System

Abstract

A system is provided.SOLUTION: A system, comprising: means for collecting physiological information from a user; means for storing the collected physiological information in a cloud database; means for obtaining the physiological information from the cloud database and analyzing the physiological information using a generative AI model; means for generating optimal health advice for each user based on the analysis result; and means for providing the generated health advice to the user's device in real time.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 modern society, men and women in their 30s to 50s, especially those who lead busy lives due to work and family commitments, face the challenge of finding it difficult to devote sufficient time to health management. Furthermore, the overabundance of health information available on the internet and in the media can make it difficult to determine what information is appropriate for them. Conventional healthcare apps and books only provide one-way information and are unable to provide advice optimized for individual users. Therefore, there is a demand for personalized health management support services tailored to each individual user. [Means for solving the problem]

[0005] To solve the above-mentioned problems, the present invention provides a system including: means for collecting biometric data, such as activity data, sleep data, and dietary data, acquired from a user; means for storing the collected biometric data in a cloud database; means for acquiring the biometric data from the cloud database and analyzing the data using a generative AI model; means for generating optimal health advice for each user based on the analysis results; and means for providing the generated health advice to the user's device in real time. This system allows users to receive personalized advice based on their specific health condition and effectively manage their health. This system continuously monitors the user's health condition and provides appropriate advice in real time, thereby contributing to improving the user's health literacy and lifestyle habits.

[0006] A "user" is an individual who uses the health management system and provides their health data.

[0007] "Activity data" is information about the user's daily physical activity, specifically data including the number of steps taken, calories burned, amount of exercise, and the like.

[0008] "Sleep data" is information related to the user's sleep, specifically data including the amount of sleep, the quality of sleep, the amount of deep sleep time, and the like.

[0009] "Dietary data" is information related to the user's diet, specifically data including the contents of the diet, calorie intake, and nutrient balance.

[0010] "Biometric data" refers to various types of data related to the user's physical condition, and refers to a wide range of information including activity data, sleep data, and dietary data.

[0011] A "cloud database" is a remote database system accessible via the Internet for storing and managing collected biometric data.

[0012] A "generative AI model" is an artificial intelligence model used to analyze collected data, and includes algorithms that evaluate and predict a user's health status and lifestyle rhythm.

[0013] "Data analysis" is the process of evaluating a user's health condition and lifestyle rhythm through processing by a generative AI model based on collected biometric data.

[0014] "Health advice" refers to health advice and recommendations that are optimized for each individual user based on the results of data analysis by a generative AI model.

[0015] A "terminal" is a device used by a user, specifically referring to electronic devices such as smartwatches and smartphones.

[0016] "Provided in real time" means that the results of data collection and analysis are provided to users immediately without delay. [Brief explanation of the drawings]

[0017] [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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] The present invention is a system that provides individually optimized health advice by collecting and analyzing a user's biometric data.

[0039] Data collection

[0040] Device:

[0041] A user's smartwatch continuously monitors the user's daily activity data, sleep data, diet data, etc. This data is sent to the user's smartphone, which then uploads it to a cloud database. For example, if a user walks 10,000 steps in a day, the step count data will be recorded by the smartwatch, sent to the smartphone, and stored in the cloud database.

[0042] Data analysis

[0043] server:

[0044] The user's biometric data obtained from the cloud database is input into a generative AI model for data analysis. The generative AI model analyzes the user's health condition and lifestyle rhythm from the collected biometric data and identifies specific patterns and abnormalities. For example, it analyzes a user's sleep data over a week and derives the result that "the average sleep time is less than 6 hours."

[0045] Generating health advice

[0046] server:

[0047] Based on the results of the data analysis, the system generates optimal health advice for each user, such as recommendations to go to bed earlier to improve sleep or meditation to relax.

[0048] Providing health advice

[0049] server:

[0050] The generated health advice is provided to the user's device (smartphone) in real time. The user's smartphone notifies the user of the received advice and displays it to them. The user can then check this advice and incorporate it into their daily lives.

[0051] Specific examples

[0052] For example, if a user's recent sleep data indicates that they have not had enough sleep, the system operates as follows:

[0053] 1. Data Collection:

[0054] The user's smartwatch collects sleep data each night and sends it via their smartphone to a cloud database, which stores the user's sleep data.

[0055] 2. Data Analysis:

[0056] The server retrieves the user's sleep data from a cloud database and analyzes it using a generative AI model. The analysis result is that "the average sleep time over the past week was less than 6 hours."

[0057] 3. Health advice generation:

[0058] Based on the analysis results, the generative AI model generates advice for the user, such as "avoid caffeine after dinner and meditate to relax before going to bed."

[0059] 4. Providing health advice:

[0060] The server sends the generated advice to the user's smartphone, which then notifies the user of the received advice and displays it to them. The user can then check the displayed advice and apply it to their daily lives to improve their health.

[0061] In this way, the system of the present invention provides personalized advice based on the user's individual health condition and supports the user's health management. Through continuous data collection and analysis, the system provides a means for the user to maintain optimal health.

[0062] The processing flow will be explained below.

[0063] Step 1:

[0064] Device: The user's smartwatch continuously monitors daily activity data (e.g., steps taken, heart rate, calories burned, etc.), sleep data (e.g., sleep duration, sleep quality, etc.), and dietary data (e.g., dietary content, calories consumed, etc.). The collected data is sent to the user's smartphone.

[0065] Step 2:

[0066] Terminal: The smartphone receives the data sent from the smartwatch and uploads it to a cloud database via the internet, allowing the user's biometric data to be stored remotely.

[0067] Step 3:

[0068] Server: Retrieves user biometric data from a cloud database, including user activity, sleep, and dietary data.

[0069] Step 4:

[0070] Server: The acquired biometric data is input into the generative AI model and data analysis is performed. The generative AI model evaluates the user's health condition and lifestyle rhythm and identifies specific patterns and abnormalities. For example, analysis results may include information such as "average sleep time less than 6 hours" or "lack of exercise."

[0071] Step 5:

[0072] Server: Based on the analysis results of the generative AI model, it generates optimal health advice for each user, such as recommending going to bed earlier to improve sleep time or walking to increase exercise.

[0073] Step 6:

[0074] Server: The server sends the generated health advice to the user's device (smartphone) in real time. The user's smartphone notifies them of the advice received.

[0075] Step 7:

[0076] User: The user checks the health advice displayed on their smartphone and incorporates it into their daily routine, for example, reducing caffeine intake before bedtime or practicing meditation to relax.

[0077] Step 8:

[0078] Server: Based on the health advice provided by the user, the generative AI model then continuously monitors the data, collecting and analyzing new biometric data to track the user's health improvement and provide further advice.

[0079] Through this series of steps, users receive personalized health advice that can be applied to their daily lives to improve and maintain their health.

[0080] Example 1

[0081] 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."

[0082] In modern society, health management is a major focus, but many people face difficulties in accurately understanding their own health status and obtaining appropriate advice. In particular, there is a lack of systems that centrally manage daily activity, sleep, and dietary data and provide individually optimized health advice based on this data. This makes it difficult for users to find the lifestyle habits that are best for them.

[0083] 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.

[0084] In this invention, the server includes means for collecting biometric data such as activity data, sleep data, and dietary data obtained from users, means for storing the collected biometric data in a cloud database via a communication device, means for acquiring the biometric data from the cloud database and analyzing the data using a generative AI model, means for generating optimal health advice for each user based on the analysis results, means for providing the generated health advice to the user's mobile device in real time, and means for continuously monitoring changes in the biometric data and updating the advice, thereby enabling users to accurately understand their own health status and receive individually optimized health advice in real time.

[0085] "Activity data" refers to data related to the user's daily physical activity, such as the number of steps taken, calories burned, and exercise time.

[0086] "Sleep data" refers to data related to the user's nighttime sleep, such as the amount of sleep, quality of sleep, and sleep cycle.

[0087] "Dietary data" refers to data related to the daily meals a user takes, such as the contents of the meals, calories, and nutrients.

[0088] "Biometric data" refers to all data that indicates the user's health condition, such as the user's activity data, sleep data, and dietary data.

[0089] "Communication devices" are electronic devices for sending and receiving data, such as smartphones, tablets, and personal computers.

[0090] A "cloud database" is a remote data storage system accessed over the Internet.

[0091] A "generative AI model" is an algorithm or model that uses artificial intelligence technology to analyze, predict, and generate data.

[0092] "Data analysis" is the act of analyzing collected biometric data using statistical methods and machine learning to extract meaningful information.

[0093] "Health advice" refers to specific suggestions or guidelines provided to improve the user's health based on the analysis results.

[0094] A "mobile terminal" is a portable information and communication device such as a smartphone or tablet.

[0095] "Monitoring" is the act of continuously observing and collecting specific data or situations.

[0096] "Updating advice" refers to the regular reassessment of the health advice provided based on ongoing data monitoring, and the amendments or changes made as necessary.

[0097] The present invention is a system for supporting a user's health management. This system collects the user's biometric data, stores it in a cloud database, analyzes the data using a generative AI model, and provides health advice based on the analysis results.

[0098] Data collection

[0099] Device:

[0100] A user's smartwatch continuously monitors daily activity data, sleep data, diet data, etc. The smartwatch transmits this data to the user's smartphone, which then uploads the received data to a cloud database. For example, if a user walks 10,000 steps in a day, the step count data is recorded on the smartwatch and stored in the cloud database via the smartphone.

[0101] Data accumulation

[0102] server:

[0103] A cloud database continuously accumulates each user's biometric data, and a server retrieves the data from the cloud database and prepares it for input into the generative AI model.

[0104] Data analysis

[0105] server:

[0106] Biometric data obtained from a cloud database is input into a generative AI model for data analysis. The generative AI model analyzes the user's health status and lifestyle rhythms from the collected biometric data to discover specific patterns and abnormalities.

[0107] Example: A generative AI model analyzes a user's sleep data from the past week and concludes that their average sleep time is less than 6 hours.

[0108] Generating health advice

[0109] server:

[0110] Based on the results of the data analysis, the system generates optimal health advice for each user, such as recommendations to go to bed earlier to improve sleep or meditation to relax.

[0111] Providing health advice

[0112] server:

[0113] The generated health advice is provided to the user's device (smartphone) in real time. The smartphone notifies the user of the received advice and displays it to the user. The user can then check this advice and incorporate it into their daily life.

[0114] Example: The server generates advice such as "avoid caffeine after dinner and practice meditation to relax before going to bed" and sends it to the smartphone. The user's smartphone notifies the advice and displays it to the user.

[0115] Health advice application and updates

[0116] User:

[0117] The user reviews the provided health advice and applies it to their daily life. New biometric data is collected through the user's activities and stored again in the cloud database. The generative AI model continuously analyzes the data based on the new data and provides the latest health advice.

[0118] Specific examples

[0119] For example, if a user's recent sleep data indicates that they have not had enough sleep, the system operates as follows:

[0120] 1. Data collection: The user's smartwatch collects nightly sleep data and sends it to a cloud database via their smartphone. The cloud database stores the user's sleep data.

[0121] 2. Data analysis: The server retrieves the user's sleep data from the cloud database and analyzes it using a generative AI model. The analysis result is that "the average sleep time over the past week was less than 6 hours."

[0122] 3. Generating health advice: Based on the analysis results, the generative AI model generates advice for the user, such as "avoid caffeine after dinner and practice meditation to relax before going to bed."

[0123] 4. Providing health advice: The server sends the generated advice to the user's smartphone. The user's smartphone notifies the user of the received advice and displays it to them. The user can then review the displayed advice and apply it to their daily lives to improve their health.

[0124] Here is an example of a prompt to input to a generative AI model:

[0125] Analyze the user's sleep data from the last week. Find specific patterns or anomalies in the data and generate optimal health advice for the user. For example, if the average sleep time for the last week is less than 6 hours, avoid caffeine after dinner and practice relaxation meditation before bed.

[0126] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0127] Step 1: Data collection

[0128] Subject: Terminal

[0129] A user's smartwatch continuously monitors daily activity data, sleep data, dietary data, etc. Input from the smartwatch includes data on the user's steps, sleep time, calorie intake, etc. The smartwatch transmits this data to the user's smartphone. Specifically, the smartwatch uses sensors to record the user's steps and heart rate, and transfers this data to the smartphone via wireless communication such as Bluetooth. The smartphone receives this data and temporarily stores it locally.

[0130] Input: Activity data, sleep data, and food data from your smartwatch

[0131] Output: Data transfer to smartphone

[0132] Step 2: Data accumulation

[0133] Subject: Server

[0134] The smartphone uploads the collected data to a cloud database. Specifically, the smartphone sends the data to a cloud server via an internet connection. This data is then stored in a database managed by the cloud server.

[0135] Input: Biometric data sent from smartphone

[0136] Output: Data storage in cloud database

[0137] Step 3: Data analysis

[0138] Subject: Server

[0139] Biometric data obtained from a cloud database is input into a generative AI model for data analysis. Specifically, the server obtains a specific user's biometric data from the cloud database and inputs it into the generative AI model. The generative AI model then analyzes the data using machine learning algorithms to detect specific patterns and anomalies. For example, it analyzes sleep data from the past week and generates a result such as an average sleep time of less than six hours.

[0140] Input: Biometric data retrieved from a cloud database

[0141] Output: Analysis result (e.g., average sleep time is less than 6 hours)

[0142] Step 4: Generate health advice

[0143] Subject: Server

[0144] Based on the results of data analysis, optimal health advice is generated for each user. Specifically, the system receives the analysis results of the generative AI model and starts the process of generating health advice based on these results. For example, based on the analysis results, it generates specific advice such as "avoid caffeine after dinner" or "practice meditation to relax before bed."

[0145] Input: Data analysis results

[0146] Output: Health advice

[0147] Step 5: Providing health advice

[0148] Subject: Server

[0149] The generated health advice is provided to the user's device (smartphone) in real time. Specifically, the server sends the generated advice to the user's smartphone, which displays it as a notification. The user can then check the displayed advice and incorporate it into their daily life.

[0150] Enter: Health Advice

[0151] Output: Advice notification sent to user's smartphone

[0152] Step 6: Applying Advice and Updating

[0153] Subject: User

[0154] The user confirms the provided health advice and applies it to their daily life. New biometric data is collected through the user's activities and stored again in the cloud database. The generative AI model continuously analyzes the data based on the new data and provides the latest health advice. For example, if the user follows the advice to "avoid caffeine after dinner," subsequent data is collected again to monitor the effectiveness of the advice.

[0155] Input: New biometric data for the user

[0156] Output: Updated health advice

[0157] (Application example 1)

[0158] 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."

[0159] Conventional health management systems only collect and analyze a user's biometric data to provide health advice, but do not cover the user's specific lifestyle habits, particularly meal menu suggestions. This makes it difficult for users to select an appropriate meal menu based on their health condition, and users often struggle with meal choices, especially in their busy daily lives. Therefore, an objective of the present invention is to provide a system that not only provides health advice based on a user's biometric data, but also suggests optimal meal menus for each individual user.

[0160] 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.

[0161] In this invention, the server includes means for collecting biometric data such as activity data, sleep data, and dietary data obtained from users, means for storing the collected biometric data in a cloud database, means for acquiring the biometric data from the cloud database and analyzing the data using a generative AI model, means for generating optimal health advice for each user based on the analysis results, means for providing the generated health advice to the user's device in real time, and means for recommending optimal meal menus to the user based on the analysis results, thereby making it possible to propose meal menus that correspond to the health condition of each user.

[0162] "Activity data" is information relating to the physical activity of the user in their daily life, such as the amount of exercise, number of steps, and distance traveled.

[0163] "Sleep data" is information related to the user's bedtime, wake-up time, quality and depth of sleep, sleep cycle, and the like.

[0164] "Dietary data" refers to information relating to the types, amounts, nutritional components, and timing of meals consumed by the user.

[0165] "Biometric data" refers to data that encompasses a variety of information about the user's body, such as activity data, sleep data, and dietary data.

[0166] A "cloud database" is a remote data storage system that stores and accesses data over the Internet.

[0167] A "generative AI model" is an artificial intelligence model trained to generate personalized, optimal advice based on a user's biometric data.

[0168] "Data analysis" is the process of processing and analyzing collected biometric data to evaluate the user's health status and lifestyle rhythm.

[0169] "Health Advice" means specific instructions or recommendations for improving or maintaining health provided to each user based on the results of data analysis.

[0170] "Providing in real time" means that analysis results and health advice are transmitted to the user's device immediately or nearly instantly.

[0171] "Recommending meals" means suggesting suitable foods and meal plans based on the user's biometric data and health advice.

[0172] This invention is a system that collects biometric data such as activity data, sleep data, and dietary data of a user and provides health advice and meal menus optimized for each individual user based on that data. This system is realized by the following specific procedures and components.

[0173] Data collection

[0174] Device:

[0175] A user's smartwatch continuously monitors their daily activity data, sleep data, diet data, etc. This data is sent to the user's smartphone, which then uploads it to a cloud database. For example, if a user walks 10,000 steps in a day, the step count data is recorded by the smartwatch and stored in the cloud database via the smartphone.

[0176] Data analysis

[0177] server:

[0178] The user's biometric data obtained from the cloud database is input into a generative AI model for data analysis. The generative AI model analyzes the user's health condition and lifestyle rhythm from the collected biometric data and identifies specific patterns and abnormalities. For example, it analyzes a user's sleep data over a week and derives the result that "the average sleep time is less than 6 hours."

[0179] Health advice and meal menu generation

[0180] server:

[0181] Based on the results of the data analysis, the system generates optimal health advice for each user, such as recommendations to go to bed earlier to improve sleep or meditation to relax.

[0182] Furthermore, the server recommends optimal meal options for the user based on the analysis results. For example, if the health advice is "low-calorie diets are recommended," the server will suggest low-calorie meal options.

[0183] Providing health advice and meal menus

[0184] server:

[0185] The generated health advice and meal menus are provided to the user's device (smartphone) in real time. The user's smartphone notifies the user of the received advice and menus and displays them to the user. The user can check these advice and menus and incorporate them into their daily lives.

[0186] Specific examples

[0187] If a user's recent sleep data indicates that they have not had enough sleep, the system operates as follows.

[0188] 1. Data Collection:

[0189] The user's smartwatch collects sleep data each night and sends it via their smartphone to a cloud database, which stores the user's sleep data.

[0190] 2. Data Analysis:

[0191] The server retrieves the user's sleep data from a cloud database and analyzes it using a generative AI model. The analysis result is that "the average sleep time over the past week was less than 6 hours."

[0192] 3. Health advice and meal menu generation:

[0193] Based on the analysis results, the generative AI model generates advice for the user, such as "avoid caffeine after dinner in the evening and practice meditation to relax before bed." If a low-calorie diet is recommended, it will suggest meal options such as "salad," "grilled chicken," and "vegetable soup."

[0194] 4. Providing health advice and meal menus:

[0195] The server sends the generated advice and meal menu to the user's smartphone. The user's smartphone notifies the user of the received advice and menu and displays them to the user. The user can check the displayed advice and menu and apply them to their daily life to improve their health.

[0196] Prompt Sentence Examples

[0197] Example prompt (data processing approach)

[0198] average_sleep_hours: 5.5,

[0199] weekly_step_count: 70000,

[0200] calorie_intake: 2500,

[0201] stress_level: 3 Stress level scale from 1 to 5

[0202] In this way, the system of the present invention can support the user's overall health management by providing personalized advice and dietary suggestions based on the user's individual health condition.

[0203] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0204] Step 1:

[0205] Data collection

[0206] Input: User's daily activity data, sleep data, and diet data

[0207] The server and device collect the day's activity, sleep, and dietary data from the user's smartwatch. This data is recorded by the smartwatch and sent to the user's smartphone. The smartphone then uploads the received data to a cloud database. Through this process, all biometric data is stored in the cloud and made available for subsequent data analysis.

[0208] Output: User biometric data stored in a cloud database

[0209] Step 2:

[0210] Data Acquisition

[0211] Input: User biometric data stored in a cloud database

[0212] The server periodically retrieves the user's biometric data from the cloud database, ensuring that the most recent data is always available for analysis. This data includes the user's daily activity, sleep, and dietary data.

[0213] Output: Captured user biometric data

[0214] Step 3:

[0215] Data analysis

[0216] Input: Captured user biometric data

[0217] The server inputs the acquired biometric data into a generative AI model to analyze the user's health condition and lifestyle. The generative AI model detects specific patterns and abnormalities (e.g., lack of sleep, excessive activity, poor nutrition). For example, it can analyze a user's sleep data over a week and derive the result that "the average sleep time is less than 6 hours."

