System

Abstract

A system is provided.SOLUTION: A system including means for collecting biological information of a user, means for transmitting the collected biological information to an edge server, means for analyzing the biological information in the edge server and monitoring and predicting a health condition, means for generating individual health advice on the basis of an analysis result and notifying the user, means for proposing a health product, means for anonymizing the biological information and selling the biological information to an external organization, means for constantly monitoring position information of the user and detecting an abnormal behavior pattern, and means for confirming safety in a disaster.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, personal health management is becoming increasingly important. However, conventional health management systems do not adequately collect and analyze biometric information in real time, making it difficult to provide health advice tailored to each individual user. Furthermore, they do not adequately detect abnormal behavior using location information or quickly confirm the safety of individuals in the event of a disaster. Furthermore, when it comes to external sales of collected health data, there are no methods in place for anonymizing the data or for securely exchanging it. Therefore, a new system is needed to strengthen user health management and promote preventive medicine. [Means for solving the problem]

[0005] This invention solves the aforementioned problems using the following means. First, a smartphone is used as a means for collecting the user's biometric information. The smartphone continuously acquires biometric information such as heart rate, step count, and sleep patterns and transmits it to an edge server using an encrypted communication protocol. The edge server then analyzes the received biometric information in real time using an AI algorithm to monitor and predict the user's health status. Based on the analysis results, the server generates personalized health advice for the user and notifies the smartphone. It also suggests health products and services to help improve the user's health. The invention also provides a means for anonymizing the collected health data and selling it to medical institutions and pharmaceutical companies. This allows the data to be used for research and service development while maintaining data security. Furthermore, the system includes a means for constantly monitoring the user's location information, notifying the user if an abnormal behavior pattern is detected, and quickly confirming the user's safety in the event of a disaster. This enables comprehensive support for the user's health management and safety.

[0006] "Biometric information" refers to physical data such as a user's heart rate, number of steps, sleep patterns, and body temperature.

[0007] An "edge server" is a server for analyzing data collected from users. It is installed close to users and improves the speed and efficiency of data processing.

[0008] A "smartphone" is a portable electronic device with communication capabilities, and is a device that has the means to collect and transmit biometric information through applications.

[0009] "Analysis" refers to the process of processing the biometric information received by the edge server using AI algorithms to derive the user's health condition and its predictions.

[0010] "Health advice" refers to recommendations and guidelines for maintaining and improving health that are provided to users based on the analysis results from the edge server.

[0011] "Health products" refers to goods and services aimed at maintaining or improving the user's health, including supplements, fitness equipment, and gym use.

[0012] "Anonymization" is the process of converting data so that it cannot identify individual users, and is a technology that enables secure data sharing.

[0013] "External sales" refers to the act of selling anonymized health data to third parties such as medical institutions and pharmaceutical companies.

[0014] "Location information" is data used to identify a user's current location using GPS or other technologies.

[0015] "Abnormal behavior patterns" refer to user actions or movement trajectories that differ from normal behavior, and are an indicator for detecting potential risks.

[0016] "Disaster safety confirmation" is the process of checking a user's safety based on their location information and notifying family and friends when a natural disaster or emergency occurs. [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] This invention is a system that manages a user's health condition in real time and provides appropriate health advice. This system consists of a device (smartphone) that collects the user's biometric information, a server (edge ​​server) that analyzes and predicts that information, and an interface that notifies the user.

[0039] System Overview

[0040] This system first collects the user's biometric information. This is performed by a device (smartphone), which continuously collects biometric information such as heart rate, number of steps, sleep patterns, and body temperature. This data is then sent to a server (edge ​​server) using an encrypted communication protocol.

[0041] The edge server is equipped with advanced AI algorithms that analyze the received biometric information in real time. This analysis allows the user's current health status to be monitored and future health risks to be predicted. Based on the analysis results, personalized health advice supervised by experts is generated and communicated to the user.

[0042] The server also suggests health products, selecting appropriate products and services based on the user's health condition and notifying the device. It also has the ability to anonymize the collected health data and safely sell it to external organizations (medical institutions, pharmaceutical companies, etc.). This allows the data to be used for research and development of new health services while maintaining its security.

[0043] Location monitoring is also a very important function. The device constantly obtains the user's location information and sends it to a server. The server analyzes this data and detects abnormal behavioral patterns. If an abnormality is detected, the user is immediately notified. In addition, in the event of a disaster, the location information can be used to quickly check the user's safety and automatically notify family and friends.

[0044] Specific examples

[0045] 1. Daily health management

[0046] User A starts jogging at 7:00 a.m. The device measures the heart rate and number of steps in real time and sends the data to the edge server.

[0047] The server analyzes the received data, generates advice on the appropriate timing for hydration and jogging pace, and notifies the device.

[0048] User A receives the notification and can continue jogging in a healthy manner based on the advice.

[0049] 2. Long-term sedentary monitoring

[0050] User B works in an office while sitting for a long time. The terminal measures the time spent sitting and sends the data to the server.

[0051] The server detects that the user has been sitting still for a long period of time and generates and sends a notification to the device to encourage the user to stand up and stretch periodically.

[0052] User B receives the notification and stretches for health reasons.

[0053] 3. Sleep monitoring

[0054] User C goes to bed and the device measures his / her sleep patterns. The data is sent to the server for analysis.

[0055] The server analyzes the time spent in deep sleep and the quality of sleep, generates advice to improve performance the next day, and notifies the device.

[0056] User C receives the advice and takes action to improve their sleep quality.

[0057] 4. Confirming safety during disasters

[0058] When a disaster such as an earthquake occurs, user D is in the affected area.

[0059] The device sends its location information to a server, which then verifies the user's location in the disaster area.

[0060] The server checks the safety of user D and automatically notifies family and friends.

[0061] In this way, this system manages the user's health in real time and helps improve their health through appropriate advice and product suggestions.

[0062] The processing flow will be explained below.

[0063] Step 1:

[0064] The device continuously collects the user's biometric information (heart rate, number of steps, sleep patterns, body temperature, etc.) using sensors.

[0065] Step 2:

[0066] The device uses a communication protocol (e.g., HTTPS) to encrypt the collected biometric information and send it to the edge server. The transmitted data is given a timestamp.

[0067] Step 3:

[0068] The server stores the received data in a database in NoSQL format, with a structure suitable for real-time analysis.

[0069] Step 4:

[0070] The server feeds the data into AI algorithms for real-time analysis, including predictive models using historical data.

[0071] Step 5:

[0072] The server evaluates the user's current health status based on the analysis results and identifies predicted health risks.

[0073] Step 6:

[0074] The server generates health advice that has been supervised by experts and transmits the advice to the terminal.

[0075] Step 7:

[0076] The device will notify the user of the health advice it receives, which will be displayed as a pop-up message and / or an audio alert.

[0077] Step 8:

[0078] The server then recommends appropriate health products and services based on the user's health status, and the recommendations are sent to the device.

[0079] Step 9:

[0080] The device displays suggestions for health products and services to users, including links to purchase products and gym discounts.

[0081] Step 10:

[0082] The server anonymizes the collected biometric information and prepares it for sale to external organizations (medical institutions, pharmaceutical companies, etc.). The sales data does not include any information that can identify individual users.

[0083] Step 11:

[0084] The device continuously collects the user's location information and sends it to a server, which is also encrypted.

[0085] Step 12:

[0086] The server analyzes location information in real time and detects abnormal behavior that deviates from normal patterns of behavior.

[0087] Step 13:

[0088] If the server detects any abnormal behavioral patterns, it will immediately alert the user and, if necessary, notify emergency contacts.

[0089] Step 14:

[0090] In the event of a disaster, the server will quickly check the safety of registered users based on their location information, and the results will be automatically sent to registered family and friends.

[0091] Through these steps, the system can manage users' health status in real time, provide appropriate health advice, and respond quickly in emergencies.

[0092] Example 1

[0093] 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."

[0094] In modern society, it is important to manage a user's health status in real time and provide appropriate individualized health guidance. However, current systems have difficulty integrating multiple functions, such as collecting and analyzing biometric information, generating health guidance, and confirming safety in the event of a disaster. Therefore, there is a need to efficiently manage both the user's health and confirm their safety. Furthermore, security and privacy protection of biometric information are also major issues, and a system for utilizing data while satisfying these needs is required.

[0095] 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.

[0096] In this invention, the server includes means for collecting user biometric information, means for transmitting the collected biometric information to an edge device, means for analyzing the biometric information at the edge device and monitoring and predicting the health status, means for generating individual health advice based on the analysis results and notifying the user, means for suggesting health products, means for anonymizing the biometric information and providing it to an external organization, means for constantly monitoring the user's location information and detecting abnormal movement patterns, and means for confirming the user's safety in the event of a disaster. This enables the secure collection and analysis of the user's biometric information, real-time health management, and rapid safety confirmation in the event of a disaster.

[0097] "User" means an individual who uses the system to manage their own health status.

[0098] "Biometric information" refers to data about the body, such as heart rate, number of steps taken, sleep patterns, and body temperature.

[0099] A "means" is a method or device used to achieve a particular purpose.

[0100] "Edge devices" refers to multiple devices, typically including servers, that process and analyze collected data.

[0101] "Analysis" is the process of examining collected biometric information in detail and finding meaning.

[0102] "Health" refers to a user's physical and mental state, as indicated by biometric information such as heart rate and body temperature.

[0103] "Health Guidance" refers to specific advice or suggestions to help users improve or maintain their health.

[0104] "Notification" means a message or alert intended to convey information from the system to the user.

[0105] "Health products" refers to goods and services that help users improve or maintain their health.

[0106] "Anonymization" is the process of processing data so that it cannot be used to identify individuals.

[0107] An "external institution" is an external organization that uses a user's biometric information, such as a medical institution or pharmaceutical company.

[0108] "Location Information" means data about a user's geographic location.

[0109] "Abnormal behavior patterns" are data that show movements or situations that differ from normal behavior.

[0110] "Safety confirmation during a disaster" refers to the process of confirming the safety of users when a disaster such as an earthquake or typhoon occurs.

[0111] This invention is a system for managing a user's health status in real time and providing appropriate health guidance. The system mainly consists of a terminal that collects the user's biometric information, an edge device that analyzes the information, and an interface that sends notifications.

[0112] First, the system collects the user's biometric information through a mobile device such as a smartphone, including heart rate, steps taken, sleep patterns, body temperature, etc. The smartphone records the user's condition in real time using data from built-in sensors and external devices.

[0113] The collected biometric information is then securely transmitted to the edge device (server) using encrypted communication protocols (e.g., SSL or TLS). The server then analyzes the received data using advanced AI algorithms (e.g., TENSORFLOW® or PyTorch). This allows the user's current health status to be monitored and future health risks to be predicted.

[0114] Based on the analysis results, the server generates a personalized health guide, supervised by an expert, including specific advice on exercise, diet, rest, etc. The advice is then encrypted and sent back to the smartphone, where it is displayed to the user as a push notification or in-app alert.

[0115] In addition, the server will suggest health products and services tailored to the user's health condition, including supplements, fitness programs, medical devices, etc. The suggestions will also be sent to the device, providing the user with the opportunity to receive appropriate products and services.

[0116] The collected biometric information is anonymized by the server and provided to external organizations such as medical institutions and pharmaceutical companies, which makes it possible to provide data for new health services and research while maintaining data security and privacy.

[0117] Smartphones also constantly collect the user's location information and send it to a server. The server analyzes the location information and promptly notifies the user if it detects abnormal behavior patterns or an emergency. This is particularly useful in the event of a disaster such as an earthquake or typhoon, as it allows for quick safety confirmation and automatically sends safety confirmation notifications to the user's family and friends.

[0118] As a concrete example, consider the case where User A starts jogging at 7:00 AM. The device measures the heart rate and number of steps in real time and sends them to the edge device. The server analyzes this data, generates advice on appropriate hydration timing and jogging pace, and notifies the device. User A receives this advice and continues jogging in a healthy manner.

