System

Abstract

A system is provided.SOLUTION: This system includes a means for acquiring life data and medical examination data from a user, a means for shaping the acquired life data and medical examination data according to a format, a means for storing the shaped data in a database, a means for analyzing the stored data and predicting a health risk, and a means for presenting a coping method based on the predicted health risk.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In today's busy lifestyles, people find it difficult to adequately manage their health, which results in increased health risks. Furthermore, as medical care becomes more sophisticated and complex, the burden on medical staff increases. Given this background, a system that can predict individual health risks in advance and propose appropriate countermeasures is needed, but efficient and practical methods for achieving this are lacking. [Means for solving the problem]

[0005] In order to solve the above problems, we propose a system that uses the following means.

[0006] The system includes a means for acquiring lifestyle and medical data from a user, a means for formatting the acquired data, and a means for storing the data in a database. It also includes a means for analyzing the stored data and predicting health risks, and a means for suggesting countermeasures based on the predicted health risks. Furthermore, by including a means for receiving biometric data from a wearable device transmitted from a user terminal, a means for calculating health risks using an AI model, and a means for selecting and suggesting effective countermeasures based on the performance of other users, it is possible to provide an efficient and practical system that identifies individual health risks in advance and suggests appropriate countermeasures.

[0007] "Lifestyle data" refers to information related to an individual's daily life, including habits such as eating, exercise, sleeping, drinking, and smoking.

[0008] "Medical data" refers to data including medical information such as an individual's health check results, medical history, medical records, and prescriptions obtained at a medical institution.

[0009] "User" refers to the individual person who uses this system and the entity that inputs and provides the data.

[0010] "User terminal" refers to electronic devices used by users, such as computers, smartphones, tablets, and wearable devices.

[0011] "Server" refers to the central system that receives, stores, and analyzes data sent by users.

[0012] A "database" is a digital storage system for organizing and storing collected lifestyle and medical data.

[0013] An "AI model" refers to a computer program or algorithm used to analyze data using artificial intelligence.

[0014] "Health risk" refers to the possibility of future health problems or illnesses occurring based on certain lifestyle habits or health conditions.

[0015] "Coping strategies" refer to specific actions or measures taken to reduce or avoid predicted health risks.

[0016] "Biometric data" refers to data relating to the user's physical condition, and includes information such as heart rate, blood pressure, body temperature, and number of steps taken.

[0017] "Format" refers to the prescribed form or structure used when arranging data. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0022] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0023] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0024] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 1, a 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.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0032] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0036] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] The present invention relates to a system that collects and analyzes lifestyle data and medical data of a user, predicts health risks, and proposes countermeasures. An embodiment of this system will be described below.

[0040] overview

[0041] The system works in conjunction with a server and a user device. The user device collects lifestyle data and biometric data from wearable devices and sends it to the server. The server formats the received data and stores it in a database. It then uses an AI model to predict health risks and recommend appropriate countermeasures.

[0042] Data collection

[0043] User terminal

[0044] The user terminal accepts lifestyle data entered by the user. Specifically, the user enters details of meals, the time and type of exercise, and sleep duration through an application. The terminal also has the function of acquiring biometric data such as heart rate and number of steps taken from the wearable device. This data is periodically (for example, every night) transmitted to a server.

[0045] Data reduction and analysis

[0046] server

[0047] The server receives data sent from user devices. It formats the data and detects, complements, and corrects missing or outlier values. The formatted data is then stored in a database. This ensures data consistency and quality.

[0048] Health risk prediction

[0049] server

[0050] The server applies an AI model to analyze the cosmetic surgery data stored in the database. The AI ​​model is trained based on past medical and lifestyle data, and can accurately predict each user's health risks. Specifically, it calculates the probability of developing certain diseases such as heart disease and diabetes.

[0051] Proposal of measures

[0052] server

[0053] Based on the predicted health risk, the server generates specific measures. For example, a user at high risk of heart disease may be recommended to engage in daily aerobic exercise and eat a low-salt diet. It also suggests more effective measures by learning from past data and learning from measures that other users have successfully implemented.

[0054] User terminal

[0055] The countermeasure information generated by the server is sent to the user's device. The user's device notifies the user of this information and displays specific actions to be taken in daily life in the form of lists and reminders. This allows the user to smoothly implement countermeasures based on health risks.

[0056] Specific examples

[0057] Example 1: Specific example of data collection

[0058] User terminal

[0059] The user inputs the ingredients and calories of breakfast into a health app, and the heart rate data from the smartwatch is sent to a server every night.

[0060] server

[0061] The server receives the data sent from the user terminal, formats it according to the format, and stores it in a database.

[0062] Example 2: A concrete example of health risk prediction

[0063] server

[0064] Using an AI model, we predict User A's risk of heart disease based on data from the past year. As a result, we determine that User A has a high risk of heart disease.

[0065] Example 3: Specific example of proposed measures

[0066] server

[0067] User A is recommended to walk 30 minutes a day and restrict his diet. As a success story, data is also presented showing another user, User B, who reduced his risk of heart disease by taking similar measures.

[0068] User terminal

[0069] Notify user A of the proposed measures and set a walking reminder.

[0070] In this way, users can grasp their own health risks early, take appropriate measures, and continuously manage their health. The system contributes to improving health awareness not only for individuals but also for society as a whole.

[0071] The processing flow will be explained below.

[0072] Step 1:

[0073] Users input lifestyle data into devices such as smartphones and tablets. Specifically, they record dietary habits, exercise types and durations, sleep durations, etc. through an application. In addition, wearable devices are used to automatically read biometric data such as heart rate and number of steps taken.

[0074] Step 2:

[0075] The user device periodically (for example, every night) transmits the collected life data and biometric data to the server. Data transmission is set to be automatic, but it can also be transmitted manually as needed.

[0076] Step 3:

[0077] The server receives data sent from the user's device, formats it according to the format, and validates the data to ensure consistency and accuracy. If missing or outliers are detected, they are supplemented or corrected according to pre-defined processing rules.

[0078] Step 4:

[0079] The formatted data is stored in a database, a digital storage system organized for each user, where all necessary information, including past medical and lifestyle data, is centrally managed.

[0080] Step 5:

[0081] The server applies an AI model based on the data stored in the database. The AI ​​model is trained to predict specific health risks and analyzes each user's lifestyle and medical data. Specifically, it calculates the risk of heart disease, diabetes, etc.

[0082] Step 6:

[0083] The server generates specific countermeasures based on the health risks calculated by the AI ​​model. The countermeasures are quantitative and include specific guidelines for action, such as "walk 30 minutes every day" or "reduce salt intake in the diet."

[0084] Step 7:

[0085] Furthermore, the server provides additional reference information based on the measures taken by other users and their results. The system stores information on successful measures and their execution results in the past, so by referring to this information, it is possible to suggest effective measures.

[0086] Step 8:

[0087] The generated countermeasure information is sent to the user's device, which notifies the user of the proposed countermeasures and provides an interface for setting reminders and action plans, making it easier for the user to implement the proposed countermeasures in their daily lives.

[0088] Step 9:

[0089] The user implements the suggested measures and inputs the results back into the application, recording feedback on the implemented measures, such as the duration of walking or changes in diet.

[0090] Step 10:

[0091] The user device sends the execution result data to the server. The server evaluates the effectiveness of the countermeasures based on the received execution results. The evaluation results are used to retrain the AI ​​model, continuously improving the prediction accuracy of the entire system and the quality of the countermeasure proposals.

[0092] By repeating the above steps, users can continuously manage their health, and the system appropriately manages individual health risks.

[0093] Example 1

[0094] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0095] Modern society requires effective management of personal lifestyle and medical data, early detection of health risks, and the implementation of appropriate countermeasures. However, current systems often lack a consistent approach to data acquisition, processing, analysis, and the presentation of countermeasures, resulting in insufficient management of individual health risks. Furthermore, integrating biometric and lifestyle data from wearable devices is expected to enable more accurate health risk prediction and countermeasures, but achieving this requires advanced data processing capabilities, which presents a challenge.

[0096] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0097] In this invention, the server includes means for acquiring life data and medical data from a user, means for formatting the acquired life data and medical data according to a format, means for storing the formatted data in a data management device, means for analyzing the stored data and predicting health risks, means for suggesting countermeasures based on the predicted health risks, means for collecting life data and biological data by a user terminal and periodically transmitting them to the server, means for the server to detect missing values ​​and abnormal values ​​in the data and complete or correct them, and means for the server to generate countermeasures based on the health risk prediction results and transmit them to the user terminal. This ensures data consistency and quality, enabling highly accurate health risk predictions and effective countermeasure suggestions.

[0098] "Lifestyle data" refers to information about the user's daily life, including details of meals, exercise times and types, and sleep duration.

[0099] "Medical data" refers to information about a user's health condition collected by a medical institution, including diagnostic results, treatment details, and drug use records.

[0100] A "user terminal" is a device that a user uses to input data about their daily life and receive data from a wearable device, and includes smartphones, tablets, etc.

[0101] A "wearable device" is a device worn on the user's body that collects biometric data, and includes smartwatches and fitness trackers.

[0102] A "data management device" is a device that is connected to a server and includes a database and storage system for storing collected and formatted data.

[0103] The "server" is a central control device that receives, formats, analyzes, and stores data sent from user terminals, predicts health risks, and generates countermeasures.

[0104] A "missing value" refers to a state in which part of the acquired data is missing, and the data is information that needs to be supplemented.

[0105] An "abnormal value" refers to a value that is outside the normal range or is unnatural among the acquired data, and is information that requires the data to be corrected.

[0106] "Health Risk" is a prediction result that indicates the risk of health problems or diseases that the user may suffer from in the future.

[0107] "Countermeasures" refer to specific actions or countermeasures recommended for predicted health risks.

[0108] Overall system overview

[0109] This invention is a system that collects and analyzes users' lifestyle and medical data, predicts health risks, and suggests countermeasures. The system operates in cooperation with a server and user terminal.

[0110] Data collection

[0111] User terminal

[0112] The user terminal is responsible for collecting lifestyle data and biometric data from the wearable device. Specifically, the user enters details of meals, the time and type of exercise, and sleep duration through the application. In addition, biometric data such as heart rate and number of steps taken is automatically acquired from the wearable device and periodically sent to the server. The user terminal can be a smartphone or tablet.

[0113] Example: A user enters the ingredients and calories they ate for breakfast into a health app, and their heart rate data from their smartwatch is sent to a server every night.

[0114] Data reduction and analysis

[0115] server

[0116] The server receives data sent from user terminals. It formats the data and detects, complements, and corrects missing or outlier values. The formatted data is then stored in a data management device. This data organization process ensures the consistency and quality of the data.

[0117] Example: The server receives data from all connected devices overnight, records it in a reception log, and fills in missing calorie information with the average value from past records.

[0118] Health risk prediction

[0119] server

[0120] The server applies an AI model to analyze the plastic surgery data stored in the database. This AI model is trained based on past medical and lifestyle data and accurately predicts each user's health risks. Specifically, it calculates the probability of developing certain diseases such as heart disease and diabetes.

[0121] Example: The server calls an AI model, sets the past year's data as input parameters, and predicts user A's risk of heart disease, which is determined to be high risk.

[0122] Proposal of measures

[0123] server

[0124] The server generates specific measures based on predicted health risks. These measures are based on past success stories and expert opinions. For example, a user at high risk of heart disease might be recommended to engage in daily aerobic exercise and eat a low-salt diet.

[0125] Example: A server creates a plan for a high-risk heart disease individual that recommends 30 minutes of walking each day.

[0126] User terminal

[0127] The countermeasure information generated by the server is sent to the user's device and notified to the user. The user's device displays this information in the form of notifications and reminders, allowing the user to smoothly implement countermeasures based on health risks.

[0128] Example: When a user's device receives new information about measures, a pop-up notification will be displayed, and a reminder will be set to encourage them to walk 30 minutes daily.

[0129] Prompt Sentence Examples

[0130] "Please explain the process of collecting the user's breakfast data and heart rate data and sending them to the server."

[0131] "Please tell me the algorithm flow for predicting the health risks of users using an AI model."

[0132] In this way, the present invention is a system that enables users to grasp health risks early and manage their health continuously, contributing to improving health awareness not only among individuals but also among society as a whole.

[0133] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0134] Step 1:

[0135] Data collection

[0136] User

[0137] Users input lifestyle data into the health app, such as meal details (breakfast, lunch, dinner), exercise time and type, and sleep duration. The data is entered into specific fields in the app and saved to the device by pressing the "Submit" button.

[0138] Input: Lifestyle data entered by the user (meals, exercise, sleep time)

[0139] Output: Life data stored on the device

[0140] Step 2:

[0141] Biometric data acquisition

[0142] Terminal

[0143] Devices (such as smartwatches and fitness trackers) collect biometric data such as heart rate and number of steps in real time, and this data is automatically recorded by dedicated applications within the device.

[0144] Input: Biometric data obtained from wearable devices (heart rate, number of steps)

[0145] Output: Biometric data stored in the device

[0146] Step 3:

[0147] Data transmission

[0148] Terminal

[0149] The device periodically (for example, every night) transmits the collected life and biometric data to the server. The data is encrypted and transmitted using an API.

[0150] Input: Life data and biometric data stored on the device

[0151] Output: Life and biological data sent to the server

[0152] Step 4:

[0153] Data reception

[0154] server

[0155] The server receives the data sent from the user terminal and temporarily stores the received data in a temporary storage area.

[0156] Input: Life data and biometric data sent from the device

[0157] Output: Data stored in the temporary storage area on the server

[0158] Step 5:

[0159] Data Shaping and Cleaning

[0160] server

[0161] The server formats the data in the temporary storage area according to the format. It then detects missing or abnormal values ​​and complements or corrects them based on the set rules. The formatted and corrected data is then stored in the actual storage area (data management device).

[0162] Input: Raw data stored in the temporary storage area

[0163] Output: Formatted data stored in the data management device

[0164] Step 6:

[0165] Data analysis

[0166] server

[0167] The server receives the formatted data from the data management device and analyzes it based on the AI ​​model, which predicts the health risks of each individual user, such as calculating a risk score for heart disease or diabetes.

[0168] Input: Formatted data stored in the data management device

[0169] Output: Health risk prediction results from the AI ​​model

[0170] Step 7:

[0171] Countermeasure generation

[0172] server

[0173] The server generates specific countermeasures based on predicted health risks, and creates the optimal action plan for the user by referencing past success stories and expert opinions.

[0174] Input: Health risk prediction results from an AI model

[0175] Output: Generated concrete countermeasure plan

[0176] Step 8:

[0177] Notification of measures

[0178] Terminal

[0179] The countermeasure information generated by the server is sent to the user's terminal and notified to the user. The terminal displays the received countermeasure information in the form of a list or reminder.

[0180] Input: Specific countermeasure plan sent from the server

[0181] Output: Notification of countermeasure plan, reminder setting

[0182] By following these steps, users can quickly identify their own health risks and take measures, enabling continuous health management.

[0183] (Application example 1)

[0184] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0185] Conventional health management systems only collect and analyze users' health data, but lack real-time feedback and suggested measures. In particular, when considering use in brick-and-mortar stores, it is necessary to immediately grasp the user's health status and take appropriate measures, but no specific technology exists for this purpose. The present invention aims to improve this situation by providing a system that collects and analyzes the health data of users visiting a store in real time and suggests measures.

[0186] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0187] In this invention, the server includes a means for acquiring lifestyle data and medical data from a user, a means for formatting the acquired lifestyle data and medical data, and a means for storing the formatted data in a database. This allows the server to collect biometric data of users visiting a physical store in real time through smart glasses and instantly analyze and notify their health status. Furthermore, by providing countermeasure information to store staff and users based on the analysis results, countermeasures to reduce health risks can be quickly implemented.

[0188] "Lifestyle data" refers to information about the user's activities and behavior in daily life, specifically data such as dietary content, type and time of exercise, and sleep duration.

[0189] "Medical data" refers to information such as the user's physical condition and diagnostic results obtained at a medical institution, specifically data such as blood pressure, blood sugar level, heart rate, and diagnostic results.

[0190] "User terminal" refers to a device owned by a user, including a smartphone, tablet, personal computer, etc.

[0191] "Smart glasses" refers to a wearable device worn by the user that acquires biometric data in real time through sensors and displays and transmits that data.

[0192] "Biometric data" refers to information that indicates the user's physical condition, specifically data such as heart rate, number of steps, and body temperature.

[0193] A "database" refers to a system for systematically storing and managing acquired and formatted data, and has a structure that facilitates searching and analysis.

[0194] "AI model" refers to algorithms and software that use machine learning and artificial intelligence to analyze data, recognize patterns, and derive predictions.

[0195] "Health risk" refers to an indicator that shows the likelihood that a user will develop a specific health disorder or disease, and is calculated based on the analysis results of the AI ​​model.

[0196] "Countermeasures information" refers to specific actions and measures suggested based on health risks, including suggestions for exercise methods and advice on dietary management.

[0197] "Store staff" refers to employees working in physical stores who are responsible for dealing with customers and providing services.

[0198] This invention relates to a system that collects and analyzes a user's lifestyle and medical data, predicts health risks, and proposes appropriate measures. The system includes a user terminal, smart glasses, a database, and a server.

[0199] Data collection

[0200] The user device collects lifestyle data and biometric data from the wearable device. Specifically, it records meal details, exercise duration and type, and sleep duration entered by the user through a smartphone application. It also collects biometric data such as heart rate, step count, and body temperature in real time using smart glasses. This data is then transmitted from the user device to a server.

[0201] Data organization and storage

[0202] The server receives and formats the data sent from the user's device. Specifically, it standardizes the data format and detects, complements, and corrects missing or outlier values. The formatted data is then stored in the database. This ensures the consistency and quality of the data.

[0203] Health risk prediction

[0204] The server applies a generative AI model to analyze the cosmetic surgery data stored in the database. The AI ​​model is trained based on past medical and lifestyle data, and can accurately predict individual users' health risks. For example, it calculates the probability of developing certain diseases such as heart disease or diabetes.

[0205] Proposal of measures

[0206] Based on the predicted health risks, the server generates specific measures. For example, it recommends daily aerobic exercise and a low-salt diet for users at high risk of heart disease. It also references success stories and provides recommendations based on effective measures taken by other users.

[0207] The generated information is sent to the user's device and smart glasses. The user device displays this information in the form of a list or reminder, helping the user to easily implement the measures. Meanwhile, the smart glasses provide real-time feedback in the store, instantly notifying the user and store staff of the information, enabling prompt health management.

[0208] Specific examples

[0209] For example, when a user wears smart glasses, they can collect real-time data such as a heart rate of 80, steps taken 5,000, and a body temperature of 36.5°C. This data is immediately sent to a server and analyzed by a generative AI model. As a result, the user may be predicted to have a low risk of heart disease and receive instructions to maintain their health.

[0210] Prompt Sentence Examples

[0211] "Based on the data of my heart rate of 80, number of steps taken 5000, and body temperature of 36.5°C, please analyze whether there are any health risks and display the results."

[0212] As described above, this system supports health management in physical stores by identifying users' health risks early and providing appropriate countermeasures.

[0213] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0214] Step 1:

[0215] The user device and smart glasses collect lifestyle data and biometric data. Lifestyle data is obtained by users entering information such as dietary habits, exercise time, and sleep time through a smartphone application. Biometric data, on the other hand, is data such as heart rate, number of steps, and body temperature collected in real time by the smart glasses. This data is then collected on the user device.

[0216] Step 2:

[0217] The user device sends the collected life data and biometric data to the server. At this time, the data is organized according to a specific format. The input is the various data collected on the user device, and the data sent to the destination server is in the organized format.

[0218] Step 3:

[0219] The server formats the received data according to the format. Specifically, it detects missing or outlier values ​​and performs supplementation or correction as necessary. This process generates consistent data that can be stored in a database. The input is the data sent from the user device, and the output is the formatted data.

[0220] Step 4:

[0221] The server stores the formatted data in a database. This database is used to manage a wide range of information, including the user's past lifestyle and medical data. The input is the formatted data, and the output is the data stored in the database.

[0222] Step 5:

[0223] The server applies a generative AI model to analyze the data stored in the database. The AI ​​model is trained based on past medical and lifestyle data. This model predicts individual user health risks with high accuracy. The input is the data stored in the database, and the output is the predicted health risk.

[0224] Step 6:

[0225] The server generates specific measures based on the predicted health risks. For example, it recommends daily aerobic exercise and a low-salt diet for a user at high risk of heart disease. The input is the predicted health risks, and the output is the measures.

[0226] Step 7:

[0227] The server sends the generated countermeasure information to the user terminal and the smart glasses. The user terminal displays the countermeasure information to the user in the form of a list or reminder. Meanwhile, the smart glasses display the countermeasure information in real time in the physical store and immediately notify the store staff and the user. The input is the countermeasure information, and the output is the countermeasure information notified to the user and the store staff.

[0228] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0229] The present invention relates to a system that combines lifestyle data, medical data, and emotion data of a user to predict health risks and propose appropriate measures. An embodiment of this system will be described below.

[0230] System Overview

[0231] This system works in cooperation with a server, user device, and emotion engine. The user device collects lifestyle data, biometric data from wearable devices, and emotion data, and sends this to the server. The server formats the received data and stores it in a database. The system then uses an AI model and emotion engine to predict health risks and suggest appropriate countermeasures.

[0232] Data collection

[0233] User terminal

[0234] The user terminal accepts lifestyle data entered by the user. Specifically, the user enters details of meals, the time and type of exercise, and sleep duration through an application. Furthermore, the wearable device automatically reads biometric data such as heart rate and number of steps taken. This data is periodically (for example, every night) transmitted to a server.

[0235] Collecting Emotional Data

[0236] Emotion Engine

[0237] The emotion engine acquires the user's emotional data. Specifically, it has the function of analyzing emotions from the user's facial expressions, voice, text input, etc. This evaluates stress and psychological state and generates data accordingly.

[0238] Data reduction and analysis

[0239] server

[0240] The server receives data sent from the user device and the emotion engine. This data is formatted and validated to ensure consistency and accuracy. If missing or outliers are detected, they are supplemented or corrected according to pre-defined processing rules.