[0218] Output: Analysis results of health condition and lifestyle rhythm

[0219] Step 4:

[0220] Health advice and meal plan generation

[0221] Input: Analysis results of health status and lifestyle rhythm

[0222] Based on the analysis results, the server generates optimal health advice for each user. Specific advice includes recommendations for going to bed early to improve sleep and meditating to relax. Furthermore, based on this, the server recommends optimal meal menus for the user. For example, if a low-calorie diet is recommended, the server will suggest meal options such as "salad," "grilled chicken," and "vegetable soup."

[0223] Output: Health advice and meal menu

[0224] Step 5:

[0225] Providing health advice and meal menus

[0226] Input: Health advice and meal plans

[0227] The server sends the generated health advice and meal menu to the user's device (smartphone), which then notifies and displays the advice and menu to the user in real time, allowing the user to take appropriate action on the spot.

[0228] Output: Health advice and meal menu provided to the user

[0229] Step 6:

[0230] Action Feedback

[0231] Input: User's health advice and diet plan implementation results

[0232] After the user puts the health advice and dietary menu into practice, changes in activity, sleep, and dietary data are monitored again. This feedback data is again stored in the cloud database and used for the next data analysis. This allows the server to track changes in the user's behavior and generate more precise advice and menus.

[0233] Output: Feedback data

[0234] 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.

[0235] The present invention is a system that collects and analyzes a user's biometric and emotional data to provide individually optimized health and mental health advice.

[0236] Data collection

[0237] Device:

[0238] The user's smartwatch continuously monitors daily activity data (e.g., number of steps, heart rate, calories burned, etc.), sleep data (e.g., sleep duration, sleep quality, etc.), and dietary data (e.g., dietary content, calories consumed, etc.). This data is sent to the user's smartphone, which then uploads it to a cloud database. Meanwhile, the emotion engine collects the user's emotional data via devices such as smartphones. The emotional data is used to understand the user's emotional state using means such as voice recognition, face recognition, and text analysis.

[0239] Data storage

[0240] server:

[0241] The cloud database receives the biometric and emotional data sent from the smartwatch and emotion engine, stores them in the database, and manages them centrally. For example, if a user walks 10,000 steps in a day and sleeps for five hours during the night, the step count data and sleep data will be recorded by the smartwatch, and if the emotion engine recognizes that the user's stress level is high, the data will be stored in the cloud database.

[0242] Data analysis

[0243] server:

[0244] The user's biometric and emotional data obtained from a cloud database is input into a generative AI model for data analysis. The generative AI model contains algorithms that use this data to evaluate and predict the user's health condition, lifestyle, and even emotional state. For example, it analyzes a user's data over a week and derives the result that "the user's average sleep time is less than six hours and they are emotionally stressed."

[0245] Generating health advice

[0246] server:

[0247] Based on the analysis results of the generative AI model, optimal health and mental health advice is generated for the user, such as "recommending going to bed early to improve sleep and meditating to manage stress."

[0248] Providing health advice

[0249] server:

[0250] The generated health and mental health advice is provided to the user's device (smartphone) in real time. The user's smartphone notifies the user of this advice and displays it to them. The user can then review this advice and incorporate it into their daily lives.

[0251] Specific examples

[0252] For example, if a user's recent sleep data indicates that they have not been getting enough sleep, the system operates as follows:

[0253] 1. Data Collection:

[0254] The user's smartwatch collects sleep data every night and sends it to a cloud database via their smartphone, while the emotion engine collects the user's daily emotional data and uploads it to the cloud database.

[0255] 2. Data Analysis:

[0256] The server retrieves the user's sleep and emotional data from a cloud database and analyzes it using a generative AI model. The analysis results include "Over the past week, the average sleep time has been less than six hours, and emotional stress is high."

[0257] 3. Health advice generation:

[0258] Based on the analysis results, the generative AI model generates health and mental health advice for the user, such as "avoid caffeine after dinner and practice meditation to relax before going to bed."

[0259] 4. Providing health advice:

[0260] The server sends the generated advice to the user's smartphone, which notifies the user of the received advice and displays it to the user. The user can then review the displayed advice and apply it to their daily lives.

[0261] In this way, the system of the present invention provides personalized advice based on the user's health and emotional state, helping the user to manage their health and maintain and improve their mental health, thereby enabling comprehensive health management in the user's daily life.

[0262] The processing flow will be explained below.

[0263] Step 1:

[0264] Device: The user's smartwatch continuously monitors daily activity data (e.g., number of steps, heart rate, calories burned, etc.), sleep data (e.g., sleep duration, sleep quality, etc.), and dietary data (e.g., dietary content, calories consumed, etc.). The collected data is sent to the user's smartphone. The emotion engine also collects the user's emotion data using methods such as voice recognition, face recognition, and text analysis. For example, the emotion engine may recognize "high stress level" from text while the user is using a diary app on their smartphone.

[0265] Step 2:

[0266] Terminal: The smartphone uploads the biometric and emotional data sent from the smartwatch and emotion engine to a cloud database, allowing the user's biometric and emotional data to be centrally managed.

[0267] Step 3:

[0268] Server: Retrieves the user's activity data, sleep data, dietary data, and emotion data from the cloud database. For example, retrieves "User A's step count data, sleep data, dietary data, and emotion data for the past week" from the cloud database.

[0269] Step 4:

[0270] Server: The acquired data is input into the generative AI model and analyzed. The generative AI model contains algorithms that evaluate and predict the user's health condition, lifestyle, and emotional state. For example, the analysis results might reveal that "User A's average sleep time is less than six hours and that he is emotionally stressed."

[0271] Step 5:

[0272] Server: Generates optimal health and mental health advice for users based on the analysis results of the generative AI model. For example, it generates advice such as "To improve sleep, avoid caffeine after dinner and practice meditation to relax before bed," and "Recommend hobbies to reduce stress."

[0273] Step 6:

[0274] Server: Provides generated health and mental health advice to the user's smartphone in real time. For example, if the generated advice is "avoid caffeine" and "try meditation," it is sent to the user's smartphone.

[0275] Step 7:

[0276] Device: The user's smartphone notifies them of the received advice and displays it to them. By checking the advice displayed on their smartphone, the user can incorporate it into their daily lives.

[0277] Step 8:

[0278] User: The user follows the advice displayed on their smartphone and changes their behavior, for example, avoiding caffeine before bedtime, practicing meditation to relax, or starting a hobby to reduce stress.

[0279] Step 9:

[0280] Server: Based on the health and mental health advice the user implements, the generative AI model then continuously monitors the data, collecting and analyzing new biometric and emotional data. This allows it to track improvements in the user's health and provide further advice. For example, it analyzes the data from several weeks after the initial implementation of the advice and generates next-step advice.

[0281] Through this series of steps, users receive individually tailored health and mental health advice that can be applied to their daily lives to achieve continuous health management.

[0282] Example 2

[0283] 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."

[0284] Conventional healthcare systems typically collect and analyze only a user's biometric data, making it difficult to provide advice that takes into account emotional changes and stress levels. As a result, they fail to achieve comprehensive improvement in health status and are unable to provide optimal health advice that meets the user's needs. It is also difficult to deliver personalized advice based on the user's lifestyle in real time. In contrast, the present invention aims to comprehensively analyze a user's emotional data and provide more personalized advice for health and mental health.

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

[0286] In this invention, the server includes means for collecting user emotional data using voice recognition, facial recognition, and text analysis, means for storing the user's biometric data and emotional data in a cloud database, and means for acquiring the user's biometric data and emotional data, generating prompt sentences, and inputting the prompt sentences into a generative AI model to generate health and mental health advice, thereby enabling personalized health advice to be provided in real time based on the user's biometric data and emotional data.

[0287] "User" refers to an individual who provides biometric and emotional data and receives health advice from the system.

[0288] "Biometric data" refers to data that indicates the user's physical condition, such as the user's daily activity level, heart rate, calories burned, sleep time, sleep quality, dietary content, and calories consumed.

[0289] "Emotional data" refers to data that indicates the user's emotions and mental state, obtained by analyzing the user's tone of voice, facial expression, text content, etc.

[0290] "Cloud database" refers to a database accessible via the Internet and a data storage system for storing users' biometric and emotional data.

[0291] A "generative AI model" is an artificial intelligence model generated by learning large amounts of data, and is used for data analysis and generating health advice.

[0292] A "prompt sentence" refers to an input sentence that provides a generative AI model with information that serves as the basis for analysis and advice generation.

[0293] "Health Advice" refers to specific suggestions or instructions provided to improve a user's physical or mental health based on the analysis of the user's biometric and emotional data.

[0294] "Real-time" refers to the time range in which users can provide data and receive results or advice almost immediately.

[0295] "Speech recognition" refers to the technology of converting voice data into text data and analyzing its content.

[0296] "Facial recognition" refers to the technology of analyzing facial image data and identifying facial expressions and features.

[0297] "Text analysis" refers to the technology of analyzing text data and identifying emotions and intentions from its content.

[0298] The present invention is a system that collects and analyzes a user's biometric and emotional data to provide individually optimized health and mental health advice. A concrete example is shown below.

[0299] Overall system configuration

[0300] The system consists of the following main components: the user's device (smartwatch or smartphone), a cloud database, a generative AI model, and a set of servers. The role and operation of each component are described in detail below.

[0301] Hardware and Software

[0302] Device:

[0303] The user uses a smartwatch to measure daily activity data (number of steps, heart rate, calories burned, etc.) and sleep data (sleep time, sleep quality, etc.). The smartphone supports the necessary data collection and has a built-in emotion engine. Specifically, the smartphone uses applications for speech recognition, facial recognition, and text analysis. For example, it uses the Google Speech-to-Text API for speech recognition, OpenCV and Dlib for facial recognition, and natural language processing libraries (e.g., NLTK and spaCy) for text analysis.

[0304] Cloud Database:

[0305] The database stores the user's biometric and emotional data, typically in a cloud-based database such as Amazon RDS or Google Cloud SQL.

[0306] Generative AI models:

[0307] The generative AI model uses deep learning frameworks, such as TensorFlow and PyTorch, to analyze a user's biometric and emotional data and generate health and mental health advice.

[0308] server:

[0309] The server communicates with a cloud database, performs data analysis, and runs a generative AI model, such as an EC2 instance from AWS (Amazon Web Services). The advice generation part uses a natural language generation model (e.g., GPT-3).

[0310] Data collection

[0311] Device:

[0312] The smartwatch worn by the user collects activity and sleep data and transmits the collected data in real time to a smartphone. The smartphone also uses an emotion engine to perform voice recognition, face recognition, and text analysis to collect emotion data, which is then uploaded to a cloud database.

[0313] Data analysis

[0314] server:

[0315] The server inputs the user's biometric and emotional data obtained from the cloud database into the generative AI model for analysis. The generative AI model contains algorithms that use this data to evaluate and predict the user's health condition, lifestyle, and even emotional state. As a concrete example, the prompt sentence is shown below.

[0316] "Analyze this user's average sleep time and stress level over the past week and generate appropriate health advice."

[0317] "Generate specific suggestions for maintaining health for the next week based on the user's biometric and emotional data."

[0318] Generating and providing health advice

[0319] server:

[0320] Based on the analysis results of the generative AI model, it generates individually optimized health and mental health advice, such as "avoid caffeine after dinner" or "practice meditation to relax before bed."

[0321] Device:

[0322] The generated advice is sent to the user's smartphone in real time and displayed as a push notification, allowing the user to check the notification and incorporate the advice into their daily life.

[0323] This system will provide users with personalized health advice based on their biometric and emotional data, helping them maintain and improve their physical and mental health.

[0324] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0325] Step 1:

[0326] Data collection

[0327] The device (smartwatch) collects the user's daily activity data (number of steps, heart rate, calories burned, etc.) and sleep data (sleep time, sleep quality, etc.). The smartphone receives this data via Bluetooth and then collects emotional data using voice recognition, facial recognition, and text analysis. Specifically, emotions are analyzed from the user's tone of voice when talking on the phone, facial expressions captured by the camera, and the content of text messages. Data input is real-time data from the smartwatch and smartphone, and output is accumulated in the form of collected data from each device.

[0328] Step 2:

[0329] Data transmission and storage

[0330] The biometric and emotional data collected by the device (smartphone) is sent to a cloud database via the internet. At this time, SSL / TLS encryption is used to ensure data security. The cloud server processes the received data and stores it in a database. Specifically, it is stored in a cloud-based database such as Amazon RDS. The data input is the biometric and emotional data sent from the smartphone, and the output is stored in the database.

[0331] Step 3:

[0332] Data Acquisition and Analysis

[0333] The server retrieves a specific user's data from a cloud database. This API call retrieves biometric and emotional data from the past week. The retrieved data is then input into a generative AI model. For example, a deep learning model built using TensorFlow or PyTorch is used. The generative AI model analyzes the data and evaluates the user's health and emotional state. The input is the data retrieved from the cloud database, and the output is the evaluation results analyzed by the AI ​​model.

[0334] Step 4:

[0335] Prompt generation and advice generation

[0336] The server generates a prompt sentence based on the analysis results of the generative AI model. The generated prompt sentence is input into a natural language generation model to generate specific health advice. Specific examples of prompt sentences include, "Please analyze this user's average sleep time and stress level over the past week and generate appropriate health advice," and "Please generate specific suggestions for maintaining health over the next week based on the user's biometric and emotional data." The input is the analysis results of the generative AI model and the prompt sentence, and the output is health and mental health advice.

[0337] Step 5:

[0338] Providing advice

[0339] The server sends the generated health advice to the user's smartphone. The smartphone displays the received advice to the user as a push notification. When the user taps the notification, a dedicated application opens and the user can check the detailed advice. The input is the generated health advice, and the output is the notification and displayed advice to the user.

[0340] Through these steps, the system can comprehensively analyze a user's biometric and emotional data and provide individually optimized health advice in real time.

[0341] (Application example 2)

[0342] 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."

[0343] In today's busy lifestyles, it is important for individual users to appropriately manage their own health and emotional state and improve their quality of life. However, conventional systems mainly provide health advice using only the user's biometric data, and are insufficient in providing specific content that takes emotional data into account or improving mental health. In particular, they lack the ability to optimize content according to the user's stress and emotional state, so more effective health management support is needed.

[0344] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0345] In this invention, the server includes means for collecting biometric data such as activity data, sleep data, and dietary data obtained from a user, means for storing the collected biometric data in a cloud database, means for acquiring the biometric data from the cloud database and analyzing the data using a generative AI model, means for generating optimal health advice for each user based on the analysis results, means for providing the generated health advice to the user's device in real time, means for performing voice recognition and facial recognition of the user and collecting emotional data, means for storing the collected emotional data in the cloud database, means for analyzing the emotional data and evaluating the user's mental health state, means for optimizing content to be viewed based on the user's health state and emotional state, and means for displaying the optimized content. This enables more personalized health management and mental health support based on both the user's biometric data and emotional data.

[0346] "Activity data" is information about the user's physical activity, such as the amount of exercise, number of steps, and calories burned each day.

[0347] "Sleep data" refers to information related to sleep, such as the user's sleep time, sleep quality, and sleep cycle.

[0348] "Dietary data" refers to information about meals such as the contents of meals consumed by the user, calorie intake, and nutritional information.

[0349] "Biometric data" refers to all information related to the user's physical condition and activities, such as activity data, sleep data, and dietary data.

[0350] A "cloud database" is a database for storing and managing data via the Internet.

[0351] A "generative AI model" is an algorithmic model that uses machine learning and artificial intelligence to generate specific outcomes or predictions from input data.

[0352] "Data analysis" is the process of extracting information from collected data using certain algorithms or models, and making evaluations and predictions.

[0353] "Health advice" refers to specific suggestions and instructions for improving and maintaining health provided to users based on the results of data analysis.

[0354] "Emotional data" is information about a user's emotional state collected through speech and facial recognition.

[0355] "Speech recognition" is a technology that analyzes the content of a user's speech and extracts information from the speech.

[0356] "Facial recognition" is a technology that analyzes a user's facial expressions and features to determine their emotions and identity.

[0357] "Mental health status" is an assessment of the user's emotional and mental state.

[0358] "Content optimization" is the process of providing the most appropriate content for a user to view based on their health and emotional state.

[0359] "Audio content" refers to the videos a user watches, the music they listen to, and other entertainment media.

[0360] "Providing in real time" means notifying and displaying analysis results and advice to users immediately on the spot.

[0361] The present invention provides a system for collecting and analyzing biometric and emotional data of a user, and then providing individually optimized health and mental health advice in real time. The system is configured as follows.

[0362] Data collection

[0363] The smartwatch, which is the user's device, continuously monitors daily activity data (e.g., steps taken, heart rate, calories burned) and sleep data (e.g., sleep duration and quality) and uploads the data to a cloud database via the smartphone. Additionally, smart glasses equipped with an emotion engine recognize the user's voice and facial expressions to collect emotion data.

[0364] Data storage

[0365] The server uses a cloud database to centrally manage and store the collected biometric and emotional data. For example, if a user walks 10,000 steps a day and gets five hours of sleep a night, but their facial expressions and voice indicate a high stress level, the data will be stored in the cloud database.

[0366] Data analysis

[0367] The server inputs the user's biometric and emotional data obtained from the cloud database into the generative AI model for data analysis. The generative AI model contains algorithms that evaluate the user's health and emotional state from this data. For example, it can derive a result such as "the average sleep time is less than six hours and the emotional stress level is high."

[0368] Generating and delivering health advice and content

[0369] The server generates optimal health advice for the user based on the analysis results of the generative AI model. Furthermore, it optimizes the content to be viewed based on the user's health and emotional state. For example, it may provide relaxing music and yoga videos along with health advice such as "avoid caffeine after dinner and practice meditation to relax before bed."

[0370] These advice and content are displayed in real time on the user's smartphone or smart glasses, allowing the user to review the displayed advice and incorporate it into their daily life.

[0371] Specific examples

[0372] For example, if a user's recent sleep data indicates that they haven't been getting enough sleep, the system could:

[0373] 1. Data collection: The smart watch collects the user's sleep data every night and sends it to the cloud database. At the same time, the smart glasses collect the user's daily emotional data and upload it to the cloud database.

[0374] 2. Data analysis: The server retrieves the user's sleep and emotional data from the cloud database and analyzes it using a generative AI model. The analysis results include "Over the past week, the average sleep time has been less than six hours, and the user is experiencing high emotional stress."

[0375] 3. Generating health advice: Based on the analysis results, the generative AI model generates health advice for the user, such as "avoid caffeine after dinner and practice relaxation meditation before going to bed." In addition, relaxing music and yoga videos are provided.

[0376] 4. Providing health advice: These advice and content will be displayed in real time on the user's smartphone or smart glasses.

[0377] Prompt Sentence Examples

[0378] If the user is in a "high stress" state on a daily basis, the following prompt sentence is input to the generative AI model:

[0379] User biometric data: Heart rate 90, sleep time 4 hours, steps 3000

[0380] Emotion data: Facial expressions indicate high stress levels, and voice indicates fatigue levels

[0381] Problem: Not getting enough sleep, high stress levels

[0382] Based on this data, it recommends relaxing content to help relieve stress and provides health advice.

[0383] As described above, the system of the present invention is capable of providing personalized health management and mental health support based on the user's health and emotional state.

[0384] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0385] Step 1:

[0386] The smartwatch and smart glasses, which are the user's devices, collect the user's activity data and emotional data and send it to a cloud database via the smartphone. Specifically, the smartwatch continuously monitors the user's steps, heart rate, and calories burned, while the smart glasses simultaneously recognize the user's voice and facial expressions to collect emotional data. The input at this stage is the user's daily activity data and emotional data, and the output is biometric data and emotional data stored in the cloud database.

[0387] Step 2:

[0388] The server retrieves the biometric and emotional data stored in the cloud database. Specifically, it uses the database API to extract the necessary data and stores it in temporary memory for analysis. The input at this stage is the biometric and emotional data in the cloud database, and the output is the data to be analyzed stored in the server's memory.

[0389] Step 3:

[0390] The server inputs biometric and emotional data into a generative AI model and performs data analysis. Specifically, it uses machine learning algorithms to evaluate and predict the user's health and emotional state. The input at this stage is the biometric and emotional data stored in the server's memory, and the output is an evaluation result indicating the user's health and emotional state.

[0391] Step 4:

[0392] The server generates optimal health advice and viewing content for the user based on the analysis results of the generative AI model. For example, relaxing music and yoga videos are selected along with health advice such as "avoid caffeine after dinner and practice meditation to relax before bed." The input at this stage is the analysis results of the generative AI model, and the output is health advice and optimized viewing content.