[0119] Additionally, personalized advice can be generated by inputting prompts like the following into the generative AI model:

[0120] "Generate optimal health advice based on the following user situation: User A started jogging at 7 AM, their current heart rate is 150 bpm, and they have taken 1500 steps."

[0121] In this way, the system manages the user's health condition in real time and provides appropriate advice and product suggestions, thereby helping to improve the user's health and ensure safety.

[0122] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0123] Step 1:

[0124] The user launches the dedicated app and begins their daily activities. For example, to start jogging, they launch the app and press the start button.

[0125] Input: User operation (launching an app, pressing the start button)

[0126] Output: The app's sensors are activated and data collection begins.

[0127] Step 2:

[0128] The device (smartphone) collects biometric information such as heart rate, number of steps, sleep patterns, and body temperature in real time from built-in sensors and external devices. The sensors detect the user's physical movements and acquire the data.

[0129] Input: User's biometric information (heart rate, steps, sleep pattern, body temperature)

[0130] Output: A dataset of collected biometric information

[0131] Step 3:

[0132] The collected biometric information is securely transmitted to the edge device (server) using encrypted communication protocols (e.g., SSL or TLS). The device periodically divides the data into packets and transmits them to the server.

[0133] Input: Biometric dataset

[0134] Output: Encrypted data packet

[0135] Step 4:

[0136] The server stores the received biometric information in a database and analyzes it using advanced AI algorithms (such as TensorFlow and PyTorch). The server calculates heart rate fluctuations and step rhythms to check for abnormalities.

[0137] Input: Encrypted data packet

[0138] Output: Analysis results (heart rate fluctuations, step rhythm, presence or absence of abnormalities)

[0139] Step 5:

[0140] Based on the analysis results, the server evaluates the user's current health status and predicts future health risks, for example, using models to predict elevated blood pressure and abnormal heart rate.

[0141] Input: Analysis results (heart rate fluctuations, step rhythm, presence or absence of abnormalities)

[0142] Output: Health status assessment and health risk prediction

[0143] Step 6:

[0144] The server generates personalized health advice supervised by experts based on the health status assessment and health risk prediction results. Using a generative AI model, it creates the most appropriate advice for the user in text format.

[0145] Input: Health status assessment and health risk prediction

[0146] Output: Health advice text message

[0147] Step 7:

[0148] The server encrypts the generated health advice and sends it to the device, which then decrypts the received message and notifies the user.

[0149] Input: Health advice text message

[0150] Output: Encrypted health advice message

[0151] Step 8:

[0152] The device will provide users with health guidance via push notifications and in-app alerts, such as messages like "stay hydrated."

[0153] Input: Encrypted health advice message

[0154] Output: A notification message that is displayed to the user.

[0155] Step 9:

[0156] The server will suggest health products and services based on the user's health status, and this information will also be encrypted and sent to the device, where it will be displayed as a notification.

[0157] Input: Health status assessment and health risk prediction

[0158] Output: Encrypted health product suggestion message

[0159] Step 10:

[0160] The server anonymizes the biometric information and safely provides it to external organizations such as medical institutions and pharmaceutical companies. The information is then anonymized and stored in a database.

[0161] Input: Biometric dataset

[0162] Output: Anonymized dataset

[0163] Step 11:

[0164] The device constantly acquires the user's location information and periodically sends it to the server, which then monitors for abnormal behavior patterns and notifies the user if necessary.

[0165] Input: User's location

[0166] Output: A dataset of location information

[0167] Step 12:

[0168] When a disaster such as an earthquake or typhoon occurs, the device sends its location information to the server in real time, and the server quickly checks the user's safety and automatically notifies family and friends.

[0169] Input: Location information at the time of disaster

[0170] Output: Safety confirmation notification

[0171] (Application example 1)

[0172] 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."

[0173] In recent years, users have become increasingly interested in managing their health, creating a demand for systems that provide advice tailored to individual health conditions. However, existing systems only collect and analyze users' biometric information, making it difficult to optimally reflect it in their actual social lives and consumer behavior. Furthermore, they lack safety confirmation functions in the event of a disaster, delaying the provision of comprehensive health management systems. Therefore, a new system is needed that utilizes users' health information to support their behavior in the real world.

[0174] 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.

[0175] In this invention, the server includes means for collecting user biometric information, means for transmitting the collected biometric information to an edge server, means for analyzing the biometric information in the edge server and monitoring and predicting the health status, means for generating personalized health advice based on the analysis results and notifying the user, means for proposing health products, means for anonymizing the biometric information and selling it to an external organization, means for constantly monitoring the user's location information and detecting abnormal behavioral patterns, means for confirming the user's safety in the event of a disaster, and means for proposing products in a physical store based on the user's health information. This enables appropriate behavior and product suggestions in the real world based on the user's health information.

[0176] "User biometric information" refers to numerical data related to a person's physical condition, such as heart rate, number of steps, body temperature, and sleep patterns.

[0177] An "edge server" is a server that receives data sent by users in real time and performs analysis and predictions.

[0178] "Health monitoring and prediction tools" refers to algorithms and programs that analyze collected biometric information to determine current health status and predict future risks.

[0179] "Individualized health advice" is advice based on the analysis of a user's biometric information, providing suggestions for improving behaviors and habits that are optimal for each individual's health condition.

[0180] "Health product suggestion" is the process of recommending appropriate products and services to users according to their analyzed health status.

[0181] "Biometric de-identification" is the process of removing or modifying personal information from collected biometric data so that it cannot be used to identify individual users.

[0182] "External organizations" are third-party organizations, such as medical institutions and pharmaceutical companies, that conduct research or provide services using users' biometric information.

[0183] "Means for constantly monitoring location information" refers to technologies and devices that continuously obtain the user's current location and identify abnormal behavior or trends during disasters.

[0184] "Abnormal behavior" refers to behavior or movement patterns that are different from normal and are predicted to pose a risk to the user's health or safety.

[0185] "Means for checking safety during a disaster" is a system that uses the user's location information to confirm the user's safety when a disaster occurs and notifies family members and other relevant parties of that information.

[0186] The "means of making product suggestions within a physical store" is a system that analyzes a user's health information and makes real-time suggestions about products that are recommended for purchase within the store where the user is currently located.

[0187] To implement the present invention, a system is constructed and operated in the following manner.

[0188] System configuration

[0189] 1. Data collection and transmission

[0190] The user uses a device (smartphone) to collect biometric information. This smartphone is equipped with a heart rate sensor and an acceleration sensor to acquire biometric information such as heart rate, number of steps, sleep pattern, and body temperature. The collected data is sent to the edge server using an encrypted communication protocol (e.g., TLS / SSL).

[0191] 2. Data Analysis

[0192] The edge server analyzes the received data using AI algorithms (e.g., TensorFlow, PyTorch, etc.), which monitors the user's current health status and predicts future health risks. The analysis results are updated in real time.

[0193] 3. Advice and Notice

[0194] Based on the analysis results, the edge server generates individual health advice, which is then sent to the user's smartphone via push notification. The advice helps users improve their health behavior and lifestyle habits.

[0195] 4. Health product suggestions

[0196] The edge server selects products and services suited to the user's health condition, and the user receives real-time recommendations, including health foods and fitness products.

[0197] 5. Anonymization and sale of data

[0198] The biometric information will be anonymized and sold to external organizations (e.g., medical institutions, pharmaceutical companies, etc.) This process will use secure data anonymization techniques to ensure the protection of personal information.

[0199] 6. Location Monitoring and Anomaly Detection

[0200] The user's smartphone constantly collects location information and sends it to the edge server. The edge server analyzes this data and detects abnormal behavioral patterns. If an abnormality is detected, the user is immediately notified. In addition, in the event of a disaster, the user's safety is quickly confirmed based on the location information, and family and friends are automatically notified.

[0201] 7. Product proposals in physical stores

[0202] The app will suggest products based on health information while users are in physical stores. These suggestions are optimized based on the user's current location within the store and sent to the user's smartphone. For example, a user with high stress levels will be recommended products with a relaxation effect.

[0203] Examples and prompts

[0204] Examples:

[0205] User A visits a physical store called "Health Shop." User A's smartphone collects real-time information such as heart rate, number of steps, and body temperature. The edge server analyzes this information and detects that User A's recent stress level has been high. Based on this, the server recommends relaxing herbal teas and stress-relieving products sold in the store. This information is displayed on User A's smartphone via push notification, allowing User A to purchase products based on that information.

[0206] Example prompt sentence:

[0207] "I want to create a smartphone app that collects heart rate, steps, and body temperature in real time, analyzes the user's health status, and recommends appropriate health products. If it detects that the user is feeling stressed, it should suggest products that are effective for relieving stress in a physical store."

[0208] This will enable appropriate actions and product suggestions in the real world based on the user's health information, improving the user's quality of life.

[0209] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0210] Step 1:

[0211] The user's smartphone collects biometric information such as heart rate, number of steps, and body temperature using the smartphone's built-in heart rate sensor and accelerometer. The input here is the user's real-time biometric information, and the output is the collected biometric data.

[0212] Step 2:

[0213] The device sends the collected biometric data to the edge server using an encrypted communication protocol (e.g., TLS / SSL). With this operation, the input is the data sent through the encryption protocol, and the output is the secure data transfer to the edge server.

[0214] Step 3:

[0215] The edge server analyzes the received biometric data using a generative AI model (e.g., TensorFlow or PyTorch). The input is the biometric data received by the edge server, and the output is the user's current health status and predicted future health risks.

[0216] Step 4:

[0217] The server generates individual health advice based on the analysis results. This advice generation is based on an algorithm supervised by experts. The input here is the health status analysis data, and the output is health advice to be notified to the user.

[0218] Step 5:

[0219] The device receives health advice via push notifications from the server. These notifications include specific behavioral guidelines and recommended products. The input is advice data based on the analysis results, and the output is a notification to the user.

[0220] Step 6:

[0221] The server suggests health products and related services that are appropriate for the user's health status using a real-time recommendation algorithm, whose input is the user's current health status data and whose output is a list of recommended health products.

[0222] Step 7:

[0223] The terminal displays recommended health products and services to support in-store activities. The input here is the recommended product information sent from the server, and the output is the product information displayed on the user's smartphone screen.

[0224] Step 8:

[0225] The server anonymizes the biometric data and sells it to external organizations. This process uses data anonymization tools and techniques. The input is biometric data and the output is anonymized data.

[0226] Step 9:

[0227] The device constantly monitors the user's location information and sends it to the server. This operation uses location information technologies such as GPS and Wi-Fi. The input is the user's location data, and the output is location information data sent to the edge server.

[0228] Step 10:

[0229] The server analyzes the received location data and detects abnormal behavioral patterns. In the event of a disaster, it also checks the user's safety and notifies relevant parties. The input is location data and disaster data, and the output is notifications of abnormal behavior and safety information.

[0230] This will enable appropriate actions and product suggestions in the real world based on the user's health information, improving the user's quality of life.

[0231] 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.

[0232] This invention is a system for managing a user's health status and providing appropriate health advice. By combining it with an emotion engine that recognizes the user's emotions, it is possible to provide optimal emotional advice. This system includes a device (smartphone) that collects the user's biometric information, an edge server, and an emotion engine.

[0233] System Overview

[0234] This system is configured as follows.

[0235] 1. Device (smartphone)

[0236] It collects biometric information such as the user's heart rate, steps, sleep patterns, and body temperature.

[0237] The biometric information is encrypted and sent to the edge server.

[0238] It uses a camera and microphone to analyze the user's facial expressions and voice and sends the data to an emotion engine.

[0239] 2. Edge Server

[0240] The biometric information sent from the terminal is received and stored in a database.

[0241] Using advanced AI algorithms, it analyzes biometric information in real time and monitors health conditions.

[0242] Based on the analysis results, the user's health risks are predicted.

[0243] It takes into account data from the emotion engine to generate personalized health advice.

[0244] Propose health products.

[0245] The collected health data is anonymized and securely sold to external organizations (medical institutions, pharmaceutical companies, etc.).

[0246] Monitors user location information and detects abnormal behavioral patterns.