[0241] Storage in the database

[0242] server

[0243] The formatted data is stored in a database, a digital storage system organized for each user, which centrally manages all necessary information, including past medical data, lifestyle data, and emotional data.

[0244] Health risk prediction

[0245] server

[0246] The server applies an AI model and emotion engine based on the data stored in the database. The AI ​​model is trained to predict specific health risks and analyzes each user's lifestyle, medical, and emotional data. Specifically, it calculates the risk of heart disease, diabetes, and other conditions.

[0247] Proposal of measures

[0248] server

[0249] Based on the predicted health risks, the server generates specific measures. The measures are quantitative and include specific guidelines for action, such as "walk 30 minutes every day" or "reduce salt intake in the diet." They also include measures to address stress and psychological states.

[0250] server

[0251] Furthermore, the server provides additional reference information based on the measures taken by other users and their results. The system stores information on successful measures and their execution results in the past, so by referring to this information, it is possible to suggest effective measures.

[0252] User Notification

[0253] User terminal

[0254] The generated countermeasure information is sent to the user's device, which notifies the user of the proposed countermeasures and provides an interface for setting reminders and action plans, making it easier for the user to implement the proposed countermeasures in their daily lives.

[0255] Gathering feedback

[0256] User

[0257] The user implements the suggested measures and inputs the results back into the application, which records feedback on the implemented measures, such as the duration of walking or changes in diet.

[0258] server

[0259] The server evaluates the effectiveness of countermeasures based on the received feedback data, and the evaluation results are used to retrain the AI ​​model and emotion engine, continuously improving the system's overall prediction accuracy and the quality of countermeasure proposals.

[0260] Specific examples

[0261] Example 1: Specific example of data collection

[0262] User terminal

[0263] The user enters the details of their meals and calories into a health app, and their smartwatch sends heart rate data to a server every night.

[0264] Emotion Engine

[0265] Users enter their daily emotions and stress levels into the app, and the app also uses the device's camera and microphone to collect data for emotional analysis.

[0266] server

[0267] The server receives data sent from the user device and emotion engine, formats it according to the format, and stores it in a database.

[0268] Example 2: A concrete example of health risk prediction

[0269] server

[0270] Using an AI model, we predict User A's risk of heart disease based on data from the past year. We also take into account emotional data and determine whether User A is in a state of high stress. As a result, we determine that User A is at high risk of heart disease.

[0271] Example 3: Specific example of proposed measures

[0272] server

[0273] User A is advised to take up 30 minutes of walking every day and restrict his diet. He is also advised to take up a hobby that has a relaxing effect and set aside time for stretching. As a success story, data is also presented showing how another user, User B, reduced his risk of heart disease by taking similar measures.

[0274] User terminal

[0275] Notify user A of the suggested measures and provide the ability to set walking reminders and stretching times.

[0276] In this way, users can grasp their own health risks early, take appropriate measures, and continuously manage their health. Furthermore, by taking emotional data into account, the system can also reflect stress and psychological state in health management.

[0277] The processing flow will be explained below.

[0278] Step 1:

[0279] Users input lifestyle data into devices such as smartphones and tablets. Specifically, they record dietary habits, exercise types and durations, sleep durations, etc. through an application. In addition, wearable devices are used to automatically read biometric data such as heart rate and number of steps taken.

[0280] Step 2:

[0281] The user inputs emotional data into the device. Specifically, this is done in the form of inputting daily emotions and stress levels. An emotion engine also runs, using the device's camera and microphone to analyze emotions from the user's facial expressions and voice.

[0282] Step 3:

[0283] The user terminal periodically (for example, every night) transmits the collected life data, biometric data, and emotional data to the server. Data transmission is automatic, but can also be performed manually if necessary.

[0284] Step 4:

[0285] The server receives data sent from the user's device, formats it according to the format, and validates the data to ensure consistency and accuracy. If missing or outliers are detected, they are supplemented or corrected according to pre-defined processing rules.

[0286] Step 5:

[0287] The formatted data is stored in a database, a digital storage system organized for each user, which centrally manages all necessary information, including past medical data, lifestyle data, and emotional data.

[0288] Step 6:

[0289] The server applies an AI model and emotion engine based on the data stored in the database. The AI ​​model is trained to predict specific health risks and analyzes each user's lifestyle, medical, and emotional data. Specifically, it calculates the risk of heart disease, diabetes, and other conditions, and also adjusts the risk based on emotional data.

[0290] Step 7:

[0291] The server generates specific countermeasures based on the health risks calculated by the AI ​​model and emotion engine. The countermeasures are quantitative and include specific guidelines for action, such as "walk 30 minutes every day" or "reduce salt intake in the diet." They also include measures to address stress and psychological states.

[0292] Step 8:

[0293] Furthermore, the server provides additional reference information based on the measures taken by other users and their results. The system stores information on successful measures and their execution results in the past, so by referring to this information, it is possible to suggest effective measures.

[0294] Step 9:

[0295] The generated countermeasure information is sent to the user's device, which notifies the user of the proposed countermeasures and provides an interface for setting reminders and action plans, making it easier for the user to implement the proposed countermeasures in their daily lives.

[0296] Step 10:

[0297] The user implements the suggested measures and inputs the results back into the application, recording feedback on the implemented measures, such as the duration of walking or changes in diet.

[0298] Step 11:

[0299] The user device sends the execution result data to the server. The server evaluates the effectiveness of the countermeasures based on the received execution results. The evaluation results are used to retrain the AI ​​model and emotion engine, continuously improving the prediction accuracy of the entire system and the quality of the countermeasure proposals.

[0300] By repeating the above steps, users can continuously manage their health and the system appropriately manages individual health risks. In addition, by taking emotion data into account, stress and psychological state can also be reflected in health management.

[0301] Example 2

[0302] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0303] Conventional health management systems predict health risks based solely on lifestyle and medical data, which means they are unable to make highly accurate predictions that take into account the user's psychological state and stress level. Furthermore, there is a lack of methods for effectively collecting feedback on proposed measures and using it to improve the overall system.

[0304] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring life data, medical data, and emotional data from the user, means for formatting the acquired life data, medical data, and emotional data according to a format, and means for storing the formatted data in a database. This enables comprehensive data analysis including emotional data to improve the accuracy of health risk prediction. In addition, feedback on proposed measures is acquired from the user, and the effectiveness is evaluated and reflected in the entire system, enabling sustainable and effective health management.

[0305] "Lifestyle data" is information about the user's daily life, including the contents of meals, the time and type of exercise, and the amount of sleep.

[0306] "Medical data" refers to information relating to medical treatment at a medical institution, including diagnosis results, prescription details, medical history, and the like.

[0307] "Emotion data" is information about the user's psychological state and emotions, and includes data analyzed from facial expressions, voice, text input, and the like.

[0308] A "user terminal" is a digital device used by a user, and includes a smartphone, tablet, personal computer, etc.

[0309] A "wearable device" is a digital device that can be worn by a user, including smart watches, fitness trackers, etc.

[0310] A "database" is an information system for storing and centrally managing organized data.

[0311] "Health risk" refers to the likelihood that a user will experience a particular health problem, including risks such as heart disease and diabetes.

[0312] "Countermeasures" are specific courses of action proposed to reduce predicted health risks, including improvements in exercise and diet.

[0313] "Feedback" is information about the results of a user's implementation of a proposed measure, and is used to evaluate the effectiveness of the measure.

[0314] An "AI model" is a collection of algorithms that use artificial intelligence to analyze data and train it for a specific purpose (in this case, predicting health risks).

[0315] This invention relates to a system that integrates a user's lifestyle data, medical data, and emotional data to predict health risks and propose specific countermeasures. The system is composed of a server, a user terminal, and an emotion engine.

[0316] Data collection

[0317] User terminal

[0318] Users use a health app to input daily lifestyle data such as what they eat, the type and duration of exercise, and how much sleep they get. Furthermore, wearable devices automatically collect biometric data such as heart rate and number of steps taken. This data is then sent to a server at a set frequency (e.g., every night).

[0319] Emotion Engine

[0320] The emotion engine generates emotion data by analyzing the user's facial expressions, voice, and text input. For example, it uses the device's camera to recognize facial expressions and assess stress and psychological state.

[0321] Data Formatting and Storage

[0322] server

[0323] The server formats the data received from the user device and emotion engine. Specifically, it converts lifestyle data, medical data, and emotion data into standard formats and checks for consistency and accuracy. The formatted data is stored in a database for later analysis.

[0324] Health risk prediction

[0325] server

[0326] The server uses the data stored in the database to apply a pre-trained AI model. The AI ​​model analyzes the user's lifestyle, medical, and emotional data to predict specific health risks (e.g., heart disease, diabetes). Taking emotional data into account improves the accuracy of risk predictions.

[0327] Proposal of measures

[0328] server

[0329] Based on the predicted health risks, the server generates specific measures, such as "walk 30 minutes a day" or "reduce salt intake in your diet," and provides additional information based on the success stories of other users.

[0330] User Notification

[0331] User terminal

[0332] The generated countermeasure information is sent to the user's device, which notifies the user of the proposed countermeasures and provides an interface for setting reminders and action plans, making it easier for the user to implement the proposed countermeasures in their daily lives.

[0333] Gathering and implementing feedback

[0334] User

[0335] The user implements the suggested measures and then inputs the results back into the application, recording feedback on the measures taken, such as the duration of walking or changes in diet.

[0336] server

[0337] The server evaluates the effectiveness of countermeasures based on the received feedback data, and this evaluation result is used to retrain the AI ​​model and emotion engine, continuously improving the prediction accuracy and quality of countermeasure proposals for the entire system.

[0338] Specific examples

[0339] Example 1: Specific example of data collection

[0340] A user enters "Breakfast: 200g rice, 1 egg, 1 bowl of miso soup" into a health app, and sends heart rate data from the smartwatch (heart rate: 70 BPM) to the server. In the emotion engine, the user enters "I'm stressed out at work today" into the app, and emotion data is captured using the device's camera and microphone. The server receives this data, formats it into a standard format, and stores it in a database.

[0341] Example 2: A concrete example of health risk prediction

[0342] Based on data from the past year, the server uses an AI model to predict User A's "heart disease risk: 65%." The emotion engine also determines that User A's "stress level: high," and reflects this in the risk assessment.

[0343] Example 3: Specific example of proposed measures

[0344] The server proposes specific measures to User A, such as "walking 30 minutes every day" and "reducing salt intake at breakfast by 1 gram." It also provides reference information on past successes of other users who have taken similar measures. The user device notifies User A of this information and provides a function to set reminders, etc.

[0345] Prompt Sentence Examples

[0346] Please explain the specific processing steps of the program that analyzes emotional and lifestyle data to predict the user's health risks and suggest appropriate measures.

[0347] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0348] Step 1:

[0349] Data collection

[0350] input

[0351] Lifestyle data such as dietary details, type and duration of exercise, and sleep time entered by users through health apps, as well as biometric data such as heart rate and number of steps sent from wearable devices.

[0352] Specific actions

[0353] A user enters "Breakfast: 200g rice, 1 egg, 1 cup of miso soup" into a health app. The smartwatch automatically acquires "Heart rate: 70 BPM" data and sends it to the server via the device. The user also records "30 minutes of jogging" in an exercise app.

[0354] output

[0355] A series of life data and biometric data transmitted from the user terminal.

[0356] Step 2:

[0357] Data Formatting

[0358] input

[0359] Life data, medical data, and emotion data sent from the user device and emotion engine.

[0360] Specific actions

[0361] The server receives data such as "Breakfast: 200g rice, 1 egg, 1 bowl of miso soup." It also receives heart rate data such as "70 BPM" and converts it into a standard format. It also receives emotional data such as "User's stress level: High."

[0362] output

[0363] Formatted data in a standard format. For example, meal data is formatted into a detailed format such as "Category: Breakfast, Items: 200g rice, 1 egg, 1 bowl of miso soup."

[0364] Step 3:

[0365] Storage in the database

[0366] input

[0367] Lifestyle data, medical data, and emotional data formatted in a standard format.

[0368] Specific actions

[0369] The server stores the formatted data in a database. The database stores the data in a format organized for each user. For example, lifestyle data and medical data are stored linked to the user ID.

[0370] output

[0371] Formatted data stored in a database.

[0372] Step 4:

[0373] Health risk prediction

[0374] input

[0375] Formatted data stored in a database.

[0376] Specific actions

[0377] An AI model analyzes the data in the database to predict specific health risks (e.g., heart disease risk, diabetes risk), and an emotion engine takes the analyzed emotional data into account to calculate an overall risk score.

[0378] output

[0379] Each user's health risk score. For example, User A's "Heart disease risk: 65%" is output.

[0380] Step 5:

[0381] Proposal of measures

[0382] input

[0383] The server-predicted health risk score.

[0384] Specific actions

[0385] The server generates specific measures based on the predicted health risks, such as "recommended 30 minutes of walking every day" or "reduce salt intake by 1 gram in your diet."

[0386] output

[0387] Specific health measures. For example, suggestions such as "walk 30 minutes every day" and "reduce salt intake at breakfast" are output to User A.

[0388] Step 6:

[0389] User Notification

[0390] input

[0391] Specific health measures proposed by the server.

[0392] Specific actions

[0393] The server sends the proposed measures to the user terminal, which notifies the user of the proposed measures and sets a reminder function for a specific time.

[0394] output

[0395] Countermeasures notified to the user device. For example, a reminder to start walking at 8:00 p.m. is set and notified on the user device.

[0396] Step 7:

[0397] Gathering feedback

[0398] input

[0399] Feedback on the actions taken by the user.

[0400] Specific actions

[0401] The user enters "I walked for 30 minutes" into the application. The server receives the feedback data and evaluates the effectiveness of the measure.

[0402] output

[0403] Feedback data collected. The server uses this data to retrain the AI ​​model and emotion engine.

[0404] Step 8:

[0405] System Improvements

[0406] input

[0407] Feedback data.

[0408] Specific actions

[0409] The server analyzes the effectiveness of countermeasures based on the feedback data, and uses the results of this analysis to retrain the AI ​​model and emotion engine, improving the prediction accuracy of the entire system and the quality of countermeasure proposals.

[0410] output

[0411] An improved system. By continuing to incorporate feedback, we can provide users with even better health management methods.

[0412] (Application example 2)

[0413] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0414] In recent years, as people have become more interested in health management, there has been an increasing demand for systems that can predict health risks in daily life in real time and suggest appropriate countermeasures. However, conventional systems only consider users' lifestyle and medical data, and lack emotional data and real-time countermeasure suggestions. Furthermore, there are not enough health management systems available to improve the user experience in physical stores. There is a need to solve these issues, comprehensively manage users' health risks, and provide health-related services in physical stores.

[0415] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring lifestyle data and medical data from the user, means for formatting the acquired lifestyle data and medical data according to a format, means for storing the formatted data in a database, means for analyzing the stored data and predicting health risks, means for suggesting countermeasures based on the predicted health risks, and means for evaluating the user's health data in real time and suggesting appropriate products and services. This allows the user to understand their own health risks in real time and receive suggestions for appropriate products and services in physical stores.

[0416] "Lifestyle data" refers to information such as dietary habits, exercise habits, and sleep duration in the user's daily life.

[0417] "Medical data" refers to information related to the medical examinations and treatments a user receives at a medical institution.

[0418] "Format" refers to the rules and format for arranging data.

[0419] "Database" refers to an information system for managing and storing formatted data.

[0420] "Health risk" refers to the likelihood that a user will develop a particular health problem.

[0421] "Coping strategies" refer to specific behavioral or lifestyle changes suggested based on predicted health risks.

[0422] "Wearable device" refers to a digital device worn by a user that is capable of collecting data.

[0423] "Biometric data" refers to data obtained from the body, such as heart rate, body temperature, and number of steps taken.

[0424] An "AI model" refers to an algorithm that uses machine learning to analyze and predict data.

[0425] "User terminal" refers to a communication device such as a smartphone or tablet used by a user.

[0426] "Real-time" refers to the fact that the entire process, from data collection to analysis and proposals, is carried out instantly.

[0427] "Product" refers to an item offered to help manage health.

[0428] "Services" refer to actions or experiences proposed to help with health management.

[0429] The present invention relates to a system that predicts health risks by combining lifestyle data, medical data, and emotional data of a user and proposes appropriate measures. An embodiment of this system will be described below.

[0430] System Overview

[0431] This system works in cooperation with a server, user device, and emotion analysis engine. The user device collects lifestyle data, biometric data from wearable devices, and emotion data, and sends this to the server. The server formats the received data and stores it in a database. It then uses an AI model and emotion analysis engine to predict health risks and recommend appropriate countermeasures.

[0432] Data collection

[0433] User terminal

[0434] The user terminal accepts lifestyle and medical data entered by the user. Specifically, the user enters details of meals, exercise time and type, sleep duration, etc. via an application. Furthermore, the wearable device automatically reads biometric data such as heart rate and number of steps taken. This data is periodically sent to the server.

[0435] Collecting Emotional Data

[0436] Sentiment Analysis Engine

[0437] The emotion analysis engine acquires user emotional data. Specifically, it has the ability to analyze emotions from the user's facial expressions, voice, text input, etc. This allows it to evaluate stress and psychological state and generate data accordingly.

[0438] Data reduction and analysis

[0439] server

[0440] The server receives data sent from user devices and the sentiment analysis engine. It formats the data and validates it for consistency and accuracy. If missing or outliers are detected, they are filled in or corrected according to pre-defined processing rules.

[0441] Storage in the database

[0442] server

[0443] The formatted data is stored in a database, a digital storage system organized for each user, which centrally manages all necessary information, including past medical data, lifestyle data, and emotional data.

[0444] Health risk prediction

[0445] server

[0446] The server applies an AI model and emotion analysis engine based on the data stored in the database. The AI ​​model is trained to predict specific health risks and analyzes each user's lifestyle, medical, and emotional data. Specifically, it calculates the risk of heart disease, diabetes, and other conditions.

[0447] Proposal of measures

[0448] server

[0449] Based on the predicted health risks, the server generates specific countermeasures. The countermeasures are quantitative and include guidelines for action such as "walk 30 minutes every day" or "reduce salt intake in the diet." They also include measures to address stress and psychological states. In addition, the server provides additional reference information based on the countermeasures taken by other users and their results. By referring to past successful countermeasures and their execution results, the server can suggest effective countermeasures.

[0450] User Notification

[0451] User terminal

[0452] The generated countermeasure information is sent to the user's device, which notifies the user of the proposed countermeasures and provides an interface for setting reminders and action plans, making it easier for the user to implement the proposed countermeasures in their daily lives.

[0453] Gathering feedback

[0454] User

[0455] The user implements the suggested measures and inputs the results back into the application, which records feedback on the implemented measures, such as the duration of walking or changes in diet.

[0456] server

[0457] The server evaluates the effectiveness of countermeasures based on the received feedback data, and the evaluation results are used to retrain the AI ​​model and sentiment analysis engine, continuously improving the system's overall prediction accuracy and the quality of countermeasure proposals.

[0458] Specific examples

[0459] Example 1: Specific example of data collection

[0460] For example, a user can input their diet and calories into a health app, and send their heart rate data from their smartwatch to a server every night. The emotion analysis engine uses the device's camera and microphone to capture data for analyzing emotions, as the user inputs their daily emotions and stress levels. The server receives this data, formats it, and stores it in a database.

[0461] Example 2: A concrete example of health risk prediction

[0462] Based on data from the past year, an AI model predicts User A's risk of heart disease. It also takes into account emotional data and determines that User A is in a state of high stress. As a result, it determines that User A is at high risk of heart disease.

[0463] Example 3: Specific example of proposed measures

[0464] User A is suggested to walk 30 minutes a day and restrict his / her diet. Furthermore, suggestions are given for taking up a hobby that has a relaxing effect and setting aside time for stretching. Data is also presented as a success story showing how another user, User B, reduced their risk of heart disease by taking similar measures. The user device notifies User A of these suggestions and provides the ability to set walking reminders and stretching times.

[0465] Prompt Sentence Examples

[0466] Evaluate the user's health status based on their health data (heart rate, stress level, etc.) and suggest appropriate products and services. For example, for a user with a high heart rate and high stress level, recommend relaxing aroma oils or healthy foods. User data will be provided in the following format:

[0467] Data format: {"Heart rate": 85, "Stress level": 7, "Sleep hours": 6}

[0468] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0469] Step 1: Data collection

[0470] The user terminal receives lifestyle data (meal details, exercise time, sleep time, etc.) and medical data entered by the user. It also automatically acquires biometric data such as heart rate and step count from the wearable device. The emotion analysis engine collects emotional data (facial expressions, voice, text input). Input: Data from the user, and data from the wearable device and emotion engine. Output: Collected lifestyle data, medical data, biometric data, and emotional data.

[0471] Step 2: Send data

[0472] The user terminal formats the collected data according to the format, and after the data is formatted, it sends it to the server. Input: Collected data Output: Formatted data

[0473] Step 3: Data Formatting and Storage

[0474] The server receives data sent from the user terminal and formats it according to the format. The data is validated to ensure consistency and accuracy, and supplemented or corrected as necessary. The formatted data is stored in the database. Input: Formatted data Output: Data stored in the database

[0475] Step 4: Predicting health risks

[0476] The server uses the data stored in the database to predict health risks using an AI model and sentiment analysis engine. Specifically, it calculates the risk of heart disease and diabetes. Input: Data in the database Output: Predicted health risks

[0477] Step 5: Generate countermeasures

[0478] The server generates specific measures based on the predicted health risks. For example, "walk 30 minutes every day" or "reduce salt intake in your diet." It may also suggest a hobby that has a relaxing effect as a stress reliever. Input: Predicted health risks Output: Generated measures

[0479] Step 6: Notify users

[0480] The server sends the generated measures to the user's device, which then notifies the user. An interface is provided to help set reminders and plan actions, helping users to easily implement the proposed measures in their daily lives. Input: Generated measure information Output: Measures notified to the user

[0481] Step 7: Gather feedback

[0482] The user implements the proposed measures and inputs the results into the user terminal again. The feedback data is sent to the server again, and the effectiveness of the measures is evaluated. Input: Feedback data after the measures are implemented. Output: Evaluated effectiveness of the measures.