[0393] Step 5:

[0394] The server sends the generated health advice and audiovisual content to the user's device (smartphone or smart glasses) in real time. Specifically, it notifies the user of the advice and plays the audiovisual content using a notification API or the device's software interface. The input of this stage is the generated health advice and audiovisual content, and the output is the advice displayed on the user's device and the audiovisual content played.

[0395] Through these steps, a system is realized that provides personalized health management and mental health support based on the user's health and emotional state.

[0396] 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.

[0397] 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.

[0398] 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.

[0399] [Second embodiment]

[0400] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0401] 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.

[0402] 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).

[0403] 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.

[0404] 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.

[0405] 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).

[0406] 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.

[0407] 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.

[0408] 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.

[0409] 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.

[0410] 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.

[0411] 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."

[0412] The present invention is a system that provides individually optimized health advice by collecting and analyzing a user's biometric data.

[0413] Data collection

[0414] Device:

[0415] A user's smartwatch continuously monitors the user's daily activity data, sleep data, diet data, etc. This data is sent to the user's smartphone, which then uploads it to a cloud database. For example, if a user walks 10,000 steps in a day, the step count data will be recorded by the smartwatch, sent to the smartphone, and stored in the cloud database.

[0416] Data analysis

[0417] server:

[0418] The user's biometric data obtained from the cloud database is input into a generative AI model for data analysis. The generative AI model analyzes the user's health condition and lifestyle rhythm from the collected biometric data and identifies specific patterns and abnormalities. For example, it analyzes a user's sleep data over a week and derives the result that "the average sleep time is less than 6 hours."

[0419] Generating health advice

[0420] server:

[0421] Based on the results of the data analysis, the system generates optimal health advice for each user, such as recommendations to go to bed earlier to improve sleep or meditation to relax.

[0422] Providing health advice

[0423] server:

[0424] The generated health advice is provided to the user's device (smartphone) in real time. The user's smartphone notifies the user of the received advice and displays it to them. The user can then check this advice and incorporate it into their daily lives.

[0425] Specific examples

[0426] For example, if a user's recent sleep data indicates that they have not had enough sleep, the system operates as follows:

[0427] 1. Data Collection:

[0428] The user's smartwatch collects sleep data each night and sends it via their smartphone to a cloud database, which stores the user's sleep data.

[0429] 2. Data Analysis:

[0430] The server retrieves the user's sleep data from a cloud database and analyzes it using a generative AI model. The analysis result is that "the average sleep time over the past week was less than 6 hours."

[0431] 3. Health advice generation:

[0432] Based on the analysis results, the generative AI model generates advice for the user, such as "avoid caffeine after dinner and meditate to relax before going to bed."

[0433] 4. Providing health advice:

[0434] The server sends the generated advice to the user's smartphone, which then notifies the user of the received advice and displays it to them. The user can then check the displayed advice and apply it to their daily lives to improve their health.

[0435] In this way, the system of the present invention provides personalized advice based on the user's individual health condition and supports the user's health management. Through continuous data collection and analysis, the system provides a means for the user to maintain optimal health.

[0436] The processing flow will be explained below.

[0437] Step 1:

[0438] Device: The user's smartwatch continuously monitors daily activity data (e.g., steps taken, heart rate, calories burned, etc.), sleep data (e.g., sleep duration, sleep quality, etc.), and dietary data (e.g., dietary content, calories consumed, etc.). The collected data is sent to the user's smartphone.

[0439] Step 2:

[0440] Terminal: The smartphone receives the data sent from the smartwatch and uploads it to a cloud database via the internet, allowing the user's biometric data to be stored remotely.

[0441] Step 3:

[0442] Server: Retrieves user biometric data from a cloud database, including user activity, sleep, and dietary data.

[0443] Step 4:

[0444] Server: The acquired biometric data is input into the generative AI model and data analysis is performed. The generative AI model evaluates the user's health condition and lifestyle rhythm and identifies specific patterns and abnormalities. For example, analysis results may include information such as "average sleep time less than 6 hours" or "lack of exercise."

[0445] Step 5:

[0446] Server: Based on the analysis results of the generative AI model, it generates optimal health advice for each user, such as recommending going to bed earlier to improve sleep time or walking to increase exercise.

[0447] Step 6:

[0448] Server: The server sends the generated health advice to the user's device (smartphone) in real time. The user's smartphone notifies them of the advice received.

[0449] Step 7:

[0450] User: The user checks the health advice displayed on their smartphone and incorporates it into their daily routine, for example, reducing caffeine intake before bedtime or practicing meditation to relax.

[0451] Step 8:

[0452] Server: Based on the health advice provided by the user, the generative AI model then continuously monitors the data, collecting and analyzing new biometric data to track the user's health improvement and provide further advice.

[0453] Through this series of steps, users receive personalized health advice that can be applied to their daily lives to improve and maintain their health.

[0454] Example 1

[0455] 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."

[0456] In modern society, health management is a major focus, but many people face difficulties in accurately understanding their own health status and obtaining appropriate advice. In particular, there is a lack of systems that centrally manage daily activity, sleep, and dietary data and provide individually optimized health advice based on this data. This makes it difficult for users to find the lifestyle habits that are best for them.

[0457] 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.

[0458] In this invention, the server includes means for collecting biometric data such as activity data, sleep data, and dietary data obtained from users, means for storing the collected biometric data in a cloud database via a communication device, means for acquiring the biometric data from the cloud database and analyzing the data using a generative AI model, means for generating optimal health advice for each user based on the analysis results, means for providing the generated health advice to the user's mobile device in real time, and means for continuously monitoring changes in the biometric data and updating the advice, thereby enabling users to accurately understand their own health status and receive individually optimized health advice in real time.

[0459] "Activity data" refers to data related to the user's daily physical activity, such as the number of steps taken, calories burned, and exercise time.

[0460] "Sleep data" refers to data related to the user's nighttime sleep, such as the amount of sleep, quality of sleep, and sleep cycle.

[0461] "Dietary data" refers to data related to the daily meals a user takes, such as the contents of the meals, calories, and nutrients.

[0462] "Biometric data" refers to all data that indicates the user's health condition, such as the user's activity data, sleep data, and dietary data.

[0463] "Communication devices" are electronic devices for sending and receiving data, such as smartphones, tablets, and personal computers.

[0464] A "cloud database" is a remote data storage system accessed over the Internet.

[0465] A "generative AI model" is an algorithm or model that uses artificial intelligence technology to analyze, predict, and generate data.

[0466] "Data analysis" is the act of analyzing collected biometric data using statistical methods and machine learning to extract meaningful information.

[0467] "Health advice" refers to specific suggestions or guidelines provided to improve the user's health based on the analysis results.

[0468] A "mobile terminal" is a portable information and communication device such as a smartphone or tablet.

[0469] "Monitoring" is the act of continuously observing and collecting specific data or situations.

[0470] "Updating advice" refers to the regular reassessment of the health advice provided based on ongoing data monitoring, and the amendments or changes made as necessary.

[0471] The present invention is a system for supporting a user's health management. This system collects the user's biometric data, stores it in a cloud database, analyzes the data using a generative AI model, and provides health advice based on the analysis results.

[0472] Data collection

[0473] Device:

[0474] A user's smartwatch continuously monitors daily activity data, sleep data, diet data, etc. The smartwatch transmits this data to the user's smartphone, which then uploads the received data to a cloud database. For example, if a user walks 10,000 steps in a day, the step count data is recorded on the smartwatch and stored in the cloud database via the smartphone.

[0475] Data accumulation

[0476] server:

[0477] A cloud database continuously accumulates each user's biometric data, and a server retrieves the data from the cloud database and prepares it for input into the generative AI model.

[0478] Data analysis

[0479] server:

[0480] Biometric data obtained from a cloud database is input into a generative AI model for data analysis. The generative AI model analyzes the user's health status and lifestyle rhythms from the collected biometric data to discover specific patterns and abnormalities.

[0481] Example: A generative AI model analyzes a user's sleep data from the past week and concludes that their average sleep time is less than 6 hours.

[0482] Generating health advice

[0483] server:

[0484] Based on the results of the data analysis, the system generates optimal health advice for each user, such as recommendations to go to bed earlier to improve sleep or meditation to relax.

[0485] Providing health advice

[0486] server:

[0487] The generated health advice is provided to the user's device (smartphone) in real time. The smartphone notifies the user of the received advice and displays it to the user. The user can then check this advice and incorporate it into their daily life.

[0488] Example: The server generates advice such as "avoid caffeine after dinner and practice meditation to relax before going to bed" and sends it to the smartphone. The user's smartphone notifies the advice and displays it to the user.

[0489] Health advice application and updates

[0490] User:

[0491] The user reviews the provided health advice and applies it to their daily life. New biometric data is collected through the user's activities and stored again in the cloud database. The generative AI model continuously analyzes the data based on the new data and provides the latest health advice.

[0492] Specific examples

[0493] For example, if a user's recent sleep data indicates that they have not had enough sleep, the system operates as follows:

[0494] 1. Data collection: The user's smartwatch collects nightly sleep data and sends it to a cloud database via their smartphone. The cloud database stores the user's sleep data.

[0495] 2. Data analysis: The server retrieves the user's sleep data from the cloud database and analyzes it using a generative AI model. The analysis result is that "the average sleep time over the past week was less than 6 hours."

[0496] 3. Generating health advice: Based on the analysis results, the generative AI model generates advice for the user, such as "avoid caffeine after dinner and practice meditation to relax before going to bed."

[0497] 4. Providing health advice: The server sends the generated advice to the user's smartphone. The user's smartphone notifies the user of the received advice and displays it to them. The user can then review the displayed advice and apply it to their daily lives to improve their health.

[0498] Here is an example of a prompt to input to a generative AI model:

[0499] Analyze the user's sleep data from the last week. Find specific patterns or anomalies in the data and generate optimal health advice for the user. For example, if the average sleep time for the last week is less than 6 hours, avoid caffeine after dinner and practice relaxation meditation before bed.

[0500] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0501] Step 1: Data collection

[0502] Subject: Terminal

[0503] A user's smartwatch continuously monitors daily activity data, sleep data, dietary data, etc. Input from the smartwatch includes data on the user's steps, sleep time, calorie intake, etc. The smartwatch transmits this data to the user's smartphone. Specifically, the smartwatch uses sensors to record the user's steps and heart rate, and transfers this data to the smartphone via wireless communication such as Bluetooth. The smartphone receives this data and temporarily stores it locally.

[0504] Input: Activity data, sleep data, and food data from your smartwatch

[0505] Output: Data transfer to smartphone

[0506] Step 2: Data accumulation

[0507] Subject: Server

[0508] The smartphone uploads the collected data to a cloud database. Specifically, the smartphone sends the data to a cloud server via an internet connection. This data is then stored in a database managed by the cloud server.

[0509] Input: Biometric data sent from smartphone

[0510] Output: Data storage in cloud database

[0511] Step 3: Data analysis

[0512] Subject: Server

[0513] Biometric data obtained from a cloud database is input into a generative AI model for data analysis. Specifically, the server obtains a specific user's biometric data from the cloud database and inputs it into the generative AI model. The generative AI model then analyzes the data using machine learning algorithms to detect specific patterns and anomalies. For example, it analyzes sleep data from the past week and generates a result such as an average sleep time of less than six hours.

[0514] Input: Biometric data retrieved from a cloud database

[0515] Output: Analysis result (e.g., average sleep time is less than 6 hours)

[0516] Step 4: Generate health advice

[0517] Subject: Server

[0518] Based on the results of data analysis, optimal health advice is generated for each user. Specifically, the system receives the analysis results of the generative AI model and starts the process of generating health advice based on these results. For example, based on the analysis results, it generates specific advice such as "avoid caffeine after dinner" or "practice meditation to relax before bed."

[0519] Input: Data analysis results

[0520] Output: Health advice

[0521] Step 5: Providing health advice

[0522] Subject: Server

[0523] The generated health advice is provided to the user's device (smartphone) in real time. Specifically, the server sends the generated advice to the user's smartphone, which displays it as a notification. The user can then check the displayed advice and incorporate it into their daily life.

[0524] Enter: Health Advice

[0525] Output: Advice notification sent to user's smartphone

[0526] Step 6: Applying Advice and Updating

[0527] Subject: User

[0528] The user confirms the provided health advice and applies it to their daily life. New biometric data is collected through the user's activities and stored again in the cloud database. The generative AI model continuously analyzes the data based on the new data and provides the latest health advice. For example, if the user follows the advice to "avoid caffeine after dinner," subsequent data is collected again to monitor the effectiveness of the advice.

[0529] Input: New biometric data for the user

[0530] Output: Updated health advice

[0531] (Application example 1)

[0532] 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."

[0533] Conventional health management systems only collect and analyze a user's biometric data to provide health advice, but do not cover the user's specific lifestyle habits, particularly meal menu suggestions. This makes it difficult for users to select an appropriate meal menu based on their health condition, and users often struggle with meal choices, especially in their busy daily lives. Therefore, an objective of the present invention is to provide a system that not only provides health advice based on a user's biometric data, but also suggests optimal meal menus for each individual user.

[0534] 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.

[0535] In this invention, the server includes means for collecting biometric data such as activity data, sleep data, and dietary data obtained from users, means for storing the collected biometric data in a cloud database, means for acquiring the biometric data from the cloud database and analyzing the data using a generative AI model, means for generating optimal health advice for each user based on the analysis results, means for providing the generated health advice to the user's device in real time, and means for recommending optimal meal menus to the user based on the analysis results, thereby making it possible to propose meal menus that correspond to the health condition of each user.

[0536] "Activity data" is information relating to the physical activity of the user in their daily life, such as the amount of exercise, number of steps, and distance traveled.

[0537] "Sleep data" is information related to the user's bedtime, wake-up time, quality and depth of sleep, sleep cycle, and the like.

[0538] "Dietary data" refers to information relating to the types, amounts, nutritional components, and timing of meals consumed by the user.

[0539] "Biometric data" refers to data that encompasses a variety of information about the user's body, such as activity data, sleep data, and dietary data.

[0540] A "cloud database" is a remote data storage system that stores and accesses data over the Internet.

[0541] A "generative AI model" is an artificial intelligence model trained to generate personalized, optimal advice based on a user's biometric data.

[0542] "Data analysis" is the process of processing and analyzing collected biometric data to evaluate the user's health status and lifestyle rhythm.

[0543] "Health Advice" means specific instructions or recommendations for improving or maintaining health provided to each user based on the results of data analysis.

[0544] "Providing in real time" means that analysis results and health advice are transmitted to the user's device immediately or nearly instantly.

[0545] "Recommending meals" means suggesting suitable foods and meal plans based on the user's biometric data and health advice.

[0546] This invention is a system that collects biometric data such as activity data, sleep data, and dietary data of a user and provides health advice and meal menus optimized for each individual user based on that data. This system is realized by the following specific procedures and components.

[0547] Data collection

[0548] Device:

[0549] A user's smartwatch continuously monitors their daily activity data, sleep data, diet data, etc. This data is sent to the user's smartphone, which then uploads it to a cloud database. For example, if a user walks 10,000 steps in a day, the step count data is recorded by the smartwatch and stored in the cloud database via the smartphone.

[0550] Data analysis

[0551] server:

[0552] The user's biometric data obtained from the cloud database is input into a generative AI model for data analysis. The generative AI model analyzes the user's health condition and lifestyle rhythm from the collected biometric data and identifies specific patterns and abnormalities. For example, it analyzes a user's sleep data over a week and derives the result that "the average sleep time is less than 6 hours."

[0553] Health advice and meal menu generation

[0554] server:

[0555] Based on the results of the data analysis, the system generates optimal health advice for each user, such as recommendations to go to bed earlier to improve sleep or meditation to relax.

[0556] Furthermore, the server recommends optimal meal options for the user based on the analysis results. For example, if the health advice is "low-calorie diets are recommended," the server will suggest low-calorie meal options.

[0557] Providing health advice and meal menus

[0558] server:

[0559] The generated health advice and meal menus are provided to the user's device (smartphone) in real time. The user's smartphone notifies the user of the received advice and menus and displays them to the user. The user can check these advice and menus and incorporate them into their daily lives.

[0560] Specific examples

[0561] If a user's recent sleep data indicates that they have not had enough sleep, the system operates as follows.

[0562] 1. Data Collection:

[0563] The user's smartwatch collects sleep data each night and sends it via their smartphone to a cloud database, which stores the user's sleep data.

[0564] 2. Data Analysis:

[0565] The server retrieves the user's sleep data from a cloud database and analyzes it using a generative AI model. The analysis result is that "the average sleep time over the past week was less than 6 hours."

[0566] 3. Health advice and meal menu generation:

[0567] Based on the analysis results, the generative AI model generates advice for the user, such as "avoid caffeine after dinner in the evening and practice meditation to relax before bed." If a low-calorie diet is recommended, it will suggest meal options such as "salad," "grilled chicken," and "vegetable soup."

[0568] 4. Providing health advice and meal menus:

[0569] The server sends the generated advice and meal menu to the user's smartphone. The user's smartphone notifies the user of the received advice and menu and displays them to the user. The user can check the displayed advice and menu and apply them to their daily life to improve their health.

[0570] Prompt Sentence Examples

[0571] Example prompt (data processing approach)

[0572] average_sleep_hours: 5.5,

[0573] weekly_step_count: 70000,

[0574] calorie_intake: 2500,

[0575] stress_level: 3 Stress level scale from 1 to 5

[0576] In this way, the system of the present invention can support the user's overall health management by providing personalized advice and dietary suggestions based on the user's individual health condition.

[0577] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0578] Step 1:

[0579] Data collection

[0580] Input: User's daily activity data, sleep data, and diet data

[0581] The server and device collect the day's activity, sleep, and dietary data from the user's smartwatch. This data is recorded by the smartwatch and sent to the user's smartphone. The smartphone then uploads the received data to a cloud database. Through this process, all biometric data is stored in the cloud and made available for subsequent data analysis.

[0582] Output: User biometric data stored in a cloud database

[0583] Step 2:

[0584] Data Acquisition

[0585] Input: User biometric data stored in a cloud database

[0586] The server periodically retrieves the user's biometric data from the cloud database, ensuring that the most recent data is always available for analysis. This data includes the user's daily activity, sleep, and dietary data.

[0587] Output: Captured user biometric data

[0588] Step 3:

[0589] Data analysis

[0590] Input: Captured user biometric data

[0591] The server inputs the acquired biometric data into a generative AI model to analyze the user's health condition and lifestyle. The generative AI model detects specific patterns and abnormalities (e.g., lack of sleep, excessive activity, poor nutrition). For example, it can analyze a user's sleep data over a week and derive the result that "the average sleep time is less than 6 hours."

[0592] Output: Analysis results of health condition and lifestyle rhythm

[0593] Step 4:

[0594] Health advice and meal plan generation

[0595] Input: Analysis results of health status and lifestyle rhythm

[0596] Based on the analysis results, the server generates optimal health advice for each user. Specific advice includes recommendations for going to bed early to improve sleep and meditating to relax. Furthermore, based on this, the server recommends optimal meal menus for the user. For example, if a low-calorie diet is recommended, the server will suggest meal options such as "salad," "grilled chicken," and "vegetable soup."

[0597] Output: Health advice and meal menu

[0598] Step 5:

[0599] Providing health advice and meal menus

[0600] Input: Health advice and meal plans

[0601] The server sends the generated health advice and meal menu to the user's device (smartphone), which then notifies and displays the advice and menu to the user in real time, allowing the user to take appropriate action on the spot.

[0602] Output: Health advice and meal menu provided to the user

[0603] Step 6:

[0604] Action Feedback

[0605] Input: User's health advice and diet plan implementation results

[0606] After the user puts the health advice and dietary menu into practice, changes in activity, sleep, and dietary data are monitored again. This feedback data is again stored in the cloud database and used for the next data analysis. This allows the server to track changes in the user's behavior and generate more precise advice and menus.

[0607] Output: Feedback data

[0608] 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.

[0609] The present invention is a system that collects and analyzes a user's biometric and emotional data to provide individually optimized health and mental health advice.

[0610] Data collection

[0611] Device:

[0612] The user's smartwatch continuously monitors daily activity data (e.g., number of steps, heart rate, calories burned, etc.), sleep data (e.g., sleep duration, sleep quality, etc.), and dietary data (e.g., dietary content, calories consumed, etc.). This data is sent to the user's smartphone, which then uploads it to a cloud database. Meanwhile, the emotion engine collects the user's emotional data via devices such as smartphones. The emotional data is used to understand the user's emotional state using means such as voice recognition, face recognition, and text analysis.