[0247] In the event of a disaster, the system will check the safety of users and automatically notify them.

[0248] 3. Emotion Engine

[0249] Recognizes user emotions through facial expression recognition and voice analysis.

[0250] The recognized emotion information is sent to the edge server.

[0251] Customize health advice and product suggestions based on emotional information.

[0252] Specific examples

[0253] 1. Daily health management

[0254] User A starts jogging in the morning. The device measures the heart rate and number of steps and sends the data to the edge server.

[0255] The server analyzes the data and notifies the user when it is time to hydrate.

[0256] The emotion engine analyzes User A's facial expressions and voice, and if it senses that the user is feeling very tired, it will advise them to slow down their pace.

[0257] 2. Long-term sedentary monitoring

[0258] User B has been sitting in the office for a long time. The device detects this and sends a message to the server.

[0259] The server analyzes how long you have been sitting and notifies you to stretch periodically.

[0260] The emotion engine detects from User B's facial expression that he is losing concentration and advises him to take a short break.

[0261] 3. Sleep monitoring using emotion recognition

[0262] User C goes to bed at night. The device measures his sleep pattern and sends it to the server.

[0263] The server analyzes the quality of sleep and suggests a wake-up time that matches the next day's schedule.

[0264] The emotion engine analyzes facial expressions and tone of voice before bed, and if anxiety is detected, it suggests relaxing music and content.

[0265] 4. Safety confirmation including emotion analysis during disasters

[0266] An earthquake occurs and user D is in the affected area. The device sends location information to the server.

[0267] The server analyzes the safety of user D and automatically notifies family and friends.

[0268] The emotion engine analyzes user D's facial expressions and voice and provides support information to reduce stress as needed.

[0269] In this way, the system, which combines an emotion engine, can gain a deeper understanding of the user's health condition and provide optimal emotional support, thereby improving the user's quality of life.

[0270] The processing flow will be explained below.

[0271] Step 1:

[0272] The device uses sensors to continuously collect biometric information such as the user's heart rate, number of steps taken, sleep patterns, and body temperature.

[0273] Step 2:

[0274] The device uses a communication protocol (e.g., HTTPS) to encrypt the collected biometric information and send it to the edge server. The transmitted data is given a timestamp.

[0275] Step 3:

[0276] The device collects the user's facial expressions and voice using a camera and microphone, and processes the data to send it to the emotion engine.

[0277] Step 4:

[0278] The emotion engine analyzes facial and voice data sent from the device and recognizes the user's emotions in real time using AI algorithms.

[0279] Step 5:

[0280] The emotion engine sends the recognized emotion information to the edge server.

[0281] Step 6:

[0282] The server receives the biometric information sent from the device and stores it in a database. The database is in NoSQL format and has a structure suitable for real-time analysis.

[0283] Step 7:

[0284] The server then inputs the received biometric information into an AI algorithm for real-time analysis, which includes predictive models using past data.

[0285] Step 8:

[0286] The server also integrates emotional data from the emotion engine into the analysis results to assess the user's current health status and identify predicted health risks.

[0287] Step 9:

[0288] The server takes into account the analysis results and emotional information to generate personalized health advice for the user, which is optimized according to the user's emotional state.

[0289] Step 10:

[0290] The server sends the generated health advice to the device, which displays it as a pop-up notification or a voice message.

[0291] Step 11:

[0292] The device receives health advice from the server and notifies the user, who then checks the notification and follows the advice.

[0293] Step 12:

[0294] The server then recommends appropriate health products and services based on the user's health status, and also reflects their emotional information.

[0295] Step 13:

[0296] The device receives health product and service recommendations from the server and displays them to the user, including links to purchase products and gym discounts.

[0297] Step 14:

[0298] The server anonymizes the collected biometric information and prepares it for sale to external organizations (medical institutions, pharmaceutical companies, etc.). The sales data does not include any information that can identify individual users.

[0299] Step 15:

[0300] The device continuously collects the user's location information and sends it to a server, which is also encrypted.

[0301] Step 16:

[0302] The server analyzes location information in real time and detects abnormal behavior that deviates from normal patterns of behavior.

[0303] Step 17:

[0304] If the server detects any abnormal behavioral patterns, it will immediately alert the user and, if necessary, notify emergency contacts.

[0305] Step 18:

[0306] In the event of a disaster, the server will quickly check the safety of registered users based on their location information, and the results will be automatically sent to registered family and friends.

[0307] This system provides comprehensive support from daily health management to emergency response, improving the health and safety of users.

[0308] Example 2

[0309] 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."

[0310] In modern society, personal health and mental health management are becoming increasingly important. However, conventional health management systems are limited to collecting biometric information and simple data analysis, making it difficult to provide comprehensive health advice that takes into account the user's emotional state. Furthermore, issues such as the security of collected data, privacy protection, and rapid safety confirmation in the event of a disaster remain unresolved. Therefore, there is a need for a system that can simultaneously analyze a user's biometric information and emotional data to provide comprehensive health management and safety.

[0311] 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.

[0312] In this invention, the server includes means for receiving and analyzing the user's biometric information and monitoring and predicting the health condition, means for analyzing facial expressions and voice data using an emotion engine, and means for generating personalized health advice based on the analysis results and emotion information and notifying the user. This makes it possible to comprehensively analyze the user's biometric information and emotional state and provide personalized health advice.

[0313] "Biometric information" is data that indicates the user's physical condition, such as heart rate, number of steps, sleep pattern, and body temperature.

[0314] An "edge server" is a server located at the edge of a network for the purpose of collecting, analyzing, and storing data.

[0315] An "emotion engine" is software and algorithms that analyze facial expressions and voice to recognize a user's emotional state.

[0316] "Data analysis" is the process of applying statistical processing and machine learning algorithms to collected data to extract meaningful information.

[0317] "Health advice" is specific course of action or recommendations provided based on a user's health or emotional state.

[0318] "Health products" are goods and services that aim to maintain or improve the health of users.

[0319] "Anonymization" is the process of removing personally identifiable information from data, enhancing the protection of personal information.

[0320] An "external organization" is a corporation or organization outside the system, such as a medical institution or pharmaceutical company.

[0321] "Location information" is data that indicates a user's current geographic location and is collected using technologies such as GPS and Wi-Fi.

[0322] "Abnormal behavioral patterns" refer to user activity or behavior that is significantly different from normal behavior and may pose a danger or health risk.

[0323] "Safety confirmation" is a process for checking whether users are safe in the event of a disaster, and notifying them if necessary.

[0324] MODE FOR CARRYING OUT THE INVENTION

[0325] This invention is a system for managing a user's health status and providing appropriate health advice. By combining it with an emotion engine that recognizes the user's emotions, it is possible to provide optimal emotional advice. The system includes a device (smartphone) that collects the user's biometric information, an edge server, and an emotion engine.

[0326] System Overview

[0327] This system is configured as follows.

[0328] 1. Device (smartphone)

[0329] Dedicated sensors and apps are used to collect biometric information such as the user's heart rate, number of steps, sleep patterns, body temperature, etc. Examples include heart rate measurement apps, pedometer apps, and sleep trackers.

[0330] The biometric information is encrypted and sent to the edge server using advanced encryption methods such as AES-256.

[0331] It uses a camera and microphone to analyze the user's facial expressions and voice and sends the data to an emotion engine.

[0332] 2. Edge Server

[0333] The biometric information sent from the device is received and stored in a database, typically an SQL database or NoSQL database.

[0334] It uses advanced AI algorithms to analyze biometric information and monitor health conditions in real time, leveraging machine learning libraries such as TensorFlow and PyTorch.

[0335] Based on the analysis results, the app predicts the user's health risks. For example, if their heart rate is abnormally high or their walking pattern is different from normal, the app notifies the user of the risk via push notification or email.

[0336] Taking into account data from the emotion engine, personalized health advice is generated and communicated to the user, including specific courses of action and recommendations.

[0337] Propose health products, such as supplements to help recover from fatigue or relaxation products.

[0338] We anonymize collected health data and sell it securely to external organizations (medical institutions, pharmaceutical companies, etc.) We use pseudonymization and data masking techniques to prevent individuals from being identified.

[0339] It monitors your location and detects unusual patterns of activity, which can help detect prolonged inactivity or unusual movement patterns.

[0340] In the event of a disaster, the app will check the user's safety and automatically notify them. When an earthquake or other disaster occurs, it will analyze the user's safety based on location information and notify family and friends.

[0341] 3. Emotion Engine

[0342] It recognizes the user's emotions through facial expression recognition and voice analysis. For example, it uses image processing libraries such as OpenCV to analyze facial expressions and evaluate stress and fatigue levels.

[0343] The recognized emotion information is sent to the edge server and used to generate health advice.

[0344] Personalize health advice and product recommendations based on emotional information, for example, suggesting relaxation techniques if the user is tired.

[0345] This system makes it possible to comprehensively analyze a user's biometric and emotional data and provide personalized health advice.

[0346] Examples of specific examples and prompts

[0347] 1. Daily health management

[0348] The device measures user A's heart rate and sends it to the edge server.

[0349] The server analyzes the measurement data and notifies the user when it is time to hydrate.

[0350] The emotion engine analyzes user A's facial expressions and, if it determines that the user is feeling very tired, notifies them to slow down.

[0351] Example prompt sentence:

[0352] Generate specific health advice based on the user's biometric information. Input heart rate and step count data while jogging, and create prompts based on that data to advise the optimal time to hydrate.

[0353] 2. Long-term sedentary monitoring

[0354] The device detects the amount of time that user B has been sitting and sends this information to the edge server.

[0355] The server analyzes this data and periodically notifies you to stretch.

[0356] The emotion engine analyzes user B's facial expression data and, if it determines that their concentration is declining, notifies them to take a short break.

[0357] Example prompt sentence:

[0358] Generate specific advice for users who sit for long periods in the office, encouraging them to stretch regularly. Furthermore, create prompts based on facial expression data to suggest taking a break if concentration is declining.

[0359] 3. Sleep monitoring using emotion recognition

[0360] The device measures user C's sleep pattern and sends it to the edge server.

[0361] The server analyzes sleep data and suggests a wake-up time that fits the next day's schedule.

[0362] The emotion engine analyzes user C's facial expressions and voice before bed, and if it detects anxiety, it recommends relaxing music.

[0363] Example prompt sentence:

[0364] Analyze the quality of your sleep based on your sleep patterns and suggest the optimal time to wake up based on your schedule for the next day. Furthermore, create prompts that suggest relaxation methods based on your facial expressions and tone of voice before going to bed.

[0365] 4. Safety confirmation including emotion analysis during disasters

[0366] An earthquake occurs, and the device sends user D's location information to the edge server.

[0367] The server automatically analyzes user D's safety and notifies family and friends.

[0368] The emotion engine analyzes user D's facial expressions and voice and provides support information to reduce stress.

[0369] Example prompt sentence:

[0370] Explain a program that checks the user's safety during a disaster and automatically notifies family and friends. Furthermore, analyze the user's emotional data and create prompts that provide support information to reduce stress.

[0371] This system can monitor the user's health and emotional state in real time and provide appropriate support, which can significantly improve the user's quality of life.

[0372] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0373] Step 1:

[0374] The device collects the user's biometric information. Specifically, it obtains heart rate, number of steps, sleep patterns, body temperature, etc. using dedicated sensors and apps (e.g., heart rate measurement apps, pedometer apps, sleep trackers). The input is the user's biometric data, and the output is the collected biometric information.

[0375] Step 2:

[0376] The device encrypts the biometric information collected. Advanced encryption methods such as AES-256 are used to securely protect the data. The input is the collected biometric information, and the output is the encrypted biometric information.

[0377] Step 3:

[0378] The device sends encrypted biometric information to the edge server. HTTPS or other communication protocols are used to ensure secure data transfer. The input is encrypted biometric information, and the output is the biometric information sent to the server.

[0379] Step 4:

[0380] The server receives the encrypted biometric information sent from the terminal and decrypts it. The input is the encrypted biometric information, and the output is the decrypted biometric information.