[0483] Step 8: System Improvement

[0484] The server uses the collected feedback data to retrain the AI ​​model and sentiment analysis engine, improving the overall system's prediction accuracy and the quality of countermeasure proposals. Input: Feedback data Output: Improved AI model and countermeasure proposal system

[0485] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0486] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0487] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0488] [Second embodiment]

[0489] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0490] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0491] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0492] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0493] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0494] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0495] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0496] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0497] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0498] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0499] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0500] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0501] The present invention relates to a system that collects and analyzes lifestyle data and medical data of a user, predicts health risks, and proposes countermeasures. An embodiment of this system will be described below.

[0502] overview

[0503] The system works in conjunction with a server and a user device. The user device collects lifestyle data and biometric data from wearable devices and sends it to the server. The server formats the received data and stores it in a database. It then uses an AI model to predict health risks and recommend appropriate countermeasures.

[0504] Data collection

[0505] User terminal

[0506] The user terminal accepts lifestyle data entered by the user. Specifically, the user enters details of meals, the time and type of exercise, and sleep duration through an application. The terminal also has the function of acquiring biometric data such as heart rate and number of steps taken from the wearable device. This data is periodically (for example, every night) transmitted to a server.

[0507] Data reduction and analysis

[0508] server

[0509] The server receives data sent from user devices. It formats the data and detects, complements, and corrects missing or outlier values. The formatted data is then stored in a database. This ensures data consistency and quality.

[0510] Health risk prediction

[0511] server

[0512] The server applies an AI model to analyze the cosmetic surgery data stored in the database. The AI ​​model is trained based on past medical and lifestyle data, and can accurately predict each user's health risks. Specifically, it calculates the probability of developing certain diseases such as heart disease and diabetes.

[0513] Proposal of measures

[0514] server

[0515] Based on the predicted health risk, the server generates specific measures. For example, a user at high risk of heart disease may be recommended to engage in daily aerobic exercise and eat a low-salt diet. It also suggests more effective measures by learning from past data and learning from measures that other users have successfully implemented.

[0516] User terminal

[0517] The countermeasure information generated by the server is sent to the user's device. The user's device notifies the user of this information and displays specific actions to be taken in daily life in the form of lists and reminders. This allows the user to smoothly implement countermeasures based on health risks.

[0518] Specific examples

[0519] Example 1: Specific example of data collection

[0520] User terminal

[0521] The user inputs the ingredients and calories of breakfast into a health app, and the heart rate data from the smartwatch is sent to a server every night.

[0522] server

[0523] The server receives the data sent from the user terminal, formats it according to the format, and stores it in a database.

[0524] Example 2: A concrete example of health risk prediction

[0525] server

[0526] Using an AI model, we predict User A's risk of heart disease based on data from the past year. As a result, we determine that User A has a high risk of heart disease.

[0527] Example 3: Specific example of proposed measures

[0528] server

[0529] User A is recommended to walk 30 minutes a day and restrict his diet. As a success story, data is also presented showing another user, User B, who reduced his risk of heart disease by taking similar measures.

[0530] User terminal

[0531] Notify user A of the proposed measures and set a walking reminder.

[0532] In this way, users can grasp their own health risks early, take appropriate measures, and continuously manage their health. The system contributes to improving health awareness not only for individuals but also for society as a whole.

[0533] The processing flow will be explained below.

[0534] Step 1:

[0535] Users input lifestyle data into devices such as smartphones and tablets. Specifically, they record dietary habits, exercise types and durations, sleep durations, etc. through an application. In addition, wearable devices are used to automatically read biometric data such as heart rate and number of steps taken.

[0536] Step 2:

[0537] The user device periodically (for example, every night) transmits the collected life data and biometric data to the server. Data transmission is set to be automatic, but it can also be transmitted manually as needed.

[0538] Step 3:

[0539] The server receives data sent from the user device, formats it according to the format, and validates the data to ensure consistency and accuracy. If missing or outliers are detected, they are supplemented or corrected according to pre-defined processing rules.

[0540] Step 4:

[0541] The formatted data is stored in a database, a digital storage system organized for each user, where all necessary information, including past medical and lifestyle data, is centrally managed.

[0542] Step 5:

[0543] The server applies an AI model based on the data stored in the database. The AI ​​model is trained to predict specific health risks and analyzes each user's lifestyle and medical data. Specifically, it calculates the risk of heart disease, diabetes, etc.

[0544] Step 6:

[0545] The server generates specific countermeasures based on the health risks calculated by the AI ​​model. The countermeasures are quantitative and include specific guidelines for action, such as "walk 30 minutes every day" or "reduce salt intake in the diet."

[0546] Step 7:

[0547] Furthermore, the server provides additional reference information based on the measures taken by other users and their results. The system stores information on successful measures and their execution results in the past, so by referring to this information, it is possible to suggest effective measures.

[0548] Step 8:

[0549] The generated countermeasure information is sent to the user's device, which notifies the user of the proposed countermeasures and provides an interface for setting reminders and action plans, making it easier for the user to implement the proposed countermeasures in their daily lives.

[0550] Step 9:

[0551] The user implements the suggested measures and inputs the results back into the application, which records feedback on the implemented measures, such as the duration of walking or changes in diet.

[0552] Step 10:

[0553] The user device sends the execution result data to the server. The server evaluates the effectiveness of the countermeasures based on the received execution results. The evaluation results are used to retrain the AI ​​model, continuously improving the prediction accuracy of the entire system and the quality of the countermeasure proposals.

[0554] By repeating the above steps, users can continuously manage their health, and the system appropriately manages individual health risks.

[0555] Example 1

[0556] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0557] Modern society requires effective management of personal lifestyle and medical data, early detection of health risks, and the implementation of appropriate countermeasures. However, current systems often lack a consistent approach to data acquisition, processing, analysis, and the presentation of countermeasures, resulting in insufficient management of individual health risks. Furthermore, integrating biometric and lifestyle data from wearable devices is expected to enable more accurate health risk prediction and countermeasures, but achieving this requires advanced data processing capabilities, which presents a challenge.

[0558] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0559] In this invention, the server includes means for acquiring life data and medical data from a user, means for formatting the acquired life data and medical data according to a format, means for storing the formatted data in a data management device, means for analyzing the stored data and predicting health risks, means for suggesting countermeasures based on the predicted health risks, means for collecting life data and biological data by a user terminal and periodically transmitting them to the server, means for the server to detect missing values ​​and abnormal values ​​in the data and complete or correct them, and means for the server to generate countermeasures based on the health risk prediction results and transmit them to the user terminal. This ensures data consistency and quality, enabling highly accurate health risk predictions and effective countermeasure suggestions.

[0560] "Lifestyle data" refers to information about the user's daily life, including details of meals, exercise times and types, and sleep duration.

[0561] "Medical data" refers to information about a user's health condition collected by a medical institution, including diagnostic results, treatment details, and drug use records.

[0562] A "user terminal" is a device that a user uses to input data about their daily life and receive data from a wearable device, and includes smartphones, tablets, etc.

[0563] A "wearable device" is a device worn on the user's body that collects biometric data, and includes smartwatches and fitness trackers.

[0564] A "data management device" is a device that is connected to a server and includes a database and storage system for storing collected and formatted data.

[0565] The "server" is a central control device that receives, formats, analyzes, and stores data sent from user terminals, predicts health risks, and generates countermeasures.

[0566] A "missing value" refers to a state in which part of the acquired data is missing, and the data is information that needs to be supplemented.

[0567] An "abnormal value" refers to a value that is outside the normal range or is unnatural among the acquired data, and is information that requires the data to be corrected.

[0568] "Health Risk" is a prediction result that indicates the risk of health problems or diseases that the user may suffer from in the future.

[0569] "Countermeasures" refer to specific actions or countermeasures recommended for predicted health risks.

[0570] Overall system overview

[0571] This invention is a system that collects and analyzes users' lifestyle and medical data, predicts health risks, and suggests countermeasures. The system operates in cooperation with a server and user terminal.

[0572] Data collection

[0573] User terminal

[0574] The user terminal is responsible for collecting lifestyle data and biometric data from the wearable device. Specifically, the user enters details of meals, the time and type of exercise, and sleep duration through the application. In addition, biometric data such as heart rate and number of steps taken is automatically acquired from the wearable device and periodically sent to the server. The user terminal can be a smartphone or tablet.

[0575] Example: A user enters the ingredients and calories they ate for breakfast into a health app, and their heart rate data from their smartwatch is sent to a server every night.

[0576] Data reduction and analysis

[0577] server

[0578] The server receives data sent from user terminals. It formats the data and detects, complements, and corrects missing or outlier values. The formatted data is then stored in a data management device. This data organization process ensures the consistency and quality of the data.

[0579] Example: The server receives data from all connected devices overnight, records it in a reception log, and fills in missing calorie information with the average value from past records.

[0580] Health risk prediction

[0581] server

[0582] The server applies an AI model to analyze the plastic surgery data stored in the database. This AI model is trained based on past medical and lifestyle data and accurately predicts each user's health risks. Specifically, it calculates the probability of developing certain diseases such as heart disease and diabetes.

[0583] Example: The server calls an AI model, sets the past year's data as input parameters, and predicts user A's risk of heart disease, which is determined to be high risk.

[0584] Proposal of measures

[0585] server

[0586] The server generates specific measures based on predicted health risks. These measures are based on past success stories and expert opinions. For example, a user at high risk of heart disease might be recommended to engage in daily aerobic exercise and eat a low-salt diet.

[0587] Example: A server creates a plan for a high-risk heart disease individual that recommends 30 minutes of walking each day.

[0588] User terminal

[0589] The countermeasure information generated by the server is sent to the user's device and notified to the user. The user's device displays this information in the form of notifications and reminders, allowing the user to smoothly implement countermeasures based on health risks.

[0590] Example: When a user's device receives new information about measures, a pop-up notification will be displayed, and a reminder will be set to encourage them to walk 30 minutes daily.

[0591] Prompt Sentence Examples

[0592] "Please explain the process of collecting the user's breakfast data and heart rate data and sending them to the server."

[0593] "Please tell me the algorithm flow for predicting the health risks of users using an AI model."

[0594] In this way, the present invention is a system that enables users to grasp health risks early and manage their health continuously, contributing to improving health awareness not only among individuals but also among society as a whole.

[0595] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0596] Step 1:

[0597] Data collection

[0598] User

[0599] Users input lifestyle data into the health app, such as meal details (breakfast, lunch, dinner), exercise time and type, and sleep duration. The data is entered into specific fields in the app and saved to the device by pressing the "Submit" button.

[0600] Input: Lifestyle data entered by the user (meals, exercise, sleep time)

[0601] Output: Life data stored on the device

[0602] Step 2:

[0603] Biometric data acquisition

[0604] Terminal

[0605] Devices (such as smartwatches and fitness trackers) collect biometric data such as heart rate and number of steps in real time, and this data is automatically recorded by dedicated applications within the device.

[0606] Input: Biometric data obtained from wearable devices (heart rate, number of steps)

[0607] Output: Biometric data stored in the device

[0608] Step 3:

[0609] Data transmission

[0610] Terminal

[0611] The device periodically (for example, every night) transmits the collected life and biometric data to the server. The data is encrypted and transmitted using an API.

[0612] Input: Life data and biometric data stored on the device

[0613] Output: Life and biological data sent to the server

[0614] Step 4:

[0615] Data reception

[0616] server

[0617] The server receives the data sent from the user terminal and temporarily stores the received data in a temporary storage area.

[0618] Input: Life data and biometric data sent from the device

[0619] Output: Data stored in the temporary storage area on the server

[0620] Step 5:

[0621] Data Shaping and Cleaning

[0622] server

[0623] The server formats the data in the temporary storage area according to the format. It then detects missing or abnormal values ​​and complements or corrects them based on the set rules. The formatted and corrected data is then stored in the permanent storage area (data management device).

[0624] Input: Raw data stored in the temporary storage area

[0625] Output: Formatted data stored in the data management device

[0626] Step 6:

[0627] Data analysis

[0628] server

[0629] The server receives the formatted data from the data management device and analyzes it based on the AI ​​model, which predicts the health risks of each individual user, such as calculating a risk score for heart disease or diabetes.

[0630] Input: Formatted data stored in the data management device

[0631] Output: Health risk prediction results from the AI ​​model

[0632] Step 7:

[0633] Countermeasure generation

[0634] server

[0635] The server generates specific countermeasures based on predicted health risks, and creates the optimal action plan for the user by referencing past success stories and expert opinions.

[0636] Input: Health risk prediction results from an AI model

[0637] Output: Generated concrete countermeasure plan

[0638] Step 8:

[0639] Notification of measures

[0640] Terminal

[0641] The countermeasure information generated by the server is sent to the user's terminal and notified to the user. The terminal displays the received countermeasure information in the form of a list or reminder.

[0642] Input: Specific countermeasure plan sent from the server

[0643] Output: Notification of countermeasure plan, reminder setting

[0644] By following these steps, users can quickly identify their own health risks and take measures, enabling continuous health management.

[0645] (Application example 1)

[0646] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0647] Conventional health management systems only collect and analyze users' health data, but lack real-time feedback and suggested measures. In particular, when considering use in brick-and-mortar stores, it is necessary to immediately grasp the user's health status and take appropriate measures, but no specific technology exists for this purpose. The present invention aims to improve this situation by providing a system that collects and analyzes the health data of users visiting a store in real time and suggests measures.

[0648] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0649] In this invention, the server includes a means for acquiring lifestyle data and medical data from a user, a means for formatting the acquired lifestyle data and medical data, and a means for storing the formatted data in a database. This allows the server to collect biometric data of users visiting a physical store in real time through smart glasses and instantly analyze and notify their health status. Furthermore, by providing countermeasure information to store staff and users based on the analysis results, countermeasures to reduce health risks can be quickly implemented.

[0650] "Lifestyle data" refers to information about the user's activities and behavior in daily life, specifically data such as dietary content, type and time of exercise, and sleep duration.

[0651] "Medical data" refers to information such as the user's physical condition and diagnostic results obtained at a medical institution, specifically data such as blood pressure, blood sugar level, heart rate, and diagnostic results.

[0652] "User terminal" refers to a device owned by a user, including a smartphone, tablet, personal computer, etc.

[0653] "Smart glasses" refers to a wearable device worn by the user that acquires biometric data in real time through sensors and displays and transmits that data.

[0654] "Biometric data" refers to information that indicates the user's physical condition, specifically data such as heart rate, number of steps, and body temperature.

[0655] A "database" refers to a system for systematically storing and managing acquired and formatted data, and has a structure that facilitates searching and analysis.

[0656] "AI model" refers to algorithms and software that use machine learning and artificial intelligence to analyze data, recognize patterns, and derive predictions.

[0657] "Health risk" refers to an indicator that shows the likelihood that a user will develop a specific health disorder or disease, and is calculated based on the analysis results of the AI ​​model.

[0658] "Countermeasures information" refers to specific actions and measures suggested based on health risks, including suggestions for exercise methods and advice on dietary management.

[0659] "Store staff" refers to employees working in physical stores who are responsible for dealing with customers and providing services.

[0660] This invention relates to a system that collects and analyzes a user's lifestyle and medical data, predicts health risks, and proposes appropriate measures. The system includes a user terminal, smart glasses, a database, and a server.

[0661] Data collection

[0662] The user device collects lifestyle data and biometric data from the wearable device. Specifically, it records meal details, exercise duration and type, and sleep duration entered by the user through a smartphone application. It also collects biometric data such as heart rate, step count, and body temperature in real time using smart glasses. This data is then transmitted from the user device to a server.

[0663] Data organization and storage

[0664] The server receives and formats the data sent from the user's device. Specifically, it standardizes the data format and detects, complements, and corrects missing or outlier values. The formatted data is then stored in the database. This ensures the consistency and quality of the data.

[0665] Health risk prediction

[0666] The server applies a generative AI model to analyze the cosmetic surgery data stored in the database. The AI ​​model is trained based on past medical and lifestyle data, and can accurately predict individual users' health risks. For example, it calculates the probability of developing certain diseases such as heart disease or diabetes.

[0667] Proposal of measures

[0668] Based on the predicted health risks, the server generates specific measures. For example, it recommends daily aerobic exercise and a low-salt diet for users at high risk of heart disease. It also references success stories and provides recommendations based on effective measures taken by other users.

[0669] The generated information is sent to the user's device and smart glasses. The user device displays this information in the form of a list or reminder, helping the user to easily implement the measures. Meanwhile, the smart glasses provide real-time feedback in the store, instantly notifying the user and store staff of the information, enabling prompt health management.

[0670] Specific examples

[0671] For example, when a user wears smart glasses, they can collect real-time data such as a heart rate of 80, steps taken 5,000, and a body temperature of 36.5°C. This data is immediately sent to a server and analyzed by a generative AI model. As a result, the user may be predicted to have a low risk of heart disease and receive instructions to maintain their health.

[0672] Prompt Sentence Examples

[0673] "Based on the data of my heart rate of 80, number of steps taken 5000, and body temperature of 36.5°C, please analyze whether there are any health risks and display the results."

[0674] As described above, this system supports health management in physical stores by identifying users' health risks early and providing appropriate countermeasures.

[0675] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0676] Step 1:

[0677] The user device and smart glasses collect lifestyle data and biometric data. Lifestyle data is obtained by users entering information such as dietary habits, exercise time, and sleep time through a smartphone application. Biometric data, on the other hand, is data such as heart rate, number of steps, and body temperature collected in real time by the smart glasses. This data is then collected on the user device.

[0678] Step 2:

[0679] The user device sends the collected life data and biometric data to the server. At this time, the data is organized according to a specific format. The input is the various data collected on the user device, and the data sent to the destination server is in the organized format.

[0680] Step 3:

[0681] The server formats the received data according to the format. Specifically, it detects missing or outlier values ​​and performs supplementation or correction as necessary. This process generates consistent data that can be stored in a database. The input is the data sent from the user device, and the output is the formatted data.

[0682] Step 4:

[0683] The server stores the formatted data in a database. This database is used to manage a wide range of information, including the user's past lifestyle and medical data. The input is the formatted data, and the output is the data stored in the database.

[0684] Step 5:

[0685] The server applies a generative AI model to analyze the data stored in the database. The AI ​​model is trained based on past medical and lifestyle data. This model predicts individual user health risks with high accuracy. The input is the data stored in the database, and the output is the predicted health risk.

[0686] Step 6:

[0687] The server generates specific measures based on the predicted health risks. For example, it recommends daily aerobic exercise and a low-salt diet for a user at high risk of heart disease. The input is the predicted health risks, and the output is the measures.

[0688] Step 7:

[0689] The server sends the generated countermeasure information to the user terminal and the smart glasses. The user terminal displays the countermeasure information to the user in the form of a list or reminder. Meanwhile, the smart glasses display the countermeasure information in real time in the physical store and immediately notify the store staff and the user. The input is the countermeasure information, and the output is the countermeasure information notified to the user and the store staff.

[0690] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0691] The present invention relates to a system that combines lifestyle data, medical data, and emotion data of a user to predict health risks and propose appropriate measures. An embodiment of this system will be described below.

[0692] System Overview

[0693] This system works in cooperation with a server, user device, and emotion engine. The user device collects lifestyle data, biometric data from wearable devices, and emotion data, and sends this to the server. The server formats the received data and stores it in a database. The system then uses an AI model and emotion engine to predict health risks and suggest appropriate countermeasures.

[0694] Data collection

[0695] User terminal

[0696] The user terminal accepts lifestyle data entered by the user. Specifically, the user enters details of meals, the time and type of exercise, and sleep duration through an application. Furthermore, the wearable device automatically reads biometric data such as heart rate and number of steps taken. This data is periodically (for example, every night) transmitted to a server.

[0697] Collecting Emotional Data

[0698] Emotion Engine

[0699] The emotion engine acquires the user's emotional data. Specifically, it has the function of analyzing emotions from the user's facial expressions, voice, text input, etc. This evaluates stress and psychological state and generates data accordingly.

[0700] Data reduction and analysis

[0701] server

[0702] The server receives data sent from the user device and the emotion engine. This data is formatted and validated to ensure consistency and accuracy. If missing or outliers are detected, they are supplemented or corrected according to pre-defined processing rules.

[0703] Storage in the database

[0704] server

[0705] The formatted data is stored in a database, a digital storage system organized for each user, which centrally manages all necessary information, including past medical data, lifestyle data, and emotional data.

[0706] Health risk prediction

[0707] server

[0708] The server applies an AI model and emotion engine based on the data stored in the database. The AI ​​model is trained to predict specific health risks and analyzes each user's lifestyle, medical, and emotional data. Specifically, it calculates the risk of heart disease, diabetes, and other conditions.

[0709] Proposal of measures

[0710] server

[0711] Based on the predicted health risks, the server generates specific measures. The measures are quantitative and include specific guidelines for action, such as "walk 30 minutes every day" or "reduce salt intake in the diet." They also include measures to address stress and psychological states.

[0712] server

[0713] Furthermore, the server provides additional reference information based on the measures taken by other users and their results. The system stores information on successful measures and their execution results in the past, so by referring to this information, it is possible to suggest effective measures.

[0714] User Notification

[0715] User terminal

[0716] The generated countermeasure information is sent to the user's device, which notifies the user of the proposed countermeasures and provides an interface for setting reminders and action plans, making it easier for the user to implement the proposed countermeasures in their daily lives.

[0717] Gathering feedback

[0718] User

[0719] The user implements the suggested measures and inputs the results back into the application, recording feedback on the implemented measures, such as the duration of walking or changes in diet.

[0720] server

[0721] The server evaluates the effectiveness of countermeasures based on the received feedback data, and the evaluation results are used to retrain the AI ​​model and emotion engine, continuously improving the system's overall prediction accuracy and the quality of countermeasure proposals.

[0722] Specific examples

[0723] Example 1: Specific example of data collection

[0724] User terminal

[0725] The user enters the details of their meals and calories into a health app, and their smartwatch sends heart rate data to a server every night.