[0613] Data storage

[0614] server:

[0615] The cloud database receives the biometric and emotional data sent from the smartwatch and emotion engine, stores them in the database, and manages them centrally. For example, if a user walks 10,000 steps in a day and sleeps for five hours during the night, the step count data and sleep data will be recorded by the smartwatch, and if the emotion engine recognizes that the user's stress level is high, the data will be stored in the cloud database.

[0616] Data analysis

[0617] server:

[0618] The user's biometric and emotional data obtained from a cloud database is input into a generative AI model for data analysis. The generative AI model contains algorithms that use this data to evaluate and predict the user's health condition, lifestyle, and even emotional state. For example, it analyzes a user's data over a week and derives the result that "the user's average sleep time is less than six hours and they are emotionally stressed."

[0619] Generating health advice

[0620] server:

[0621] Based on the analysis results of the generative AI model, optimal health and mental health advice is generated for the user, such as "recommending going to bed early to improve sleep and meditating to manage stress."

[0622] Providing health advice

[0623] server:

[0624] The generated health and mental health advice is provided to the user's device (smartphone) in real time. The user's smartphone notifies the user of this advice and displays it to them. The user can then review this advice and incorporate it into their daily lives.

[0625] Specific examples

[0626] For example, if a user's recent sleep data indicates that they have not been getting enough sleep, the system operates as follows:

[0627] 1. Data Collection:

[0628] The user's smartwatch collects sleep data every night and sends it to a cloud database via their smartphone, while the emotion engine collects the user's daily emotional data and uploads it to the cloud database.

[0629] 2. Data Analysis:

[0630] The server retrieves the user's sleep and emotional data from a cloud database and analyzes it using a generative AI model. The analysis results include "Over the past week, the average sleep time has been less than six hours, and emotional stress is high."

[0631] 3. Health advice generation:

[0632] Based on the analysis results, the generative AI model generates health and mental health advice for the user, such as "avoid caffeine after dinner and practice meditation to relax before going to bed."

[0633] 4. Providing health advice:

[0634] The server sends the generated advice to the user's smartphone, which notifies the user of the received advice and displays it to the user. The user can then review the displayed advice and apply it to their daily lives.

[0635] In this way, the system of the present invention provides personalized advice based on the user's health and emotional state, helping the user to manage their health and maintain and improve their mental health, thereby enabling comprehensive health management in the user's daily life.

[0636] The processing flow will be explained below.

[0637] Step 1:

[0638] Device: The user's smartwatch continuously monitors daily activity data (e.g., number of steps, heart rate, calories burned, etc.), sleep data (e.g., sleep duration, sleep quality, etc.), and dietary data (e.g., dietary content, calories consumed, etc.). The collected data is sent to the user's smartphone. The emotion engine also collects the user's emotion data using methods such as voice recognition, face recognition, and text analysis. For example, the emotion engine may recognize "high stress level" from text while the user is using a diary app on their smartphone.

[0639] Step 2:

[0640] Terminal: The smartphone uploads the biometric and emotional data sent from the smartwatch and emotion engine to a cloud database, allowing the user's biometric and emotional data to be centrally managed.

[0641] Step 3:

[0642] Server: Retrieves the user's activity data, sleep data, dietary data, and emotion data from the cloud database. For example, retrieves "User A's step count data, sleep data, dietary data, and emotion data for the past week" from the cloud database.

[0643] Step 4:

[0644] Server: The acquired data is input into the generative AI model and analyzed. The generative AI model contains algorithms that evaluate and predict the user's health condition, lifestyle, and emotional state. For example, the analysis results might reveal that "User A's average sleep time is less than six hours and that he is emotionally stressed."

[0645] Step 5:

[0646] Server: Generates optimal health and mental health advice for users based on the analysis results of the generative AI model. For example, it generates advice such as "To improve sleep, avoid caffeine after dinner and practice meditation to relax before bed," and "Recommend hobbies to reduce stress."

[0647] Step 6:

[0648] Server: Provides generated health and mental health advice to the user's smartphone in real time. For example, if the generated advice is "avoid caffeine" and "try meditation," it is sent to the user's smartphone.

[0649] Step 7:

[0650] Device: The user's smartphone notifies them of the received advice and displays it to them. By checking the advice displayed on their smartphone, the user can incorporate it into their daily lives.

[0651] Step 8:

[0652] User: The user follows the advice displayed on their smartphone and changes their behavior, for example, avoiding caffeine before bedtime, practicing meditation to relax, or starting a hobby to reduce stress.

[0653] Step 9:

[0654] Server: Based on the health and mental health advice the user implements, the generative AI model then continuously monitors the data, collecting and analyzing new biometric and emotional data. This allows it to track improvements in the user's health and provide further advice. For example, it analyzes the data from several weeks after the initial implementation of the advice and generates next-step advice.

[0655] Through this series of steps, users receive individually tailored health and mental health advice that can be applied to their daily lives to achieve continuous health management.

[0656] Example 2

[0657] 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."

[0658] Conventional healthcare systems typically collect and analyze only a user's biometric data, making it difficult to provide advice that takes into account emotional changes and stress levels. As a result, they fail to achieve comprehensive improvement in health status and are unable to provide optimal health advice that meets the user's needs. It is also difficult to deliver personalized advice based on the user's lifestyle in real time. In contrast, the present invention aims to comprehensively analyze a user's emotional data and provide more personalized advice for health and mental health.

[0659] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0660] In this invention, the server includes means for collecting user emotional data using voice recognition, facial recognition, and text analysis, means for storing the user's biometric data and emotional data in a cloud database, and means for acquiring the user's biometric data and emotional data, generating prompt sentences, and inputting the prompt sentences into a generative AI model to generate health and mental health advice, thereby enabling personalized health advice to be provided in real time based on the user's biometric data and emotional data.

[0661] "User" refers to an individual who provides biometric and emotional data and receives health advice from the system.

[0662] "Biometric data" refers to data that indicates the user's physical condition, such as the user's daily activity level, heart rate, calories burned, sleep time, sleep quality, dietary content, and calories consumed.

[0663] "Emotional data" refers to data that indicates the user's emotions and mental state, obtained by analyzing the user's tone of voice, facial expression, text content, etc.

[0664] "Cloud database" refers to a database accessible via the Internet and a data storage system for storing users' biometric and emotional data.

[0665] A "generative AI model" is an artificial intelligence model generated by learning large amounts of data, and is used for data analysis and generating health advice.

[0666] A "prompt sentence" refers to an input sentence that provides a generative AI model with information that serves as the basis for analysis and advice generation.

[0667] "Health Advice" refers to specific suggestions or instructions provided to improve a user's physical or mental health based on the analysis of the user's biometric and emotional data.

[0668] "Real-time" refers to the time range in which users can provide data and receive results or advice almost immediately.

[0669] "Speech recognition" refers to the technology of converting voice data into text data and analyzing its content.

[0670] "Facial recognition" refers to the technology of analyzing facial image data and identifying facial expressions and features.

[0671] "Text analysis" refers to the technology of analyzing text data and identifying emotions and intentions from its content.

[0672] The present invention is a system that collects and analyzes a user's biometric and emotional data to provide individually optimized health and mental health advice. A concrete example is shown below.

[0673] Overall system configuration

[0674] The system consists of the following main components: the user's device (smartwatch or smartphone), a cloud database, a generative AI model, and a set of servers. The role and operation of each component are described in detail below.

[0675] Hardware and Software

[0676] Device:

[0677] The user uses a smartwatch to measure daily activity data (number of steps, heart rate, calories burned, etc.) and sleep data (sleep time, sleep quality, etc.). The smartphone supports the necessary data collection and has a built-in emotion engine. Specifically, the smartphone uses applications for speech recognition, facial recognition, and text analysis. For example, it uses the Google Speech-to-Text API for speech recognition, OpenCV and Dlib for facial recognition, and natural language processing libraries (e.g., NLTK and spaCy) for text analysis.

[0678] Cloud Database:

[0679] The database stores the user's biometric and emotional data, typically in a cloud-based database such as Amazon RDS or Google Cloud SQL.

[0680] Generative AI models:

[0681] The generative AI model uses deep learning frameworks, such as TensorFlow and PyTorch, to analyze a user's biometric and emotional data and generate health and mental health advice.

[0682] server:

[0683] The server communicates with a cloud database, performs data analysis, and runs a generative AI model, such as an EC2 instance from AWS (Amazon Web Services). The advice generation part uses a natural language generation model (e.g., GPT-3).

[0684] Data collection

[0685] Device:

[0686] The smartwatch worn by the user collects activity and sleep data and transmits the collected data in real time to a smartphone. The smartphone also uses an emotion engine to perform voice recognition, face recognition, and text analysis to collect emotion data, which is then uploaded to a cloud database.

[0687] Data analysis

[0688] server:

[0689] The server inputs the user's biometric and emotional data obtained from the cloud database into the generative AI model for analysis. The generative AI model contains algorithms that use this data to evaluate and predict the user's health condition, lifestyle, and even emotional state. As a concrete example, the prompt sentence is shown below.

[0690] "Analyze this user's average sleep time and stress level over the past week and generate appropriate health advice."

[0691] "Generate specific suggestions for maintaining health for the next week based on the user's biometric and emotional data."

[0692] Generating and providing health advice

[0693] server:

[0694] Based on the analysis results of the generative AI model, it generates individually optimized health and mental health advice, such as "avoid caffeine after dinner" or "practice meditation to relax before bed."

[0695] Device:

[0696] The generated advice is sent to the user's smartphone in real time and displayed as a push notification, allowing the user to check the notification and incorporate the advice into their daily life.

[0697] This system will provide users with personalized health advice based on their biometric and emotional data, helping them maintain and improve their physical and mental health.

[0698] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0699] Step 1:

[0700] Data collection

[0701] The device (smartwatch) collects the user's daily activity data (number of steps, heart rate, calories burned, etc.) and sleep data (sleep time, sleep quality, etc.). The smartphone receives this data via Bluetooth and then collects emotional data using voice recognition, facial recognition, and text analysis. Specifically, emotions are analyzed from the user's tone of voice when talking on the phone, facial expressions captured by the camera, and the content of text messages. Data input is real-time data from the smartwatch and smartphone, and output is accumulated in the form of collected data from each device.

[0702] Step 2:

[0703] Data transmission and storage

[0704] The biometric and emotional data collected by the device (smartphone) is sent to a cloud database via the internet. At this time, SSL / TLS encryption is used to ensure data security. The cloud server processes the received data and stores it in a database. Specifically, it is stored in a cloud-based database such as Amazon RDS. The data input is the biometric and emotional data sent from the smartphone, and the output is stored in the database.

[0705] Step 3:

[0706] Data Acquisition and Analysis

[0707] The server retrieves a specific user's data from a cloud database. This API call retrieves biometric and emotional data from the past week. The retrieved data is then input into a generative AI model. For example, a deep learning model built using TensorFlow or PyTorch is used. The generative AI model analyzes the data and evaluates the user's health and emotional state. The input is the data retrieved from the cloud database, and the output is the evaluation results analyzed by the AI ​​model.

[0708] Step 4:

[0709] Prompt generation and advice generation

[0710] The server generates a prompt sentence based on the analysis results of the generative AI model. The generated prompt sentence is input into a natural language generation model to generate specific health advice. Specific examples of prompt sentences include, "Please analyze this user's average sleep time and stress level over the past week and generate appropriate health advice," and "Please generate specific suggestions for maintaining health over the next week based on the user's biometric and emotional data." The input is the analysis results of the generative AI model and the prompt sentence, and the output is health and mental health advice.

[0711] Step 5:

[0712] Providing advice

[0713] The server sends the generated health advice to the user's smartphone. The smartphone displays the received advice to the user as a push notification. When the user taps the notification, a dedicated application opens and the user can check the detailed advice. The input is the generated health advice, and the output is the notification and displayed advice to the user.

[0714] Through these steps, the system can comprehensively analyze a user's biometric and emotional data and provide individually optimized health advice in real time.

[0715] (Application example 2)

[0716] 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."

[0717] In today's busy lifestyles, it is important for individual users to appropriately manage their own health and emotional state and improve their quality of life. However, conventional systems mainly provide health advice using only the user's biometric data, and are insufficient in providing specific content that takes emotional data into account or improving mental health. In particular, they lack the ability to optimize content according to the user's stress and emotional state, so more effective health management support is needed.

[0718] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0719] In this invention, the server includes means for collecting biometric data such as activity data, sleep data, and dietary data obtained from a user, means for storing the collected biometric data in a cloud database, means for acquiring the biometric data from the cloud database and analyzing the data using a generative AI model, means for generating optimal health advice for each user based on the analysis results, means for providing the generated health advice to the user's device in real time, means for performing voice recognition and facial recognition of the user and collecting emotional data, means for storing the collected emotional data in the cloud database, means for analyzing the emotional data and evaluating the user's mental health state, means for optimizing content to be viewed based on the user's health state and emotional state, and means for displaying the optimized content. This enables more personalized health management and mental health support based on both the user's biometric data and emotional data.

[0720] "Activity data" is information about the user's physical activity, such as the amount of exercise, number of steps, and calories burned each day.

[0721] "Sleep data" refers to information related to sleep, such as the user's sleep time, sleep quality, and sleep cycle.

[0722] "Dietary data" refers to information about meals such as the contents of meals consumed by the user, calorie intake, and nutritional information.

[0723] "Biometric data" refers to all information related to the user's physical condition and activities, such as activity data, sleep data, and dietary data.

[0724] A "cloud database" is a database for storing and managing data via the Internet.

[0725] A "generative AI model" is an algorithmic model that uses machine learning and artificial intelligence to generate specific outcomes or predictions from input data.

[0726] "Data analysis" is the process of extracting information from collected data using certain algorithms or models, and making evaluations and predictions.

[0727] "Health advice" refers to specific suggestions and instructions for improving and maintaining health provided to users based on the results of data analysis.

[0728] "Emotional data" is information about a user's emotional state collected through speech and facial recognition.

[0729] "Speech recognition" is a technology that analyzes the content of a user's speech and extracts information from the speech.

[0730] "Facial recognition" is a technology that analyzes a user's facial expressions and features to determine their emotions and identity.

[0731] "Mental health status" is an assessment of the user's emotional and mental state.

[0732] "Content optimization" is the process of providing the most appropriate content for a user to view based on their health and emotional state.

[0733] "Audio content" refers to the videos a user watches, the music they listen to, and other entertainment media.

[0734] "Providing in real time" means notifying and displaying analysis results and advice to users immediately on the spot.

[0735] The present invention provides a system for collecting and analyzing biometric and emotional data of a user, and then providing individually optimized health and mental health advice in real time. The system is configured as follows.

[0736] Data collection

[0737] The smartwatch, which is the user's device, continuously monitors daily activity data (e.g., steps taken, heart rate, calories burned) and sleep data (e.g., sleep duration and quality) and uploads the data to a cloud database via the smartphone. Additionally, smart glasses equipped with an emotion engine recognize the user's voice and facial expressions to collect emotion data.

[0738] Data storage

[0739] The server uses a cloud database to centrally manage and store the collected biometric and emotional data. For example, if a user walks 10,000 steps a day and gets five hours of sleep a night, but their facial expressions and voice indicate a high stress level, the data will be stored in the cloud database.

[0740] Data analysis

[0741] The server inputs the user's biometric and emotional data obtained from the cloud database into the generative AI model for data analysis. The generative AI model contains algorithms that evaluate the user's health and emotional state from this data. For example, it can derive a result such as "the average sleep time is less than six hours and the emotional stress level is high."

[0742] Generating and delivering health advice and content

[0743] The server generates optimal health advice for the user based on the analysis results of the generative AI model. Furthermore, it optimizes the content to be viewed based on the user's health and emotional state. For example, it may provide relaxing music and yoga videos along with health advice such as "avoid caffeine after dinner and practice meditation to relax before bed."

[0744] These advice and content are displayed in real time on the user's smartphone or smart glasses, allowing the user to review the displayed advice and incorporate it into their daily life.

[0745] Specific examples

[0746] For example, if a user's recent sleep data indicates that they haven't been getting enough sleep, the system could:

[0747] 1. Data collection: The smart watch collects the user's sleep data every night and sends it to the cloud database. At the same time, the smart glasses collect the user's daily emotional data and upload it to the cloud database.

[0748] 2. Data analysis: The server retrieves the user's sleep and emotional data from the cloud database and analyzes it using a generative AI model. The analysis results include "Over the past week, the average sleep time has been less than six hours, and the user is experiencing high emotional stress."

[0749] 3. Generating health advice: Based on the analysis results, the generative AI model generates health advice for the user, such as "avoid caffeine after dinner and practice relaxation meditation before going to bed." In addition, relaxing music and yoga videos are provided.

[0750] 4. Providing health advice: These advice and content will be displayed in real time on the user's smartphone or smart glasses.

[0751] Prompt Sentence Examples

[0752] If the user is in a "high stress" state on a daily basis, the following prompt sentence is input to the generative AI model:

[0753] User biometric data: Heart rate 90, sleep time 4 hours, steps 3000

[0754] Emotion data: Facial expressions indicate high stress levels, and voice indicates fatigue levels

[0755] Problem: Not getting enough sleep, high stress levels

[0756] Based on this data, it recommends relaxing content to help relieve stress and provides health advice.

[0757] As described above, the system of the present invention is capable of providing personalized health management and mental health support based on the user's health and emotional state.

[0758] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0759] Step 1:

[0760] The smartwatch and smart glasses, which are the user's devices, collect the user's activity data and emotional data and send it to a cloud database via the smartphone. Specifically, the smartwatch continuously monitors the user's steps, heart rate, and calories burned, while the smart glasses simultaneously recognize the user's voice and facial expressions to collect emotional data. The input at this stage is the user's daily activity data and emotional data, and the output is biometric data and emotional data stored in the cloud database.

[0761] Step 2:

[0762] The server retrieves the biometric and emotional data stored in the cloud database. Specifically, it uses the database API to extract the necessary data and stores it in temporary memory for analysis. The input at this stage is the biometric and emotional data in the cloud database, and the output is the data to be analyzed stored in the server's memory.

[0763] Step 3:

[0764] The server inputs biometric and emotional data into a generative AI model and performs data analysis. Specifically, it uses machine learning algorithms to evaluate and predict the user's health and emotional state. The input at this stage is the biometric and emotional data stored in the server's memory, and the output is an evaluation result indicating the user's health and emotional state.

[0765] Step 4:

[0766] The server generates optimal health advice and viewing content for the user based on the analysis results of the generative AI model. For example, relaxing music and yoga videos are selected along with health advice such as "avoid caffeine after dinner and practice meditation to relax before bed." The input at this stage is the analysis results of the generative AI model, and the output is health advice and optimized viewing content.

[0767] Step 5:

[0768] The server sends the generated health advice and audiovisual content to the user's device (smartphone or smart glasses) in real time. Specifically, it notifies the user of the advice and plays the audiovisual content using a notification API or the device's software interface. The input of this stage is the generated health advice and audiovisual content, and the output is the advice displayed on the user's device and the audiovisual content played.

[0769] Through these steps, a system is realized that provides personalized health management and mental health support based on the user's health and emotional state.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] [Third embodiment]

[0774] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0775] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0776] 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).

[0777] 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.

[0778] 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.

[0779] 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).

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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."

[0786] The present invention is a system that provides individually optimized health advice by collecting and analyzing a user's biometric data.

[0787] Data collection

[0788] Device:

[0789] A user's smartwatch continuously monitors the user's daily activity data, sleep data, diet data, etc. This data is sent to the user's smartphone, which then uploads it to a cloud database. For example, if a user walks 10,000 steps in a day, the step count data will be recorded by the smartwatch, sent to the smartphone, and stored in the cloud database.

[0790] Data analysis

[0791] server:

[0792] The user's biometric data obtained from the cloud database is input into a generative AI model for data analysis. The generative AI model analyzes the user's health condition and lifestyle rhythm from the collected biometric data and identifies specific patterns and abnormalities. For example, it analyzes a user's sleep data over a week and derives the result that "the average sleep time is less than 6 hours."

[0793] Generating health advice

[0794] server:

[0795] Based on the results of the data analysis, the system generates optimal health advice for each user, such as recommendations to go to bed earlier to improve sleep or meditation to relax.

[0796] Providing health advice

[0797] server:

[0798] The generated health advice is provided to the user's device (smartphone) in real time. The user's smartphone notifies the user of the received advice and displays it to them. The user can then check this advice and incorporate it into their daily lives.

[0799] Specific examples

[0800] For example, if a user's recent sleep data indicates that they have not had enough sleep, the system operates as follows:

[0801] 1. Data Collection:

[0802] The user's smartwatch collects sleep data each night and sends it via their smartphone to a cloud database, which stores the user's sleep data.