[0381] Step 5:

[0382] The server stores the decoded biometric information in a database. SQL databases and NoSQL databases are used to efficiently manage data. The input is the decoded biometric information, and the output is stored in the database.

[0383] Step 6:

[0384] The server analyzes the stored biometric information in real time. Using machine learning libraries such as TensorFlow and PyTorch, an AI model analyzes the data. The input is the stored biometric information, and the output is the analysis results. The analysis results are used to assess the user's health status and risk.

[0385] Step 7:

[0386] The server predicts health risks based on the analysis results. If the heart rate is abnormally high or the walking pattern is different from normal, the server evaluates the risk and notifies the user. The input is the analysis results, and the output is a risk notification message.

[0387] Step 8:

[0388] The device collects the user's facial expressions and voice and sends the data to the emotion engine. The input is the user's facial and voice data, and the output is the data sent to the emotion engine.

[0389] Step 9:

[0390] The emotion engine performs facial expression recognition and voice analysis. It uses image processing libraries such as OpenCV to analyze facial expressions and evaluate the user's emotional state and stress level. The input is facial expression and voice data, and the output is the emotion analysis results.

[0391] Step 10:

[0392] The server generates personalized health advice based on the results of emotion analysis and notifies the user. The advice, including specific guidelines and recommendations, is generated and sent to the user via push notification or email. The input is the emotion analysis results and the analysis results of biometric information, and the output is a health advice message.

[0393] Step 11:

[0394] The server proposes health products. It proposes appropriate health products (e.g., supplements, relaxation goods) by taking into account the user's biometric information and emotional state. The input is the biometric information and the results of emotional analysis, and the output is a product proposal message.

[0395] Step 12:

[0396] The health data collected by the server is anonymized. Pseudonymization and data masking techniques are used to prevent individuals from being identified. The input is decoded biometric information, and the output is anonymized data.

[0397] Step 13:

[0398] The server sells the anonymized data to external organizations. The data is securely provided to medical institutions, pharmaceutical companies, etc. The input is anonymized data, and the output is data provided to external organizations.

[0399] Step 14:

[0400] The server monitors the user's location information and detects abnormal behavior patterns. It detects cases of prolonged inactivity or unusual movement patterns and notifies the user. The input is the user's location information, and the output is abnormal behavior detection and notification.

[0401] Step 15:

[0402] The server checks the user's safety in the event of a disaster. When a disaster such as an earthquake occurs, it analyzes the user's safety based on location information and automatically notifies family and friends. It also utilizes an emotion engine to provide support information as needed. The input is location information and emotion data at the time of the disaster, and the output is a safety confirmation and notification message.

[0403] (Application example 2)

[0404] 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."

[0405] Conventional healthcare systems were able to monitor a user's health status and provide advice, but they did not address health management that took into account the user's emotional state or provide lifestyle suggestions based on that. Furthermore, there was no system that could recommend and deliver optimal foods based on the user's health and emotional state. This left users without a way to easily achieve an appropriate diet that matched their own health and emotional state.

[0406] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting the user's biometric information, means for transmitting the collected biometric information to the edge server, means for analyzing the biometric information in the edge server and monitoring and predicting the health status, means for generating personalized health advice based on the analysis results and notifying the user, means for proposing health products, means for anonymizing the biometric information and selling it to external organizations, means for constantly monitoring the user's location information and detecting abnormal behavioral patterns, means for confirming the user's safety in the event of a disaster, means for proposing optimal foods based on the user's health status and emotions, and means for ordering the foods based on the suggestions. This enables the recommendation and delivery of optimal foods according to the user's health status and emotional state.

[0407] "Means for collecting user biometric information" refers to devices or systems that collect health-related data of users, such as heart rate, body temperature, number of steps, and sleep patterns.

[0408] The "means for transmitting biometric information to the edge server" refers to a device or system that transmits collected health-related data of a user to the edge server.

[0409] "Means for analyzing biometric information on an edge server and monitoring and predicting health status" refers to a device or system that analyzes transmitted biometric information, monitors the user's health status in real time, and predicts future health risks.

[0410] "Means for generating individual health advice based on the analysis results and notifying the user" refers to a device or system that generates health advice tailored to each individual user based on the results of analysis performed by the edge server and notifies the user of this advice.

[0411] The "means for proposing health products" refers to a device or system that proposes appropriate health-related products based on the user's health condition and analysis results.

[0412] "Means for anonymizing biometric information and selling it to external organizations" refers to devices or systems that convert collected biometric information into a form that makes it impossible to identify individual users and then sell it to external institutions or organizations.

[0413] "Means for constantly monitoring a user's location information and detecting abnormal behavioral patterns" refers to a device or system that constantly monitors a user's location information and detects behavioral patterns that differ from normal.

[0414] "Means for confirming safety during a disaster" refers to devices or systems that confirm the safety of users when a disaster occurs and automatically notify them of necessary information.

[0415] The "means for suggesting optimal foods based on the user's health condition and emotions" refers to a device or system that suggests optimal foods to a user based on the user's health condition and emotional data.

[0416] A "means for ordering food based on the suggestions" is a device or system that allows a user to order the suggested food.

[0417] This invention is a system that takes into account the user's health condition and emotions, and proposes and delivers the most suitable foods. This system not only collects and analyzes the user's biometric and emotional data to provide health advice, but also proposes the most suitable foods based on the user's health and emotional state, enabling the user to order food based on these suggestions, creating a new service.

[0418] The system consists of the following:

[0419] 1. Device (smartphone)

[0420] Biometric data collection: Using smartphone sensors, biometric data such as the user's heart rate, body temperature, steps taken, and sleep patterns are collected.

[0421] Emotion data collection: The camera and microphone are used to analyze the user's facial expressions and voice, and the data is sent to the emotion engine.

[0422] Data transmission: The collected biometric information and emotion data are encrypted and sent to the edge server.

[0423] 2. Edge Server

[0424] Data reception and storage: Receives biometric and emotional data sent from the device and stores it in a database.

[0425] Real-time analysis: Advanced AI algorithms are used to analyze biometric and emotional data, and the results are used to monitor health conditions and predict health risks.

[0426] Advice generation: Based on the analysis results, individual health advice is generated and notified to the user.

[0427] Product Suggestion: Recommend appropriate health products based on health advice.

[0428] Data anonymization: Health data collected is anonymized so that it can be securely provided to external organizations.

[0429] Location monitoring: Constantly monitors user location information to detect abnormal behavioral patterns. Confirm safety in the event of a disaster.

[0430] Food Suggestion and Ordering: Recommends the best foods based on the user's health and emotional state, and allows the user to order food based on the suggestions.

[0431] Hardware and software used

[0432] Hardware: Smartphones, edge servers

[0433] Software: Biometric information collection app, emotion recognition engine, data analysis AI, database management system, communication protocol (encryption)

[0434] Specific examples

[0435] When a user returns home from work, their smartphone detects their fatigue based on their heart rate, body temperature, and facial expression. The edge server analyzes this data and sends the user a notification suggesting a sushi set with green tea for relaxation. If the user agrees to the suggestion, the system sends an order to the nearest restaurant and arranges for food delivery.

[0436] Example prompts for generative AI models

[0437] Input data: Heart rate = 80, Body temperature = 36.5°C, Sleep pattern = irregular, Facial expression = fatigue, Tone of voice = stress

[0438] Output data: Suggested food = Sushi set with relaxing green tea

[0439] This allows users to easily order the best foods for their health and emotional state.

[0440] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0441] Step 1:

[0442] Data collection

[0443] The device (smartphone) collects the user's biometric information. Specifically, it obtains data such as heart rate, body temperature, number of steps, and sleep patterns from sensors. It also uses the device's camera and microphone to analyze the user's facial expressions and voice and generate emotional data.

[0444] Input: Sensor, Camera, Microphone

[0445] Output: Heart rate, body temperature, steps, sleep patterns, facial expression data, voice data

[0446] Step 2:

[0447] Data transmission

[0448] The biometric and emotional data collected by the device is encrypted and sent to the edge server via a highly secure communication protocol (e.g., HTTPS).

[0449] Input: Heart rate, body temperature, steps, sleep pattern, facial expression data, voice data

[0450] Output: Encrypted data packet

[0451] Step 3:

[0452] Data reception and storage

[0453] The server receives the transmitted data and stores it in a database. Specifically, it uses a database management system (DBMS) to record the transmitted biometric and emotional data in an appropriate format.

[0454] Input: Encrypted data packet

[0455] Output: Biometric and emotional data stored in a database

[0456] Step 4:

[0457] Real-time analytics

[0458] The server analyzes the stored data in real time, using advanced AI algorithms to analyze biometric and emotional data to assess health and emotional status, and predict health risks based on the results.

[0459] Input: Biometric and emotional data stored in a database

[0460] Output: Health status assessment, emotional status assessment, health risk prediction

[0461] Step 5:

[0462] Advice Generation

[0463] The server generates personalized health advice based on the analysis results, specifically using a generative AI model to create health advice tailored to the user.

[0464] Input: Health status assessment, emotional status assessment, health risk prediction

[0465] Output: personalized health advice

[0466] Step 6:

[0467] notification

[0468] The server notifies the device of the generated health advice, specifically by sending the advice directly to the user via mobile notifications or email.

[0469] Input: personalized health advice

[0470] Output: User notification

[0471] Step 7:

[0472] food suggestions

[0473] The server will suggest the best food based on the user's health and emotional state, and will create a list of food delivery options based on the results of data analysis.

[0474] Input: Health status assessment, emotional status assessment

[0475] Output: Food suggestion list

[0476] Step 8:

[0477] Food ordering

[0478] The user selects the suggested food items and confirms the order. Specifically, the user confirms the food selection on the device and sends the order to the nearest restaurant.

[0479] Input: Food suggestion list

[0480] Output: Restaurant order data

[0481] Step 9:

[0482] Delivery arrangements

[0483] The restaurant receives the order and arranges for the food to be delivered. Specifically, the restaurant that received the order uses a delivery service to deliver the food to the user.

[0484] Input: Restaurant order data

[0485] Output: Food delivered to the user

[0486] In this way, the system can utilize the user's health and emotional information to provide optimal health advice and food suggestions, thereby improving the user's quality of life.

[0487] 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.

[0488] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0489] 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.

[0490] [Second embodiment]

[0491] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0492] 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.

[0493] 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).

[0494] 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.

[0495] 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.

[0496] 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).

[0497] 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.

[0498] 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.

[0499] 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.

[0500] 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.

[0501] 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.

[0502] 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."

[0503] This invention is a system that manages a user's health condition in real time and provides appropriate health advice. This system consists of a device (smartphone) that collects the user's biometric information, a server (edge ​​server) that analyzes and predicts that information, and an interface that notifies the user.

[0504] System Overview

[0505] This system first collects the user's biometric information. This is performed by a device (smartphone), which continuously collects biometric information such as heart rate, number of steps, sleep patterns, and body temperature. This data is then sent to a server (edge ​​server) using an encrypted communication protocol.

[0506] The edge server is equipped with advanced AI algorithms that analyze the received biometric information in real time. This analysis allows the user's current health status to be monitored and future health risks to be predicted. Based on the analysis results, personalized health advice supervised by experts is generated and communicated to the user.

[0507] The server also suggests health products, selecting appropriate products and services based on the user's health condition and notifying the device. It also has the ability to anonymize the collected health data and safely sell it to external organizations (medical institutions, pharmaceutical companies, etc.). This allows the data to be used for research and development of new health services while maintaining its security.

[0508] Location monitoring is also a very important function. The device constantly obtains the user's location information and sends it to a server. The server analyzes this data and detects abnormal behavioral patterns. If an abnormality is detected, the user is immediately notified. In addition, in the event of a disaster, the location information can be used to quickly check the user's safety and automatically notify family and friends.

[0509] Specific examples

[0510] 1. Daily health management

[0511] User A starts jogging at 7:00 a.m. The device measures the heart rate and number of steps in real time and sends the data to the edge server.

[0512] The server analyzes the received data, generates advice on the appropriate timing for hydration and jogging pace, and notifies the device.