[0726] Emotion Engine

[0727] Users enter their daily emotions and stress levels into the app, and the app also uses the device's camera and microphone to collect data for emotional analysis.

[0728] server

[0729] The server receives data sent from the user device and emotion engine, formats it according to the format, and stores it in a database.

[0730] Example 2: A concrete example of health risk prediction

[0731] server

[0732] Using an AI model, we predict User A's risk of heart disease based on data from the past year. We also take into account emotional data and determine whether User A is in a state of high stress. As a result, we determine that User A is at high risk of heart disease.

[0733] Example 3: Specific example of proposed measures

[0734] server

[0735] User A is advised to take up 30 minutes of walking every day and restrict his diet. He is also advised to take up a hobby that has a relaxing effect and set aside time for stretching. As a success story, data is also presented showing how another user, User B, reduced his risk of heart disease by taking similar measures.

[0736] User terminal

[0737] Notify user A of the suggested measures and provide the ability to set walking reminders and stretching times.

[0738] In this way, users can grasp their own health risks early, take appropriate measures, and continuously manage their health. Furthermore, by taking emotional data into account, the system can also reflect stress and psychological state in health management.

[0739] The processing flow will be explained below.

[0740] Step 1:

[0741] Users input lifestyle data into devices such as smartphones and tablets. Specifically, they record dietary habits, exercise types and durations, sleep durations, etc. through an application. In addition, wearable devices are used to automatically read biometric data such as heart rate and number of steps taken.

[0742] Step 2:

[0743] The user inputs emotional data into the device. Specifically, this is done in the form of inputting daily emotions and stress levels. An emotion engine also runs, using the device's camera and microphone to analyze emotions from the user's facial expressions and voice.

[0744] Step 3:

[0745] The user terminal periodically (for example, every night) transmits the collected life data, biometric data, and emotional data to the server. Data transmission is automatic, but can also be performed manually if necessary.

[0746] Step 4:

[0747] The server receives data sent from the user device, formats it according to the format, and validates the data to ensure consistency and accuracy. If missing or outliers are detected, they are supplemented or corrected according to pre-defined processing rules.

[0748] Step 5:

[0749] The formatted data is stored in a database, a digital storage system organized for each user, which centrally manages all necessary information, including past medical data, lifestyle data, and emotional data.

[0750] Step 6:

[0751] The server applies an AI model and emotion engine based on the data stored in the database. The AI ​​model is trained to predict specific health risks and analyzes each user's lifestyle, medical, and emotional data. Specifically, it calculates the risk of heart disease, diabetes, and other conditions, and also adjusts the risk based on emotional data.

[0752] Step 7:

[0753] The server generates specific countermeasures based on the health risks calculated by the AI ​​model and emotion engine. The countermeasures are quantitative and include specific guidelines for action, such as "walk 30 minutes every day" or "reduce salt intake in the diet." They also include measures to address stress and psychological states.

[0754] Step 8:

[0755] Furthermore, the server provides additional reference information based on the measures taken by other users and their results. The system stores information on successful measures and their execution results in the past, so by referring to this information, it is possible to suggest effective measures.

[0756] Step 9:

[0757] The generated countermeasure information is sent to the user's device, which notifies the user of the proposed countermeasures and provides an interface for setting reminders and action plans, making it easier for the user to implement the proposed countermeasures in their daily lives.

[0758] Step 10:

[0759] The user implements the suggested measures and inputs the results back into the application, recording feedback on the implemented measures, such as the duration of walking or changes in diet.

[0760] Step 11:

[0761] The user device sends the execution result data to the server. The server evaluates the effectiveness of the countermeasures based on the received execution results. The evaluation results are used to retrain the AI ​​model and emotion engine, continuously improving the prediction accuracy of the entire system and the quality of the countermeasure proposals.

[0762] By repeating the above steps, users can continuously manage their health and the system appropriately manages individual health risks. In addition, by taking emotion data into account, stress and psychological state can also be reflected in health management.

[0763] Example 2

[0764] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0765] Conventional health management systems predict health risks based solely on lifestyle and medical data, which means they are unable to make highly accurate predictions that take into account the user's psychological state and stress level. Furthermore, there is a lack of methods for effectively collecting feedback on proposed measures and using it to improve the overall system.

[0766] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring life data, medical data, and emotional data from the user, means for formatting the acquired life data, medical data, and emotional data according to a format, and means for storing the formatted data in a database. This enables comprehensive data analysis including emotional data to improve the accuracy of health risk prediction. In addition, feedback on proposed measures is acquired from the user, and the effectiveness is evaluated and reflected in the entire system, enabling sustainable and effective health management.

[0767] "Lifestyle data" is information about the user's daily life, including the contents of meals, the time and type of exercise, and the amount of sleep.

[0768] "Medical data" refers to information relating to medical treatment at a medical institution, including diagnosis results, prescription details, medical history, and the like.

[0769] "Emotion data" is information about the user's psychological state and emotions, and includes data analyzed from facial expressions, voice, text input, and the like.

[0770] A "user terminal" is a digital device used by a user, and includes a smartphone, tablet, personal computer, etc.

[0771] A "wearable device" is a digital device that can be worn by a user, including smart watches, fitness trackers, etc.

[0772] A "database" is an information system for storing and centrally managing organized data.

[0773] "Health risk" refers to the likelihood that a user will experience a particular health problem, including risks such as heart disease and diabetes.

[0774] "Countermeasures" are specific courses of action proposed to reduce predicted health risks, including improvements in exercise and diet.

[0775] "Feedback" is information about the results of a user's implementation of a proposed measure, and is used to evaluate the effectiveness of the measure.

[0776] An "AI model" is a collection of algorithms that use artificial intelligence to analyze data and train it for a specific purpose (in this case, predicting health risks).

[0777] This invention relates to a system that integrates a user's lifestyle data, medical data, and emotional data to predict health risks and propose specific countermeasures. The system is composed of a server, a user terminal, and an emotion engine.

[0778] Data collection

[0779] User terminal

[0780] Users use a health app to input daily lifestyle data such as what they eat, the type and duration of exercise, and how much sleep they get. Furthermore, wearable devices automatically collect biometric data such as heart rate and number of steps taken. This data is then sent to a server at a set frequency (e.g., every night).

[0781] Emotion Engine

[0782] The emotion engine generates emotion data by analyzing the user's facial expressions, voice, and text input. For example, it uses the device's camera to recognize facial expressions and assess stress and psychological state.

[0783] Data Formatting and Storage

[0784] server

[0785] The server formats the data received from the user device and emotion engine. Specifically, it converts lifestyle data, medical data, and emotion data into standard formats and checks for consistency and accuracy. The formatted data is stored in a database for later analysis.

[0786] Health risk prediction

[0787] server

[0788] The server uses the data stored in the database to apply a pre-trained AI model. The AI ​​model analyzes the user's lifestyle, medical, and emotional data to predict specific health risks (e.g., heart disease, diabetes). Taking emotional data into account improves the accuracy of risk predictions.

[0789] Proposal of measures

[0790] server

[0791] Based on the predicted health risks, the server generates specific measures, such as "walk 30 minutes a day" or "reduce salt intake in your diet," and provides additional information based on the success stories of other users.

[0792] User Notification

[0793] User terminal

[0794] The generated countermeasure information is sent to the user's device, which notifies the user of the proposed countermeasures and provides an interface for setting reminders and action plans, making it easier for the user to implement the proposed countermeasures in their daily lives.

[0795] Gathering and implementing feedback

[0796] User

[0797] The user implements the suggested measures and then inputs the results back into the application, recording feedback on the measures taken, such as the duration of walking or changes in diet.

[0798] server

[0799] The server evaluates the effectiveness of countermeasures based on the received feedback data, and this evaluation result is used to retrain the AI ​​model and emotion engine, continuously improving the prediction accuracy and quality of countermeasure proposals for the entire system.

[0800] Specific examples

[0801] Example 1: Specific example of data collection

[0802] A user enters "Breakfast: 200g rice, 1 egg, 1 bowl of miso soup" into a health app, and sends heart rate data from the smartwatch (heart rate: 70 BPM) to the server. In the emotion engine, the user enters "I'm stressed out at work today" into the app, and emotion data is captured using the device's camera and microphone. The server receives this data, formats it into a standard format, and stores it in a database.

[0803] Example 2: A concrete example of health risk prediction

[0804] Based on data from the past year, the server uses an AI model to predict User A's "heart disease risk: 65%." The emotion engine also determines that User A's "stress level: high," and reflects this in the risk assessment.

[0805] Example 3: Specific example of proposed measures

[0806] The server proposes specific measures to User A, such as "walking 30 minutes every day" and "reducing salt intake at breakfast by 1 gram." It also provides reference information on past successes of other users who have taken similar measures. The user device notifies User A of this information and provides a function to set reminders, etc.

[0807] Prompt Sentence Examples

[0808] Please explain the specific processing steps of the program that analyzes emotional and lifestyle data to predict the user's health risks and suggest appropriate measures.

[0809] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0810] Step 1:

[0811] Data collection

[0812] input

[0813] Lifestyle data such as dietary details, type and duration of exercise, and sleep time entered by users through health apps, as well as biometric data such as heart rate and number of steps sent from wearable devices.

[0814] Specific actions

[0815] A user enters "Breakfast: 200g rice, 1 egg, 1 cup of miso soup" into a health app. The smartwatch automatically acquires "Heart rate: 70 BPM" data and sends it to the server via the device. The user also records "30 minutes of jogging" in an exercise app.

[0816] output

[0817] A series of life data and biometric data transmitted from the user terminal.

[0818] Step 2:

[0819] Data Formatting

[0820] input

[0821] Life data, medical data, and emotion data sent from the user device and emotion engine.

[0822] Specific actions

[0823] The server receives data such as "Breakfast: 200g rice, 1 egg, 1 bowl of miso soup." It also receives heart rate data such as "70 BPM" and converts it into a standard format. It also receives emotional data such as "User's stress level: High."

[0824] output

[0825] Formatted data in a standard format. For example, meal data is formatted into a detailed format such as "Category: Breakfast, Items: 200g rice, 1 egg, 1 bowl of miso soup."

[0826] Step 3:

[0827] Storage in the database

[0828] input

[0829] Lifestyle data, medical data, and emotional data formatted in a standard format.

[0830] Specific actions

[0831] The server stores the formatted data in a database. The database stores the data in a format organized for each user. For example, lifestyle data and medical data are stored linked to the user ID.

[0832] output

[0833] Formatted data stored in a database.

[0834] Step 4:

[0835] Health risk prediction

[0836] input

[0837] Formatted data stored in a database.

[0838] Specific actions

[0839] An AI model analyzes the data in the database to predict specific health risks (e.g., heart disease risk, diabetes risk), and an emotion engine takes the analyzed emotional data into account to calculate an overall risk score.

[0840] output

[0841] Each user's health risk score. For example, User A's "Heart disease risk: 65%" is output.

[0842] Step 5:

[0843] Proposal of measures

[0844] input

[0845] The server-predicted health risk score.

[0846] Specific actions

[0847] The server generates specific measures based on the predicted health risks, such as "recommended 30 minutes of walking every day" or "reduce salt intake by 1 gram in your diet."

[0848] output

[0849] Specific health measures. For example, suggestions such as "walk 30 minutes every day" and "reduce salt intake at breakfast" are output to User A.

[0850] Step 6:

[0851] User Notification

[0852] input

[0853] Specific health measures proposed by the server.

[0854] Specific actions

[0855] The server sends the proposed measures to the user terminal, which notifies the user of the proposed measures and sets a reminder function for a specific time.

[0856] output

[0857] Countermeasures notified to the user device. For example, a reminder to start walking at 8:00 p.m. is set and notified on the user device.

[0858] Step 7:

[0859] Gathering feedback

[0860] input

[0861] Feedback on the actions taken by the user.

[0862] Specific actions

[0863] The user enters "I walked for 30 minutes" into the application. The server receives the feedback data and evaluates the effectiveness of the measure.

[0864] output

[0865] Feedback data collected. The server uses this data to retrain the AI ​​model and emotion engine.

[0866] Step 8:

[0867] System Improvements

[0868] input

[0869] Feedback data.

[0870] Specific actions

[0871] The server analyzes the effectiveness of countermeasures based on the feedback data, and uses the results of this analysis to retrain the AI ​​model and emotion engine, improving the prediction accuracy of the entire system and the quality of countermeasure proposals.

[0872] output

[0873] An improved system. By continuing to incorporate feedback, we can provide users with even better health management methods.

[0874] (Application example 2)

[0875] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0876] In recent years, as people have become more interested in health management, there has been an increasing demand for systems that can predict health risks in daily life in real time and suggest appropriate countermeasures. However, conventional systems only consider users' lifestyle and medical data, and lack emotional data and real-time countermeasure suggestions. Furthermore, there are not enough health management systems available to improve the user experience in physical stores. There is a need to solve these issues, comprehensively manage users' health risks, and provide health-related services in physical stores.

[0877] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring lifestyle data and medical data from the user, means for formatting the acquired lifestyle data and medical data according to a format, means for storing the formatted data in a database, means for analyzing the stored data and predicting health risks, means for suggesting countermeasures based on the predicted health risks, and means for evaluating the user's health data in real time and suggesting appropriate products and services. This allows the user to understand their own health risks in real time and receive suggestions for appropriate products and services in physical stores.

[0878] "Lifestyle data" refers to information such as dietary habits, exercise habits, and sleep duration in the user's daily life.

[0879] "Medical data" refers to information related to the medical examinations and treatments a user receives at a medical institution.

[0880] "Format" refers to the rules and format for arranging data.

[0881] "Database" refers to an information system for managing and storing formatted data.

[0882] "Health risk" refers to the likelihood that a user will develop a particular health problem.

[0883] "Coping strategies" refer to specific behavioral or lifestyle changes suggested based on predicted health risks.

[0884] "Wearable device" refers to a digital device worn by a user that is capable of collecting data.

[0885] "Biometric data" refers to data obtained from the body, such as heart rate, body temperature, and number of steps taken.

[0886] An "AI model" refers to an algorithm that uses machine learning to analyze and predict data.

[0887] "User terminal" refers to a communication device such as a smartphone or tablet used by a user.

[0888] "Real-time" refers to the fact that the entire process, from data collection to analysis and proposals, is carried out instantly.

[0889] "Product" refers to an item offered to help manage health.

[0890] "Services" refer to actions or experiences proposed to help with health management.

[0891] The present invention relates to a system that predicts health risks by combining lifestyle data, medical data, and emotional data of a user and proposes appropriate measures. An embodiment of this system will be described below.

[0892] System Overview

[0893] This system works in cooperation with a server, user device, and emotion analysis engine. The user device collects lifestyle data, biometric data from wearable devices, and emotion data, and sends this to the server. The server formats the received data and stores it in a database. It then uses an AI model and emotion analysis engine to predict health risks and recommend appropriate countermeasures.

[0894] Data collection

[0895] User terminal

[0896] The user terminal accepts lifestyle and medical data entered by the user. Specifically, the user enters details of meals, exercise time and type, sleep duration, etc. via an application. Furthermore, the wearable device automatically reads biometric data such as heart rate and number of steps taken. This data is periodically sent to the server.

[0897] Collecting Emotional Data

[0898] Sentiment Analysis Engine

[0899] The emotion analysis engine acquires user emotional data. Specifically, it has the ability to analyze emotions from the user's facial expressions, voice, text input, etc. This allows it to evaluate stress and psychological state and generate data accordingly.

[0900] Data reduction and analysis

[0901] server

[0902] The server receives data sent from user devices and the sentiment analysis engine. It formats the data and validates it for consistency and accuracy. If missing or outliers are detected, they are filled in or corrected according to pre-defined processing rules.

[0903] Storage in the database

[0904] server

[0905] The formatted data is stored in a database, a digital storage system organized for each user, which centrally manages all necessary information, including past medical data, lifestyle data, and emotional data.

[0906] Health risk prediction

[0907] server

[0908] The server applies an AI model and emotion analysis engine based on the data stored in the database. The AI ​​model is trained to predict specific health risks and analyzes each user's lifestyle, medical, and emotional data. Specifically, it calculates the risk of heart disease, diabetes, and other conditions.

[0909] Proposal of measures

[0910] server

[0911] Based on the predicted health risks, the server generates specific countermeasures. The countermeasures are quantitative and include guidelines for action such as "walk 30 minutes every day" or "reduce salt intake in the diet." They also include measures to address stress and psychological states. In addition, the server provides additional reference information based on the countermeasures taken by other users and their results. By referring to past successful countermeasures and their execution results, the server can suggest effective countermeasures.

[0912] User Notification

[0913] User terminal

[0914] The generated countermeasure information is sent to the user's device, which notifies the user of the proposed countermeasures and provides an interface for setting reminders and action plans, making it easier for the user to implement the proposed countermeasures in their daily lives.

[0915] Gathering feedback

[0916] User

[0917] The user implements the suggested measures and inputs the results back into the application, which records feedback on the implemented measures, such as the duration of walking or changes in diet.

[0918] server

[0919] The server evaluates the effectiveness of countermeasures based on the received feedback data, and the evaluation results are used to retrain the AI ​​model and sentiment analysis engine, continuously improving the system's overall prediction accuracy and the quality of countermeasure proposals.

[0920] Specific examples

[0921] Example 1: Specific example of data collection

[0922] For example, a user can input their diet and calories into a health app, and send their heart rate data from their smartwatch to a server every night. The emotion analysis engine uses the device's camera and microphone to capture data for analyzing emotions, as the user inputs their daily emotions and stress levels. The server receives this data, formats it, and stores it in a database.

[0923] Example 2: A concrete example of health risk prediction

[0924] Based on data from the past year, an AI model predicts User A's risk of heart disease. It also takes into account emotional data and determines that User A is in a state of high stress. As a result, it determines that User A is at high risk of heart disease.

[0925] Example 3: Specific example of proposed measures

[0926] User A is suggested to walk 30 minutes a day and restrict his / her diet. Furthermore, suggestions are given for taking up a hobby that has a relaxing effect and setting aside time for stretching. Data is also presented as a success story showing how another user, User B, reduced their risk of heart disease by taking similar measures. The user device notifies User A of these suggestions and provides the ability to set walking reminders and stretching times.

[0927] Prompt Sentence Examples

[0928] Evaluate the user's health status based on their health data (heart rate, stress level, etc.) and suggest appropriate products and services. For example, for a user with a high heart rate and high stress level, recommend relaxing aroma oils or healthy foods. User data will be provided in the following format:

[0929] Data format: {"Heart rate": 85, "Stress level": 7, "Sleep hours": 6}

[0930] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0931] Step 1: Data collection

[0932] The user terminal receives lifestyle data (meal details, exercise time, sleep time, etc.) and medical data entered by the user. It also automatically acquires biometric data such as heart rate and step count from the wearable device. The emotion analysis engine collects emotional data (facial expressions, voice, text input). Input: Data from the user, and data from the wearable device and emotion engine. Output: Collected lifestyle data, medical data, biometric data, and emotional data.

[0933] Step 2: Send data

[0934] The user terminal formats the collected data according to the format, and after the data is formatted, it sends it to the server. Input: Collected data Output: Formatted data

[0935] Step 3: Data Formatting and Storage

[0936] The server receives data sent from the user terminal and formats it according to the format. The data is validated to ensure consistency and accuracy, and supplemented or corrected as necessary. The formatted data is stored in the database. Input: Formatted data Output: Data stored in the database

[0937] Step 4: Predicting health risks

[0938] The server uses the data stored in the database to predict health risks using an AI model and sentiment analysis engine. Specifically, it calculates the risk of heart disease and diabetes. Input: Data in the database Output: Predicted health risks

[0939] Step 5: Generate countermeasures

[0940] The server generates specific measures based on the predicted health risks. For example, "walk 30 minutes every day" or "reduce salt intake in your diet." It may also suggest a hobby that has a relaxing effect as a stress reliever. Input: Predicted health risks Output: Generated measures

[0941] Step 6: Notify users

[0942] The server sends the generated measures to the user's device, which then notifies the user. An interface is provided to help set reminders and plan actions, helping users to easily implement the proposed measures in their daily lives. Input: Generated measure information Output: Measures notified to the user

[0943] Step 7: Gather feedback

[0944] The user implements the proposed measures and inputs the results into the user terminal again. The feedback data is sent to the server again, and the effectiveness of the measures is evaluated. Input: Feedback data after the measures are implemented. Output: Evaluated effectiveness of the measures.

[0945] Step 8: System Improvement

[0946] The server uses the collected feedback data to retrain the AI ​​model and sentiment analysis engine, improving the overall system's prediction accuracy and the quality of countermeasure proposals. Input: Feedback data Output: Improved AI model and countermeasure proposal system

[0947] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0948] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0949] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0950] [Third embodiment]

[0951] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0952] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0953] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0954] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0955] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0956] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0957] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0958] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0959] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0960] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0961] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0962] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0963] The present invention relates to a system that collects and analyzes lifestyle data and medical data of a user, predicts health risks, and proposes countermeasures. An embodiment of this system will be described below.

[0964] overview

[0965] The system works in conjunction with a server and a user device. The user device collects lifestyle data and biometric data from wearable devices and sends it to the server. The server formats the received data and stores it in a database. It then uses an AI model to predict health risks and recommend appropriate countermeasures.

[0966] Data collection

[0967] User terminal

[0968] The user terminal accepts lifestyle data entered by the user. Specifically, the user enters details of meals, the time and type of exercise, and sleep duration through an application. The terminal also has the function of acquiring biometric data such as heart rate and number of steps taken from the wearable device. This data is periodically (for example, every night) transmitted to a server.

[0969] Data reduction and analysis

[0970] server

[0971] The server receives data sent from user devices. It formats the data and detects, complements, and corrects missing or outlier values. The formatted data is then stored in a database. This ensures data consistency and quality.

[0972] Health risk prediction

[0973] server

[0974] The server applies an AI model to analyze the cosmetic surgery data stored in the database. The AI ​​model is trained based on past medical and lifestyle data, and can accurately predict each user's health risks. Specifically, it calculates the probability of developing certain diseases such as heart disease and diabetes.