[0803] 2. Data Analysis:

[0804] The server retrieves the user's sleep data from a cloud database and analyzes it using a generative AI model. The analysis result is that "the average sleep time over the past week was less than 6 hours."

[0805] 3. Health advice generation:

[0806] Based on the analysis results, the generative AI model generates advice for the user, such as "avoid caffeine after dinner and meditate to relax before going to bed."

[0807] 4. Providing health advice:

[0808] The server sends the generated advice to the user's smartphone, which then notifies the user of the received advice and displays it to them. The user can then check the displayed advice and apply it to their daily lives to improve their health.

[0809] In this way, the system of the present invention provides personalized advice based on the user's individual health condition and supports the user's health management. Through continuous data collection and analysis, the system provides a means for the user to maintain optimal health.

[0810] The processing flow will be explained below.

[0811] Step 1:

[0812] Device: The user's smartwatch continuously monitors daily activity data (e.g., steps taken, heart rate, calories burned, etc.), sleep data (e.g., sleep duration, sleep quality, etc.), and dietary data (e.g., dietary content, calories consumed, etc.). The collected data is sent to the user's smartphone.

[0813] Step 2:

[0814] Terminal: The smartphone receives the data sent from the smartwatch and uploads it to a cloud database via the internet, allowing the user's biometric data to be stored remotely.

[0815] Step 3:

[0816] Server: Retrieves user biometric data from a cloud database, including user activity, sleep, and dietary data.

[0817] Step 4:

[0818] Server: The acquired biometric data is input into the generative AI model and data analysis is performed. The generative AI model evaluates the user's health condition and lifestyle rhythm and identifies specific patterns and abnormalities. For example, analysis results may include information such as "average sleep time less than 6 hours" or "lack of exercise."

[0819] Step 5:

[0820] Server: Based on the analysis results of the generative AI model, it generates optimal health advice for each user, such as recommending going to bed earlier to improve sleep time or walking to increase exercise.

[0821] Step 6:

[0822] Server: The server sends the generated health advice to the user's device (smartphone) in real time. The user's smartphone notifies them of the advice received.

[0823] Step 7:

[0824] User: The user checks the health advice displayed on their smartphone and incorporates it into their daily routine, for example, reducing caffeine intake before bedtime or practicing meditation to relax.

[0825] Step 8:

[0826] Server: Based on the health advice provided by the user, the generative AI model then continuously monitors the data, collecting and analyzing new biometric data to track the user's health improvement and provide further advice.

[0827] Through this series of steps, users receive personalized health advice that can be applied to their daily lives to improve and maintain their health.

[0828] Example 1

[0829] 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."

[0830] In modern society, health management is a major focus, but many people face difficulties in accurately understanding their own health status and obtaining appropriate advice. In particular, there is a lack of systems that centrally manage daily activity, sleep, and dietary data and provide individually optimized health advice based on this data. This makes it difficult for users to find the lifestyle habits that are best for them.

[0831] 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.

[0832] In this invention, the server includes means for collecting biometric data such as activity data, sleep data, and dietary data obtained from users, means for storing the collected biometric data in a cloud database via a communication device, means for acquiring the biometric data from the cloud database and analyzing the data using a generative AI model, means for generating optimal health advice for each user based on the analysis results, means for providing the generated health advice to the user's mobile device in real time, and means for continuously monitoring changes in the biometric data and updating the advice, thereby enabling users to accurately understand their own health status and receive individually optimized health advice in real time.

[0833] "Activity data" refers to data related to the user's daily physical activity, such as the number of steps taken, calories burned, and exercise time.

[0834] "Sleep data" refers to data related to the user's nighttime sleep, such as the amount of sleep, quality of sleep, and sleep cycle.

[0835] "Dietary data" refers to data related to the daily meals a user takes, such as the contents of the meals, calories, and nutrients.

[0836] "Biometric data" refers to all data that indicates the user's health condition, such as the user's activity data, sleep data, and dietary data.

[0837] "Communication devices" are electronic devices for sending and receiving data, such as smartphones, tablets, and personal computers.

[0838] A "cloud database" is a remote data storage system accessed over the Internet.

[0839] A "generative AI model" is an algorithm or model that uses artificial intelligence technology to analyze, predict, and generate data.

[0840] "Data analysis" is the act of analyzing collected biometric data using statistical methods and machine learning to extract meaningful information.

[0841] "Health advice" refers to specific suggestions or guidelines provided to improve the user's health based on the analysis results.

[0842] A "mobile terminal" is a portable information and communication device such as a smartphone or tablet.

[0843] "Monitoring" is the act of continuously observing and collecting specific data or situations.

[0844] "Updating advice" refers to the regular reassessment of the health advice provided based on ongoing data monitoring, and the amendments or changes made as necessary.

[0845] The present invention is a system for supporting a user's health management. This system collects the user's biometric data, stores it in a cloud database, analyzes the data using a generative AI model, and provides health advice based on the analysis results.

[0846] Data collection

[0847] Device:

[0848] A user's smartwatch continuously monitors daily activity data, sleep data, diet data, etc. The smartwatch transmits this data to the user's smartphone, which then uploads the received data to a cloud database. For example, if a user walks 10,000 steps in a day, the step count data is recorded on the smartwatch and stored in the cloud database via the smartphone.

[0849] Data accumulation

[0850] server:

[0851] A cloud database continuously accumulates each user's biometric data, and a server retrieves the data from the cloud database and prepares it for input into the generative AI model.

[0852] Data analysis

[0853] server:

[0854] Biometric data obtained from a cloud database is input into a generative AI model for data analysis. The generative AI model analyzes the user's health status and lifestyle rhythms from the collected biometric data to discover specific patterns and abnormalities.

[0855] Example: A generative AI model analyzes a user's sleep data from the past week and concludes that their average sleep time is less than 6 hours.

[0856] Generating health advice

[0857] server:

[0858] Based on the results of the data analysis, the system generates optimal health advice for each user, such as recommendations to go to bed earlier to improve sleep or meditation to relax.

[0859] Providing health advice

[0860] server:

[0861] The generated health advice is provided to the user's device (smartphone) in real time. The smartphone notifies the user of the received advice and displays it to the user. The user can then check this advice and incorporate it into their daily life.

[0862] Example: The server generates advice such as "avoid caffeine after dinner and practice meditation to relax before going to bed" and sends it to the smartphone. The user's smartphone notifies the advice and displays it to the user.

[0863] Health advice application and updates

[0864] User:

[0865] The user reviews the provided health advice and applies it to their daily life. New biometric data is collected through the user's activities and stored again in the cloud database. The generative AI model continuously analyzes the data based on the new data and provides the latest health advice.

[0866] Specific examples

[0867] For example, if a user's recent sleep data indicates that they have not had enough sleep, the system operates as follows:

[0868] 1. Data collection: The user's smartwatch collects nightly sleep data and sends it to a cloud database via their smartphone. The cloud database stores the user's sleep data.

[0869] 2. Data analysis: The server retrieves the user's sleep data from the cloud database and analyzes it using a generative AI model. The analysis result is that "the average sleep time over the past week was less than 6 hours."

[0870] 3. Generating health advice: Based on the analysis results, the generative AI model generates advice for the user, such as "avoid caffeine after dinner and practice meditation to relax before going to bed."

[0871] 4. Providing health advice: The server sends the generated advice to the user's smartphone. The user's smartphone notifies the user of the received advice and displays it to them. The user can then review the displayed advice and apply it to their daily lives to improve their health.

[0872] Here is an example of a prompt to input to a generative AI model:

[0873] Analyze the user's sleep data from the last week. Find specific patterns or anomalies in the data and generate optimal health advice for the user. For example, if the average sleep time for the last week is less than 6 hours, avoid caffeine after dinner and practice relaxation meditation before bed.

[0874] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0875] Step 1: Data collection

[0876] Subject: Terminal

[0877] A user's smartwatch continuously monitors daily activity data, sleep data, dietary data, etc. Input from the smartwatch includes data on the user's steps, sleep time, calorie intake, etc. The smartwatch transmits this data to the user's smartphone. Specifically, the smartwatch uses sensors to record the user's steps and heart rate, and transfers this data to the smartphone via wireless communication such as Bluetooth. The smartphone receives this data and temporarily stores it locally.

[0878] Input: Activity data, sleep data, and food data from your smartwatch

[0879] Output: Data transfer to smartphone

[0880] Step 2: Data accumulation

[0881] Subject: Server

[0882] The smartphone uploads the collected data to a cloud database. Specifically, the smartphone sends the data to a cloud server via an internet connection. This data is then stored in a database managed by the cloud server.

[0883] Input: Biometric data sent from smartphone

[0884] Output: Data storage in cloud database

[0885] Step 3: Data analysis

[0886] Subject: Server

[0887] Biometric data obtained from a cloud database is input into a generative AI model for data analysis. Specifically, the server obtains a specific user's biometric data from the cloud database and inputs it into the generative AI model. The generative AI model then analyzes the data using machine learning algorithms to detect specific patterns and anomalies. For example, it analyzes sleep data from the past week and generates a result such as an average sleep time of less than six hours.

[0888] Input: Biometric data retrieved from a cloud database

[0889] Output: Analysis result (e.g., average sleep time is less than 6 hours)

[0890] Step 4: Generate health advice

[0891] Subject: Server

[0892] Based on the results of data analysis, optimal health advice is generated for each user. Specifically, the system receives the analysis results of the generative AI model and starts the process of generating health advice based on these results. For example, based on the analysis results, it generates specific advice such as "avoid caffeine after dinner" or "practice meditation to relax before bed."

[0893] Input: Data analysis results

[0894] Output: Health advice

[0895] Step 5: Providing health advice

[0896] Subject: Server

[0897] The generated health advice is provided to the user's device (smartphone) in real time. Specifically, the server sends the generated advice to the user's smartphone, which displays it as a notification. The user can then check the displayed advice and incorporate it into their daily life.

[0898] Enter: Health Advice

[0899] Output: Advice notification sent to user's smartphone

[0900] Step 6: Applying Advice and Updating

[0901] Subject: User

[0902] The user confirms the provided health advice and applies it to their daily life. New biometric data is collected through the user's activities and stored again in the cloud database. The generative AI model continuously analyzes the data based on the new data and provides the latest health advice. For example, if the user follows the advice to "avoid caffeine after dinner," subsequent data is collected again to monitor the effectiveness of the advice.

[0903] Input: New biometric data for the user

[0904] Output: Updated health advice

[0905] (Application example 1)

[0906] 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."

[0907] Conventional health management systems only collect and analyze a user's biometric data to provide health advice, but do not cover the user's specific lifestyle habits, particularly meal menu suggestions. This makes it difficult for users to select an appropriate meal menu based on their health condition, and users often struggle with meal choices, especially in their busy daily lives. Therefore, an objective of the present invention is to provide a system that not only provides health advice based on a user's biometric data, but also suggests optimal meal menus for each individual user.

[0908] 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.

[0909] In this invention, the server includes means for collecting biometric data such as activity data, sleep data, and dietary data obtained from users, means for storing the collected biometric data in a cloud database, means for acquiring the biometric data from the cloud database and analyzing the data using a generative AI model, means for generating optimal health advice for each user based on the analysis results, means for providing the generated health advice to the user's device in real time, and means for recommending optimal meal menus to the user based on the analysis results, thereby making it possible to propose meal menus that correspond to the health condition of each user.

[0910] "Activity data" is information relating to the physical activity of the user in their daily life, such as the amount of exercise, number of steps, and distance traveled.

[0911] "Sleep data" is information related to the user's bedtime, wake-up time, quality and depth of sleep, sleep cycle, and the like.

[0912] "Dietary data" refers to information relating to the types, amounts, nutritional components, and timing of meals consumed by the user.

[0913] "Biometric data" refers to data that encompasses a variety of information about the user's body, such as activity data, sleep data, and dietary data.

[0914] A "cloud database" is a remote data storage system that stores and accesses data over the Internet.

[0915] A "generative AI model" is an artificial intelligence model trained to generate personalized, optimal advice based on a user's biometric data.

[0916] "Data analysis" is the process of processing and analyzing collected biometric data to evaluate the user's health status and lifestyle rhythm.

[0917] "Health Advice" means specific instructions or recommendations for improving or maintaining health provided to each user based on the results of data analysis.

[0918] "Providing in real time" means that analysis results and health advice are transmitted to the user's device immediately or nearly instantly.

[0919] "Recommending meals" means suggesting suitable foods and meal plans based on the user's biometric data and health advice.

[0920] This invention is a system that collects biometric data such as activity data, sleep data, and dietary data of a user and provides health advice and meal menus optimized for each individual user based on that data. This system is realized by the following specific procedures and components.

[0921] Data collection

[0922] Device:

[0923] A user's smartwatch continuously monitors their daily activity data, sleep data, diet data, etc. This data is sent to the user's smartphone, which then uploads it to a cloud database. For example, if a user walks 10,000 steps in a day, the step count data is recorded by the smartwatch and stored in the cloud database via the smartphone.

[0924] Data analysis

[0925] server:

[0926] The user's biometric data obtained from the cloud database is input into a generative AI model for data analysis. The generative AI model analyzes the user's health condition and lifestyle rhythm from the collected biometric data and identifies specific patterns and abnormalities. For example, it analyzes a user's sleep data over a week and derives the result that "the average sleep time is less than 6 hours."

[0927] Health advice and meal menu generation

[0928] server:

[0929] Based on the results of the data analysis, the system generates optimal health advice for each user, such as recommendations to go to bed earlier to improve sleep or meditation to relax.

[0930] Furthermore, the server recommends optimal meal options for the user based on the analysis results. For example, if the health advice is "low-calorie diets are recommended," the server will suggest low-calorie meal options.

[0931] Providing health advice and meal menus

[0932] server:

[0933] The generated health advice and meal menus are provided to the user's device (smartphone) in real time. The user's smartphone notifies the user of the received advice and menus and displays them to the user. The user can check these advice and menus and incorporate them into their daily lives.

[0934] Specific examples

[0935] If a user's recent sleep data indicates that they have not had enough sleep, the system operates as follows.

[0936] 1. Data Collection:

[0937] The user's smartwatch collects sleep data each night and sends it via their smartphone to a cloud database, which stores the user's sleep data.

[0938] 2. Data Analysis:

[0939] The server retrieves the user's sleep data from a cloud database and analyzes it using a generative AI model. The analysis result is that "the average sleep time over the past week was less than 6 hours."

[0940] 3. Health advice and meal menu generation:

[0941] Based on the analysis results, the generative AI model generates advice for the user, such as "avoid caffeine after dinner in the evening and practice meditation to relax before bed." If a low-calorie diet is recommended, it will suggest meal options such as "salad," "grilled chicken," and "vegetable soup."

[0942] 4. Providing health advice and meal menus:

[0943] The server sends the generated advice and meal menu to the user's smartphone. The user's smartphone notifies the user of the received advice and menu and displays them to the user. The user can check the displayed advice and menu and apply them to their daily life to improve their health.

[0944] Prompt Sentence Examples

[0945] Example prompt (data processing approach)

[0946] average_sleep_hours: 5.5,

[0947] weekly_step_count: 70000,

[0948] calorie_intake: 2500,

[0949] stress_level: 3 Stress level scale from 1 to 5

[0950] In this way, the system of the present invention can support the user's overall health management by providing personalized advice and dietary suggestions based on the user's individual health condition.

[0951] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0952] Step 1:

[0953] Data collection

[0954] Input: User's daily activity data, sleep data, and diet data

[0955] The server and device collect the day's activity, sleep, and dietary data from the user's smartwatch. This data is recorded by the smartwatch and sent to the user's smartphone. The smartphone then uploads the received data to a cloud database. Through this process, all biometric data is stored in the cloud and made available for subsequent data analysis.

[0956] Output: User biometric data stored in a cloud database

[0957] Step 2:

[0958] Data Acquisition

[0959] Input: User biometric data stored in a cloud database

[0960] The server periodically retrieves the user's biometric data from the cloud database, ensuring that the most recent data is always available for analysis. This data includes the user's daily activity, sleep, and dietary data.

[0961] Output: Captured user biometric data

[0962] Step 3:

[0963] Data analysis

[0964] Input: Captured user biometric data

[0965] The server inputs the acquired biometric data into a generative AI model to analyze the user's health condition and lifestyle. The generative AI model detects specific patterns and abnormalities (e.g., lack of sleep, excessive activity, poor nutrition). For example, it can analyze a user's sleep data over a week and derive the result that "the average sleep time is less than 6 hours."

[0966] Output: Analysis results of health condition and lifestyle rhythm

[0967] Step 4:

[0968] Health advice and meal plan generation

[0969] Input: Analysis results of health status and lifestyle rhythm

[0970] Based on the analysis results, the server generates optimal health advice for each user. Specific advice includes recommendations for going to bed early to improve sleep and meditating to relax. Furthermore, based on this, the server recommends optimal meal menus for the user. For example, if a low-calorie diet is recommended, the server will suggest meal options such as "salad," "grilled chicken," and "vegetable soup."

[0971] Output: Health advice and meal menu

[0972] Step 5:

[0973] Providing health advice and meal menus

[0974] Input: Health advice and meal plans

[0975] The server sends the generated health advice and meal menu to the user's device (smartphone), which then notifies and displays the advice and menu to the user in real time, allowing the user to take appropriate action on the spot.

[0976] Output: Health advice and meal menu provided to the user

[0977] Step 6:

[0978] Action Feedback

[0979] Input: User's health advice and diet plan implementation results

[0980] After the user puts the health advice and dietary menu into practice, changes in activity, sleep, and dietary data are monitored again. This feedback data is again stored in the cloud database and used for the next data analysis. This allows the server to track changes in the user's behavior and generate more precise advice and menus.

[0981] Output: Feedback data

[0982] 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.

[0983] The present invention is a system that collects and analyzes a user's biometric and emotional data to provide individually optimized health and mental health advice.

[0984] Data collection

[0985] Device:

[0986] The user's smartwatch continuously monitors daily activity data (e.g., number of steps, heart rate, calories burned, etc.), sleep data (e.g., sleep duration, sleep quality, etc.), and dietary data (e.g., dietary content, calories consumed, etc.). This data is sent to the user's smartphone, which then uploads it to a cloud database. Meanwhile, the emotion engine collects the user's emotional data via devices such as smartphones. The emotional data is used to understand the user's emotional state using means such as voice recognition, face recognition, and text analysis.

[0987] Data storage

[0988] server:

[0989] The cloud database receives the biometric and emotional data sent from the smartwatch and emotion engine, stores them in the database, and manages them centrally. For example, if a user walks 10,000 steps in a day and sleeps for five hours during the night, the step count data and sleep data will be recorded by the smartwatch, and if the emotion engine recognizes that the user's stress level is high, the data will be stored in the cloud database.

[0990] Data analysis

[0991] server:

[0992] The user's biometric and emotional data obtained from a cloud database is input into a generative AI model for data analysis. The generative AI model contains algorithms that use this data to evaluate and predict the user's health condition, lifestyle, and even emotional state. For example, it analyzes a user's data over a week and derives the result that "the user's average sleep time is less than six hours and they are emotionally stressed."

[0993] Generating health advice

[0994] server:

[0995] Based on the analysis results of the generative AI model, optimal health and mental health advice is generated for the user, such as "recommending going to bed early to improve sleep and meditating to manage stress."

[0996] Providing health advice

[0997] server:

[0998] The generated health and mental health advice is provided to the user's device (smartphone) in real time. The user's smartphone notifies the user of this advice and displays it to them. The user can then review this advice and incorporate it into their daily lives.

[0999] Specific examples

[1000] For example, if a user's recent sleep data indicates that they have not been getting enough sleep, the system operates as follows:

[1001] 1. Data Collection:

[1002] The user's smartwatch collects sleep data every night and sends it to a cloud database via their smartphone, while the emotion engine collects the user's daily emotional data and uploads it to the cloud database.

[1003] 2. Data Analysis:

[1004] The server retrieves the user's sleep and emotional data from a cloud database and analyzes it using a generative AI model. The analysis results include "Over the past week, the average sleep time has been less than six hours, and emotional stress is high."

[1005] 3. Health advice generation:

[1006] Based on the analysis results, the generative AI model generates health and mental health advice for the user, such as "avoid caffeine after dinner and practice meditation to relax before going to bed."

[1007] 4. Providing health advice:

[1008] The server sends the generated advice to the user's smartphone, which notifies the user of the received advice and displays it to the user. The user can then review the displayed advice and apply it to their daily lives.

[1009] In this way, the system of the present invention provides personalized advice based on the user's health and emotional state, helping the user to manage their health and maintain and improve their mental health, thereby enabling comprehensive health management in the user's daily life.

[1010] The processing flow will be explained below.