[0513] User A receives the notification and can continue jogging in a healthy manner based on the advice.

[0514] 2. Long-term sedentary monitoring

[0515] User B works in an office while sitting for a long time. The terminal measures the time spent sitting and sends the data to the server.

[0516] The server detects that the user has been sitting still for a long period of time and generates and sends a notification to the device to encourage the user to stand up and stretch periodically.

[0517] User B receives the notification and stretches for health reasons.

[0518] 3. Sleep monitoring

[0519] User C goes to bed and the device measures his / her sleep patterns. The data is sent to the server for analysis.

[0520] The server analyzes the time spent in deep sleep and the quality of sleep, generates advice to improve performance the next day, and notifies the device.

[0521] User C receives the advice and takes action to improve their sleep quality.

[0522] 4. Confirming safety during disasters

[0523] When a disaster such as an earthquake occurs, user D is in the affected area.

[0524] The device sends its location information to a server, which then verifies the user's location in the disaster area.

[0525] The server checks the safety of user D and automatically notifies family and friends.

[0526] In this way, this system manages the user's health in real time and helps improve their health through appropriate advice and product suggestions.

[0527] The processing flow will be explained below.

[0528] Step 1:

[0529] The device continuously collects the user's biometric information (heart rate, number of steps, sleep patterns, body temperature, etc.) using sensors.

[0530] Step 2:

[0531] The device uses a communication protocol (e.g., HTTPS) to encrypt the collected biometric information and send it to the edge server. The transmitted data is given a timestamp.

[0532] Step 3:

[0533] The server stores the received data in a database in NoSQL format, with a structure suitable for real-time analysis.

[0534] Step 4:

[0535] The server feeds the data into AI algorithms for real-time analysis, including predictive models using historical data.

[0536] Step 5:

[0537] The server evaluates the user's current health status based on the analysis results and identifies predicted health risks.

[0538] Step 6:

[0539] The server generates health advice that has been supervised by experts and transmits the advice to the terminal.

[0540] Step 7:

[0541] The device will notify the user of the health advice it receives, which will be displayed as a pop-up message and / or an audio alert.

[0542] Step 8:

[0543] The server then recommends appropriate health products and services based on the user's health status, and the recommendations are sent to the device.

[0544] Step 9:

[0545] The device displays suggestions for health products and services to users, including links to purchase products and gym discounts.

[0546] Step 10:

[0547] The server anonymizes the collected biometric information and prepares it for sale to external organizations (medical institutions, pharmaceutical companies, etc.). The sales data does not include any information that can identify individual users.

[0548] Step 11:

[0549] The device continuously collects the user's location information and sends it to a server, which is also encrypted.

[0550] Step 12:

[0551] The server analyzes location information in real time and detects abnormal behavior that deviates from normal patterns of behavior.

[0552] Step 13:

[0553] If the server detects any abnormal behavioral patterns, it will immediately alert the user and, if necessary, notify emergency contacts.

[0554] Step 14:

[0555] In the event of a disaster, the server will quickly check the safety of registered users based on their location information, and the results will be automatically sent to registered family and friends.

[0556] Through these steps, the system can manage users' health status in real time, provide appropriate health advice, and respond quickly in emergencies.

[0557] Example 1

[0558] 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."

[0559] In modern society, it is important to manage a user's health status in real time and provide appropriate individualized health guidance. However, current systems have difficulty integrating multiple functions, such as collecting and analyzing biometric information, generating health guidance, and confirming safety in the event of a disaster. Therefore, there is a need to efficiently manage both the user's health and confirm their safety. Furthermore, security and privacy protection of biometric information are also major issues, and a system for utilizing data while satisfying these needs is required.

[0560] 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.

[0561] In this invention, the server includes means for collecting user biometric information, means for transmitting the collected biometric information to an edge device, means for analyzing the biometric information at the edge device and monitoring and predicting the health status, means for generating individual health advice based on the analysis results and notifying the user, means for suggesting health products, means for anonymizing the biometric information and providing it to an external organization, means for constantly monitoring the user's location information and detecting abnormal movement patterns, and means for confirming the user's safety in the event of a disaster. This enables the secure collection and analysis of the user's biometric information, real-time health management, and rapid safety confirmation in the event of a disaster.

[0562] "User" means an individual who uses the system to manage their own health status.

[0563] "Biometric information" refers to data about the body, such as heart rate, number of steps taken, sleep patterns, and body temperature.

[0564] A "means" is a method or device used to achieve a particular purpose.

[0565] "Edge devices" refers to multiple devices, typically including servers, that process and analyze collected data.

[0566] "Analysis" is the process of examining collected biometric information in detail and finding meaning.

[0567] "Health" refers to a user's physical and mental state, as indicated by biometric information such as heart rate and body temperature.

[0568] "Health Guidance" refers to specific advice or suggestions to help users improve or maintain their health.

[0569] "Notification" means a message or alert intended to convey information from the system to the user.

[0570] "Health products" refers to goods and services that help users improve or maintain their health.

[0571] "Anonymization" is the process of processing data so that it cannot be used to identify individuals.

[0572] An "external institution" is an external organization that uses a user's biometric information, such as a medical institution or pharmaceutical company.

[0573] "Location Information" means data about a user's geographic location.

[0574] "Abnormal behavior patterns" are data that show movements or situations that differ from normal behavior.

[0575] "Safety confirmation during a disaster" refers to the process of confirming the safety of users when a disaster such as an earthquake or typhoon occurs.

[0576] This invention is a system for managing a user's health status in real time and providing appropriate health guidance. The system mainly consists of a terminal that collects the user's biometric information, an edge device that analyzes the information, and an interface that sends notifications.

[0577] First, the system collects the user's biometric information through a mobile device such as a smartphone, including heart rate, steps taken, sleep patterns, body temperature, etc. The smartphone records the user's condition in real time using data from built-in sensors and external devices.

[0578] The collected biometric information is then securely transmitted to the edge device (server) using encrypted communication protocols (e.g., SSL or TLS). The server then analyzes the received data using advanced AI algorithms (e.g., TensorFlow or PyTorch). This allows the user's current health status to be monitored and future health risks to be predicted.

[0579] Based on the analysis results, the server generates a personalized health guide, supervised by an expert, including specific advice on exercise, diet, rest, etc. The advice is then encrypted and sent back to the smartphone, where it is displayed to the user as a push notification or in-app alert.

[0580] In addition, the server will suggest health products and services tailored to the user's health condition, including supplements, fitness programs, medical devices, etc. The suggestions will also be sent to the device, providing the user with the opportunity to receive appropriate products and services.

[0581] The collected biometric information is anonymized by the server and provided to external organizations such as medical institutions and pharmaceutical companies, which makes it possible to provide data for new health services and research while maintaining data security and privacy.

[0582] Smartphones also constantly collect the user's location information and send it to a server. The server analyzes the location information and promptly notifies the user if it detects abnormal behavior patterns or an emergency. This is particularly useful in the event of a disaster such as an earthquake or typhoon, as it allows for quick safety confirmation and automatically sends safety confirmation notifications to the user's family and friends.

[0583] As a concrete example, consider the case where User A starts jogging at 7:00 AM. The device measures the heart rate and number of steps in real time and sends them to the edge device. The server analyzes this data, generates advice on appropriate hydration timing and jogging pace, and notifies the device. User A receives this advice and continues jogging in a healthy manner.

[0584] Additionally, personalized advice can be generated by inputting prompts like the following into the generative AI model:

[0585] "Generate optimal health advice based on the following user situation: User A started jogging at 7 AM, their current heart rate is 150 bpm, and they have taken 1500 steps."

[0586] In this way, the system manages the user's health condition in real time and provides appropriate advice and product suggestions, thereby helping to improve the user's health and ensure safety.

[0587] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0588] Step 1:

[0589] The user launches the dedicated app and begins their daily activities. For example, to start jogging, they launch the app and press the start button.

[0590] Input: User operation (launching an app, pressing the start button)

[0591] Output: The app's sensors are activated and data collection begins.

[0592] Step 2:

[0593] The device (smartphone) collects biometric information such as heart rate, number of steps, sleep patterns, and body temperature in real time from built-in sensors and external devices. The sensors detect the user's physical movements and acquire the data.

[0594] Input: User's biometric information (heart rate, steps, sleep pattern, body temperature)

[0595] Output: A dataset of collected biometric information

[0596] Step 3:

[0597] The collected biometric information is securely transmitted to the edge device (server) using encrypted communication protocols (e.g., SSL or TLS). The device periodically divides the data into packets and transmits them to the server.

[0598] Input: Biometric dataset

[0599] Output: Encrypted data packet

[0600] Step 4:

[0601] The server stores the received biometric information in a database and analyzes it using advanced AI algorithms (such as TensorFlow and PyTorch). The server calculates heart rate fluctuations and step rhythms to check for abnormalities.

[0602] Input: Encrypted data packet

[0603] Output: Analysis results (heart rate fluctuations, step rhythm, presence or absence of abnormalities)

[0604] Step 5:

[0605] Based on the analysis results, the server evaluates the user's current health status and predicts future health risks, for example, using models to predict elevated blood pressure and abnormal heart rate.

[0606] Input: Analysis results (heart rate fluctuations, step rhythm, presence or absence of abnormalities)

[0607] Output: Health status assessment and health risk prediction

[0608] Step 6:

[0609] The server generates personalized health advice supervised by experts based on the health status assessment and health risk prediction results. Using a generative AI model, it creates the most appropriate advice for the user in text format.

[0610] Input: Health status assessment and health risk prediction

[0611] Output: Health advice text message

[0612] Step 7:

[0613] The server encrypts the generated health advice and sends it to the device, which then decrypts the received message and notifies the user.

[0614] Input: Health advice text message

[0615] Output: Encrypted health advice message

[0616] Step 8:

[0617] The device will provide users with health guidance via push notifications and in-app alerts, such as messages like "stay hydrated."

[0618] Input: Encrypted health advice message

[0619] Output: A notification message that is displayed to the user.

[0620] Step 9:

[0621] The server will suggest health products and services based on the user's health status, and this information will also be encrypted and sent to the device, where it will be displayed as a notification.

[0622] Input: Health status assessment and health risk prediction

[0623] Output: Encrypted health product suggestion message

[0624] Step 10:

[0625] The server anonymizes the biometric information and safely provides it to external organizations such as medical institutions and pharmaceutical companies. The information is then anonymized and stored in a database.

[0626] Input: Biometric dataset

[0627] Output: Anonymized dataset

[0628] Step 11:

[0629] The device constantly acquires the user's location information and periodically sends it to the server, which then monitors for abnormal behavior patterns and notifies the user if necessary.

[0630] Input: User's location

[0631] Output: A dataset of location information

[0632] Step 12:

[0633] When a disaster such as an earthquake or typhoon occurs, the device sends its location information to the server in real time, and the server quickly checks the user's safety and automatically notifies family and friends.

[0634] Input: Location information at the time of disaster

[0635] Output: Safety confirmation notification

[0636] (Application example 1)

[0637] 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."

[0638] In recent years, users have become increasingly interested in managing their health, creating a demand for systems that provide advice tailored to individual health conditions. However, existing systems only collect and analyze users' biometric information, making it difficult to optimally reflect it in their actual social lives and consumer behavior. Furthermore, they lack safety confirmation functions in the event of a disaster, delaying the provision of comprehensive health management systems. Therefore, a new system is needed that utilizes users' health information to support their behavior in the real world.

[0639] 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.

[0640] In this invention, the server includes means for collecting user biometric information, means for transmitting the collected biometric information to an edge server, means for analyzing the biometric information in the edge server and monitoring and predicting the health status, means for generating personalized health advice based on the analysis results and notifying the user, means for proposing health products, means for anonymizing the biometric information and selling it to an external organization, means for constantly monitoring the user's location information and detecting abnormal behavioral patterns, means for confirming the user's safety in the event of a disaster, and means for proposing products in a physical store based on the user's health information. This enables appropriate behavior and product suggestions in the real world based on the user's health information.