[0975] Proposal of measures

[0976] server

[0977] Based on the predicted health risk, the server generates specific measures. For example, a user at high risk of heart disease may be recommended to engage in daily aerobic exercise and eat a low-salt diet. It also suggests more effective measures by learning from past data and learning from measures that other users have successfully implemented.

[0978] User terminal

[0979] The countermeasure information generated by the server is sent to the user's device. The user's device notifies the user of this information and displays specific actions to be taken in daily life in the form of lists and reminders. This allows the user to smoothly implement countermeasures based on health risks.

[0980] Specific examples

[0981] Example 1: Specific example of data collection

[0982] User terminal

[0983] The user inputs the ingredients and calories of breakfast into a health app, and the heart rate data from the smartwatch is sent to a server every night.

[0984] server

[0985] The server receives the data sent from the user terminal, formats it according to the format, and stores it in a database.

[0986] Example 2: A concrete example of health risk prediction

[0987] server

[0988] Using an AI model, we predict User A's risk of heart disease based on data from the past year. As a result, we determine that User A has a high risk of heart disease.

[0989] Example 3: Specific example of proposed measures

[0990] server

[0991] User A is recommended to walk 30 minutes a day and restrict his diet. As a success story, data is also presented showing another user, User B, who reduced his risk of heart disease by taking similar measures.

[0992] User terminal

[0993] Notify user A of the proposed measures and set a walking reminder.

[0994] In this way, users can grasp their own health risks early, take appropriate measures, and continuously manage their health. The system contributes to improving health awareness not only for individuals but also for society as a whole.

[0995] The processing flow will be explained below.

[0996] Step 1:

[0997] Users input lifestyle data into devices such as smartphones and tablets. Specifically, they record dietary habits, exercise types and durations, sleep durations, etc. through an application. In addition, wearable devices are used to automatically read biometric data such as heart rate and number of steps taken.

[0998] Step 2:

[0999] The user device periodically (for example, every night) transmits the collected life data and biometric data to the server. Data transmission is set to be automatic, but it can also be transmitted manually as needed.

[1000] Step 3:

[1001] The server receives data sent from the user device, formats it according to the format, and validates the data to ensure consistency and accuracy. If missing or outliers are detected, they are supplemented or corrected according to pre-defined processing rules.

[1002] Step 4:

[1003] The formatted data is stored in a database, a digital storage system organized for each user, where all necessary information, including past medical and lifestyle data, is centrally managed.

[1004] Step 5:

[1005] The server applies an AI model based on the data stored in the database. The AI ​​model is trained to predict specific health risks and analyzes each user's lifestyle and medical data. Specifically, it calculates the risk of heart disease, diabetes, etc.

[1006] Step 6:

[1007] The server generates specific countermeasures based on the health risks calculated by the AI ​​model. The countermeasures are quantitative and include specific guidelines for action, such as "walk 30 minutes every day" or "reduce salt intake in the diet."

[1008] Step 7:

[1009] Furthermore, the server provides additional reference information based on the measures taken by other users and their results. The system stores information on successful measures and their execution results in the past, so by referring to this information, it is possible to suggest effective measures.

[1010] Step 8:

[1011] The generated countermeasure information is sent to the user's device, which notifies the user of the proposed countermeasures and provides an interface for setting reminders and action plans, making it easier for the user to implement the proposed countermeasures in their daily lives.

[1012] Step 9:

[1013] The user implements the suggested measures and inputs the results back into the application, recording feedback on the implemented measures, such as the duration of walking or changes in diet.

[1014] Step 10:

[1015] The user device sends the execution result data to the server. The server evaluates the effectiveness of the countermeasures based on the received execution results. The evaluation results are used to retrain the AI ​​model, continuously improving the prediction accuracy of the entire system and the quality of the countermeasure proposals.

[1016] By repeating the above steps, users can continuously manage their health, and the system appropriately manages individual health risks.

[1017] Example 1

[1018] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1019] Modern society requires effective management of personal lifestyle and medical data, early detection of health risks, and the implementation of appropriate countermeasures. However, current systems often lack a consistent approach to data acquisition, processing, analysis, and the presentation of countermeasures, resulting in insufficient management of individual health risks. Furthermore, integrating biometric and lifestyle data from wearable devices is expected to enable more accurate health risk prediction and countermeasures, but achieving this requires advanced data processing capabilities, which presents a challenge.

[1020] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1021] In this invention, the server includes means for acquiring life data and medical data from a user, means for formatting the acquired life data and medical data according to a format, means for storing the formatted data in a data management device, means for analyzing the stored data and predicting health risks, means for suggesting countermeasures based on the predicted health risks, means for collecting life data and biological data by a user terminal and periodically transmitting them to the server, means for the server to detect missing values ​​and abnormal values ​​in the data and complete or correct them, and means for the server to generate countermeasures based on the health risk prediction results and transmit them to the user terminal. This ensures data consistency and quality, enabling highly accurate health risk predictions and effective countermeasure suggestions.

[1022] "Lifestyle data" refers to information about the user's daily life, including details of meals, exercise times and types, and sleep duration.

[1023] "Medical data" refers to information about a user's health condition collected by a medical institution, including diagnostic results, treatment details, and drug use records.

[1024] A "user terminal" is a device that a user uses to input data about their daily life and receive data from a wearable device, and includes smartphones, tablets, etc.

[1025] A "wearable device" is a device worn on the user's body that collects biometric data, and includes smartwatches and fitness trackers.

[1026] A "data management device" is a device that is connected to a server and includes a database and storage system for storing collected and formatted data.

[1027] The "server" is a central control device that receives, formats, analyzes, and stores data sent from user terminals, predicts health risks, and generates countermeasures.

[1028] A "missing value" refers to a state in which part of the acquired data is missing, and the data is information that needs to be supplemented.

[1029] An "abnormal value" refers to a value that is outside the normal range or is unnatural among the acquired data, and is information that requires the data to be corrected.

[1030] "Health Risk" is a prediction result that indicates the risk of health problems or diseases that the user may suffer from in the future.

[1031] "Countermeasures" refer to specific actions or countermeasures recommended for predicted health risks.

[1032] Overall system overview

[1033] This invention is a system that collects and analyzes users' lifestyle and medical data, predicts health risks, and suggests countermeasures. The system operates in cooperation with a server and user terminal.

[1034] Data collection

[1035] User terminal

[1036] The user terminal is responsible for collecting lifestyle data and biometric data from the wearable device. Specifically, the user enters details of meals, the time and type of exercise, and sleep duration through the application. In addition, biometric data such as heart rate and number of steps taken is automatically acquired from the wearable device and periodically sent to the server. The user terminal can be a smartphone or tablet.

[1037] Example: A user enters the ingredients and calories they ate for breakfast into a health app, and their heart rate data from their smartwatch is sent to a server every night.

[1038] Data reduction and analysis

[1039] server

[1040] The server receives data sent from user terminals. It formats the data and detects, complements, and corrects missing or outlier values. The formatted data is then stored in a data management device. This data organization process ensures the consistency and quality of the data.

[1041] Example: The server receives data from all connected devices overnight, records it in a reception log, and fills in missing calorie information with the average value from past records.

[1042] Health risk prediction

[1043] server

[1044] The server applies an AI model to analyze the plastic surgery data stored in the database. This AI model is trained based on past medical and lifestyle data and accurately predicts each user's health risks. Specifically, it calculates the probability of developing certain diseases such as heart disease and diabetes.

[1045] Example: The server calls an AI model, sets the past year's data as input parameters, and predicts user A's risk of heart disease, which is determined to be high risk.

[1046] Proposal of measures

[1047] server

[1048] The server generates specific measures based on predicted health risks. These measures are based on past success stories and expert opinions. For example, a user at high risk of heart disease might be recommended to engage in daily aerobic exercise and eat a low-salt diet.

[1049] Example: A server creates a plan for a high-risk heart disease individual that recommends 30 minutes of walking each day.

[1050] User terminal

[1051] The countermeasure information generated by the server is sent to the user's device and notified to the user. The user's device displays this information in the form of notifications and reminders, allowing the user to smoothly implement countermeasures based on health risks.

[1052] Example: When a user's device receives new information about measures, a pop-up notification will be displayed, and a reminder will be set to encourage them to walk 30 minutes daily.

[1053] Prompt Sentence Examples

[1054] "Please explain the process of collecting the user's breakfast data and heart rate data and sending them to the server."

[1055] "Please tell me the algorithm flow for predicting the health risks of users using an AI model."

[1056] In this way, the present invention is a system that enables users to grasp health risks early and manage their health continuously, contributing to improving health awareness not only among individuals but also among society as a whole.

[1057] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1058] Step 1:

[1059] Data collection

[1060] User

[1061] Users input lifestyle data into the health app, such as meal details (breakfast, lunch, dinner), exercise time and type, and sleep duration. The data is entered into specific fields in the app and saved to the device by pressing the "Send" button.

[1062] Input: Lifestyle data entered by the user (meals, exercise, sleep time)

[1063] Output: Life data stored on the device

[1064] Step 2:

[1065] Biometric data acquisition

[1066] Terminal

[1067] Devices (such as smartwatches and fitness trackers) collect biometric data such as heart rate and number of steps in real time, and this data is automatically recorded by dedicated applications within the device.

[1068] Input: Biometric data obtained from wearable devices (heart rate, number of steps)

[1069] Output: Biometric data stored in the device

[1070] Step 3:

[1071] Data transmission

[1072] Terminal

[1073] The device periodically (for example, every night) transmits the collected life and biometric data to the server. The data is encrypted and transmitted using an API.

[1074] Input: Life data and biometric data stored on the device

[1075] Output: Life and biological data sent to the server

[1076] Step 4:

[1077] Data reception

[1078] server

[1079] The server receives the data sent from the user terminal and temporarily stores the received data in a temporary storage area.

[1080] Input: Life data and biometric data sent from the device

[1081] Output: Data stored in the temporary storage area on the server

[1082] Step 5:

[1083] Data Shaping and Cleaning

[1084] server

[1085] The server formats the data in the temporary storage area according to the format. It then detects missing or abnormal values ​​and complements or corrects them based on the set rules. The formatted and corrected data is then stored in the permanent storage area (data management device).

[1086] Input: Raw data stored in the temporary storage area

[1087] Output: Formatted data stored in the data management device

[1088] Step 6:

[1089] Data analysis

[1090] server

[1091] The server receives the formatted data from the data management device and analyzes it based on the AI ​​model, which predicts the health risks of each individual user, such as calculating a risk score for heart disease or diabetes.

[1092] Input: Formatted data stored in the data management device

[1093] Output: Health risk prediction results from the AI ​​model

[1094] Step 7:

[1095] Countermeasure generation

[1096] server

[1097] The server generates specific countermeasures based on predicted health risks, and creates the optimal action plan for the user by referencing past success stories and expert opinions.

[1098] Input: Health risk prediction results from an AI model

[1099] Output: Generated concrete countermeasure plan

[1100] Step 8:

[1101] Notification of measures

[1102] Terminal

[1103] The countermeasure information generated by the server is sent to the user's terminal and notified to the user. The terminal displays the received countermeasure information in the form of a list or reminder.

[1104] Input: Specific countermeasure plan sent from the server

[1105] Output: Notification of countermeasure plan, reminder setting

[1106] By following these steps, users can quickly identify their own health risks and take measures, enabling continuous health management.

[1107] (Application example 1)

[1108] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1109] Conventional health management systems only collect and analyze users' health data, but lack real-time feedback and suggested measures. In particular, when considering use in brick-and-mortar stores, it is necessary to immediately grasp the user's health status and take appropriate measures, but no specific technology exists for this purpose. The present invention aims to improve this situation by providing a system that collects and analyzes the health data of users visiting a store in real time and suggests measures.

[1110] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1111] In this invention, the server includes a means for acquiring lifestyle data and medical data from a user, a means for formatting the acquired lifestyle data and medical data, and a means for storing the formatted data in a database. This allows the server to collect biometric data of users visiting a physical store in real time through smart glasses and instantly analyze and notify their health status. Furthermore, by providing countermeasure information to store staff and users based on the analysis results, countermeasures to reduce health risks can be quickly implemented.

[1112] "Lifestyle data" refers to information about the user's activities and behavior in daily life, specifically data such as dietary content, type and time of exercise, and sleep duration.

[1113] "Medical data" refers to information such as the user's physical condition and diagnostic results obtained at a medical institution, specifically data such as blood pressure, blood sugar level, heart rate, and diagnostic results.

[1114] "User terminal" refers to a device owned by a user, including a smartphone, tablet, personal computer, etc.

[1115] "Smart glasses" refers to a wearable device worn by the user that acquires biometric data in real time through sensors and displays and transmits that data.

[1116] "Biometric data" refers to information that indicates the user's physical condition, specifically data such as heart rate, number of steps, and body temperature.

[1117] A "database" refers to a system for systematically storing and managing acquired and formatted data, and has a structure that facilitates searching and analysis.

[1118] "AI model" refers to algorithms and software that use machine learning and artificial intelligence to analyze data, recognize patterns, and derive predictions.

[1119] "Health risk" refers to an indicator that shows the likelihood that a user will develop a specific health disorder or disease, and is calculated based on the analysis results of the AI ​​model.

[1120] "Countermeasures information" refers to specific actions and measures suggested based on health risks, including suggestions for exercise methods and advice on dietary management.

[1121] "Store staff" refers to employees working in physical stores who are responsible for dealing with customers and providing services.

[1122] This invention relates to a system that collects and analyzes a user's lifestyle and medical data, predicts health risks, and proposes appropriate measures. The system includes a user terminal, smart glasses, a database, and a server.

[1123] Data collection

[1124] The user device collects lifestyle data and biometric data from the wearable device. Specifically, it records meal details, exercise duration and type, and sleep duration entered by the user through a smartphone application. It also collects biometric data such as heart rate, step count, and body temperature in real time using smart glasses. This data is then transmitted from the user device to a server.

[1125] Data organization and storage

[1126] The server receives and formats the data sent from the user's device. Specifically, it standardizes the data format and detects, complements, and corrects missing or outlier values. The formatted data is then stored in the database. This ensures the consistency and quality of the data.

[1127] Health risk prediction

[1128] The server applies a generative AI model to analyze the cosmetic surgery data stored in the database. The AI ​​model is trained based on past medical and lifestyle data, and can accurately predict individual users' health risks. For example, it calculates the probability of developing certain diseases such as heart disease or diabetes.

[1129] Proposal of measures

[1130] Based on the predicted health risks, the server generates specific measures. For example, it recommends daily aerobic exercise and a low-salt diet for users at high risk of heart disease. It also references success stories and provides recommendations based on effective measures taken by other users.

[1131] The generated information is sent to the user's device and smart glasses. The user device displays this information in the form of a list or reminder, helping the user to easily implement the measures. Meanwhile, the smart glasses provide real-time feedback in the store, instantly notifying the user and store staff of the information, enabling prompt health management.

[1132] Specific examples

[1133] For example, when a user wears smart glasses, they can collect real-time data such as a heart rate of 80, steps taken 5,000, and a body temperature of 36.5°C. This data is immediately sent to a server and analyzed by a generative AI model. As a result, the user may be predicted to have a low risk of heart disease and receive instructions to maintain their health.

[1134] Prompt Sentence Examples

[1135] "Based on the data of my heart rate of 80, number of steps taken 5000, and body temperature of 36.5°C, please analyze whether there are any health risks and display the results."

[1136] As described above, this system supports health management in physical stores by identifying users' health risks early and providing appropriate measures.

[1137] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1138] Step 1:

[1139] The user device and smart glasses collect lifestyle data and biometric data. Lifestyle data is obtained by users entering information such as dietary habits, exercise time, and sleep time through a smartphone application. Biometric data, on the other hand, is data such as heart rate, number of steps, and body temperature collected in real time by the smart glasses. This data is then collected on the user device.

[1140] Step 2:

[1141] The user device sends the collected life data and biometric data to the server. At this time, the data is organized according to a specific format. The input is the various data collected on the user device, and the data sent to the destination server is in the organized format.

[1142] Step 3:

[1143] The server formats the received data according to the format. Specifically, it detects missing or outlier values ​​and performs supplementation or correction as necessary. This process generates consistent data that can be stored in a database. The input is the data sent from the user device, and the output is the formatted data.

[1144] Step 4:

[1145] The server stores the formatted data in a database. This database is used to manage a wide range of information, including the user's past lifestyle and medical data. The input is the formatted data, and the output is the data stored in the database.

[1146] Step 5:

[1147] The server applies a generative AI model to analyze the data stored in the database. The AI ​​model is trained based on past medical and lifestyle data. This model predicts individual user health risks with high accuracy. The input is the data stored in the database, and the output is the predicted health risk.

[1148] Step 6:

[1149] The server generates specific measures based on the predicted health risks. For example, it recommends daily aerobic exercise and a low-salt diet for a user at high risk of heart disease. The input is the predicted health risks, and the output is the measures.

[1150] Step 7:

[1151] The server sends the generated countermeasure information to the user terminal and the smart glasses. The user terminal displays the countermeasure information to the user in the form of a list or reminder. Meanwhile, the smart glasses display the countermeasure information in real time in the physical store and immediately notify the store staff and the user. The input is the countermeasure information, and the output is the countermeasure information notified to the user and the store staff.

[1152] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1153] The present invention relates to a system that combines lifestyle data, medical data, and emotion data of a user to predict health risks and propose appropriate measures. An embodiment of this system will be described below.

[1154] System Overview

[1155] This system works in cooperation with a server, user device, and emotion engine. The user device collects lifestyle data, biometric data from wearable devices, and emotion data, and sends this to the server. The server formats the received data and stores it in a database. The system then uses an AI model and emotion engine to predict health risks and suggest appropriate countermeasures.

[1156] Data collection

[1157] User terminal

[1158] The user terminal accepts lifestyle data entered by the user. Specifically, the user enters details of meals, the time and type of exercise, and sleep duration through an application. Furthermore, the wearable device automatically reads biometric data such as heart rate and number of steps taken. This data is periodically (for example, every night) transmitted to a server.

[1159] Collecting Emotional Data

[1160] Emotion Engine

[1161] The emotion engine acquires the user's emotional data. Specifically, it has the function of analyzing emotions from the user's facial expressions, voice, text input, etc. This evaluates stress and psychological state and generates data accordingly.

[1162] Data reduction and analysis

[1163] server

[1164] The server receives data sent from the user device and the emotion engine. This data is formatted and validated to ensure consistency and accuracy. If missing or outliers are detected, they are supplemented or corrected according to pre-defined processing rules.

[1165] Storage in the database

[1166] server

[1167] The formatted data is stored in a database, a digital storage system organized for each user, which centrally manages all necessary information, including past medical data, lifestyle data, and emotional data.

[1168] Health risk prediction

[1169] server

[1170] The server applies an AI model and emotion engine based on the data stored in the database. The AI ​​model is trained to predict specific health risks and analyzes each user's lifestyle, medical, and emotional data. Specifically, it calculates the risk of heart disease, diabetes, and other conditions.

[1171] Proposal of measures

[1172] server

[1173] Based on the predicted health risks, the server generates specific measures. The measures are quantitative and include specific guidelines for action, such as "walk 30 minutes every day" or "reduce salt intake in the diet." They also include measures to address stress and psychological states.

[1174] server

[1175] Furthermore, the server provides additional reference information based on the measures taken by other users and their results. The system stores information on successful measures and their execution results in the past, so by referring to this information, it is possible to suggest effective measures.

[1176] User Notification

[1177] User terminal

[1178] The generated countermeasure information is sent to the user's device, which notifies the user of the proposed countermeasures and provides an interface for setting reminders and action plans, making it easier for the user to implement the proposed countermeasures in their daily lives.

[1179] Gathering feedback

[1180] User

[1181] The user implements the suggested measures and inputs the results back into the application, recording feedback on the implemented measures, such as the duration of walking or changes in diet.

[1182] server

[1183] The server evaluates the effectiveness of countermeasures based on the received feedback data, and the evaluation results are used to retrain the AI ​​model and emotion engine, continuously improving the system's overall prediction accuracy and the quality of countermeasure proposals.

[1184] Specific examples

[1185] Example 1: Specific example of data collection

[1186] User terminal

[1187] The user enters the details of their meals and calories into a health app, and their smartwatch sends heart rate data to a server every night.

[1188] Emotion Engine

[1189] Users enter their daily emotions and stress levels into the app, and the app also uses the device's camera and microphone to collect data for emotional analysis.

[1190] server

[1191] The server receives data sent from the user device and emotion engine, formats it according to the format, and stores it in a database.

[1192] Example 2: A concrete example of health risk prediction

[1193] server

[1194] Using an AI model, we predict User A's risk of heart disease based on data from the past year. We also take into account emotional data and determine whether User A is in a state of high stress. As a result, we determine that User A is at high risk of heart disease.

[1195] Example 3: Specific example of proposed measures

[1196] server

[1197] User A is advised to take up 30 minutes of walking every day and restrict his diet. He is also advised to take up a hobby that has a relaxing effect and set aside time for stretching. As a success story, data is also presented showing how another user, User B, reduced his risk of heart disease by taking similar measures.

[1198] User terminal

[1199] Notify user A of the suggested measures and provide the ability to set walking reminders and stretching times.

[1200] In this way, users can grasp their own health risks early, take appropriate measures, and continuously manage their health. Furthermore, by taking emotional data into account, the system can also reflect stress and psychological state in health management.

[1201] The processing flow will be explained below.

[1202] Step 1:

[1203] Users input lifestyle data into devices such as smartphones and tablets. Specifically, they record dietary habits, exercise types and durations, sleep durations, etc. through an application. In addition, wearable devices are used to automatically read biometric data such as heart rate and number of steps taken.

[1204] Step 2:

[1205] The user inputs emotional data into the device. Specifically, this is done in the form of inputting daily emotions and stress levels. An emotion engine also runs, using the device's camera and microphone to analyze emotions from the user's facial expressions and voice.

[1206] Step 3:

[1207] The user terminal periodically (for example, every night) transmits the collected life data, biometric data, and emotional data to the server. Data transmission is automatic, but can also be performed manually if necessary.