[1011] Step 1:

[1012] Device: The user's smartwatch continuously monitors daily activity data (e.g., number of steps, heart rate, calories burned, etc.), sleep data (e.g., sleep duration, sleep quality, etc.), and dietary data (e.g., dietary content, calories consumed, etc.). The collected data is sent to the user's smartphone. The emotion engine also collects the user's emotion data using methods such as voice recognition, face recognition, and text analysis. For example, the emotion engine may recognize "high stress level" from text while the user is using a diary app on their smartphone.

[1013] Step 2:

[1014] Terminal: The smartphone uploads the biometric and emotional data sent from the smartwatch and emotion engine to a cloud database, allowing the user's biometric and emotional data to be centrally managed.

[1015] Step 3:

[1016] Server: Retrieves the user's activity data, sleep data, dietary data, and emotion data from the cloud database. For example, retrieves "User A's step count data, sleep data, dietary data, and emotion data for the past week" from the cloud database.

[1017] Step 4:

[1018] Server: The acquired data is input into the generative AI model and analyzed. The generative AI model contains algorithms that evaluate and predict the user's health condition, lifestyle, and emotional state. For example, the analysis results might reveal that "User A's average sleep time is less than six hours and that he is emotionally stressed."

[1019] Step 5:

[1020] Server: Generates optimal health and mental health advice for users based on the analysis results of the generative AI model. For example, it generates advice such as "To improve sleep, avoid caffeine after dinner and practice meditation to relax before bed," and "Recommend hobbies to reduce stress."

[1021] Step 6:

[1022] Server: Provides generated health and mental health advice to the user's smartphone in real time. For example, if the generated advice is "avoid caffeine" and "try meditation," it is sent to the user's smartphone.

[1023] Step 7:

[1024] Device: The user's smartphone notifies them of the received advice and displays it to them. By checking the advice displayed on their smartphone, the user can incorporate it into their daily lives.

[1025] Step 8:

[1026] User: The user follows the advice displayed on their smartphone and changes their behavior, for example, avoiding caffeine before bedtime, practicing meditation to relax, or starting a hobby to reduce stress.

[1027] Step 9:

[1028] Server: Based on the health and mental health advice the user implements, the generative AI model then continuously monitors the data, collecting and analyzing new biometric and emotional data. This allows it to track improvements in the user's health and provide further advice. For example, it analyzes the data from several weeks after the initial implementation of the advice and generates next-step advice.

[1029] Through this series of steps, users receive individually tailored health and mental health advice that can be applied to their daily lives to achieve continuous health management.

[1030] Example 2

[1031] 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."

[1032] Conventional healthcare systems typically collect and analyze only a user's biometric data, making it difficult to provide advice that takes into account emotional changes and stress levels. As a result, they fail to achieve comprehensive improvement in health status and are unable to provide optimal health advice that meets the user's needs. It is also difficult to deliver personalized advice based on the user's lifestyle in real time. In contrast, the present invention aims to comprehensively analyze a user's emotional data and provide more personalized advice for health and mental health.

[1033] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1034] In this invention, the server includes means for collecting user emotional data using voice recognition, facial recognition, and text analysis, means for storing the user's biometric data and emotional data in a cloud database, and means for acquiring the user's biometric data and emotional data, generating prompt sentences, and inputting the prompt sentences into a generative AI model to generate health and mental health advice, thereby enabling personalized health advice to be provided in real time based on the user's biometric data and emotional data.

[1035] "User" refers to an individual who provides biometric and emotional data and receives health advice from the system.

[1036] "Biometric data" refers to data that indicates the user's physical condition, such as the user's daily activity level, heart rate, calories burned, sleep time, sleep quality, dietary content, and calories consumed.

[1037] "Emotional data" refers to data that indicates the user's emotions and mental state, obtained by analyzing the user's tone of voice, facial expression, text content, etc.

[1038] "Cloud database" refers to a database accessible via the Internet and a data storage system for storing users' biometric and emotional data.

[1039] A "generative AI model" is an artificial intelligence model generated by learning large amounts of data, and is used for data analysis and generating health advice.

[1040] A "prompt sentence" refers to an input sentence that provides a generative AI model with information that serves as the basis for analysis and advice generation.

[1041] "Health Advice" refers to specific suggestions or instructions provided to improve a user's physical or mental health based on the analysis of the user's biometric and emotional data.

[1042] "Real-time" refers to the time range in which users can provide data and receive results or advice almost immediately.

[1043] "Speech recognition" refers to the technology of converting voice data into text data and analyzing its content.

[1044] "Facial recognition" refers to the technology of analyzing facial image data and identifying facial expressions and features.

[1045] "Text analysis" refers to the technology of analyzing text data and identifying emotions and intentions from its content.

[1046] The present invention is a system that collects and analyzes a user's biometric and emotional data to provide individually optimized health and mental health advice. A concrete example is shown below.

[1047] Overall system configuration

[1048] The system consists of the following main components: the user's device (smartwatch or smartphone), a cloud database, a generative AI model, and a set of servers. The role and operation of each component are described in detail below.

[1049] Hardware and Software

[1050] Device:

[1051] The user uses a smartwatch to measure daily activity data (number of steps, heart rate, calories burned, etc.) and sleep data (sleep time, sleep quality, etc.). The smartphone supports the necessary data collection and has a built-in emotion engine. Specifically, the smartphone uses applications for speech recognition, facial recognition, and text analysis. For example, it uses the Google Speech-to-Text API for speech recognition, OpenCV and Dlib for facial recognition, and natural language processing libraries (e.g., NLTK and spaCy) for text analysis.

[1052] Cloud Database:

[1053] The database stores the user's biometric and emotional data, typically in a cloud-based database such as Amazon RDS or Google Cloud SQL.

[1054] Generative AI models:

[1055] The generative AI model uses deep learning frameworks, such as TensorFlow and PyTorch, to analyze a user's biometric and emotional data and generate health and mental health advice.

[1056] server:

[1057] The server communicates with a cloud database, performs data analysis, and runs a generative AI model, such as an EC2 instance from AWS (Amazon Web Services). The advice generation part uses a natural language generation model (e.g., GPT-3).

[1058] Data collection

[1059] Device:

[1060] The smartwatch worn by the user collects activity and sleep data and transmits the collected data in real time to a smartphone. The smartphone also uses an emotion engine to perform voice recognition, face recognition, and text analysis to collect emotion data, which is then uploaded to a cloud database.

[1061] Data analysis

[1062] server:

[1063] The server inputs the user's biometric and emotional data obtained from the cloud database into the generative AI model for analysis. The generative AI model contains algorithms that use this data to evaluate and predict the user's health condition, lifestyle, and even emotional state. As a concrete example, the prompt sentence is shown below.

[1064] "Analyze this user's average sleep time and stress level over the past week and generate appropriate health advice."

[1065] "Generate specific suggestions for maintaining health for the next week based on the user's biometric and emotional data."

[1066] Generating and providing health advice

[1067] server:

[1068] Based on the analysis results of the generative AI model, it generates individually optimized health and mental health advice, such as "avoid caffeine after dinner" or "practice meditation to relax before bed."

[1069] Device:

[1070] The generated advice is sent to the user's smartphone in real time and displayed as a push notification, allowing the user to check the notification and incorporate the advice into their daily life.

[1071] This system will provide users with personalized health advice based on their biometric and emotional data, helping them maintain and improve their physical and mental health.

[1072] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1073] Step 1:

[1074] Data collection

[1075] The device (smartwatch) collects the user's daily activity data (number of steps, heart rate, calories burned, etc.) and sleep data (sleep time, sleep quality, etc.). The smartphone receives this data via Bluetooth and then collects emotional data using voice recognition, facial recognition, and text analysis. Specifically, emotions are analyzed from the user's tone of voice when talking on the phone, facial expressions captured by the camera, and the content of text messages. Data input is real-time data from the smartwatch and smartphone, and output is accumulated in the form of collected data from each device.

[1076] Step 2:

[1077] Data transmission and storage

[1078] The biometric and emotional data collected by the device (smartphone) is sent to a cloud database via the internet. At this time, SSL / TLS encryption is used to ensure data security. The cloud server processes the received data and stores it in a database. Specifically, it is stored in a cloud-based database such as Amazon RDS. The data input is the biometric and emotional data sent from the smartphone, and the output is stored in the database.

[1079] Step 3:

[1080] Data Acquisition and Analysis

[1081] The server retrieves a specific user's data from a cloud database. This API call retrieves biometric and emotional data from the past week. The retrieved data is then input into a generative AI model. For example, a deep learning model built using TensorFlow or PyTorch is used. The generative AI model analyzes the data and evaluates the user's health and emotional state. The input is the data retrieved from the cloud database, and the output is the evaluation results analyzed by the AI ​​model.

[1082] Step 4:

[1083] Prompt generation and advice generation

[1084] The server generates a prompt sentence based on the analysis results of the generative AI model. The generated prompt sentence is input into a natural language generation model to generate specific health advice. Specific examples of prompt sentences include, "Please analyze this user's average sleep time and stress level over the past week and generate appropriate health advice," and "Please generate specific suggestions for maintaining health over the next week based on the user's biometric and emotional data." The input is the analysis results of the generative AI model and the prompt sentence, and the output is health and mental health advice.

[1085] Step 5:

[1086] Providing advice

[1087] The server sends the generated health advice to the user's smartphone. The smartphone displays the received advice to the user as a push notification. When the user taps the notification, a dedicated application opens and the user can check the detailed advice. The input is the generated health advice, and the output is the notification and displayed advice to the user.

[1088] Through these steps, the system can comprehensively analyze a user's biometric and emotional data and provide individually optimized health advice in real time.

[1089] (Application example 2)

[1090] 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."

[1091] In today's busy lifestyles, it is important for individual users to appropriately manage their own health and emotional state and improve their quality of life. However, conventional systems mainly provide health advice using only the user's biometric data, and are insufficient in providing specific content that takes emotional data into account or improving mental health. In particular, they lack the ability to optimize content according to the user's stress and emotional state, so more effective health management support is needed.

[1092] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1093] In this invention, the server includes means for collecting biometric data such as activity data, sleep data, and dietary data obtained from a user, means for storing the collected biometric data in a cloud database, means for acquiring the biometric data from the cloud database and analyzing the data using a generative AI model, means for generating optimal health advice for each user based on the analysis results, means for providing the generated health advice to the user's device in real time, means for performing voice recognition and facial recognition of the user and collecting emotional data, means for storing the collected emotional data in the cloud database, means for analyzing the emotional data and evaluating the user's mental health state, means for optimizing content to be viewed based on the user's health state and emotional state, and means for displaying the optimized content. This enables more personalized health management and mental health support based on both the user's biometric data and emotional data.

[1094] "Activity data" is information about the user's physical activity, such as the amount of exercise, number of steps, and calories burned each day.

[1095] "Sleep data" refers to information related to sleep, such as the user's sleep time, sleep quality, and sleep cycle.

[1096] "Dietary data" refers to information about meals such as the contents of meals consumed by the user, calorie intake, and nutritional information.

[1097] "Biometric data" refers to all information related to the user's physical condition and activities, such as activity data, sleep data, and dietary data.

[1098] A "cloud database" is a database for storing and managing data via the Internet.

[1099] A "generative AI model" is an algorithmic model that uses machine learning and artificial intelligence to generate specific outcomes or predictions from input data.

[1100] "Data analysis" is the process of extracting information from collected data using certain algorithms or models, and making evaluations and predictions.

[1101] "Health advice" refers to specific suggestions and instructions for improving and maintaining health provided to users based on the results of data analysis.

[1102] "Emotional data" is information about a user's emotional state collected through speech and facial recognition.

[1103] "Speech recognition" is a technology that analyzes the content of a user's speech and extracts information from the speech.

[1104] "Facial recognition" is a technology that analyzes a user's facial expressions and features to determine their emotions and identity.

[1105] "Mental health status" is an assessment of the user's emotional and mental state.

[1106] "Content optimization" is the process of providing the most appropriate content for a user to view based on their health and emotional state.

[1107] "Audio content" refers to the videos a user watches, the music they listen to, and other entertainment media.

[1108] "Providing in real time" means notifying and displaying analysis results and advice to users immediately on the spot.

[1109] The present invention provides a system for collecting and analyzing biometric and emotional data of a user, and then providing individually optimized health and mental health advice in real time. The system is configured as follows.

[1110] Data collection

[1111] The smartwatch, which is the user's device, continuously monitors daily activity data (e.g., steps taken, heart rate, calories burned) and sleep data (e.g., sleep duration and quality) and uploads the data to a cloud database via the smartphone. Additionally, smart glasses equipped with an emotion engine recognize the user's voice and facial expressions to collect emotion data.

[1112] Data storage

[1113] The server uses a cloud database to centrally manage and store the collected biometric and emotional data. For example, if a user walks 10,000 steps a day and gets five hours of sleep a night, but their facial expressions and voice indicate a high stress level, the data will be stored in the cloud database.

[1114] Data analysis

[1115] The server inputs the user's biometric and emotional data obtained from the cloud database into the generative AI model for data analysis. The generative AI model contains algorithms that evaluate the user's health and emotional state from this data. For example, it can derive a result such as "the average sleep time is less than six hours and the emotional stress level is high."

[1116] Generating and delivering health advice and content

[1117] The server generates optimal health advice for the user based on the analysis results of the generative AI model. Furthermore, it optimizes the content to be viewed based on the user's health and emotional state. For example, it may provide relaxing music and yoga videos along with health advice such as "avoid caffeine after dinner and practice meditation to relax before bed."

[1118] These advice and content are displayed in real time on the user's smartphone or smart glasses, allowing the user to review the displayed advice and incorporate it into their daily life.

[1119] Specific examples

[1120] For example, if a user's recent sleep data indicates that they haven't been getting enough sleep, the system could:

[1121] 1. Data collection: The smart watch collects the user's sleep data every night and sends it to the cloud database. At the same time, the smart glasses collect the user's daily emotional data and upload it to the cloud database.

[1122] 2. Data analysis: The server retrieves the user's sleep and emotional data from the cloud database and analyzes it using a generative AI model. The analysis results include "Over the past week, the average sleep time has been less than six hours, and the user is experiencing high emotional stress."

[1123] 3. Generating health advice: Based on the analysis results, the generative AI model generates health advice for the user, such as "avoid caffeine after dinner and practice relaxation meditation before going to bed." In addition, relaxing music and yoga videos are provided.

[1124] 4. Providing health advice: These advice and content will be displayed in real time on the user's smartphone or smart glasses.

[1125] Prompt Sentence Examples

[1126] If the user is in a "high stress" state on a daily basis, the following prompt sentence is input to the generative AI model:

[1127] User biometric data: Heart rate 90, sleep time 4 hours, steps 3000

[1128] Emotion data: Facial expressions indicate high stress levels, and voice indicates fatigue levels

[1129] Problem: Not getting enough sleep, high stress levels

[1130] Based on this data, it recommends relaxing content to help relieve stress and provides health advice.

[1131] As described above, the system of the present invention is capable of providing personalized health management and mental health support based on the user's health and emotional state.

[1132] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1133] Step 1:

[1134] The smartwatch and smart glasses, which are the user's devices, collect the user's activity data and emotional data and send it to a cloud database via the smartphone. Specifically, the smartwatch continuously monitors the user's steps, heart rate, and calories burned, while the smart glasses simultaneously recognize the user's voice and facial expressions to collect emotional data. The input at this stage is the user's daily activity data and emotional data, and the output is biometric data and emotional data stored in the cloud database.

[1135] Step 2:

[1136] The server retrieves the biometric and emotional data stored in the cloud database. Specifically, it uses the database API to extract the necessary data and stores it in temporary memory for analysis. The input at this stage is the biometric and emotional data in the cloud database, and the output is the data to be analyzed stored in the server's memory.

[1137] Step 3:

[1138] The server inputs biometric and emotional data into a generative AI model and performs data analysis. Specifically, it uses machine learning algorithms to evaluate and predict the user's health and emotional state. The input at this stage is the biometric and emotional data stored in the server's memory, and the output is an evaluation result indicating the user's health and emotional state.

[1139] Step 4:

[1140] The server generates optimal health advice and viewing content for the user based on the analysis results of the generative AI model. For example, relaxing music and yoga videos are selected along with health advice such as "avoid caffeine after dinner and practice meditation to relax before bed." The input at this stage is the analysis results of the generative AI model, and the output is health advice and optimized viewing content.

[1141] Step 5:

[1142] The server sends the generated health advice and audiovisual content to the user's device (smartphone or smart glasses) in real time. Specifically, it notifies the user of the advice and plays the audiovisual content using a notification API or the device's software interface. The input of this stage is the generated health advice and audiovisual content, and the output is the advice displayed on the user's device and the audiovisual content played.

[1143] Through these steps, a system is realized that provides personalized health management and mental health support based on the user's health and emotional state.

[1144] 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.

[1145] 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.

[1146] 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.

[1147] [Fourth embodiment]

[1148] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1149] 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.

[1150] 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).

[1151] 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.

[1152] 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.

[1153] 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).

[1154] 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.

[1155] 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.

[1156] 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.

[1157] 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.

[1158] 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.

[1159] 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.

[1160] 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."

[1161] The present invention is a system that provides individually optimized health advice by collecting and analyzing a user's biometric data.

[1162] Data collection

[1163] Device:

[1164] A user's smartwatch continuously monitors the user's daily activity data, sleep data, diet data, etc. This data is sent to the user's smartphone, which then uploads it to a cloud database. For example, if a user walks 10,000 steps in a day, the step count data will be recorded by the smartwatch, sent to the smartphone, and stored in the cloud database.

[1165] Data analysis

[1166] server:

[1167] The user's biometric data obtained from the cloud database is input into a generative AI model for data analysis. The generative AI model analyzes the user's health condition and lifestyle rhythm from the collected biometric data and identifies specific patterns and abnormalities. For example, it analyzes a user's sleep data over a week and derives the result that "the average sleep time is less than 6 hours."

[1168] Generating health advice

[1169] server:

[1170] Based on the results of the data analysis, the system generates optimal health advice for each user, such as recommendations to go to bed earlier to improve sleep or meditation to relax.

[1171] Providing health advice

[1172] server:

[1173] The generated health advice is provided to the user's device (smartphone) in real time. The user's smartphone notifies the user of the received advice and displays it to them. The user can then check this advice and incorporate it into their daily lives.

[1174] Specific examples

[1175] For example, if a user's recent sleep data indicates that they have not had enough sleep, the system operates as follows:

[1176] 1. Data Collection:

[1177] The user's smartwatch collects sleep data each night and sends it via their smartphone to a cloud database, which stores the user's sleep data.

[1178] 2. Data Analysis:

[1179] The server retrieves the user's sleep data from a cloud database and analyzes it using a generative AI model. The analysis result is that "the average sleep time over the past week was less than 6 hours."

[1180] 3. Health advice generation:

[1181] Based on the analysis results, the generative AI model generates advice for the user, such as "avoid caffeine after dinner and meditate to relax before going to bed."

[1182] 4. Providing health advice:

[1183] The server sends the generated advice to the user's smartphone, which then notifies the user of the received advice and displays it to them. The user can then check the displayed advice and apply it to their daily lives to improve their health.

[1184] In this way, the system of the present invention provides personalized advice based on the user's individual health condition and supports the user's health management. Through continuous data collection and analysis, the system provides a means for the user to maintain optimal health.

[1185] The processing flow will be explained below.

[1186] Step 1:

[1187] Device: The user's smartwatch continuously monitors daily activity data (e.g., steps taken, heart rate, calories burned, etc.), sleep data (e.g., sleep duration, sleep quality, etc.), and dietary data (e.g., dietary content, calories consumed, etc.). The collected data is sent to the user's smartphone.

[1188] Step 2:

[1189] Terminal: The smartphone receives the data sent from the smartwatch and uploads it to a cloud database via the internet, allowing the user's biometric data to be stored remotely.

[1190] Step 3:

[1191] Server: Retrieves user biometric data from a cloud database, including user activity, sleep, and dietary data.

[1192] Step 4:

[1193] Server: The acquired biometric data is input into the generative AI model and data analysis is performed. The generative AI model evaluates the user's health condition and lifestyle rhythm and identifies specific patterns and abnormalities. For example, analysis results may include information such as "average sleep time less than 6 hours" or "lack of exercise."

[1194] Step 5:

[1195] Server: Based on the analysis results of the generative AI model, it generates optimal health advice for each user, such as recommending going to bed earlier to improve sleep time or walking to increase exercise.

[1196] Step 6:

[1197] Server: The server sends the generated health advice to the user's device (smartphone) in real time. The user's smartphone notifies them of the advice received.