[0641] "User biometric information" refers to numerical data related to a person's physical condition, such as heart rate, number of steps, body temperature, and sleep patterns.

[0642] An "edge server" is a server that receives data sent by users in real time and performs analysis and predictions.

[0643] "Health monitoring and prediction tools" refers to algorithms and programs that analyze collected biometric information to determine current health status and predict future risks.

[0644] "Individualized health advice" is advice based on the analysis of a user's biometric information, providing suggestions for improving behaviors and habits that are optimal for each individual's health condition.

[0645] "Health product suggestion" is the process of recommending appropriate products and services to users according to their analyzed health status.

[0646] "Biometric de-identification" is the process of removing or modifying personal information from collected biometric data so that it cannot be used to identify individual users.

[0647] "External organizations" are third-party organizations, such as medical institutions and pharmaceutical companies, that conduct research or provide services using users' biometric information.

[0648] "Means for constantly monitoring location information" refers to technologies and devices that continuously obtain the user's current location and identify abnormal behavior or trends during disasters.

[0649] "Abnormal behavior" refers to behavior or movement patterns that are different from normal and are predicted to pose a risk to the user's health or safety.

[0650] "Means for checking safety during a disaster" is a system that uses the user's location information to confirm the user's safety when a disaster occurs and notifies family members and other relevant parties of that information.

[0651] The "means of making product suggestions within a physical store" is a system that analyzes a user's health information and makes real-time suggestions about products that are recommended for purchase within the store where the user is currently located.

[0652] To implement the present invention, a system is constructed and operated in the following manner.

[0653] System configuration

[0654] 1. Data collection and transmission

[0655] The user uses a device (smartphone) to collect biometric information. This smartphone is equipped with a heart rate sensor and an acceleration sensor to acquire biometric information such as heart rate, number of steps, sleep pattern, and body temperature. The collected data is sent to the edge server using an encrypted communication protocol (e.g., TLS / SSL).

[0656] 2. Data Analysis

[0657] The edge server analyzes the received data using AI algorithms (e.g., TensorFlow, PyTorch, etc.), which monitors the user's current health status and predicts future health risks. The analysis results are updated in real time.

[0658] 3. Advice and Notice

[0659] Based on the analysis results, the edge server generates individual health advice, which is then sent to the user's smartphone via push notification. The advice helps users improve their health behavior and lifestyle habits.

[0660] 4. Health product suggestions

[0661] The edge server selects products and services suited to the user's health condition, and the user receives real-time recommendations, including health foods and fitness products.

[0662] 5. Anonymization and sale of data

[0663] The biometric information will be anonymized and sold to external organizations (e.g., medical institutions, pharmaceutical companies, etc.) This process will use secure data anonymization techniques to ensure the protection of personal information.

[0664] 6. Location Monitoring and Anomaly Detection

[0665] The user's smartphone constantly collects location information and sends it to the edge server. The edge server analyzes this data and detects abnormal behavioral patterns. If an abnormality is detected, the user is immediately notified. In addition, in the event of a disaster, the user's safety is quickly confirmed based on the location information, and family and friends are automatically notified.

[0666] 7. Product proposals in physical stores

[0667] The app will suggest products based on health information while users are in physical stores. These suggestions are optimized based on the user's current location within the store and sent to the user's smartphone. For example, a user with high stress levels will be recommended products with a relaxation effect.

[0668] Examples and prompts

[0669] Examples:

[0670] User A visits a physical store called "Health Shop." User A's smartphone collects real-time information such as heart rate, number of steps, and body temperature. The edge server analyzes this information and detects that User A's recent stress level has been high. Based on this, the server recommends relaxing herbal teas and stress-relieving products sold in the store. This information is displayed on User A's smartphone via push notification, allowing User A to purchase products based on that information.

[0671] Example prompt sentence:

[0672] "I want to create a smartphone app that collects heart rate, steps, and body temperature in real time, analyzes the user's health status, and recommends appropriate health products. If it detects that the user is feeling stressed, it should suggest products that are effective for relieving stress in a physical store."

[0673] This will enable appropriate actions and product suggestions in the real world based on the user's health information, improving the user's quality of life.

[0674] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0675] Step 1:

[0676] The user's smartphone collects biometric information such as heart rate, number of steps, and body temperature using the smartphone's built-in heart rate sensor and accelerometer. The input here is the user's real-time biometric information, and the output is the collected biometric data.

[0677] Step 2:

[0678] The device sends the collected biometric data to the edge server using an encrypted communication protocol (e.g., TLS / SSL). With this operation, the input is the data sent through the encryption protocol, and the output is the secure data transfer to the edge server.

[0679] Step 3:

[0680] The edge server analyzes the received biometric data using a generative AI model (e.g., TensorFlow or PyTorch). The input is the biometric data received by the edge server, and the output is the user's current health status and predicted future health risks.

[0681] Step 4:

[0682] The server generates individual health advice based on the analysis results. This advice generation is based on an algorithm supervised by experts. The input here is the health status analysis data, and the output is health advice to be notified to the user.

[0683] Step 5:

[0684] The device receives health advice via push notifications from the server. These notifications include specific behavioral guidelines and recommended products. The input is advice data based on the analysis results, and the output is a notification to the user.

[0685] Step 6:

[0686] The server suggests health products and related services that are appropriate for the user's health status using a real-time recommendation algorithm, whose input is the user's current health status data and whose output is a list of recommended health products.

[0687] Step 7:

[0688] The terminal displays recommended health products and services to support in-store activities. The input here is the recommended product information sent from the server, and the output is the product information displayed on the user's smartphone screen.

[0689] Step 8:

[0690] The server anonymizes the biometric data and sells it to external organizations. This process uses data anonymization tools and techniques. The input is biometric data and the output is anonymized data.

[0691] Step 9:

[0692] The device constantly monitors the user's location information and sends it to the server. This operation uses location information technologies such as GPS and Wi-Fi. The input is the user's location data, and the output is location information data sent to the edge server.

[0693] Step 10:

[0694] The server analyzes the received location data and detects abnormal behavioral patterns. In the event of a disaster, it also checks the user's safety and notifies relevant parties. The input is location data and disaster data, and the output is notifications of abnormal behavior and safety information.

[0695] This will enable appropriate actions and product suggestions in the real world based on the user's health information, improving the user's quality of life.

[0696] 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.

[0697] This invention is a system for managing a user's health status and providing appropriate health advice. By combining it with an emotion engine that recognizes the user's emotions, it is possible to provide optimal emotional advice. This system includes a device (smartphone) that collects the user's biometric information, an edge server, and an emotion engine.

[0698] System Overview

[0699] This system is configured as follows.

[0700] 1. Device (smartphone)

[0701] It collects biometric information such as the user's heart rate, steps, sleep patterns, and body temperature.

[0702] The biometric information is encrypted and sent to the edge server.

[0703] It uses a camera and microphone to analyze the user's facial expressions and voice and sends the data to an emotion engine.

[0704] 2. Edge Server

[0705] The biometric information sent from the terminal is received and stored in a database.

[0706] Using advanced AI algorithms, it analyzes biometric information in real time and monitors health conditions.

[0707] Based on the analysis results, the user's health risks are predicted.

[0708] It takes into account data from the emotion engine to generate personalized health advice.

[0709] Propose health products.

[0710] The collected health data is anonymized and securely sold to external organizations (medical institutions, pharmaceutical companies, etc.).

[0711] Monitors user location information and detects abnormal behavioral patterns.

[0712] In the event of a disaster, the system will check the safety of users and automatically notify them.

[0713] 3. Emotion Engine

[0714] Recognizes user emotions through facial expression recognition and voice analysis.

[0715] The recognized emotion information is sent to the edge server.

[0716] Customize health advice and product suggestions based on emotional information.

[0717] Specific examples

[0718] 1. Daily health management

[0719] User A starts jogging in the morning. The device measures the heart rate and number of steps and sends the data to the edge server.

[0720] The server analyzes the data and notifies the user when it is time to hydrate.

[0721] The emotion engine analyzes User A's facial expressions and voice, and if it senses that the user is feeling very tired, it will advise them to slow down their pace.

[0722] 2. Long-term sedentary monitoring

[0723] User B has been sitting in the office for a long time. The device detects this and sends a message to the server.

[0724] The server analyzes how long you have been sitting and notifies you to stretch periodically.

[0725] The emotion engine detects from User B's facial expression that he is losing concentration and advises him to take a short break.

[0726] 3. Sleep monitoring using emotion recognition

[0727] User C goes to bed at night. The device measures his sleep pattern and sends it to the server.

[0728] The server analyzes the quality of sleep and suggests a wake-up time that matches the next day's schedule.

[0729] The emotion engine analyzes facial expressions and tone of voice before bed, and if anxiety is detected, it suggests relaxing music and content.

[0730] 4. Safety confirmation including emotion analysis during disasters

[0731] An earthquake occurs and user D is in the affected area. The device sends location information to the server.

[0732] The server analyzes the safety of user D and automatically notifies family and friends.

[0733] The emotion engine analyzes user D's facial expressions and voice and provides support information to reduce stress as needed.

[0734] In this way, the system, which combines an emotion engine, can gain a deeper understanding of the user's health condition and provide optimal emotional support, thereby improving the user's quality of life.

[0735] The processing flow will be explained below.

[0736] Step 1:

[0737] The device uses sensors to continuously collect biometric information such as the user's heart rate, number of steps taken, sleep patterns, and body temperature.

[0738] Step 2:

[0739] The device uses a communication protocol (e.g., HTTPS) to encrypt the collected biometric information and send it to the edge server. The transmitted data is given a timestamp.

[0740] Step 3:

[0741] The device collects the user's facial expressions and voice using a camera and microphone, and processes the data to send it to the emotion engine.

[0742] Step 4:

[0743] The emotion engine analyzes facial and voice data sent from the device and recognizes the user's emotions in real time using AI algorithms.

[0744] Step 5:

[0745] The emotion engine sends the recognized emotion information to the edge server.

[0746] Step 6:

[0747] The server receives the biometric information sent from the device and stores it in a database. The database is in NoSQL format and has a structure suitable for real-time analysis.

[0748] Step 7:

[0749] The server then inputs the received biometric information into an AI algorithm for real-time analysis, which includes predictive models using past data.

[0750] Step 8:

[0751] The server also integrates emotional data from the emotion engine into the analysis results to assess the user's current health status and identify predicted health risks.

[0752] Step 9:

[0753] The server takes into account the analysis results and emotional information to generate personalized health advice for the user, which is optimized according to the user's emotional state.

[0754] Step 10:

[0755] The server sends the generated health advice to the device, which displays it as a pop-up notification or a voice message.

[0756] Step 11:

[0757] The device receives health advice from the server and notifies the user, who then checks the notification and follows the advice.

[0758] Step 12:

[0759] The server then recommends appropriate health products and services based on the user's health status, and also reflects their emotional information.

[0760] Step 13:

[0761] The device receives health product and service recommendations from the server and displays them to the user, including links to purchase products and gym discounts.

[0762] Step 14:

[0763] The server anonymizes the collected biometric information and prepares it for sale to external organizations (medical institutions, pharmaceutical companies, etc.). The sales data does not include any information that can identify individual users.

[0764] Step 15:

[0765] The device continuously collects the user's location information and sends it to a server, which is also encrypted.

[0766] Step 16:

[0767] The server analyzes location information in real time and detects abnormal behavior that deviates from normal patterns of behavior.

[0768] Step 17:

[0769] If the server detects any abnormal behavioral patterns, it will immediately alert the user and, if necessary, notify emergency contacts.

[0770] Step 18:

[0771] In the event of a disaster, the server will quickly check the safety of registered users based on their location information, and the results will be automatically sent to registered family and friends.

[0772] This system provides comprehensive support from daily health management to emergency response, improving the health and safety of users.

[0773] Example 2

[0774] 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."