[1208] Step 4:

[1209] The server receives data sent from the user's device, formats it according to the format, and validates the data to ensure consistency and accuracy. If missing or outliers are detected, they are supplemented or corrected according to pre-defined processing rules.

[1210] Step 5:

[1211] The formatted data is stored in a database, a digital storage system organized for each user, which centrally manages all necessary information, including past medical data, lifestyle data, and emotional data.

[1212] Step 6:

[1213] The server applies an AI model and emotion engine based on the data stored in the database. The AI ​​model is trained to predict specific health risks and analyzes each user's lifestyle, medical, and emotional data. Specifically, it calculates the risk of heart disease, diabetes, and other conditions, and also adjusts the risk based on emotional data.

[1214] Step 7:

[1215] The server generates specific countermeasures based on the health risks calculated by the AI ​​model and emotion engine. The countermeasures are quantitative and include specific guidelines for action, such as "walk 30 minutes every day" or "reduce salt intake in the diet." They also include measures to address stress and psychological states.

[1216] Step 8:

[1217] Furthermore, the server provides additional reference information based on the measures taken by other users and their results. The system stores information on successful measures and their execution results in the past, so by referring to this information, it is possible to suggest effective measures.

[1218] Step 9:

[1219] The generated countermeasure information is sent to the user's device, which notifies the user of the proposed countermeasures and provides an interface for setting reminders and action plans, making it easier for the user to implement the proposed countermeasures in their daily lives.

[1220] Step 10:

[1221] The user implements the suggested measures and inputs the results back into the application, recording feedback on the implemented measures, such as the duration of walking or changes in diet.

[1222] Step 11:

[1223] The user device sends the execution result data to the server. The server evaluates the effectiveness of the countermeasures based on the received execution results. The evaluation results are used to retrain the AI ​​model and emotion engine, continuously improving the prediction accuracy of the entire system and the quality of the countermeasure proposals.

[1224] By repeating the above steps, users can continuously manage their health and the system appropriately manages individual health risks. In addition, by taking emotion data into account, stress and psychological state can also be reflected in health management.

[1225] Example 2

[1226] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1227] Conventional health management systems predict health risks based solely on lifestyle and medical data, which means they are unable to make highly accurate predictions that take into account the user's psychological state and stress level. Furthermore, there is a lack of methods for effectively collecting feedback on proposed measures and using it to improve the overall system.

[1228] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring life data, medical data, and emotional data from the user, means for formatting the acquired life data, medical data, and emotional data according to a format, and means for storing the formatted data in a database. This enables comprehensive data analysis including emotional data to improve the accuracy of health risk prediction. In addition, feedback on proposed measures is acquired from the user, and the effectiveness is evaluated and reflected in the entire system, enabling sustainable and effective health management.

[1229] "Lifestyle data" is information about the user's daily life, including the contents of meals, the time and type of exercise, and the amount of sleep.

[1230] "Medical data" refers to information relating to medical treatment at a medical institution, including diagnosis results, prescription details, medical history, and the like.

[1231] "Emotion data" is information about the user's psychological state and emotions, and includes data analyzed from facial expressions, voice, text input, and the like.

[1232] A "user terminal" is a digital device used by a user, and includes a smartphone, tablet, personal computer, etc.

[1233] A "wearable device" is a digital device that can be worn by a user, including smart watches, fitness trackers, etc.

[1234] A "database" is an information system for storing and centrally managing organized data.

[1235] "Health risk" refers to the likelihood that a user will experience a particular health problem, including risks such as heart disease and diabetes.

[1236] "Countermeasures" are specific courses of action proposed to reduce predicted health risks, including improvements in exercise and diet.

[1237] "Feedback" is information about the results of a user's implementation of a proposed measure, and is used to evaluate the effectiveness of the measure.

[1238] An "AI model" is a collection of algorithms that use artificial intelligence to analyze data and train it for a specific purpose (in this case, predicting health risks).

[1239] This invention relates to a system that integrates a user's lifestyle data, medical data, and emotional data to predict health risks and propose specific countermeasures. The system is composed of a server, a user terminal, and an emotion engine.

[1240] Data collection

[1241] User terminal

[1242] Users use a health app to input daily lifestyle data such as what they eat, the type and duration of exercise, and how much sleep they get. Furthermore, wearable devices automatically collect biometric data such as heart rate and number of steps taken. This data is then sent to a server at a set frequency (e.g., every night).

[1243] Emotion Engine

[1244] The emotion engine generates emotion data by analyzing the user's facial expressions, voice, and text input. For example, it uses the device's camera to recognize facial expressions and assess stress and psychological state.

[1245] Data Formatting and Storage

[1246] server

[1247] The server formats the data received from the user device and emotion engine. Specifically, it converts lifestyle data, medical data, and emotion data into standard formats and checks for consistency and accuracy. The formatted data is stored in a database for later analysis.

[1248] Health risk prediction

[1249] server

[1250] The server uses the data stored in the database to apply a pre-trained AI model. The AI ​​model analyzes the user's lifestyle, medical, and emotional data to predict specific health risks (e.g., heart disease, diabetes). Taking emotional data into account improves the accuracy of risk predictions.

[1251] Proposal of measures

[1252] server

[1253] Based on the predicted health risks, the server generates specific measures, such as "walk 30 minutes a day" or "reduce salt intake in your diet," and provides additional information based on the success stories of other users.

[1254] User Notification

[1255] User terminal

[1256] The generated countermeasure information is sent to the user's device, which notifies the user of the proposed countermeasures and provides an interface for setting reminders and action plans, making it easier for the user to implement the proposed countermeasures in their daily lives.

[1257] Gathering and implementing feedback

[1258] User

[1259] The user implements the suggested measures and then inputs the results back into the application, recording feedback on the measures taken, such as the duration of walking or changes in diet.

[1260] server

[1261] The server evaluates the effectiveness of countermeasures based on the received feedback data, and this evaluation result is used to retrain the AI ​​model and emotion engine, continuously improving the prediction accuracy and quality of countermeasure proposals for the entire system.

[1262] Specific examples

[1263] Example 1: Specific example of data collection

[1264] A user enters "Breakfast: 200g rice, 1 egg, 1 bowl of miso soup" into a health app, and sends heart rate data from the smartwatch (heart rate: 70 BPM) to the server. In the emotion engine, the user enters "I'm stressed out at work today" into the app, and emotion data is captured using the device's camera and microphone. The server receives this data, formats it into a standard format, and stores it in a database.

[1265] Example 2: A concrete example of health risk prediction

[1266] Based on data from the past year, the server uses an AI model to predict User A's "heart disease risk: 65%." The emotion engine also determines that User A's "stress level: high," and reflects this in the risk assessment.

[1267] Example 3: Specific example of proposed measures

[1268] The server proposes specific measures to User A, such as "walking 30 minutes every day" and "reducing salt intake at breakfast by 1 gram." It also provides reference information on past successes of other users who have taken similar measures. The user device notifies User A of this information and provides a function to set reminders, etc.

[1269] Prompt Sentence Examples

[1270] Please explain the specific processing steps of the program that analyzes emotional and lifestyle data to predict the user's health risks and suggest appropriate measures.

[1271] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1272] Step 1:

[1273] Data collection

[1274] input

[1275] Lifestyle data such as dietary details, type and duration of exercise, and sleep time entered by users through health apps, as well as biometric data such as heart rate and number of steps sent from wearable devices.

[1276] Specific actions

[1277] A user enters "Breakfast: 200g rice, 1 egg, 1 cup of miso soup" into a health app. The smartwatch automatically acquires "Heart rate: 70 BPM" data and sends it to the server via the device. The user also records "30 minutes of jogging" in an exercise app.

[1278] output

[1279] A series of life data and biometric data transmitted from the user terminal.

[1280] Step 2:

[1281] Data Formatting

[1282] input

[1283] Life data, medical data, and emotion data sent from the user device and emotion engine.

[1284] Specific actions

[1285] The server receives data such as "Breakfast: 200g rice, 1 egg, 1 bowl of miso soup." It also receives heart rate data such as "70 BPM" and converts it into a standard format. It also receives emotional data such as "User's stress level: High."

[1286] output

[1287] Formatted data in a standard format. For example, meal data is formatted into a detailed format such as "Category: Breakfast, Items: 200g rice, 1 egg, 1 bowl of miso soup."

[1288] Step 3:

[1289] Storage in the database

[1290] input

[1291] Lifestyle data, medical data, and emotional data formatted in a standard format.

[1292] Specific actions

[1293] The server stores the formatted data in a database. The database stores the data in a format organized for each user. For example, lifestyle data and medical data are stored linked to the user ID.

[1294] output

[1295] Formatted data stored in a database.

[1296] Step 4:

[1297] Health risk prediction

[1298] input

[1299] Formatted data stored in a database.

[1300] Specific actions

[1301] An AI model analyzes the data in the database to predict specific health risks (e.g., heart disease risk, diabetes risk), and an emotion engine takes the analyzed emotional data into account to calculate an overall risk score.

[1302] output

[1303] Each user's health risk score. For example, User A's "Heart disease risk: 65%" is output.

[1304] Step 5:

[1305] Proposal of measures

[1306] input

[1307] The server-predicted health risk score.

[1308] Specific actions

[1309] The server generates specific measures based on the predicted health risks, such as "recommended 30 minutes of walking every day" or "reduce salt intake by 1 gram in your diet."

[1310] output

[1311] Specific health measures. For example, suggestions such as "walk 30 minutes every day" and "reduce salt intake at breakfast" are output to User A.

[1312] Step 6:

[1313] User Notification

[1314] input

[1315] Specific health measures proposed by the server.

[1316] Specific actions

[1317] The server sends the proposed measures to the user terminal, which notifies the user of the proposed measures and sets a reminder function for a specific time.

[1318] output

[1319] Countermeasures notified to the user device. For example, a reminder to start walking at 8:00 p.m. is set and notified on the user device.

[1320] Step 7:

[1321] Collecting feedback

[1322] input

[1323] Feedback on the actions taken by the user.

[1324] Specific actions

[1325] The user enters "I walked for 30 minutes" into the application. The server receives the feedback data and evaluates the effectiveness of the measure.

[1326] output

[1327] Feedback data collected. The server uses this data to retrain the AI ​​model and emotion engine.

[1328] Step 8:

[1329] System Improvements

[1330] input

[1331] Feedback data.

[1332] Specific actions

[1333] The server analyzes the effectiveness of countermeasures based on the feedback data, and uses the results of this analysis to retrain the AI ​​model and emotion engine, improving the prediction accuracy of the entire system and the quality of countermeasure proposals.

[1334] output

[1335] An improved system. By continuing to incorporate feedback, we can provide users with even better health management methods.

[1336] (Application example 2)

[1337] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1338] In recent years, as people have become more interested in health management, there has been an increasing demand for systems that can predict health risks in daily life in real time and suggest appropriate countermeasures. However, conventional systems only consider users' lifestyle and medical data, and lack emotional data and real-time countermeasure suggestions. Furthermore, there are not enough health management systems available to improve the user experience in physical stores. There is a need to solve these issues, comprehensively manage users' health risks, and provide health-related services in physical stores.

[1339] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring lifestyle data and medical data from the user, means for formatting the acquired lifestyle data and medical data according to a format, means for storing the formatted data in a database, means for analyzing the stored data and predicting health risks, means for suggesting countermeasures based on the predicted health risks, and means for evaluating the user's health data in real time and suggesting appropriate products and services. This allows the user to understand their own health risks in real time and receive suggestions for appropriate products and services in physical stores.

[1340] "Lifestyle data" refers to information such as dietary habits, exercise habits, and sleep duration in the user's daily life.

[1341] "Medical data" refers to information related to the medical examinations and treatments a user receives at a medical institution.

[1342] "Format" refers to the rules and format for arranging data.

[1343] "Database" refers to an information system for managing and storing formatted data.

[1344] "Health risk" refers to the likelihood that a user will develop a particular health problem.

[1345] "Coping strategies" refer to specific behavioral or lifestyle changes suggested based on predicted health risks.

[1346] "Wearable device" refers to a digital device worn by a user that is capable of collecting data.

[1347] "Biometric data" refers to data obtained from the body, such as heart rate, body temperature, and number of steps taken.

[1348] An "AI model" refers to an algorithm that uses machine learning to analyze and predict data.

[1349] "User terminal" refers to a communication device such as a smartphone or tablet used by a user.

[1350] "Real-time" refers to the fact that the entire process, from data collection to analysis and proposals, is carried out instantly.

[1351] "Product" refers to an item offered to help manage health.

[1352] "Services" refer to actions or experiences proposed to help with health management.

[1353] The present invention relates to a system that predicts health risks by combining lifestyle data, medical data, and emotional data of a user and proposes appropriate measures. An embodiment of this system will be described below.

[1354] System Overview

[1355] This system works in cooperation with a server, user device, and emotion analysis engine. The user device collects lifestyle data, biometric data from wearable devices, and emotion data, and sends this to the server. The server formats the received data and stores it in a database. It then uses an AI model and emotion analysis engine to predict health risks and recommend appropriate countermeasures.

[1356] Data collection

[1357] User terminal

[1358] The user terminal accepts lifestyle and medical data entered by the user. Specifically, the user enters details of meals, exercise time and type, sleep duration, etc. via an application. Furthermore, the wearable device automatically reads biometric data such as heart rate and number of steps taken. This data is periodically sent to the server.

[1359] Collecting Emotional Data

[1360] Sentiment Analysis Engine

[1361] The emotion analysis engine acquires user emotional data. Specifically, it has the ability to analyze emotions from the user's facial expressions, voice, text input, etc. This allows it to evaluate stress and psychological state and generate data accordingly.

[1362] Data reduction and analysis

[1363] server

[1364] The server receives data sent from user devices and the sentiment analysis engine. It formats the data and validates it for consistency and accuracy. If missing or outliers are detected, they are filled in or corrected according to pre-defined processing rules.

[1365] Storage in the database

[1366] server

[1367] The formatted data is stored in a database, a digital storage system organized for each user, which centrally manages all necessary information, including past medical data, lifestyle data, and emotional data.

[1368] Health risk prediction

[1369] server

[1370] The server applies an AI model and emotion analysis engine based on the data stored in the database. The AI ​​model is trained to predict specific health risks and analyzes each user's lifestyle, medical, and emotional data. Specifically, it calculates the risk of heart disease, diabetes, and other conditions.

[1371] Proposal of measures

[1372] server

[1373] Based on the predicted health risks, the server generates specific countermeasures. The countermeasures are quantitative and include guidelines for action such as "walk 30 minutes every day" or "reduce salt intake in the diet." They also include measures to address stress and psychological states. In addition, the server provides additional reference information based on the countermeasures taken by other users and their results. By referring to past successful countermeasures and their execution results, the server can suggest effective countermeasures.

[1374] User Notification

[1375] User terminal

[1376] The generated countermeasure information is sent to the user's device, which notifies the user of the proposed countermeasures and provides an interface for setting reminders and action plans, making it easier for the user to implement the proposed countermeasures in their daily lives.

[1377] Collecting feedback

[1378] User

[1379] The user implements the suggested measures and inputs the results back into the application, recording feedback on the implemented measures, such as the duration of walking or changes in diet.

[1380] server

[1381] The server evaluates the effectiveness of countermeasures based on the received feedback data, and the evaluation results are used to retrain the AI ​​model and sentiment analysis engine, continuously improving the system's overall prediction accuracy and the quality of countermeasure proposals.

[1382] Specific examples

[1383] Example 1: Specific example of data collection

[1384] For example, a user can input their diet and calories into a health app, and send their heart rate data from their smartwatch to a server every night. The emotion analysis engine uses the device's camera and microphone to capture data for analyzing emotions, as the user inputs their daily emotions and stress levels. The server receives this data, formats it, and stores it in a database.

[1385] Example 2: A concrete example of health risk prediction

[1386] Based on data from the past year, an AI model predicts User A's risk of heart disease. It also takes into account emotional data and determines that User A is in a state of high stress. As a result, it determines that User A is at high risk of heart disease.

[1387] Example 3: Specific example of proposed measures

[1388] User A is suggested to walk 30 minutes a day and restrict his / her diet. Furthermore, suggestions are given for taking up a hobby that has a relaxing effect and setting aside time for stretching. Data is also presented as a success story showing how another user, User B, reduced their risk of heart disease by taking similar measures. The user device notifies User A of these suggestions and provides the ability to set walking reminders and stretching times.

[1389] Prompt Sentence Examples

[1390] Evaluate the user's health status based on their health data (heart rate, stress level, etc.) and suggest appropriate products and services. For example, for a user with a high heart rate and high stress level, recommend relaxing aroma oils or healthy foods. User data will be provided in the following format:

[1391] Data format: {"Heart rate": 85, "Stress level": 7, "Sleep hours": 6}

[1392] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1393] Step 1: Data collection

[1394] The user terminal receives lifestyle data (meal details, exercise time, sleep time, etc.) and medical data entered by the user. It also automatically acquires biometric data such as heart rate and step count from the wearable device. The emotion analysis engine collects emotional data (facial expressions, voice, text input). Input: Data from the user, and data from the wearable device and emotion engine. Output: Collected lifestyle data, medical data, biometric data, and emotional data.

[1395] Step 2: Send data

[1396] The user terminal formats the collected data according to the format, and after the data is formatted, it sends it to the server. Input: Collected data Output: Formatted data

[1397] Step 3: Data Formatting and Storage

[1398] The server receives data sent from the user terminal and formats it according to the format. The data is validated to ensure consistency and accuracy, and supplemented or corrected as necessary. The formatted data is stored in the database. Input: Formatted data Output: Data stored in the database

[1399] Step 4: Predicting health risks

[1400] The server uses the data stored in the database to predict health risks using an AI model and sentiment analysis engine. Specifically, it calculates the risk of heart disease and diabetes. Input: Data in the database Output: Predicted health risks

[1401] Step 5: Generate countermeasures

[1402] The server generates specific measures based on the predicted health risks. For example, "walk 30 minutes every day" or "reduce salt intake in your diet." It may also suggest a hobby that has a relaxing effect as a stress reliever. Input: Predicted health risks Output: Generated measures

[1403] Step 6: Notify users

[1404] The server sends the generated measures to the user's device, which then notifies the user. An interface is provided to help set reminders and plan actions, helping users to easily implement the proposed measures in their daily lives. Input: Generated measure information Output: Measures notified to the user

[1405] Step 7: Gather feedback

[1406] The user implements the proposed measures and inputs the results into the user terminal again. The feedback data is sent to the server again, and the effectiveness of the measures is evaluated. Input: Feedback data after the measures are implemented. Output: Evaluated effectiveness of the measures.

[1407] Step 8: System Improvement

[1408] The server uses the collected feedback data to retrain the AI ​​model and sentiment analysis engine, improving the overall system's prediction accuracy and the quality of countermeasure proposals. Input: Feedback data Output: Improved AI model and countermeasure proposal system

[1409] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1410] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1411] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1412] [Fourth embodiment]

[1413] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1414] 7, a 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.

[1415] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1416] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1417] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1418] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1419] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1420] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1421] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1422] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1423] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1424] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1425] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1426] The present invention relates to a system that collects and analyzes lifestyle data and medical data of a user, predicts health risks, and proposes countermeasures. An embodiment of this system will be described below.

[1427] overview

[1428] The system works in conjunction with a server and a user device. The user device collects lifestyle data and biometric data from wearable devices and sends it to the server. The server formats the received data and stores it in a database. It then uses an AI model to predict health risks and recommend appropriate countermeasures.

[1429] Data collection

[1430] User terminal

[1431] The user terminal accepts lifestyle data entered by the user. Specifically, the user enters details of meals, the time and type of exercise, and sleep duration through an application. The terminal also has the function of acquiring biometric data such as heart rate and number of steps taken from the wearable device. This data is periodically (for example, every night) transmitted to a server.

[1432] Data reduction and analysis

[1433] server

[1434] The server receives data sent from user devices. It formats the data and detects, complements, and corrects missing or outlier values. The formatted data is then stored in a database. This ensures data consistency and quality.

[1435] Health risk prediction

[1436] server

[1437] The server applies an AI model to analyze the cosmetic surgery data stored in the database. The AI ​​model is trained based on past medical and lifestyle data, and can accurately predict each user's health risks. Specifically, it calculates the probability of developing certain diseases such as heart disease and diabetes.

[1438] Proposal of measures

[1439] server

[1440] Based on the predicted health risk, the server generates specific measures. For example, a user at high risk of heart disease may be recommended to engage in daily aerobic exercise and eat a low-salt diet. It also suggests more effective measures by learning from past data and learning from measures that other users have successfully implemented.

[1441] User terminal

[1442] The countermeasure information generated by the server is sent to the user's device. The user's device notifies the user of this information and displays specific actions to be taken in daily life in the form of lists and reminders. This allows the user to smoothly implement countermeasures based on health risks.

[1443] Specific examples

[1444] Example 1: Specific example of data collection

[1445] User terminal

[1446] The user inputs the ingredients and calories of breakfast into a health app, and the heart rate data from the smartwatch is sent to a server every night.

[1447] server

[1448] The server receives the data sent from the user terminal, formats it according to the format, and stores it in a database.

[1449] Example 2: A concrete example of health risk prediction

[1450] server

[1451] Using an AI model, we predict User A's risk of heart disease based on data from the past year. As a result, we determine that User A has a high risk of heart disease.

[1452] Example 3: Specific example of proposed measures

[1453] server

[1454] User A is recommended to walk 30 minutes a day and restrict his diet. As a success story, data is also presented showing another user, User B, who reduced his risk of heart disease by taking similar measures.

[1455] User terminal

[1456] Notify user A of the proposed measures and set a walking reminder.

[1457] In this way, users can grasp their own health risks early, take appropriate measures, and continuously manage their health. The system contributes to improving health awareness not only for individuals but also for society as a whole.

[1458] The processing flow will be explained below.

[1459] Step 1:

[1460] Users input lifestyle data into devices such as smartphones and tablets. Specifically, they record dietary habits, exercise types and durations, sleep durations, etc. through an application. In addition, wearable devices are used to automatically read biometric data such as heart rate and number of steps taken.