[1198] Step 7:

[1199] User: The user checks the health advice displayed on their smartphone and incorporates it into their daily routine, for example, reducing caffeine intake before bedtime or practicing meditation to relax.

[1200] Step 8:

[1201] Server: Based on the health advice provided by the user, the generative AI model then continuously monitors the data, collecting and analyzing new biometric data to track the user's health improvement and provide further advice.

[1202] Through this series of steps, users receive personalized health advice that can be applied to their daily lives to improve and maintain their health.

[1203] Example 1

[1204] 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."

[1205] In modern society, health management is a major focus, but many people face difficulties in accurately understanding their own health status and obtaining appropriate advice. In particular, there is a lack of systems that centrally manage daily activity, sleep, and dietary data and provide individually optimized health advice based on this data. This makes it difficult for users to find the lifestyle habits that are best for them.

[1206] 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.

[1207] In this invention, the server includes means for collecting biometric data such as activity data, sleep data, and dietary data obtained from users, means for storing the collected biometric data in a cloud database via a communication device, means for acquiring the biometric data from the cloud database and analyzing the data using a generative AI model, means for generating optimal health advice for each user based on the analysis results, means for providing the generated health advice to the user's mobile device in real time, and means for continuously monitoring changes in the biometric data and updating the advice, thereby enabling users to accurately understand their own health status and receive individually optimized health advice in real time.

[1208] "Activity data" refers to data related to the user's daily physical activity, such as the number of steps taken, calories burned, and exercise time.

[1209] "Sleep data" refers to data related to the user's nighttime sleep, such as the amount of sleep, quality of sleep, and sleep cycle.

[1210] "Dietary data" refers to data related to the daily meals a user takes, such as the contents of the meals, calories, and nutrients.

[1211] "Biometric data" refers to all data that indicates the user's health condition, such as the user's activity data, sleep data, and dietary data.

[1212] "Communication devices" are electronic devices for sending and receiving data, such as smartphones, tablets, and personal computers.

[1213] A "cloud database" is a remote data storage system accessed over the Internet.

[1214] A "generative AI model" is an algorithm or model that uses artificial intelligence technology to analyze, predict, and generate data.

[1215] "Data analysis" is the act of analyzing collected biometric data using statistical methods and machine learning to extract meaningful information.

[1216] "Health advice" refers to specific suggestions or guidelines provided to improve the user's health based on the analysis results.

[1217] A "mobile terminal" is a portable information and communication device such as a smartphone or tablet.

[1218] "Monitoring" is the act of continuously observing and collecting specific data or situations.

[1219] "Updating advice" refers to the regular reassessment of the health advice provided based on ongoing data monitoring, and the amendments or changes made as necessary.

[1220] The present invention is a system for supporting a user's health management. This system collects the user's biometric data, stores it in a cloud database, analyzes the data using a generative AI model, and provides health advice based on the analysis results.

[1221] Data collection

[1222] Device:

[1223] A user's smartwatch continuously monitors daily activity data, sleep data, diet data, etc. The smartwatch transmits this data to the user's smartphone, which then uploads the received data to a cloud database. For example, if a user walks 10,000 steps in a day, the step count data is recorded on the smartwatch and stored in the cloud database via the smartphone.

[1224] Data accumulation

[1225] server:

[1226] A cloud database continuously accumulates each user's biometric data, and a server retrieves the data from the cloud database and prepares it for input into the generative AI model.

[1227] Data analysis

[1228] server:

[1229] Biometric data obtained from a cloud database is input into a generative AI model for data analysis. The generative AI model analyzes the user's health status and lifestyle rhythms from the collected biometric data to discover specific patterns and abnormalities.

[1230] Example: A generative AI model analyzes a user's sleep data from the past week and concludes that their average sleep time is less than 6 hours.

[1231] Generating health advice

[1232] server:

[1233] Based on the results of the data analysis, the system generates optimal health advice for each user, such as recommendations to go to bed earlier to improve sleep or meditation to relax.

[1234] Providing health advice

[1235] server:

[1236] The generated health advice is provided to the user's device (smartphone) in real time. The smartphone notifies the user of the received advice and displays it to the user. The user can then check this advice and incorporate it into their daily life.

[1237] Example: The server generates advice such as "avoid caffeine after dinner and practice meditation to relax before going to bed" and sends it to the smartphone. The user's smartphone notifies the advice and displays it to the user.

[1238] Health advice application and updates

[1239] User:

[1240] The user reviews the provided health advice and applies it to their daily life. New biometric data is collected through the user's activities and stored again in the cloud database. The generative AI model continuously analyzes the data based on the new data and provides the latest health advice.

[1241] Specific examples

[1242] For example, if a user's recent sleep data indicates that they have not had enough sleep, the system operates as follows:

[1243] 1. Data collection: The user's smartwatch collects nightly sleep data and sends it to a cloud database via their smartphone. The cloud database stores the user's sleep data.

[1244] 2. Data analysis: The server retrieves the user's sleep data from the cloud database and analyzes it using a generative AI model. The analysis result is that "the average sleep time over the past week was less than 6 hours."

[1245] 3. Generating health advice: Based on the analysis results, the generative AI model generates advice for the user, such as "avoid caffeine after dinner and practice meditation to relax before going to bed."

[1246] 4. Providing health advice: The server sends the generated advice to the user's smartphone. The user's smartphone notifies the user of the received advice and displays it to them. The user can then review the displayed advice and apply it to their daily lives to improve their health.

[1247] Here is an example of a prompt to input to a generative AI model:

[1248] Analyze the user's sleep data from the last week. Find specific patterns or anomalies in the data and generate optimal health advice for the user. For example, if the average sleep time for the last week is less than 6 hours, avoid caffeine after dinner and practice relaxation meditation before bed.

[1249] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1250] Step 1: Data collection

[1251] Subject: Terminal

[1252] A user's smartwatch continuously monitors daily activity data, sleep data, dietary data, etc. Input from the smartwatch includes data on the user's steps, sleep time, calorie intake, etc. The smartwatch transmits this data to the user's smartphone. Specifically, the smartwatch uses sensors to record the user's steps and heart rate, and transfers this data to the smartphone via wireless communication such as Bluetooth. The smartphone receives this data and temporarily stores it locally.

[1253] Input: Activity data, sleep data, and food data from your smartwatch

[1254] Output: Data transfer to smartphone

[1255] Step 2: Data accumulation

[1256] Subject: Server

[1257] The smartphone uploads the collected data to a cloud database. Specifically, the smartphone sends the data to a cloud server via an internet connection. This data is then stored in a database managed by the cloud server.

[1258] Input: Biometric data sent from smartphone

[1259] Output: Data storage in cloud database

[1260] Step 3: Data analysis

[1261] Subject: Server

[1262] Biometric data obtained from a cloud database is input into a generative AI model for data analysis. Specifically, the server obtains a specific user's biometric data from the cloud database and inputs it into the generative AI model. The generative AI model then analyzes the data using machine learning algorithms to detect specific patterns and anomalies. For example, it analyzes sleep data from the past week and generates a result such as an average sleep time of less than six hours.

[1263] Input: Biometric data retrieved from a cloud database

[1264] Output: Analysis result (e.g., average sleep time is less than 6 hours)

[1265] Step 4: Generate health advice

[1266] Subject: Server

[1267] Based on the results of data analysis, optimal health advice is generated for each user. Specifically, the system receives the analysis results of the generative AI model and starts the process of generating health advice based on these results. For example, based on the analysis results, it generates specific advice such as "avoid caffeine after dinner" or "practice meditation to relax before bed."

[1268] Input: Data analysis results

[1269] Output: Health advice

[1270] Step 5: Providing health advice

[1271] Subject: Server

[1272] The generated health advice is provided to the user's device (smartphone) in real time. Specifically, the server sends the generated advice to the user's smartphone, which displays it as a notification. The user can then check the displayed advice and incorporate it into their daily life.

[1273] Enter: Health Advice

[1274] Output: Advice notification sent to user's smartphone

[1275] Step 6: Applying Advice and Updating

[1276] Subject: User

[1277] The user confirms the provided health advice and applies it to their daily life. New biometric data is collected through the user's activities and stored again in the cloud database. The generative AI model continuously analyzes the data based on the new data and provides the latest health advice. For example, if the user follows the advice to "avoid caffeine after dinner," subsequent data is collected again to monitor the effectiveness of the advice.

[1278] Input: New biometric data for the user

[1279] Output: Updated health advice

[1280] (Application example 1)

[1281] 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."

[1282] Conventional health management systems only collect and analyze a user's biometric data to provide health advice, but do not cover the user's specific lifestyle habits, particularly meal menu suggestions. This makes it difficult for users to select an appropriate meal menu based on their health condition, and users often struggle with meal choices, especially in their busy daily lives. Therefore, an objective of the present invention is to provide a system that not only provides health advice based on a user's biometric data, but also suggests optimal meal menus for each individual user.

[1283] 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.

[1284] In this invention, the server includes means for collecting biometric data such as activity data, sleep data, and dietary data obtained from users, means for storing the collected biometric data in a cloud database, means for acquiring the biometric data from the cloud database and analyzing the data using a generative AI model, means for generating optimal health advice for each user based on the analysis results, means for providing the generated health advice to the user's device in real time, and means for recommending optimal meal menus to the user based on the analysis results, thereby making it possible to propose meal menus that correspond to the health condition of each user.

[1285] "Activity data" is information relating to the physical activity of the user in their daily life, such as the amount of exercise, number of steps, and distance traveled.

[1286] "Sleep data" is information related to the user's bedtime, wake-up time, quality and depth of sleep, sleep cycle, and the like.

[1287] "Dietary data" refers to information relating to the types, amounts, nutritional components, and timing of meals consumed by the user.

[1288] "Biometric data" refers to data that encompasses a variety of information about the user's body, such as activity data, sleep data, and dietary data.

[1289] A "cloud database" is a remote data storage system that stores and accesses data over the Internet.

[1290] A "generative AI model" is an artificial intelligence model trained to generate personalized, optimal advice based on a user's biometric data.

[1291] "Data analysis" is the process of processing and analyzing collected biometric data to evaluate the user's health status and lifestyle rhythm.

[1292] "Health Advice" means specific instructions or recommendations for improving or maintaining health provided to each user based on the results of data analysis.

[1293] "Providing in real time" means that analysis results and health advice are transmitted to the user's device immediately or nearly instantly.

[1294] "Recommending meals" means suggesting suitable foods and meal plans based on the user's biometric data and health advice.

[1295] This invention is a system that collects biometric data such as activity data, sleep data, and dietary data of a user and provides health advice and meal menus optimized for each individual user based on that data. This system is realized by the following specific procedures and components.

[1296] Data collection

[1297] Device:

[1298] A user's smartwatch continuously monitors their daily activity data, sleep data, diet data, etc. This data is sent to the user's smartphone, which then uploads it to a cloud database. For example, if a user walks 10,000 steps in a day, the step count data is recorded by the smartwatch and stored in the cloud database via the smartphone.

[1299] Data analysis

[1300] server:

[1301] The user's biometric data obtained from the cloud database is input into a generative AI model for data analysis. The generative AI model analyzes the user's health condition and lifestyle rhythm from the collected biometric data and identifies specific patterns and abnormalities. For example, it analyzes a user's sleep data over a week and derives the result that "the average sleep time is less than 6 hours."

[1302] Health advice and meal menu generation

[1303] server:

[1304] Based on the results of the data analysis, the system generates optimal health advice for each user, such as recommendations to go to bed earlier to improve sleep or meditation to relax.

[1305] Furthermore, the server recommends optimal meal options for the user based on the analysis results. For example, if the health advice is "low-calorie diets are recommended," the server will suggest low-calorie meal options.

[1306] Providing health advice and meal menus

[1307] server:

[1308] The generated health advice and meal menus are provided to the user's device (smartphone) in real time. The user's smartphone notifies the user of the received advice and menus and displays them to the user. The user can check these advice and menus and incorporate them into their daily lives.

[1309] Specific examples

[1310] If a user's recent sleep data indicates that they have not had enough sleep, the system operates as follows.

[1311] 1. Data Collection:

[1312] The user's smartwatch collects sleep data each night and sends it via their smartphone to a cloud database, which stores the user's sleep data.

[1313] 2. Data Analysis:

[1314] The server retrieves the user's sleep data from a cloud database and analyzes it using a generative AI model. The analysis result is that "the average sleep time over the past week was less than 6 hours."

[1315] 3. Health advice and meal menu generation:

[1316] Based on the analysis results, the generative AI model generates advice for the user, such as "avoid caffeine after dinner in the evening and practice meditation to relax before bed." If a low-calorie diet is recommended, it will suggest meal options such as "salad," "grilled chicken," and "vegetable soup."

[1317] 4. Providing health advice and meal menus:

[1318] The server sends the generated advice and meal menu to the user's smartphone. The user's smartphone notifies the user of the received advice and menu and displays them to the user. The user can check the displayed advice and menu and apply them to their daily life to improve their health.

[1319] Prompt Sentence Examples

[1320] Example prompt (data processing approach)

[1321] average_sleep_hours: 5.5,

[1322] weekly_step_count: 70000,

[1323] calorie_intake: 2500,

[1324] stress_level: 3 Stress level scale from 1 to 5

[1325] In this way, the system of the present invention can support the user's overall health management by providing personalized advice and dietary suggestions based on the user's individual health condition.

[1326] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1327] Step 1:

[1328] Data collection

[1329] Input: User's daily activity data, sleep data, and diet data

[1330] The server and device collect the day's activity, sleep, and dietary data from the user's smartwatch. This data is recorded by the smartwatch and sent to the user's smartphone. The smartphone then uploads the received data to a cloud database. Through this process, all biometric data is stored in the cloud and made available for subsequent data analysis.

[1331] Output: User biometric data stored in a cloud database

[1332] Step 2:

[1333] Data Acquisition

[1334] Input: User biometric data stored in a cloud database

[1335] The server periodically retrieves the user's biometric data from the cloud database, ensuring that the most recent data is always available for analysis. This data includes the user's daily activity, sleep, and dietary data.

[1336] Output: Captured user biometric data

[1337] Step 3:

[1338] Data analysis

[1339] Input: Captured user biometric data

[1340] The server inputs the acquired biometric data into a generative AI model to analyze the user's health condition and lifestyle. The generative AI model detects specific patterns and abnormalities (e.g., lack of sleep, excessive activity, poor nutrition). For example, it can analyze a user's sleep data over a week and derive the result that "the average sleep time is less than 6 hours."

[1341] Output: Analysis results of health condition and lifestyle rhythm

[1342] Step 4:

[1343] Health advice and meal plan generation

[1344] Input: Analysis results of health status and lifestyle rhythm

[1345] Based on the analysis results, the server generates optimal health advice for each user. Specific advice includes recommendations for going to bed early to improve sleep and meditating to relax. Furthermore, based on this, the server recommends optimal meal menus for the user. For example, if a low-calorie diet is recommended, the server will suggest meal options such as "salad," "grilled chicken," and "vegetable soup."

[1346] Output: Health advice and meal menu

[1347] Step 5:

[1348] Providing health advice and meal menus

[1349] Input: Health advice and meal plans

[1350] The server sends the generated health advice and meal menu to the user's device (smartphone), which then notifies and displays the advice and menu to the user in real time, allowing the user to take appropriate action on the spot.

[1351] Output: Health advice and meal menu provided to the user

[1352] Step 6:

[1353] Action Feedback

[1354] Input: User's health advice and diet plan implementation results

[1355] After the user puts the health advice and dietary menu into practice, changes in activity, sleep, and dietary data are monitored again. This feedback data is again stored in the cloud database and used for the next data analysis. This allows the server to track changes in the user's behavior and generate more precise advice and menus.

[1356] Output: Feedback data

[1357] 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.

[1358] The present invention is a system that collects and analyzes a user's biometric and emotional data to provide individually optimized health and mental health advice.

[1359] Data collection

[1360] Device:

[1361] The user's smartwatch continuously monitors daily activity data (e.g., number of steps, heart rate, calories burned, etc.), sleep data (e.g., sleep duration, sleep quality, etc.), and dietary data (e.g., dietary content, calories consumed, etc.). This data is sent to the user's smartphone, which then uploads it to a cloud database. Meanwhile, the emotion engine collects the user's emotional data via devices such as smartphones. The emotional data is used to understand the user's emotional state using means such as voice recognition, face recognition, and text analysis.

[1362] Data storage

[1363] server:

[1364] The cloud database receives the biometric and emotional data sent from the smartwatch and emotion engine, stores them in the database, and manages them centrally. For example, if a user walks 10,000 steps in a day and sleeps for five hours during the night, the step count data and sleep data will be recorded by the smartwatch, and if the emotion engine recognizes that the user's stress level is high, the data will be stored in the cloud database.

[1365] Data analysis

[1366] server:

[1367] The user's biometric and emotional data obtained from a cloud database is input into a generative AI model for data analysis. The generative AI model contains algorithms that use this data to evaluate and predict the user's health condition, lifestyle, and even emotional state. For example, it analyzes a user's data over a week and derives the result that "the user's average sleep time is less than six hours and they are emotionally stressed."

[1368] Generating health advice

[1369] server:

[1370] Based on the analysis results of the generative AI model, optimal health and mental health advice is generated for the user, such as "recommending going to bed early to improve sleep and meditating to manage stress."

[1371] Providing health advice

[1372] server:

[1373] The generated health and mental health advice is provided to the user's device (smartphone) in real time. The user's smartphone notifies the user of this advice and displays it to them. The user can then review this advice and incorporate it into their daily lives.

[1374] Specific examples

[1375] For example, if a user's recent sleep data indicates that they have not been getting enough sleep, the system operates as follows:

[1376] 1. Data Collection:

[1377] The user's smartwatch collects sleep data every night and sends it to a cloud database via their smartphone, while the emotion engine collects the user's daily emotional data and uploads it to the cloud database.

[1378] 2. Data Analysis:

[1379] The server retrieves the user's sleep and emotional data from a cloud database and analyzes it using a generative AI model. The analysis results include "Over the past week, the average sleep time has been less than six hours, and emotional stress is high."

[1380] 3. Health advice generation:

[1381] Based on the analysis results, the generative AI model generates health and mental health advice for the user, such as "avoid caffeine after dinner and practice meditation to relax before going to bed."

[1382] 4. Providing health advice:

[1383] The server sends the generated advice to the user's smartphone, which notifies the user of the received advice and displays it to the user. The user can then review the displayed advice and apply it to their daily lives.

[1384] In this way, the system of the present invention provides personalized advice based on the user's health and emotional state, helping the user to manage their health and maintain and improve their mental health, thereby enabling comprehensive health management in the user's daily life.

[1385] The processing flow will be explained below.

[1386] Step 1:

[1387] Device: The user's smartwatch continuously monitors daily activity data (e.g., number of steps, heart rate, calories burned, etc.), sleep data (e.g., sleep duration, sleep quality, etc.), and dietary data (e.g., dietary content, calories consumed, etc.). The collected data is sent to the user's smartphone. The emotion engine also collects the user's emotion data using methods such as voice recognition, face recognition, and text analysis. For example, the emotion engine may recognize "high stress level" from text while the user is using a diary app on their smartphone.

[1388] Step 2:

[1389] Terminal: The smartphone uploads the biometric and emotional data sent from the smartwatch and emotion engine to a cloud database, allowing the user's biometric and emotional data to be centrally managed.

[1390] Step 3:

[1391] Server: Retrieves the user's activity data, sleep data, dietary data, and emotion data from the cloud database. For example, retrieves "User A's step count data, sleep data, dietary data, and emotion data for the past week" from the cloud database.

[1392] Step 4:

[1393] Server: The acquired data is input into the generative AI model and analyzed. The generative AI model contains algorithms that evaluate and predict the user's health condition, lifestyle, and emotional state. For example, the analysis results might reveal that "User A's average sleep time is less than six hours and that he is emotionally stressed."

[1394] Step 5:

[1395] Server: Generates optimal health and mental health advice for users based on the analysis results of the generative AI model. For example, it generates advice such as "To improve sleep, avoid caffeine after dinner and practice meditation to relax before bed," and "Recommend hobbies to reduce stress."

[1396] Step 6:

[1397] Server: Provides generated health and mental health advice to the user's smartphone in real time. For example, if the generated advice is "avoid caffeine" and "try meditation," it is sent to the user's smartphone.

[1398] Step 7:

[1399] Device: The user's smartphone notifies them of the received advice and displays it to them. By checking the advice displayed on their smartphone, the user can incorporate it into their daily lives.

[1400] Step 8:

[1401] User: The user follows the advice displayed on their smartphone and changes their behavior, for example, avoiding caffeine before bedtime, practicing meditation to relax, or starting a hobby to reduce stress.