[0775] In modern society, personal health and mental health management are becoming increasingly important. However, conventional health management systems are limited to collecting biometric information and simple data analysis, making it difficult to provide comprehensive health advice that takes into account the user's emotional state. Furthermore, issues such as the security of collected data, privacy protection, and rapid safety confirmation in the event of a disaster remain unresolved. Therefore, there is a need for a system that can simultaneously analyze a user's biometric information and emotional data to provide comprehensive health management and safety.

[0776] 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.

[0777] In this invention, the server includes means for receiving and analyzing the user's biometric information and monitoring and predicting the health condition, means for analyzing facial expressions and voice data using an emotion engine, and means for generating personalized health advice based on the analysis results and emotion information and notifying the user. This makes it possible to comprehensively analyze the user's biometric information and emotional state and provide personalized health advice.

[0778] "Biometric information" is data that indicates the user's physical condition, such as heart rate, number of steps, sleep pattern, and body temperature.

[0779] An "edge server" is a server located at the edge of a network for the purpose of collecting, analyzing, and storing data.

[0780] An "emotion engine" is software and algorithms that analyze facial expressions and voice to recognize a user's emotional state.

[0781] "Data analysis" is the process of applying statistical processing and machine learning algorithms to collected data to extract meaningful information.

[0782] "Health advice" is specific course of action or recommendations provided based on a user's health or emotional state.

[0783] "Health products" are goods and services that aim to maintain or improve the health of users.

[0784] "Anonymization" is the process of removing personally identifiable information from data, enhancing the protection of personal information.

[0785] An "external organization" is a corporation or organization outside the system, such as a medical institution or pharmaceutical company.

[0786] "Location information" is data that indicates a user's current geographic location and is collected using technologies such as GPS and Wi-Fi.

[0787] "Abnormal behavioral patterns" refer to user activity or behavior that is significantly different from normal behavior and may pose a danger or health risk.

[0788] "Safety confirmation" is a process for checking whether users are safe in the event of a disaster, and notifying them if necessary.

[0789] MODE FOR CARRYING OUT THE INVENTION

[0790] This invention is a system for managing a user's health status and providing appropriate health advice. By combining it with an emotion engine that recognizes the user's emotions, it is possible to provide optimal emotional advice. The system includes a device (smartphone) that collects the user's biometric information, an edge server, and an emotion engine.

[0791] System Overview

[0792] This system is configured as follows.

[0793] 1. Device (smartphone)

[0794] Dedicated sensors and apps are used to collect biometric information such as the user's heart rate, number of steps, sleep patterns, body temperature, etc. Examples include heart rate measurement apps, pedometer apps, and sleep trackers.

[0795] The biometric information is encrypted and sent to the edge server using advanced encryption methods such as AES-256.

[0796] It uses a camera and microphone to analyze the user's facial expressions and voice and sends the data to an emotion engine.

[0797] 2. Edge Server

[0798] The biometric information sent from the device is received and stored in a database, typically an SQL database or NoSQL database.

[0799] It uses advanced AI algorithms to analyze biometric information and monitor health conditions in real time, leveraging machine learning libraries such as TensorFlow and PyTorch.

[0800] Based on the analysis results, the app predicts the user's health risks. For example, if their heart rate is abnormally high or their walking pattern is different from normal, the app notifies the user of the risk via push notification or email.

[0801] Taking into account data from the emotion engine, personalized health advice is generated and communicated to the user, including specific courses of action and recommendations.

[0802] Propose health products, such as supplements to help recover from fatigue or relaxation products.

[0803] We anonymize collected health data and sell it securely to external organizations (medical institutions, pharmaceutical companies, etc.) We use pseudonymization and data masking techniques to prevent individuals from being identified.

[0804] It monitors your location and detects unusual patterns of activity, which can help detect prolonged inactivity or unusual movement patterns.

[0805] In the event of a disaster, the app will check the user's safety and automatically notify them. When an earthquake or other disaster occurs, it will analyze the user's safety based on location information and notify family and friends.

[0806] 3. Emotion Engine

[0807] It recognizes the user's emotions through facial expression recognition and voice analysis. For example, it uses image processing libraries such as OpenCV to analyze facial expressions and evaluate stress and fatigue levels.

[0808] The recognized emotion information is sent to the edge server and used to generate health advice.

[0809] Personalize health advice and product recommendations based on emotional information, for example, suggesting relaxation techniques if the user is tired.

[0810] This system makes it possible to comprehensively analyze a user's biometric and emotional data and provide personalized health advice.

[0811] Examples of specific examples and prompts

[0812] 1. Daily health management

[0813] The device measures user A's heart rate and sends it to the edge server.

[0814] The server analyzes the measurement data and notifies the user when it is time to hydrate.

[0815] The emotion engine analyzes user A's facial expressions and, if it determines that the user is feeling very tired, notifies them to slow down.

[0816] Example prompt sentence:

[0817] Generate specific health advice based on the user's biometric information. Input heart rate and step count data while jogging, and create prompts based on that data to advise the optimal time to hydrate.

[0818] 2. Long-term sedentary monitoring

[0819] The device detects the amount of time that user B has been sitting and sends this information to the edge server.

[0820] The server analyzes this data and periodically notifies you to stretch.

[0821] The emotion engine analyzes user B's facial expression data and, if it determines that their concentration is declining, notifies them to take a short break.

[0822] Example prompt sentence:

[0823] Generate specific advice for users who sit for long periods in the office, encouraging them to stretch regularly. Furthermore, create prompts based on facial expression data to suggest taking a break if concentration is declining.

[0824] 3. Sleep monitoring using emotion recognition

[0825] The device measures user C's sleep pattern and sends it to the edge server.

[0826] The server analyzes sleep data and suggests a wake-up time that fits the next day's schedule.

[0827] The emotion engine analyzes user C's facial expressions and voice before bed, and if it detects anxiety, it recommends relaxing music.

[0828] Example prompt sentence:

[0829] Analyze the quality of your sleep based on your sleep patterns and suggest the optimal time to wake up based on your schedule for the next day. Furthermore, create prompts that suggest relaxation methods based on your facial expressions and tone of voice before going to bed.

[0830] 4. Safety confirmation including emotion analysis during disasters

[0831] An earthquake occurs, and the device sends user D's location information to the edge server.

[0832] The server automatically analyzes user D's safety and notifies family and friends.

[0833] The emotion engine analyzes user D's facial expressions and voice and provides support information to reduce stress.

[0834] Example prompt sentence:

[0835] Explain a program that checks the user's safety during a disaster and automatically notifies family and friends. Furthermore, analyze the user's emotional data and create prompts that provide support information to reduce stress.

[0836] This system can monitor the user's health and emotional state in real time and provide appropriate support, which can significantly improve the user's quality of life.

[0837] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0838] Step 1:

[0839] The device collects the user's biometric information. Specifically, it obtains heart rate, number of steps, sleep patterns, body temperature, etc. using dedicated sensors and apps (e.g., heart rate measurement apps, pedometer apps, sleep trackers). The input is the user's biometric data, and the output is the collected biometric information.

[0840] Step 2:

[0841] The device encrypts the biometric information collected. Advanced encryption methods such as AES-256 are used to securely protect the data. The input is the collected biometric information, and the output is the encrypted biometric information.

[0842] Step 3:

[0843] The device sends encrypted biometric information to the edge server. HTTPS or other communication protocols are used to ensure secure data transfer. The input is encrypted biometric information, and the output is the biometric information sent to the server.

[0844] Step 4:

[0845] The server receives the encrypted biometric information sent from the terminal and decrypts it. The input is the encrypted biometric information, and the output is the decrypted biometric information.

[0846] Step 5:

[0847] The server stores the decoded biometric information in a database. SQL databases and NoSQL databases are used to efficiently manage data. The input is the decoded biometric information, and the output is stored in the database.

[0848] Step 6:

[0849] The server analyzes the stored biometric information in real time. Using machine learning libraries such as TensorFlow and PyTorch, an AI model analyzes the data. The input is the stored biometric information, and the output is the analysis results. The analysis results are used to assess the user's health status and risk.

[0850] Step 7:

[0851] The server predicts health risks based on the analysis results. If the heart rate is abnormally high or the walking pattern is different from normal, the server evaluates the risk and notifies the user. The input is the analysis results, and the output is a risk notification message.

[0852] Step 8:

[0853] The device collects the user's facial expressions and voice and sends the data to the emotion engine. The input is the user's facial and voice data, and the output is the data sent to the emotion engine.

[0854] Step 9:

[0855] The emotion engine performs facial expression recognition and voice analysis. It uses image processing libraries such as OpenCV to analyze facial expressions and evaluate the user's emotional state and stress level. The input is facial expression and voice data, and the output is the emotion analysis results.

[0856] Step 10:

[0857] The server generates personalized health advice based on the results of emotion analysis and notifies the user. The advice, including specific guidelines and recommendations, is generated and sent to the user via push notification or email. The input is the emotion analysis results and the analysis results of biometric information, and the output is a health advice message.

[0858] Step 11:

[0859] The server proposes health products. It proposes appropriate health products (e.g., supplements, relaxation goods) by taking into account the user's biometric information and emotional state. The input is the biometric information and the results of emotional analysis, and the output is a product proposal message.

[0860] Step 12:

[0861] The health data collected by the server is anonymized. Pseudonymization and data masking techniques are used to prevent individuals from being identified. The input is decoded biometric information, and the output is anonymized data.

[0862] Step 13:

[0863] The server sells the anonymized data to external organizations. The data is securely provided to medical institutions, pharmaceutical companies, etc. The input is anonymized data, and the output is data provided to external organizations.

[0864] Step 14:

[0865] The server monitors the user's location information and detects abnormal behavior patterns. It detects cases of prolonged inactivity or unusual movement patterns and notifies the user. The input is the user's location information, and the output is abnormal behavior detection and notification.

[0866] Step 15:

[0867] The server checks the user's safety in the event of a disaster. When a disaster such as an earthquake occurs, it analyzes the user's safety based on location information and automatically notifies family and friends. It also utilizes an emotion engine to provide support information as needed. The input is location information and emotion data at the time of the disaster, and the output is a safety confirmation and notification message.

[0868] (Application example 2)

[0869] 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."

[0870] Conventional healthcare systems were able to monitor a user's health status and provide advice, but they did not address health management that took into account the user's emotional state or provide lifestyle suggestions based on that. Furthermore, there was no system that could recommend and deliver optimal foods based on the user's health and emotional state. This left users without a way to easily achieve an appropriate diet that matched their own health and emotional state.

[0871] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting the user's biometric information, means for transmitting the collected biometric information to the edge server, means for analyzing the biometric information in the edge server and monitoring and predicting the health status, means for generating personalized health advice based on the analysis results and notifying the user, means for proposing health products, means for anonymizing the biometric information and selling it to external organizations, means for constantly monitoring the user's location information and detecting abnormal behavioral patterns, means for confirming the user's safety in the event of a disaster, means for proposing optimal foods based on the user's health status and emotions, and means for ordering the foods based on the suggestions. This enables the recommendation and delivery of optimal foods according to the user's health status and emotional state.

[0872] "Means for collecting user biometric information" refers to devices or systems that collect health-related data of users, such as heart rate, body temperature, number of steps, and sleep patterns.

[0873] The "means for transmitting biometric information to the edge server" refers to a device or system that transmits collected health-related data of a user to the edge server.

[0874] "Means for analyzing biometric information on an edge server and monitoring and predicting health status" refers to a device or system that analyzes transmitted biometric information, monitors the user's health status in real time, and predicts future health risks.

[0875] "Means for generating individual health advice based on the analysis results and notifying the user" refers to a device or system that generates health advice tailored to each individual user based on the results of analysis performed by the edge server and notifies the user of this advice.

[0876] The "means for proposing health products" refers to a device or system that proposes appropriate health-related products based on the user's health condition and analysis results.

[0877] "Means for anonymizing biometric information and selling it to external organizations" refers to devices or systems that convert collected biometric information into a form that makes it impossible to identify individual users and then sell it to external institutions or organizations.

[0878] "Means for constantly monitoring a user's location information and detecting abnormal behavioral patterns" refers to a device or system that constantly monitors a user's location information and detects behavioral patterns that differ from normal.