[1461] Step 2:

[1462] The user device periodically (for example, every night) transmits the collected life data and biometric data to the server. Data transmission is set to be automatic, but it can also be transmitted manually as needed.

[1463] Step 3:

[1464] The server receives data sent from the user device, formats it according to the format, and validates the data to ensure consistency and accuracy. If missing or outliers are detected, they are supplemented or corrected according to pre-defined processing rules.

[1465] Step 4:

[1466] The formatted data is stored in a database, a digital storage system organized for each user, where all necessary information, including past medical and lifestyle data, is centrally managed.

[1467] Step 5:

[1468] The server applies an AI model based on the data stored in the database. The AI ​​model is trained to predict specific health risks and analyzes each user's lifestyle and medical data. Specifically, it calculates the risk of heart disease, diabetes, etc.

[1469] Step 6:

[1470] The server generates specific countermeasures based on the health risks calculated by the AI ​​model. The countermeasures are quantitative and include specific guidelines for action, such as "walk 30 minutes every day" or "reduce salt intake in the diet."

[1471] Step 7:

[1472] Furthermore, the server provides additional reference information based on the measures taken by other users and their results. The system stores information on successful measures and their execution results in the past, so by referring to this information, it is possible to suggest effective measures.

[1473] Step 8:

[1474] The generated countermeasure information is sent to the user's device, which notifies the user of the proposed countermeasures and provides an interface for setting reminders and action plans, making it easier for the user to implement the proposed countermeasures in their daily lives.

[1475] Step 9:

[1476] The user implements the suggested measures and inputs the results back into the application, recording feedback on the implemented measures, such as the duration of walking or changes in diet.

[1477] Step 10:

[1478] The user device sends the execution result data to the server. The server evaluates the effectiveness of the countermeasures based on the received execution results. The evaluation results are used to retrain the AI ​​model, continuously improving the prediction accuracy of the entire system and the quality of the countermeasure proposals.

[1479] By repeating the above steps, users can continuously manage their health, and the system appropriately manages individual health risks.

[1480] Example 1

[1481] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1482] Modern society requires effective management of personal lifestyle and medical data, early detection of health risks, and the implementation of appropriate countermeasures. However, current systems often lack a consistent approach to data acquisition, processing, analysis, and the presentation of countermeasures, resulting in insufficient management of individual health risks. Furthermore, integrating biometric and lifestyle data from wearable devices is expected to enable more accurate health risk prediction and countermeasures, but achieving this requires advanced data processing capabilities, which presents a challenge.

[1483] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1484] In this invention, the server includes means for acquiring life data and medical data from a user, means for formatting the acquired life data and medical data according to a format, means for storing the formatted data in a data management device, means for analyzing the stored data and predicting health risks, means for suggesting countermeasures based on the predicted health risks, means for collecting life data and biological data using a user terminal and periodically transmitting them to the server, means for the server to detect missing values ​​and abnormal values ​​in the data and complete or correct them, and means for the server to generate countermeasures based on the health risk prediction results and transmit them to the user terminal. This ensures data consistency and quality, enabling highly accurate health risk predictions and effective countermeasure suggestions.

[1485] "Lifestyle data" refers to information about the user's daily life, including details of meals, exercise times and types, and sleep duration.

[1486] "Medical data" refers to information about a user's health condition collected by a medical institution, including diagnostic results, treatment details, and drug use records.

[1487] A "user terminal" is a device that a user uses to input data about their daily life and receive data from a wearable device, and includes smartphones, tablets, etc.

[1488] A "wearable device" is a device worn on the user's body that collects biometric data, and includes smartwatches and fitness trackers.

[1489] A "data management device" is a device that is connected to a server and includes a database and storage system for storing collected and formatted data.

[1490] The "server" is a central control device that receives, formats, analyzes, and stores data sent from user terminals, predicts health risks, and generates countermeasures.

[1491] A "missing value" refers to a state in which part of the acquired data is missing, and the data is information that needs to be supplemented.

[1492] An "abnormal value" refers to a value that is outside the normal range or is unnatural among the acquired data, and is information that requires the data to be corrected.

[1493] "Health Risk" is a prediction result that indicates the risk of health problems or diseases that the user may suffer from in the future.

[1494] "Countermeasures" refer to specific actions or countermeasures recommended for predicted health risks.

[1495] Overall system overview

[1496] This invention is a system that collects and analyzes users' lifestyle and medical data, predicts health risks, and suggests countermeasures. The system operates in cooperation with a server and user terminal.

[1497] Data collection

[1498] User terminal

[1499] The user terminal is responsible for collecting lifestyle data and biometric data from the wearable device. Specifically, the user enters details of meals, the time and type of exercise, and sleep duration through the application. In addition, biometric data such as heart rate and number of steps taken is automatically acquired from the wearable device and periodically sent to the server. The user terminal can be a smartphone or tablet.

[1500] Example: A user enters the ingredients and calories they ate for breakfast into a health app, and their heart rate data from their smartwatch is sent to a server every night.

[1501] Data reduction and analysis

[1502] server

[1503] The server receives data sent from user terminals. It formats the data and detects, complements, and corrects missing or outlier values. The formatted data is then stored in a data management device. This data organization process ensures the consistency and quality of the data.

[1504] Example: The server receives data from all connected devices overnight, records it in a reception log, and fills in missing calorie information with the average value from past records.

[1505] Health risk prediction

[1506] server

[1507] The server applies an AI model to analyze the plastic surgery data stored in the database. This AI model is trained based on past medical and lifestyle data and accurately predicts each user's health risks. Specifically, it calculates the probability of developing certain diseases such as heart disease and diabetes.

[1508] Example: The server calls an AI model, sets the past year's data as input parameters, and predicts user A's risk of heart disease, which is determined to be high risk.

[1509] Proposal of measures

[1510] server

[1511] The server generates specific measures based on predicted health risks. These measures are based on past success stories and expert opinions. For example, a user at high risk of heart disease might be recommended to engage in daily aerobic exercise and eat a low-salt diet.

[1512] Example: A server creates a plan for a high-risk heart disease individual that recommends 30 minutes of walking each day.

[1513] User terminal

[1514] The countermeasure information generated by the server is sent to the user's device and notified to the user. The user's device displays this information in the form of notifications and reminders, allowing the user to smoothly implement countermeasures based on health risks.

[1515] Example: When a user's device receives new information about measures, a pop-up notification will be displayed, and a reminder will be set to encourage them to walk 30 minutes daily.

[1516] Prompt Sentence Examples

[1517] "Please explain the process of collecting the user's breakfast data and heart rate data and sending them to the server."

[1518] "Please tell me the algorithm flow for predicting the health risks of users using an AI model."

[1519] In this way, the present invention is a system that enables users to grasp health risks early and manage their health continuously, contributing to improving health awareness not only among individuals but also among society as a whole.

[1520] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1521] Step 1:

[1522] Data collection

[1523] User

[1524] Users input lifestyle data into the health app, such as meal details (breakfast, lunch, dinner), exercise time and type, and sleep duration. The data is entered into specific fields in the app and saved to the device by pressing the "Send" button.

[1525] Input: Lifestyle data entered by the user (meals, exercise, sleep time)

[1526] Output: Life data stored on the device

[1527] Step 2:

[1528] Biometric data acquisition

[1529] Terminal

[1530] Devices (such as smartwatches and fitness trackers) collect biometric data such as heart rate and number of steps in real time, and this data is automatically recorded by dedicated applications within the device.

[1531] Input: Biometric data obtained from wearable devices (heart rate, number of steps)

[1532] Output: Biometric data stored in the device

[1533] Step 3:

[1534] Data transmission

[1535] Terminal

[1536] The device periodically (for example, every night) transmits the collected life and biometric data to the server. The data is encrypted and transmitted using an API.

[1537] Input: Life data and biometric data stored on the device

[1538] Output: Life and biological data sent to the server

[1539] Step 4:

[1540] Data reception

[1541] server

[1542] The server receives the data sent from the user terminal and temporarily stores the received data in a temporary storage area.

[1543] Input: Life data and biometric data sent from the device

[1544] Output: Data stored in the temporary storage area on the server

[1545] Step 5:

[1546] Data Shaping and Cleaning

[1547] server

[1548] The server formats the data in the temporary storage area according to the format. It then detects missing or abnormal values ​​and complements or corrects them based on the set rules. The formatted and corrected data is then stored in the actual storage area (data management device).

[1549] Input: Raw data stored in the temporary storage area

[1550] Output: Formatted data stored in the data management device

[1551] Step 6:

[1552] Data analysis

[1553] server

[1554] The server receives the formatted data from the data management device and analyzes it based on the AI ​​model, which predicts the health risks of each individual user, such as calculating a risk score for heart disease or diabetes.

[1555] Input: Formatted data stored in the data management device

[1556] Output: Health risk prediction results from the AI ​​model

[1557] Step 7:

[1558] Countermeasure generation

[1559] server

[1560] The server generates specific countermeasures based on predicted health risks, and creates the optimal action plan for the user by referencing past success stories and expert opinions.

[1561] Input: Health risk prediction results from an AI model

[1562] Output: Generated concrete countermeasure plan

[1563] Step 8:

[1564] Notification of measures

[1565] Terminal

[1566] The countermeasure information generated by the server is sent to the user's terminal and notified to the user. The terminal displays the received countermeasure information in the form of a list or reminder.

[1567] Input: Specific countermeasure plan sent from the server

[1568] Output: Notification of countermeasure plan, reminder setting

[1569] By following these steps, users can quickly identify their own health risks and take measures, enabling continuous health management.

[1570] (Application example 1)

[1571] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1572] Conventional health management systems only collect and analyze users' health data, but lack real-time feedback and suggested measures. In particular, when considering use in brick-and-mortar stores, it is necessary to immediately grasp the user's health status and take appropriate measures, but no specific technology exists for this purpose. The present invention aims to improve this situation by providing a system that collects and analyzes the health data of users visiting a store in real time and suggests measures.

[1573] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1574] In this invention, the server includes a means for acquiring lifestyle data and medical data from a user, a means for formatting the acquired lifestyle data and medical data, and a means for storing the formatted data in a database. This allows the server to collect biometric data of users visiting a physical store in real time through smart glasses and instantly analyze and notify their health status. Furthermore, by providing countermeasure information to store staff and users based on the analysis results, countermeasures to reduce health risks can be quickly implemented.

[1575] "Lifestyle data" refers to information about the user's activities and behavior in daily life, specifically data such as dietary content, type and time of exercise, and sleep duration.

[1576] "Medical data" refers to information such as the user's physical condition and diagnostic results obtained at a medical institution, specifically data such as blood pressure, blood sugar level, heart rate, and diagnostic results.

[1577] "User terminal" refers to a device owned by a user, including a smartphone, tablet, personal computer, etc.

[1578] "Smart glasses" refers to a wearable device worn by the user that acquires biometric data in real time through sensors and displays and transmits that data.

[1579] "Biometric data" refers to information that indicates the user's physical condition, specifically data such as heart rate, number of steps, and body temperature.

[1580] A "database" refers to a system for systematically storing and managing acquired and formatted data, and has a structure that facilitates searching and analysis.

[1581] "AI model" refers to algorithms and software that use machine learning and artificial intelligence to analyze data, recognize patterns, and derive predictions.

[1582] "Health risk" refers to an indicator that shows the likelihood that a user will develop a specific health disorder or disease, and is calculated based on the analysis results of the AI ​​model.

[1583] "Countermeasures information" refers to specific actions and measures suggested based on health risks, including suggestions for exercise methods and advice on dietary management.

[1584] "Store staff" refers to employees working in physical stores who are responsible for dealing with customers and providing services.

[1585] This invention relates to a system that collects and analyzes a user's lifestyle and medical data, predicts health risks, and proposes appropriate measures. The system includes a user terminal, smart glasses, a database, and a server.

[1586] Data collection

[1587] The user device collects lifestyle data and biometric data from the wearable device. Specifically, it records meal details, exercise duration and type, and sleep duration entered by the user through a smartphone application. It also collects biometric data such as heart rate, step count, and body temperature in real time using smart glasses. This data is then transmitted from the user device to a server.

[1588] Data organization and storage

[1589] The server receives and formats the data sent from the user's device. Specifically, it standardizes the data format and detects, complements, and corrects missing or outlier values. The formatted data is then stored in the database. This ensures the consistency and quality of the data.

[1590] Health risk prediction

[1591] The server applies a generative AI model to analyze the cosmetic surgery data stored in the database. The AI ​​model is trained based on past medical and lifestyle data, and can accurately predict individual users' health risks. For example, it calculates the probability of developing certain diseases such as heart disease or diabetes.

[1592] Proposal of measures

[1593] Based on the predicted health risks, the server generates specific measures. For example, it recommends daily aerobic exercise and a low-salt diet for users at high risk of heart disease. It also references success stories and provides recommendations based on effective measures taken by other users.

[1594] The generated information is sent to the user's device and smart glasses. The user device displays this information in the form of a list or reminder, helping the user to easily implement the measures. Meanwhile, the smart glasses provide real-time feedback in the store, instantly notifying the user and store staff of the information, enabling prompt health management.

[1595] Specific examples

[1596] For example, when a user wears smart glasses, they can collect real-time data such as a heart rate of 80, steps taken 5,000, and a body temperature of 36.5°C. This data is immediately sent to a server and analyzed by a generative AI model. As a result, the user may be predicted to have a low risk of heart disease and receive instructions to maintain their health.

[1597] Prompt Sentence Examples

[1598] "Based on the data of my heart rate of 80, number of steps taken 5000, and body temperature of 36.5°C, please analyze whether there are any health risks and display the results."

[1599] As described above, this system supports health management in physical stores by identifying users' health risks early and providing appropriate measures.

[1600] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1601] Step 1:

[1602] The user device and smart glasses collect lifestyle data and biometric data. Lifestyle data is obtained by users entering information such as dietary habits, exercise time, and sleep time through a smartphone application. Biometric data, on the other hand, is data such as heart rate, number of steps, and body temperature collected in real time by the smart glasses. This data is then collected on the user device.

[1603] Step 2:

[1604] The user device sends the collected life data and biometric data to the server. At this time, the data is organized according to a specific format. The input is the various data collected on the user device, and the data sent to the destination server is in the organized format.

[1605] Step 3:

[1606] The server formats the received data according to the format. Specifically, it detects missing or outlier values ​​and performs supplementation or correction as necessary. This process generates consistent data that can be stored in a database. The input is the data sent from the user device, and the output is the formatted data.

[1607] Step 4:

[1608] The server stores the formatted data in a database. This database is used to manage a wide range of information, including the user's past lifestyle and medical data. The input is the formatted data, and the output is the data stored in the database.

[1609] Step 5:

[1610] The server applies a generative AI model to analyze the data stored in the database. The AI ​​model is trained based on past medical and lifestyle data. This model predicts individual user health risks with high accuracy. The input is the data stored in the database, and the output is the predicted health risk.

[1611] Step 6:

[1612] The server generates specific measures based on the predicted health risks. For example, it recommends daily aerobic exercise and a low-salt diet for a user at high risk of heart disease. The input is the predicted health risks, and the output is the measures.

[1613] Step 7:

[1614] The server sends the generated countermeasure information to the user terminal and the smart glasses. The user terminal displays the countermeasure information to the user in the form of a list or reminder. Meanwhile, the smart glasses display the countermeasure information in real time in the physical store and immediately notify the store staff and the user. The input is the countermeasure information, and the output is the countermeasure information notified to the user and the store staff.

[1615] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1616] The present invention relates to a system that combines lifestyle data, medical data, and emotion data of a user to predict health risks and propose appropriate measures. An embodiment of this system will be described below.

[1617] System Overview

[1618] This system works in cooperation with a server, user device, and emotion engine. The user device collects lifestyle data, biometric data from wearable devices, and emotion data, and sends this to the server. The server formats the received data and stores it in a database. The system then uses an AI model and emotion engine to predict health risks and suggest appropriate countermeasures.

[1619] Data collection

[1620] User terminal

[1621] The user terminal accepts lifestyle data entered by the user. Specifically, the user enters details of meals, the time and type of exercise, and sleep duration through an application. Furthermore, the wearable device automatically reads biometric data such as heart rate and number of steps taken. This data is periodically (for example, every night) transmitted to a server.

[1622] Collecting Emotional Data

[1623] Emotion Engine

[1624] The emotion engine acquires the user's emotional data. Specifically, it has the function of analyzing emotions from the user's facial expressions, voice, text input, etc. This evaluates stress and psychological state and generates data accordingly.

[1625] Data reduction and analysis

[1626] server

[1627] The server receives data sent from the user device and the emotion engine. This data is formatted and validated to ensure consistency and accuracy. If missing or outliers are detected, they are supplemented or corrected according to pre-defined processing rules.

[1628] Storage in the database

[1629] server

[1630] The formatted data is stored in a database, a digital storage system organized for each user, which centrally manages all necessary information, including past medical data, lifestyle data, and emotional data.

[1631] Health risk prediction

[1632] server

[1633] The server applies an AI model and emotion engine based on the data stored in the database. The AI ​​model is trained to predict specific health risks and analyzes each user's lifestyle, medical, and emotional data. Specifically, it calculates the risk of heart disease, diabetes, and other conditions.

[1634] Proposal of measures

[1635] server

[1636] Based on the predicted health risks, the server generates specific measures. The measures are quantitative and include specific guidelines for action, such as "walk 30 minutes every day" or "reduce salt intake in the diet." They also include measures to address stress and psychological states.

[1637] server

[1638] Furthermore, the server provides additional reference information based on the measures taken by other users and their results. The system stores information on successful measures and their execution results in the past, so by referring to this information, it is possible to suggest effective measures.

[1639] User Notification

[1640] User terminal

[1641] The generated countermeasure information is sent to the user's device, which notifies the user of the proposed countermeasures and provides an interface for setting reminders and action plans, making it easier for the user to implement the proposed countermeasures in their daily lives.

[1642] Collecting feedback

[1643] User

[1644] The user implements the suggested measures and inputs the results back into the application, recording feedback on the implemented measures, such as the duration of walking or changes in diet.

[1645] server

[1646] The server evaluates the effectiveness of countermeasures based on the received feedback data, and the evaluation results are used to retrain the AI ​​model and emotion engine, continuously improving the system's overall prediction accuracy and the quality of countermeasure proposals.

[1647] Specific examples

[1648] Example 1: Specific example of data collection

[1649] User terminal

[1650] The user enters the details of their meals and calories into a health app, and their smartwatch sends heart rate data to a server every night.

[1651] Emotion Engine

[1652] Users enter their daily emotions and stress levels into the app, and the app also uses the device's camera and microphone to collect data for emotional analysis.

[1653] server

[1654] The server receives data sent from the user device and emotion engine, formats it according to the format, and stores it in a database.

[1655] Example 2: A concrete example of health risk prediction

[1656] server

[1657] Using an AI model, we predict User A's risk of heart disease based on data from the past year. We also take into account emotional data and determine whether User A is in a state of high stress. As a result, we determine that User A is at high risk of heart disease.

[1658] Example 3: Specific example of proposed measures

[1659] server

[1660] User A is advised to take up 30 minutes of walking every day and restrict his diet. He is also advised to take up a hobby that has a relaxing effect and set aside time for stretching. As a success story, data is also presented showing how another user, User B, reduced his risk of heart disease by taking similar measures.

[1661] User terminal

[1662] Notify user A of the suggested measures and provide the ability to set walking reminders and stretching times.

[1663] In this way, users can grasp their own health risks early, take appropriate measures, and continuously manage their health. Furthermore, by taking emotional data into account, the system can also reflect stress and psychological state in health management.

[1664] The processing flow will be explained below.

[1665] Step 1:

[1666] Users input lifestyle data into devices such as smartphones and tablets. Specifically, they record dietary habits, exercise types and durations, sleep durations, etc. through an application. In addition, wearable devices are used to automatically read biometric data such as heart rate and number of steps taken.

[1667] Step 2:

[1668] The user inputs emotional data into the device. Specifically, this is done in the form of inputting daily emotions and stress levels. An emotion engine also runs, using the device's camera and microphone to analyze emotions from the user's facial expressions and voice.

[1669] Step 3:

[1670] The user terminal periodically (for example, every night) transmits the collected life data, biometric data, and emotional data to the server. Data transmission is automatic, but can also be performed manually if necessary.

[1671] Step 4:

[1672] The server receives data sent from the user device, formats it according to the format, and validates the data to ensure consistency and accuracy. If missing or outliers are detected, they are supplemented or corrected according to pre-defined processing rules.

[1673] Step 5:

[1674] The formatted data is stored in a database, a digital storage system organized for each user, which centrally manages all necessary information, including past medical data, lifestyle data, and emotional data.

[1675] Step 6:

[1676] The server applies an AI model and emotion engine based on the data stored in the database. The AI ​​model is trained to predict specific health risks and analyzes each user's lifestyle, medical, and emotional data. Specifically, it calculates the risk of heart disease, diabetes, and other conditions, and also adjusts the risk based on emotional data.

[1677] Step 7:

[1678] The server generates specific countermeasures based on the health risks calculated by the AI ​​model and emotion engine. The countermeasures are quantitative and include specific guidelines for action, such as "walk 30 minutes every day" or "reduce salt intake in the diet." They also include measures to address stress and psychological states.

[1679] Step 8:

[1680] Furthermore, the server provides additional reference information based on the measures taken by other users and their results. The system stores information on successful measures and their execution results in the past, so by referring to this information, it is possible to suggest effective measures.

[1681] Step 9:

[1682] The generated countermeasure information is sent to the user's device, which notifies the user of the proposed countermeasures and provides an interface for setting reminders and action plans, making it easier for the user to implement the proposed countermeasures in their daily lives.

[1683] Step 10:

[1684] The user implements the suggested measures and inputs the results back into the application, recording feedback on the implemented measures, such as the duration of walking or changes in diet.

[1685] Step 11:

[1686] The user device sends the execution result data to the server. The server evaluates the effectiveness of the countermeasures based on the received execution results. The evaluation results are used to retrain the AI ​​model and emotion engine, continuously improving the prediction accuracy of the entire system and the quality of the countermeasure proposals.

