by the device.

[0782] Step 3:

[0783] The device sends health and emotional data to the server. This allows the server to centrally manage both types of data. The input is the data sent from the device, and the output is higher-level data stored on the server.

[0784] Step 4:

[0785] The server applies machine learning algorithms to the received health data to analyze the user's health status. If abnormalities are detected or health trends are identified, a detailed report is generated. The input is raw data, and the output is the result of the health status analysis.

[0786] Step 5:

[0787] The server analyzes the emotional state based on the collected emotional data. It evaluates what emotions the user is experiencing and generates an emotional state report based on that evaluation. The input is emotional data, and the output is the evaluation result of the emotional state.

[0788] Step 6:

[0789] The server uses a generative AI model based on the analysis results to form prompt sentences for generating personalized health improvement actions and meal suggestions. By inputting prompts into the generative AI, it outputs specific suggestions. The input is the analysis results of health and emotional states, and the output is personalized health improvement suggestions and meal menus.

[0790] Step 7:

[0791] Users view health improvement actions and dietary suggestions sent from the server on their devices and incorporate them into their daily lives. Because the suggestions are specific, users can easily incorporate them into their daily habits. The input is the suggestions from the server, and the output is the change in the user's behavior.

[0792] 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.

[0793] 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.

[0794] 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 robot 414.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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."

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0813] The following is further disclosed regarding the embodiments described above.

[0814] (Claim 1)

[0815] Means for collecting and storing personal health data,

[0816] A means for analyzing health status based on the aforementioned health data,

[0817] A means for predicting future health status based on the aforementioned analysis results,

[0818] A means for presenting health risks to the user based on the aforementioned prediction results,

[0819] A means of proposing specific improvement actions for the aforementioned health risks,

[0820] A system that includes this.

[0821] (Claim 2)

[0822] The system according to claim 1, wherein a wearable device is used to collect the aforementioned health data.

[0823] (Claim 3)

[0824] The system according to claim 1, which includes means for calculating and presenting the economic impact based on the aforementioned prediction results.

[0825] "Example 1"

[0826] (Claim 1)

[0827] Means for acquiring and recording information about an individual's health,

[0828] A means for using a machine learning algorithm to analyze the health status based on the aforementioned health information,

[0829] A means for predicting future health status using digital twin technology based on the aforementioned analysis results,

[0830] A means for notifying the user of health risks based on the aforementioned prediction results,

[0831] A means of proposing specific actions to improve the aforementioned health risks to the user,

[0832] A means for generating prompt sentences to enhance predictions and suggestions using a generative AI model,

[0833] A system that includes this.

[0834] (Claim 2)

[0835] The system according to claim 1, which uses a wearable device for acquiring the aforementioned health information.

[0836] (Claim 3)

[0837] The system according to claim 1, comprising means for evaluating and presenting the social and economic impacts based on the aforementioned prediction results.

[0838] "Application Example 1"

[0839] (Claim 1)

[0840] Means for collecting and storing personal health data,

[0841] A means for analyzing health status based on the aforementioned health data,

[0842] A means for predicting future health status based on the aforementioned analysis results,

[0843] A means for presenting health risks to the user based on the aforementioned prediction results,

[0844] A means of proposing specific improvement actions for the aforementioned health risks,

[0845] In a physical store environment, a means of providing product information and special offer information linked to the user's health data,

[0846] A system that includes this.

[0847] (Claim 2)

[0848] The system according to claim 1, wherein a wearable device is used to collect the aforementioned health data.

[0849] (Claim 3)

[0850] The system according to claim 1, which includes means for calculating and presenting the economic impact based on the aforementioned prediction results.

[0851] "Example 2 of combining an emotion engine"

[0852] (Claim 1)

[0853] Means for collecting and storing personal biometric and emotional information,

[0854] Means for analyzing the aforementioned biological information and emotional information independently or in an integrated manner,

[0855] A means for predicting future health and emotional states based on the aforementioned analysis results,

[0856] A means for presenting factors that influence health risks and emotional states based on the aforementioned prediction results,

[0857] A means of proposing specific improvement actions that correspond to the aforementioned health risks and emotional states,

[0858] A system that includes this.

[0859] (Claim 2)

[0860] The system according to claim 1, which uses a wearable device and terminal equipment to collect the aforementioned biometric information and emotional information.

[0861] (Claim 3)

[0862] The system according to claim 1, which uses a learning model that integrates the prediction results and analysis results to generate personalized advice.

[0863] "Application example 2 when combining with an emotional engine"

[0864] (Claim 1)

[0865] Means for collecting and storing personal health data and emotional data,

[0866] A means for analyzing health status and emotional status based on the aforementioned health data and emotional data,

[0867] A means for predicting future health and emotional states based on the aforementioned analysis results,

[0868] A means for presenting health risks and related emotional changes to the user based on the aforementioned prediction results,

[0869] A means of proposing specific improvement actions for the aforementioned health risks and providing dietary suggestions tailored to emotional states,

[0870] A system that includes this.

[0871] (Claim 2)

[0872] The system according to claim 1, wherein a portable device is used to collect the aforementioned health data and emotional data.

[0873] (Claim 3)

[0874] The system according to claim 1, comprising means for calculating and presenting the economic impact based on the prediction results and for evaluating the health outcomes of the dietary suggestions. [Explanation of symbols]

[0875] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means for collecting and storing personal health data, A means for analyzing health status based on the aforementioned health data, A means for predicting future health status based on the aforementioned analysis results, A means for presenting health risks to the user based on the aforementioned prediction results, A means of proposing specific improvement actions for the aforementioned health risks, A system that includes this.

2. The system according to claim 1, wherein a wearable device is used to collect the aforementioned health data.

3. The system according to claim 1, which includes means for calculating and presenting the economic impact based on the aforementioned prediction results.

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