[1402] Step 9:

[1403] Server: Based on the health and mental health advice the user implements, the generative AI model then continuously monitors the data, collecting and analyzing new biometric and emotional data. This allows it to track improvements in the user's health and provide further advice. For example, it analyzes the data from several weeks after the initial implementation of the advice and generates next-step advice.

[1404] Through this series of steps, users receive individually tailored health and mental health advice that can be applied to their daily lives to achieve continuous health management.

[1405] Example 2

[1406] 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."

[1407] Conventional healthcare systems typically collect and analyze only a user's biometric data, making it difficult to provide advice that takes into account emotional changes and stress levels. As a result, they fail to achieve comprehensive improvement in health status and are unable to provide optimal health advice that meets the user's needs. It is also difficult to deliver personalized advice based on the user's lifestyle in real time. In contrast, the present invention aims to comprehensively analyze a user's emotional data and provide more personalized advice for health and mental health.

[1408] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1409] In this invention, the server includes means for collecting user emotional data using voice recognition, facial recognition, and text analysis, means for storing the user's biometric data and emotional data in a cloud database, and means for acquiring the user's biometric data and emotional data, generating prompt sentences, and inputting the prompt sentences into a generative AI model to generate health and mental health advice, thereby enabling personalized health advice to be provided in real time based on the user's biometric data and emotional data.

[1410] "User" refers to an individual who provides biometric and emotional data and receives health advice from the system.

[1411] "Biometric data" refers to data that indicates the user's physical condition, such as the user's daily activity level, heart rate, calories burned, sleep time, sleep quality, dietary content, and calories consumed.

[1412] "Emotional data" refers to data that indicates the user's emotions and mental state, obtained by analyzing the user's tone of voice, facial expression, text content, etc.

[1413] "Cloud database" refers to a database accessible via the Internet and a data storage system for storing users' biometric and emotional data.

[1414] A "generative AI model" is an artificial intelligence model generated by learning large amounts of data, and is used for data analysis and generating health advice.

[1415] A "prompt sentence" refers to an input sentence that provides a generative AI model with information that serves as the basis for analysis and advice generation.

[1416] "Health Advice" refers to specific suggestions or instructions provided to improve a user's physical or mental health based on the analysis of the user's biometric and emotional data.

[1417] "Real-time" refers to the time range in which users can provide data and receive results or advice almost immediately.

[1418] "Speech recognition" refers to the technology of converting voice data into text data and analyzing its content.

[1419] "Facial recognition" refers to the technology of analyzing facial image data and identifying facial expressions and features.

[1420] "Text analysis" refers to the technology of analyzing text data and identifying emotions and intentions from its content.

[1421] The present invention is a system that collects and analyzes a user's biometric and emotional data to provide individually optimized health and mental health advice. A concrete example is shown below.

[1422] Overall system configuration

[1423] The system consists of the following main components: the user's device (smartwatch or smartphone), a cloud database, a generative AI model, and a set of servers. The role and operation of each component are described in detail below.

[1424] Hardware and Software

[1425] Device:

[1426] The user uses a smartwatch to measure daily activity data (number of steps, heart rate, calories burned, etc.) and sleep data (sleep time, sleep quality, etc.). The smartphone supports the necessary data collection and has a built-in emotion engine. Specifically, the smartphone uses applications for speech recognition, facial recognition, and text analysis. For example, it uses the Google Speech-to-Text API for speech recognition, OpenCV and Dlib for facial recognition, and natural language processing libraries (e.g., NLTK and spaCy) for text analysis.

[1427] Cloud Database:

[1428] The database stores the user's biometric and emotional data, typically in a cloud-based database such as Amazon RDS or Google Cloud SQL.

[1429] Generative AI models:

[1430] The generative AI model uses deep learning frameworks, such as TensorFlow and PyTorch, to analyze a user's biometric and emotional data and generate health and mental health advice.

[1431] server:

[1432] The server communicates with a cloud database, performs data analysis, and runs a generative AI model, such as an EC2 instance from AWS (Amazon Web Services). The advice generation part uses a natural language generation model (e.g., GPT-3).

[1433] Data collection

[1434] Device:

[1435] The smartwatch worn by the user collects activity and sleep data and transmits the collected data in real time to a smartphone. The smartphone also uses an emotion engine to perform voice recognition, face recognition, and text analysis to collect emotion data, which is then uploaded to a cloud database.

[1436] Data analysis

[1437] server:

[1438] The server inputs the user's biometric and emotional data obtained from the cloud database into the generative AI model for analysis. The generative AI model contains algorithms that use this data to evaluate and predict the user's health condition, lifestyle, and even emotional state. As a concrete example, the prompt sentence is shown below.

[1439] "Analyze this user's average sleep time and stress level over the past week and generate appropriate health advice."

[1440] "Generate specific suggestions for maintaining health for the next week based on the user's biometric and emotional data."

[1441] Generating and providing health advice

[1442] server:

[1443] Based on the analysis results of the generative AI model, it generates individually optimized health and mental health advice, such as "avoid caffeine after dinner" or "practice meditation to relax before bed."

[1444] Device:

[1445] The generated advice is sent to the user's smartphone in real time and displayed as a push notification, allowing the user to check the notification and incorporate the advice into their daily life.

[1446] This system will provide users with personalized health advice based on their biometric and emotional data, helping them maintain and improve their physical and mental health.

[1447] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1448] Step 1:

[1449] Data collection

[1450] The device (smartwatch) collects the user's daily activity data (number of steps, heart rate, calories burned, etc.) and sleep data (sleep time, sleep quality, etc.). The smartphone receives this data via Bluetooth and then collects emotional data using voice recognition, facial recognition, and text analysis. Specifically, emotions are analyzed from the user's tone of voice when talking on the phone, facial expressions captured by the camera, and the content of text messages. Data input is real-time data from the smartwatch and smartphone, and output is accumulated in the form of collected data from each device.

[1451] Step 2:

[1452] Data transmission and storage

[1453] The biometric and emotional data collected by the device (smartphone) is sent to a cloud database via the internet. At this time, SSL / TLS encryption is used to ensure data security. The cloud server processes the received data and stores it in a database. Specifically, it is stored in a cloud-based database such as Amazon RDS. The data input is the biometric and emotional data sent from the smartphone, and the output is stored in the database.

[1454] Step 3:

[1455] Data Acquisition and Analysis

[1456] The server retrieves a specific user's data from a cloud database. This API call retrieves biometric and emotional data from the past week. The retrieved data is then input into a generative AI model. For example, a deep learning model built using TensorFlow or PyTorch is used. The generative AI model analyzes the data and evaluates the user's health and emotional state. The input is the data retrieved from the cloud database, and the output is the evaluation results analyzed by the AI ​​model.

[1457] Step 4:

[1458] Prompt generation and advice generation

[1459] The server generates a prompt sentence based on the analysis results of the generative AI model. The generated prompt sentence is input into a natural language generation model to generate specific health advice. Specific examples of prompt sentences include, "Please analyze this user's average sleep time and stress level over the past week and generate appropriate health advice," and "Please generate specific suggestions for maintaining health over the next week based on the user's biometric and emotional data." The input is the analysis results of the generative AI model and the prompt sentence, and the output is health and mental health advice.

[1460] Step 5:

[1461] Providing advice

[1462] The server sends the generated health advice to the user's smartphone. The smartphone displays the received advice to the user as a push notification. When the user taps the notification, a dedicated application opens and the user can check the detailed advice. The input is the generated health advice, and the output is the notification and displayed advice to the user.

[1463] Through these steps, the system can comprehensively analyze a user's biometric and emotional data and provide individually optimized health advice in real time.

[1464] (Application example 2)

[1465] 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."

[1466] In today's busy lifestyles, it is important for individual users to appropriately manage their own health and emotional state and improve their quality of life. However, conventional systems mainly provide health advice using only the user's biometric data, and are insufficient in providing specific content that takes emotional data into account or improving mental health. In particular, they lack the ability to optimize content according to the user's stress and emotional state, so more effective health management support is needed.

[1467] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1468] In this invention, the server includes means for collecting biometric data such as activity data, sleep data, and dietary data obtained from a user, means for storing the collected biometric data in a cloud database, means for acquiring the biometric data from the cloud database and analyzing the data using a generative AI model, means for generating optimal health advice for each user based on the analysis results, means for providing the generated health advice to the user's device in real time, means for performing voice recognition and facial recognition of the user and collecting emotional data, means for storing the collected emotional data in the cloud database, means for analyzing the emotional data and evaluating the user's mental health state, means for optimizing content to be viewed based on the user's health state and emotional state, and means for displaying the optimized content. This enables more personalized health management and mental health support based on both the user's biometric data and emotional data.

[1469] "Activity data" is information about the user's physical activity, such as the amount of exercise, number of steps, and calories burned each day.

[1470] "Sleep data" refers to information related to sleep, such as the user's sleep time, sleep quality, and sleep cycle.

[1471] "Dietary data" refers to information about meals such as the contents of meals consumed by the user, calorie intake, and nutritional information.

[1472] "Biometric data" refers to all information related to the user's physical condition and activities, such as activity data, sleep data, and dietary data.

[1473] A "cloud database" is a database for storing and managing data via the Internet.

[1474] A "generative AI model" is an algorithmic model that uses machine learning and artificial intelligence to generate specific outcomes or predictions from input data.

[1475] "Data analysis" is the process of extracting information from collected data using certain algorithms or models, and making evaluations and predictions.

[1476] "Health advice" refers to specific suggestions and instructions for improving and maintaining health provided to users based on the results of data analysis.

[1477] "Emotional data" is information about a user's emotional state collected through speech and facial recognition.

[1478] "Speech recognition" is a technology that analyzes the content of a user's speech and extracts information from the speech.

[1479] "Facial recognition" is a technology that analyzes a user's facial expressions and features to determine their emotions and identity.

[1480] "Mental health status" is an assessment of the user's emotional and mental state.

[1481] "Content optimization" is the process of providing the most appropriate content for a user to view based on their health and emotional state.

[1482] "Audio content" refers to the videos a user watches, the music they listen to, and other entertainment media.

[1483] "Providing in real time" means notifying and displaying analysis results and advice to users immediately on the spot.

[1484] The present invention provides a system for collecting and analyzing biometric and emotional data of a user, and then providing individually optimized health and mental health advice in real time. The system is configured as follows.

[1485] Data collection

[1486] The smartwatch, which is the user's device, continuously monitors daily activity data (e.g., steps taken, heart rate, calories burned) and sleep data (e.g., sleep duration and quality) and uploads the data to a cloud database via the smartphone. Additionally, smart glasses equipped with an emotion engine recognize the user's voice and facial expressions to collect emotion data.

[1487] Data storage

[1488] The server uses a cloud database to centrally manage and store the collected biometric and emotional data. For example, if a user walks 10,000 steps a day and gets five hours of sleep a night, but their facial expressions and voice indicate a high stress level, the data will be stored in the cloud database.

[1489] Data analysis

[1490] The server inputs the user's biometric and emotional data obtained from the cloud database into the generative AI model for data analysis. The generative AI model contains algorithms that evaluate the user's health and emotional state from this data. For example, it can derive a result such as "the average sleep time is less than six hours and the emotional stress level is high."

[1491] Generating and delivering health advice and content

[1492] The server generates optimal health advice for the user based on the analysis results of the generative AI model. Furthermore, it optimizes the content to be viewed based on the user's health and emotional state. For example, it may provide relaxing music and yoga videos along with health advice such as "avoid caffeine after dinner and practice meditation to relax before bed."

[1493] These advice and content are displayed in real time on the user's smartphone or smart glasses, allowing the user to review the displayed advice and incorporate it into their daily life.

[1494] Specific examples

[1495] For example, if a user's recent sleep data indicates that they haven't been getting enough sleep, the system could:

[1496] 1. Data collection: The smart watch collects the user's sleep data every night and sends it to the cloud database. At the same time, the smart glasses collect the user's daily emotional data and upload it to the cloud database.

[1497] 2. Data analysis: The server retrieves the user's sleep and emotional data from the cloud database and analyzes it using a generative AI model. The analysis results include "Over the past week, the average sleep time has been less than six hours, and the user is experiencing high emotional stress."

[1498] 3. Generating health advice: Based on the analysis results, the generative AI model generates health advice for the user, such as "avoid caffeine after dinner and practice relaxation meditation before going to bed." In addition, relaxing music and yoga videos are provided.

[1499] 4. Providing health advice: These advice and content will be displayed in real time on the user's smartphone or smart glasses.

[1500] Prompt Sentence Examples

[1501] If the user is in a "high stress" state on a daily basis, the following prompt sentence is input to the generative AI model:

[1502] User biometric data: Heart rate 90, sleep time 4 hours, steps 3000

[1503] Emotion data: Facial expressions indicate high stress levels, and voice indicates fatigue levels

[1504] Problem: Not getting enough sleep, high stress levels

[1505] Based on this data, it recommends relaxing content to help relieve stress and provides health advice.

[1506] As described above, the system of the present invention is capable of providing personalized health management and mental health support based on the user's health and emotional state.

[1507] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1508] Step 1:

[1509] The smartwatch and smart glasses, which are the user's devices, collect the user's activity data and emotional data and send it to a cloud database via the smartphone. Specifically, the smartwatch continuously monitors the user's steps, heart rate, and calories burned, while the smart glasses simultaneously recognize the user's voice and facial expressions to collect emotional data. The input at this stage is the user's daily activity data and emotional data, and the output is biometric data and emotional data stored in the cloud database.

[1510] Step 2:

[1511] The server retrieves the biometric and emotional data stored in the cloud database. Specifically, it uses the database API to extract the necessary data and stores it in temporary memory for analysis. The input at this stage is the biometric and emotional data in the cloud database, and the output is the data to be analyzed stored in the server's memory.

[1512] Step 3:

[1513] The server inputs biometric and emotional data into a generative AI model and performs data analysis. Specifically, it uses machine learning algorithms to evaluate and predict the user's health and emotional state. The input at this stage is the biometric and emotional data stored in the server's memory, and the output is an evaluation result indicating the user's health and emotional state.

[1514] Step 4:

[1515] The server generates optimal health advice and viewing content for the user based on the analysis results of the generative AI model. For example, relaxing music and yoga videos are selected along with health advice such as "avoid caffeine after dinner and practice meditation to relax before bed." The input at this stage is the analysis results of the generative AI model, and the output is health advice and optimized viewing content.

[1516] Step 5:

[1517] The server sends the generated health advice and audiovisual content to the user's device (smartphone or smart glasses) in real time. Specifically, it notifies the user of the advice and plays the audiovisual content using a notification API or the device's software interface. The input of this stage is the generated health advice and audiovisual content, and the output is the advice displayed on the user's device and the audiovisual content played.

[1518] Through these steps, a system is realized that provides personalized health management and mental health support based on the user's health and emotional state.

[1519] 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.

[1520] 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.

[1521] 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.

[1522] 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.

[1523] 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.

[1524] 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.

[1525] 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).

[1526] 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.

[1527] 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."

[1528] 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.

[1529] 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).

[1530] 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.

[1531] 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.

[1532] 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.

[1533] 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.

[1534] 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.

[1535] 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.

[1536] 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.

[1537] 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.

[1538] 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.

[1539] 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.

[1540] The following is further disclosed regarding the above embodiment.

[1541] (Claim 1)

[1542] means for collecting biometric data, such as activity data, sleep data, and dietary data, obtained from a user;

[1543] a means for storing the collected biometric data in a cloud database;

[1544] A means for acquiring biometric data from a cloud database and performing data analysis using a generative AI model;

[1545] A means for generating optimal health advice for each user based on the analysis results;

[1546] The system includes a means for providing the generated health advice to a user's terminal in real time.

[1547] (Claim 2)

[1548] 2. The system according to claim 1, further comprising means for recommending specific lifestyle improvements based on the analysis of the user's biological data.

[1549] (Claim 3)

[1550] 2. The system according to claim 1, further comprising means for monitoring subsequent changes in the activity data, sleep data, and dietary data based on the health advice received by the user, and performing data analysis again.

[1551] "Example 1"

[1552] (Claim 1)

[1553] means for collecting biometric data, such as activity data, sleep data, and dietary data, obtained from a user;

[1554] A means for storing the collected biometric data in a cloud database via a communication device;

[1555] A means for acquiring biometric data from a cloud database and performing data analysis using a generative AI model;

[1556] A means for generating optimal health advice for each user based on the analysis results;

[1557] means for providing the generated health advice to a user's mobile device in real time;

[1558] The system includes means for continuously monitoring changes in said biometric data and updating the advice.

[1559] (Claim 2)

[1560] 10. The system of claim 1, further comprising a means for recommending specific lifestyle improvements based on the results of the data analysis.

[1561] (Claim 3)

[1562] 2. The system according to claim 1, further comprising means for monitoring subsequent changes in the activity data, sleep data, and dietary data based on the health advice received by the user, and performing data analysis again.

[1563] "Application Example 1"

[1564] (Claim 1)

[1565] means for collecting biometric data, such as activity data, sleep data, and dietary data, obtained from a user;

[1566] a means for storing the collected biometric data in a cloud database;

[1567] A means for acquiring biometric data from a cloud database and performing data analysis using a generative AI model;

[1568] A means for generating optimal health advice for each user based on the analysis results;

[1569] means for providing the generated health advice to a user's terminal in real time;

[1570] The system includes a means for recommending optimal meal menus to users based on the analysis results.

[1571] (Claim 2)

[1572] 2. The system according to claim 1, further comprising means for recommending specific lifestyle improvements based on the analysis of the user's biological data.

[1573] (Claim 3)

[1574] 2. The system according to claim 1, further comprising means for monitoring subsequent changes in the activity data, sleep data, and dietary data based on the health advice received by the user, and performing data analysis again.

[1575] "Example 2: Combining Emotion Engines"

[1576] (Claim 1)

[1577] means for collecting biometric data, such as activity data, sleep data, and dietary data, obtained from a user;

[1578] a means for storing the collected biometric data in a cloud database;

[1579] A means for acquiring biometric data from a cloud database and performing data analysis using a generative AI model;

[1580] A means for generating optimal health advice for each user based on the analysis results;

[1581] means for providing the generated health advice to a user's terminal in real time;

[1582] means for collecting user emotion data using voice recognition, facial recognition, and text analysis;

[1583] means for storing the user's biometric data and emotional data in a cloud database;

[1584] A system including means for acquiring biometric and emotional data of a user, generating prompt sentences to input into a generative AI model, and generating health and mental health advice.

[1585] (Claim 2)

[1586] 10. The system according to claim 1, further comprising means for recommending specific lifestyle improvements based on the analysis of the user's biometric data and emotional data.

[1587] (Claim 3)

[1588] 2. The system according to claim 1, further comprising means for monitoring subsequent changes in the activity data, sleep data, dietary data, and emotion data based on the health advice received by the user, and performing data analysis again.

[1589] "Application example 2 when combining emotion engines"

[1590] (Claim 1)

[1591] means for collecting biometric data, such as activity data, sleep data, and dietary data, obtained from a user;

[1592] a means for storing the collected biometric data in a cloud database;

[1593] A means for acquiring biometric data from a cloud database and performing data analysis using a generative AI model;

[1594] A means for generating optimal health advice for each user based on the analysis results;

[1595] means for providing the generated health advice to a user's terminal in real time;

[1596] A means for performing voice recognition and facial recognition of a user and collecting emotional data;

[1597] a means for storing the collected emotion data in a cloud database;

[1598] means for analyzing the emotion data and assessing the user's mental health;

[1599] A means for optimizing content viewing based on the user's health and emotional state;

[1600] A way to display optimized content

[1601] A system including:

[1602] (Claim 2)

[1603] 10. The system according to claim 1, further comprising means for recommending specific lifestyle improvements based on the analysis of the user's biometric data and emotional data.

[1604] (Claim 3)

[1605] The system of claim 1, further comprising means for monitoring subsequent changes in activity data, sleep data, and dietary data based on the health advice and optimized content received by the user, and performing data analysis again. [Explanation of symbols]

[1606] 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. means for collecting biometric data, such as activity data, sleep data, and dietary data, obtained from a user; a means for storing the collected biometric data in a cloud database; A means for acquiring biometric data from a cloud database and performing data analysis using a generative AI model; A means for generating optimal health advice for each user based on the analysis results; The system includes a means for providing the generated health advice to a user's terminal in real time.

2. The system according to claim 1, further comprising means for recommending specific lifestyle improvements based on the analysis of the user's biological data.

3. The system according to claim 1 , further comprising means for monitoring subsequent changes in the activity data, sleep data, and dietary data based on the health advice received by the user, and performing data analysis again.

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