[1687] By repeating the above steps, users can continuously manage their health and the system appropriately manages individual health risks. In addition, by taking emotion data into account, stress and psychological state can also be reflected in health management.

[1688] Example 2

[1689] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1690] Conventional health management systems predict health risks based solely on lifestyle and medical data, which means they are unable to make highly accurate predictions that take into account the user's psychological state and stress level. Furthermore, there is a lack of methods for effectively collecting feedback on proposed measures and using it to improve the overall system.

[1691] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring life data, medical data, and emotional data from the user, means for formatting the acquired life data, medical data, and emotional data according to a format, and means for storing the formatted data in a database. This enables comprehensive data analysis including emotional data to improve the accuracy of health risk prediction. In addition, feedback on proposed measures is acquired from the user, and the effectiveness is evaluated and reflected in the entire system, enabling sustainable and effective health management.

[1692] "Lifestyle data" is information about the user's daily life, including the contents of meals, the time and type of exercise, and the amount of sleep.

[1693] "Medical data" refers to information relating to medical treatment at a medical institution, including diagnosis results, prescription details, medical history, and the like.

[1694] "Emotion data" is information about the user's psychological state and emotions, and includes data analyzed from facial expressions, voice, text input, and the like.

[1695] A "user terminal" is a digital device used by a user, and includes a smartphone, tablet, personal computer, etc.

[1696] A "wearable device" is a digital device that can be worn by a user, including smart watches, fitness trackers, etc.

[1697] A "database" is an information system for storing and centrally managing organized data.

[1698] "Health risk" refers to the likelihood that a user will experience a particular health problem, including risks such as heart disease and diabetes.

[1699] "Countermeasures" are specific courses of action proposed to reduce predicted health risks, including improvements in exercise and diet.

[1700] "Feedback" is information about the results of a user's implementation of a proposed measure, and is used to evaluate the effectiveness of the measure.

[1701] An "AI model" is a collection of algorithms that use artificial intelligence to analyze data and train it for a specific purpose (in this case, predicting health risks).

[1702] This invention relates to a system that integrates a user's lifestyle data, medical data, and emotional data to predict health risks and propose specific countermeasures. The system is composed of a server, a user terminal, and an emotion engine.

[1703] Data collection

[1704] User terminal

[1705] Users use a health app to input daily lifestyle data such as what they eat, the type and duration of exercise, and how much sleep they get. Furthermore, wearable devices automatically collect biometric data such as heart rate and number of steps taken. This data is then sent to a server at a set frequency (e.g., every night).

[1706] Emotion Engine

[1707] The emotion engine generates emotion data by analyzing the user's facial expressions, voice, and text input. For example, it uses the device's camera to recognize facial expressions and assess stress and psychological state.

[1708] Data Formatting and Storage

[1709] server

[1710] The server formats the data received from the user device and emotion engine. Specifically, it converts lifestyle data, medical data, and emotion data into standard formats and checks for consistency and accuracy. The formatted data is stored in a database for later analysis.

[1711] Health risk prediction

[1712] server

[1713] The server uses the data stored in the database to apply a pre-trained AI model. The AI ​​model analyzes the user's lifestyle, medical, and emotional data to predict specific health risks (e.g., heart disease, diabetes). Taking emotional data into account improves the accuracy of risk predictions.

[1714] Proposal of measures

[1715] server

[1716] Based on the predicted health risks, the server generates specific measures, such as "walk 30 minutes a day" or "reduce salt intake in your diet," and provides additional information based on the success stories of other users.

[1717] User Notification

[1718] User terminal

[1719] The generated countermeasure information is sent to the user's device, which notifies the user of the proposed countermeasures and provides an interface for setting reminders and action plans, making it easier for the user to implement the proposed countermeasures in their daily lives.

[1720] Gathering and implementing feedback

[1721] User

[1722] The user implements the suggested measures and then inputs the results back into the application, recording feedback on the measures taken, such as the duration of walking or changes in diet.

[1723] server

[1724] The server evaluates the effectiveness of countermeasures based on the received feedback data, and this evaluation result is used to retrain the AI ​​model and emotion engine, continuously improving the prediction accuracy and quality of countermeasure proposals for the entire system.

[1725] Specific examples

[1726] Example 1: Specific example of data collection

[1727] A user enters "Breakfast: 200g rice, 1 egg, 1 bowl of miso soup" into a health app, and sends heart rate data from the smartwatch (heart rate: 70 BPM) to the server. In the emotion engine, the user enters "I'm stressed out at work today" into the app, and emotion data is captured using the device's camera and microphone. The server receives this data, formats it into a standard format, and stores it in a database.

[1728] Example 2: A concrete example of health risk prediction

[1729] Based on data from the past year, the server uses an AI model to predict User A's "heart disease risk: 65%." The emotion engine also determines that User A's "stress level: high," and reflects this in the risk assessment.

[1730] Example 3: Specific example of proposed measures

[1731] The server proposes specific measures to User A, such as "walking 30 minutes every day" and "reducing salt intake at breakfast by 1 gram." It also provides reference information on past successes of other users who have taken similar measures. The user device notifies User A of this information and provides a function to set reminders, etc.

[1732] Prompt Sentence Examples

[1733] Please explain the specific processing steps of the program that analyzes emotional and lifestyle data to predict the user's health risks and suggest appropriate measures.

[1734] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1735] Step 1:

[1736] Data collection

[1737] input

[1738] Lifestyle data such as dietary details, type and duration of exercise, and sleep time entered by users through health apps, as well as biometric data such as heart rate and number of steps sent from wearable devices.

[1739] Specific actions

[1740] A user enters "Breakfast: 200g rice, 1 egg, 1 cup of miso soup" into a health app. The smartwatch automatically acquires "Heart rate: 70 BPM" data and sends it to the server via the device. The user also records "30 minutes of jogging" in an exercise app.

[1741] output

[1742] A series of life data and biometric data transmitted from the user terminal.

[1743] Step 2:

[1744] Data Formatting

[1745] input

[1746] Life data, medical data, and emotion data sent from the user device and emotion engine.

[1747] Specific actions

[1748] The server receives data such as "Breakfast: 200g rice, 1 egg, 1 bowl of miso soup." It also receives heart rate data such as "70 BPM" and converts it into a standard format. It also receives emotional data such as "User's stress level: High."

[1749] output

[1750] Formatted data in a standard format. For example, meal data is formatted into a detailed format such as "Category: Breakfast, Items: 200g rice, 1 egg, 1 bowl of miso soup."

[1751] Step 3:

[1752] Storage in the database

[1753] input

[1754] Lifestyle data, medical data, and emotional data formatted in a standard format.

[1755] Specific actions

[1756] The server stores the formatted data in a database. The database stores the data in a format organized for each user. For example, lifestyle data and medical data are stored linked to the user ID.

[1757] output

[1758] Formatted data stored in a database.

[1759] Step 4:

[1760] Health risk prediction

[1761] input

[1762] Formatted data stored in a database.

[1763] Specific actions

[1764] An AI model analyzes the data in the database to predict specific health risks (e.g., heart disease risk, diabetes risk), and an emotion engine takes the analyzed emotional data into account to calculate an overall risk score.

[1765] output

[1766] Each user's health risk score. For example, User A's "Heart disease risk: 65%" is output.

[1767] Step 5:

[1768] Proposal of measures

[1769] input

[1770] The server-predicted health risk score.

[1771] Specific actions

[1772] The server generates specific measures based on the predicted health risks, such as "recommended 30 minutes of walking every day" or "reduce salt intake by 1 gram in your diet."

[1773] output

[1774] Specific health measures. For example, suggestions such as "walk 30 minutes every day" and "reduce salt intake at breakfast" are output to User A.

[1775] Step 6:

[1776] User Notification

[1777] input

[1778] Specific health measures proposed by the server.

[1779] Specific actions

[1780] The server sends the proposed measures to the user terminal, which notifies the user of the proposed measures and sets a reminder function for a specific time.

[1781] output

[1782] Countermeasures notified to the user device. For example, a reminder to start walking at 8:00 p.m. is set and notified on the user device.

[1783] Step 7:

[1784] Gathering feedback

[1785] input

[1786] Feedback on the actions taken by the user.

[1787] Specific actions

[1788] The user enters "I walked for 30 minutes" into the application. The server receives the feedback data and evaluates the effectiveness of the measure.

[1789] output

[1790] Feedback data collected. The server uses this data to retrain the AI ​​model and emotion engine.

[1791] Step 8:

[1792] System Improvements

[1793] input

[1794] Feedback data.

[1795] Specific actions

[1796] The server analyzes the effectiveness of countermeasures based on the feedback data, and uses the results of this analysis to retrain the AI ​​model and emotion engine, improving the prediction accuracy of the entire system and the quality of countermeasure proposals.

[1797] output

[1798] An improved system. By continuing to incorporate feedback, we can provide users with even better health management methods.

[1799] (Application example 2)

[1800] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1801] In recent years, as people have become more interested in health management, there has been an increasing demand for systems that can predict health risks in daily life in real time and suggest appropriate countermeasures. However, conventional systems only consider users' lifestyle and medical data, and lack emotional data and real-time countermeasure suggestions. Furthermore, there are not enough health management systems available to improve the user experience in physical stores. There is a need to solve these issues, comprehensively manage users' health risks, and provide health-related services in physical stores.

[1802] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring lifestyle data and medical data from the user, means for formatting the acquired lifestyle data and medical data according to a format, means for storing the formatted data in a database, means for analyzing the stored data and predicting health risks, means for suggesting countermeasures based on the predicted health risks, and means for evaluating the user's health data in real time and suggesting appropriate products and services. This allows the user to understand their own health risks in real time and receive suggestions for appropriate products and services in physical stores.

[1803] "Lifestyle data" refers to information such as dietary habits, exercise habits, and sleep duration in the user's daily life.

[1804] "Medical data" refers to information related to the medical examinations and treatments a user receives at a medical institution.

[1805] "Format" refers to the rules and format for arranging data.

[1806] "Database" refers to an information system for managing and storing formatted data.

[1807] "Health risk" refers to the likelihood that a user will develop a particular health problem.

[1808] "Coping strategies" refer to specific behavioral or lifestyle changes suggested based on predicted health risks.

[1809] "Wearable device" refers to a digital device worn by a user that is capable of collecting data.

[1810] "Biometric data" refers to data obtained from the body, such as heart rate, body temperature, and number of steps taken.

[1811] An "AI model" refers to an algorithm that uses machine learning to analyze and predict data.

[1812] "User terminal" refers to a communication device such as a smartphone or tablet used by a user.

[1813] "Real-time" refers to the fact that the entire process, from data collection to analysis and proposals, is carried out instantly.

[1814] "Product" refers to an item offered to help manage health.

[1815] "Services" refer to actions or experiences proposed to help with health management.

[1816] The present invention relates to a system that predicts health risks by combining lifestyle data, medical data, and emotional data of a user and proposes appropriate measures. An embodiment of this system will be described below.

[1817] System Overview

[1818] This system works in cooperation with a server, user device, and emotion analysis engine. The user device collects lifestyle data, biometric data from wearable devices, and emotion data, and sends this to the server. The server formats the received data and stores it in a database. It then uses an AI model and emotion analysis engine to predict health risks and recommend appropriate countermeasures.

[1819] Data collection

[1820] User terminal

[1821] The user terminal accepts lifestyle and medical data entered by the user. Specifically, the user enters details of meals, exercise time and type, sleep duration, etc. via an application. Furthermore, the wearable device automatically reads biometric data such as heart rate and number of steps taken. This data is periodically sent to the server.

[1822] Collecting Emotional Data

[1823] Sentiment Analysis Engine

[1824] The emotion analysis engine acquires user emotional data. Specifically, it has the ability to analyze emotions from the user's facial expressions, voice, text input, etc. This allows it to evaluate stress and psychological state and generate data accordingly.

[1825] Data reduction and analysis

[1826] server

[1827] The server receives data sent from user devices and the sentiment analysis engine. It formats the data and validates it for consistency and accuracy. If missing or outliers are detected, they are filled in or corrected according to pre-defined processing rules.

[1828] Storage in the database

[1829] server

[1830] The formatted data is stored in a database, a digital storage system organized for each user, which centrally manages all necessary information, including past medical data, lifestyle data, and emotional data.

[1831] Health risk prediction

[1832] server

[1833] The server applies an AI model and emotion analysis engine based on the data stored in the database. The AI ​​model is trained to predict specific health risks and analyzes each user's lifestyle, medical, and emotional data. Specifically, it calculates the risk of heart disease, diabetes, and other conditions.

[1834] Proposal of measures

[1835] server

[1836] Based on the predicted health risks, the server generates specific countermeasures. The countermeasures are quantitative and include guidelines for action such as "walk 30 minutes every day" or "reduce salt intake in the diet." They also include measures to address stress and psychological states. In addition, the server provides additional reference information based on the countermeasures taken by other users and their results. By referring to past successful countermeasures and their execution results, the server can suggest effective countermeasures.

[1837] User Notification

[1838] User terminal

[1839] The generated countermeasure information is sent to the user's device, which notifies the user of the proposed countermeasures and provides an interface for setting reminders and action plans, making it easier for the user to implement the proposed countermeasures in their daily lives.

[1840] Gathering feedback

[1841] User

[1842] The user implements the suggested measures and inputs the results back into the application, which records feedback on the implemented measures, such as the duration of walking or changes in diet.

[1843] server

[1844] The server evaluates the effectiveness of countermeasures based on the received feedback data, and the evaluation results are used to retrain the AI ​​model and sentiment analysis engine, continuously improving the system's overall prediction accuracy and the quality of countermeasure proposals.

[1845] Specific examples

[1846] Example 1: Specific example of data collection

[1847] For example, a user can input their diet and calories into a health app, and send their heart rate data from their smartwatch to a server every night. The emotion analysis engine uses the device's camera and microphone to capture data for analyzing emotions, as the user inputs their daily emotions and stress levels. The server receives this data, formats it, and stores it in a database.

[1848] Example 2: A concrete example of health risk prediction

[1849] Based on data from the past year, an AI model predicts User A's risk of heart disease. It also takes into account emotional data and determines that User A is in a state of high stress. As a result, it determines that User A is at high risk of heart disease.

[1850] Example 3: Specific example of proposed measures

[1851] User A is suggested to walk 30 minutes a day and restrict his / her diet. Furthermore, suggestions are given for taking up a hobby that has a relaxing effect and setting aside time for stretching. Data is also presented as a success story showing how another user, User B, reduced their risk of heart disease by taking similar measures. The user device notifies User A of these suggestions and provides the ability to set walking reminders and stretching times.

[1852] Prompt Sentence Examples

[1853] Evaluate the user's health status based on their health data (heart rate, stress level, etc.) and suggest appropriate products and services. For example, for a user with a high heart rate and high stress level, recommend relaxing aroma oils or healthy foods. User data will be provided in the following format:

[1854] Data format: {"Heart rate": 85, "Stress level": 7, "Sleep hours": 6}

[1855] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1856] Step 1: Data collection

[1857] The user terminal receives lifestyle data (meal details, exercise time, sleep time, etc.) and medical data entered by the user. It also automatically acquires biometric data such as heart rate and step count from the wearable device. The emotion analysis engine collects emotional data (facial expressions, voice, text input). Input: Data from the user, and data from the wearable device and emotion engine. Output: Collected lifestyle data, medical data, biometric data, and emotional data.

[1858] Step 2: Send data

[1859] The user terminal formats the collected data according to the format, and after the data is formatted, it sends it to the server. Input: Collected data Output: Formatted data

[1860] Step 3: Data Formatting and Storage

[1861] The server receives data sent from the user terminal and formats it according to the format. The data is validated to ensure consistency and accuracy, and supplemented or corrected as necessary. The formatted data is stored in the database. Input: Formatted data Output: Data stored in the database

[1862] Step 4: Predicting health risks

[1863] The server uses the data stored in the database to predict health risks using an AI model and sentiment analysis engine. Specifically, it calculates the risk of heart disease and diabetes. Input: Data in the database Output: Predicted health risks

[1864] Step 5: Generate countermeasures

[1865] The server generates specific measures based on the predicted health risks. For example, "walk 30 minutes every day" or "reduce salt intake in your diet." It may also suggest a hobby that has a relaxing effect as a stress reliever. Input: Predicted health risks Output: Generated measures

[1866] Step 6: Notify users

[1867] The server sends the generated measures to the user's device, which then notifies the user. An interface is provided to help set reminders and plan actions, helping users to easily implement the proposed measures in their daily lives. Input: Generated measure information Output: Measures notified to the user

[1868] Step 7: Gather feedback

[1869] The user implements the proposed measures and inputs the results into the user terminal again. The feedback data is sent to the server again, and the effectiveness of the measures is evaluated. Input: Feedback data after the measures are implemented. Output: Evaluated effectiveness of the measures.

[1870] Step 8: System Improvement

[1871] The server uses the collected feedback data to retrain the AI ​​model and sentiment analysis engine, improving the overall system's prediction accuracy and the quality of countermeasure proposals. Input: Feedback data Output: Improved AI model and countermeasure proposal system

[1872] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1873] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1874] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1875] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1876] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1877] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1878] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1879] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1880] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1881] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1882] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1883] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1884] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1885] 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.

[1886] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1887] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1888] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1889] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1890] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1891] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1892] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1893] The following is further disclosed regarding the above embodiment.

[1894] (Claim 1)

[1895] A means for acquiring lifestyle data and medical data from a user;

[1896] A means for formatting the acquired lifestyle data and medical data according to a format;

[1897] a means for storing the formatted data in a database;

[1898] A means for analyzing the stored data and predicting health risks;

[1899] A means for suggesting countermeasures based on predicted health risks;

[1900] A system including:

[1901] (Claim 2)

[1902] 10. The system of claim 1,

[1903] The system further includes means for receiving biometric data of the wearable device transmitted from the user terminal.

[1904] (Claim 3)

[1905] 10. The system of claim 1,

[1906] The system further includes means for calculating a health risk based on the stored data using the AI ​​model.

[1907] (Claim 4)

[1908] 10. The system of claim 1,

[1909] The system further includes a means for selecting and presenting effective measures based on the performance of other users.

[1910] "Example 1"

[1911] (Claim 1)

[1912] A means for acquiring lifestyle data and medical data from users;

[1913] A means for formatting the acquired lifestyle data and medical data according to a format;

[1914] means for storing the formatted data in a data management device;

[1915] A means for analyzing the stored data and predicting health risks;

[1916] A means for suggesting countermeasures based on predicted health risks;

[1917] A means for collecting life data and biometric data by a user terminal and periodically transmitting the data to a server;

[1918] The server detects missing or outliers in the data and provides a means to supplement and correct them.

[1919] A means for the server to generate countermeasures based on the health risk prediction results and transmit the countermeasures to the user terminal;

[1920] A system including:

[1921] (Claim 2)

[1922] 10. The system of claim 1, further comprising means for transmitting biometric data of the wearable device from the user terminal.

[1923] (Claim 3)

[1924] 10. The system of claim 1, further comprising means for calculating health risk based on the stored data using a machine learning model.

[1925] "Application Example 1"

[1926] (Claim 1)

[1927] A means for acquiring lifestyle data and medical data from a user;

[1928] A means for formatting the acquired lifestyle data and medical data according to a format;

[1929] a means for storing the formatted data in a database;

[1930] A means for analyzing the stored data and predicting health risks;

[1931] A means for suggesting countermeasures based on predicted health risks;

[1932] A means for collecting biometric data in real time using smart glasses;

[1933] A means of analyzing collected biometric data and instantly notifying users of their health status;

[1934] A means for providing countermeasure information to store staff and users;

[1935] A system including:

[1936] (Claim 2)

[1937] 10. The system of claim 1, further comprising means for receiving biometric data of the wearable device transmitted from the user terminal.

[1938] (Claim 3)

[1939] 10. The system of claim 1, further comprising means for calculating health risks based on the stored data using an AI model.

[1940] "Example 2: Combining Emotion Engines"

[1941] (Claim 1)

[1942] means for acquiring lifestyle data, medical data, and emotion data from a user;

[1943] A means for formatting the acquired lifestyle data, medical data, and emotion data according to a format;

[1944] a means for storing the formatted data in a database;

[1945] A means for analyzing the stored data and predicting health risks;

[1946] A means of suggesting countermeasures based on predicted health risks;

[1947] means for notifying a user terminal of the proposed measures;

[1948] a means for obtaining feedback from users on the proposed measures;

[1949] A means to evaluate the effectiveness of measures based on the obtained feedback and improve the entire system;

[1950] A system including:

[1951] (Claim 2)

[1952] 10. The system of claim 1, further comprising means for receiving biometric data of the wearable device transmitted from the user terminal.

[1953] (Claim 3)

[1954] 10. The system of claim 1, further comprising means for calculating health risks based on the stored data using an AI model.

[1955] "Application example 2 when combining emotion engines"

[1956] (Claim 1)

[1957] A means for acquiring lifestyle data and medical data from a user;

[1958] A means for formatting the acquired lifestyle data and medical data according to a format;

[1959] a means for storing the formatted data in a database;

[1960] A means for analyzing the stored data and predicting health risks;

[1961] A means for suggesting countermeasures based on predicted health risks;

[1962] A means to evaluate users' health data in real time and suggest appropriate products and services;

[1963] A system including:

[1964] (Claim 2)

[1965] 10. The system of claim 1, further comprising means for receiving biometric data of the wearable device transmitted from the user terminal.

[1966] (Claim 3)

[1967] 10. The system of claim 1, further comprising means for calculating health risks based on the stored data using an AI model. [Explanation of symbols]

[1968] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for acquiring lifestyle data and medical data from a user; A means for formatting the acquired lifestyle data and medical data according to a format; a means for storing the formatted data in a database; A means for analyzing the stored data and predicting health risks; A means for suggesting countermeasures based on predicted health risks; A system including:

2. 10. The system of claim 1, The system further includes means for receiving biometric data of the wearable device transmitted from the user terminal.

3. 10. The system of claim 1, The system further includes means for calculating a health risk based on the stored data using the AI ​​model.

4. 10. The system of claim 1, The system further includes a means for selecting and presenting effective measures based on the performance of other users.

Images