[0879] "Means for confirming safety during a disaster" refers to devices or systems that confirm the safety of users when a disaster occurs and automatically notify them of necessary information.

[0880] The "means for suggesting optimal foods based on the user's health condition and emotions" refers to a device or system that suggests optimal foods to a user based on the user's health condition and emotional data.

[0881] A "means for ordering food based on the suggestions" is a device or system that allows a user to order the suggested food.

[0882] This invention is a system that takes into account the user's health condition and emotions, and proposes and delivers the most suitable foods. This system not only collects and analyzes the user's biometric and emotional data to provide health advice, but also proposes the most suitable foods based on the user's health and emotional state, enabling the user to order food based on these suggestions, creating a new service.

[0883] The system consists of the following:

[0884] 1. Device (smartphone)

[0885] Biometric data collection: Using smartphone sensors, biometric data such as the user's heart rate, body temperature, steps taken, and sleep patterns are collected.

[0886] Emotion data collection: The camera and microphone are used to analyze the user's facial expressions and voice, and the data is sent to the emotion engine.

[0887] Data transmission: The collected biometric information and emotion data are encrypted and sent to the edge server.

[0888] 2. Edge Server

[0889] Data reception and storage: Receives biometric and emotional data sent from the device and stores it in a database.

[0890] Real-time analysis: Advanced AI algorithms are used to analyze biometric and emotional data, and the results are used to monitor health conditions and predict health risks.

[0891] Advice generation: Based on the analysis results, individual health advice is generated and notified to the user.

[0892] Product Suggestion: Recommend appropriate health products based on health advice.

[0893] Data anonymization: Health data collected is anonymized so that it can be securely provided to external organizations.

[0894] Location monitoring: Constantly monitors user location information to detect abnormal behavioral patterns. Confirm safety in the event of a disaster.

[0895] Food Suggestion and Ordering: Recommends the best foods based on the user's health and emotional state, and allows the user to order food based on the suggestions.

[0896] Hardware and software used

[0897] Hardware: Smartphones, edge servers

[0898] Software: Biometric information collection app, emotion recognition engine, data analysis AI, database management system, communication protocol (encryption)

[0899] Specific examples

[0900] When a user returns home from work, their smartphone detects their fatigue based on their heart rate, body temperature, and facial expression. The edge server analyzes this data and sends the user a notification suggesting a sushi set with green tea for relaxation. If the user agrees to the suggestion, the system sends an order to the nearest restaurant and arranges for food delivery.

[0901] Example prompts for generative AI models

[0902] Input data: Heart rate = 80, Body temperature = 36.5°C, Sleep pattern = irregular, Facial expression = fatigue, Tone of voice = stress

[0903] Output data: Suggested food = Sushi set with relaxing green tea

[0904] This allows users to easily order the best foods for their health and emotional state.

[0905] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0906] Step 1:

[0907] Data collection

[0908] The device (smartphone) collects the user's biometric information. Specifically, it obtains data such as heart rate, body temperature, number of steps, and sleep patterns from sensors. It also uses the device's camera and microphone to analyze the user's facial expressions and voice and generate emotional data.

[0909] Input: Sensor, Camera, Microphone

[0910] Output: Heart rate, body temperature, steps, sleep patterns, facial expression data, voice data

[0911] Step 2:

[0912] Data transmission

[0913] The biometric and emotional data collected by the device is encrypted and sent to the edge server via a highly secure communication protocol (e.g., HTTPS).

[0914] Input: Heart rate, body temperature, steps, sleep pattern, facial expression data, voice data

[0915] Output: Encrypted data packet

[0916] Step 3:

[0917] Data reception and storage

[0918] The server receives the transmitted data and stores it in a database. Specifically, it uses a database management system (DBMS) to record the transmitted biometric and emotional data in an appropriate format.

[0919] Input: Encrypted data packet

[0920] Output: Biometric and emotional data stored in a database

[0921] Step 4:

[0922] Real-time analytics

[0923] The server analyzes the stored data in real time, using advanced AI algorithms to analyze biometric and emotional data to assess health and emotional status, and predict health risks based on the results.

[0924] Input: Biometric and emotional data stored in a database

[0925] Output: Health status assessment, emotional status assessment, health risk prediction

[0926] Step 5:

[0927] Advice Generation

[0928] The server generates personalized health advice based on the analysis results, specifically using a generative AI model to create health advice tailored to the user.

[0929] Input: Health status assessment, emotional status assessment, health risk prediction

[0930] Output: personalized health advice

[0931] Step 6:

[0932] notification

[0933] The server notifies the device of the generated health advice, specifically by sending the advice directly to the user via mobile notifications or email.

[0934] Input: personalized health advice

[0935] Output: User notification

[0936] Step 7:

[0937] food suggestions

[0938] The server will suggest the best food based on the user's health and emotional state, and will create a list of food delivery options based on the results of data analysis.

[0939] Input: Health status assessment, emotional status assessment

[0940] Output: Food suggestion list

[0941] Step 8:

[0942] Food ordering

[0943] The user selects the suggested food items and confirms the order. Specifically, the user confirms the food selection on the device and sends the order to the nearest restaurant.

[0944] Input: Food suggestion list

[0945] Output: Restaurant order data

[0946] Step 9:

[0947] Delivery arrangements

[0948] The restaurant receives the order and arranges for the food to be delivered. Specifically, the restaurant that received the order uses a delivery service to deliver the food to the user.

[0949] Input: Restaurant order data

[0950] Output: Food delivered to the user

[0951] In this way, the system can utilize the user's health and emotional information to provide optimal health advice and food suggestions, thereby improving the user's quality of life.

[0952] 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.

[0953] 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.

[0954] 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.

[0955] [Third embodiment]

[0956] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0957] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0958] 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).

[0959] 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.

[0960] 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.

[0961] 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).

[0962] 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.

[0963] 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.

[0964] 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.

[0965] 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.

[0966] 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.

[0967] 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."

[0968] This invention is a system that manages a user's health condition in real time and provides appropriate health advice. This system consists of a device (smartphone) that collects the user's biometric information, a server (edge ​​server) that analyzes and predicts that information, and an interface that notifies the user.

[0969] System Overview

[0970] This system first collects the user's biometric information. This is performed by a device (smartphone), which continuously collects biometric information such as heart rate, number of steps, sleep patterns, and body temperature. This data is then sent to a server (edge ​​server) using an encrypted communication protocol.

[0971] The edge server is equipped with advanced AI algorithms that analyze the received biometric information in real time. This analysis allows the user's current health status to be monitored and future health risks to be predicted. Based on the analysis results, personalized health advice supervised by experts is generated and communicated to the user.

[0972] The server also suggests health products, selecting appropriate products and services based on the user's health condition and notifying the device. It also has the ability to anonymize the collected health data and safely sell it to external organizations (medical institutions, pharmaceutical companies, etc.). This allows the data to be used for research and development of new health services while maintaining its security.

[0973] Location monitoring is also a very important function. The device constantly obtains the user's location information and sends it to a server. The server analyzes this data and detects abnormal behavioral patterns. If an abnormality is detected, the user is immediately notified. In addition, in the event of a disaster, the location information can be used to quickly check the user's safety and automatically notify family and friends.

[0974] Specific examples

[0975] 1. Daily health management

[0976] User A starts jogging at 7:00 a.m. The device measures the heart rate and number of steps in real time and sends the data to the edge server.

[0977] The server analyzes the received data, generates advice on the appropriate timing for hydration and jogging pace, and notifies the device.

[0978] User A receives the notification and can continue jogging in a healthy manner based on the advice.

[0979] 2. Long-term sedentary monitoring

[0980] User B works in an office while sitting for a long time. The terminal measures the time spent sitting and sends the data to the server.

[0981] The server detects that the user has been sitting still for a long period of time and generates and sends a notification to the device to encourage the user to stand up and stretch periodically.

[0982] User B receives the notification and stretches for health reasons.

[0983] 3. Sleep monitoring

[0984] User C goes to bed and the device measures his / her sleep patterns. The data is sent to the server for analysis.

[0985] The server analyzes the time spent in deep sleep and the quality of sleep, generates advice to improve performance the next day, and notifies the device.

[0986] User C receives the advice and takes action to improve their sleep quality.

[0987] 4. Confirming safety during disasters

[0988] When a disaster such as an earthquake occurs, user D is in the affected area.

[0989] The device sends its location information to a server, which then verifies the user's location in the disaster area.

[0990] The server checks the safety of user D and automatically notifies family and friends.

[0991] In this way, this system manages the user's health in real time and helps improve their health through appropriate advice and product suggestions.

[0992] The processing flow will be explained below.

[0993] Step 1:

[0994] The device continuously collects the user's biometric information (heart rate, number of steps, sleep patterns, body temperature, etc.) using sensors.

[0995] Step 2:

[0996] The device uses a communication protocol (e.g., HTTPS) to encrypt the collected biometric information and send it to the edge server. The transmitted data is given a timestamp.

[0997] Step 3:

[0998] The server stores the received data in a database in NoSQL format, with a structure suitable for real-time analysis.

[0999] Step 4:

[1000] The server feeds the data into AI algorithms for real-time analysis, including predictive models using historical data.

[1001] Step 5:

[1002] The server evaluates the user's current health status based on the analysis results and identifies predicted health risks.

[1003] Step 6:

[1004] The server generates health advice that has been supervised by experts and transmits the advice to the terminal.

[1005] Step 7:

[1006] The device will notify the user of the health advice it receives, which will be displayed as a pop-up message and / or an audio alert.

[1007] Step 8:

[1008] The server then recommends appropriate health products and services based on the user's health status, and the recommendations are sent to the device.

[1009] Step 9:

[1010] The device displays suggestions for health products and services to users, including links to purchase products and gym discounts.

[1011] Step 10:

[1012] The server anonymizes the collected biometric information and prepares it for sale to external organizations (medical institutions, pharmaceutical companies, etc.). The sales data does not include any information that can identify individual users.

[1013] Step 11:

[1014] The device continuously collects the user's location information and sends it to a server, which is also encrypted.

[1015] Step 12:

[1016] The server analyzes location information in real time and detects abnormal behavior that deviates from normal patterns of behavior.

[1017] Step 13:

[1018] If the server detects any abnormal behavioral patterns, it will immediately alert the user and, if necessary, notify emergency contacts.

[1019] Step 14:

[1020] In the event of a disaster, the server will quickly check the safety of registered users based on their location information, and the results will be automatically sent to registered family and friends.

[1021] Through these steps, the system can manage users' health status in real time, provide appropriate health advice, and respond quickly in emergencies.

[1022] Example 1

[1023] 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."

[1024] In modern society, it is important to manage a user's health status in real time and provide appropriate individualized health guidance. However, current systems have difficulty integrating multiple functions, such as collecting and analyzing biometric information, generating health guidance, and confirming safety in the event of a disaster. Therefore, there is a need to efficiently manage both the user's health and confirm their safety. Furthermore, security and privacy protection of biometric information are also major issues, and a system for utilizing data while satisfying these needs is required.

[1025] 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.

[1026] In this invention, the server includes means for collecting user biometric information, means for transmitting the collected biometric information to an edge device, means for analyzing the biometric information at the edge device and monitoring and predicting the health status, means for generating individual health advice based on the analysis results and notifying the user, means for suggesting health products, means for anonymizing the biometric information and providing it to an external organization, means for constantly monitoring the user's location information and detecting abnormal movement patterns, and means for confirming the user's safety in the event of a disaster. This enables the secure collection and analysis of the user's biometric information, real-time health management, and rapid safety confirmation in the event of a disaster.

[1027] "User" means an individual who uses the system to manage their own health status.

[1028] "Biometric information" refers to data about the body, such as heart rate, number of steps taken, sleep patterns, and body temperature.

[1029] A "means" is a method or device used to achieve a particular purpose.

[1030] "Edge devices" refers to multiple devices, typically including servers, that process and analyze collected data.

[1031] "Analysis" is the process of examining collected biometric information in detail and finding meaning.

[1032] "Health" refers to a user's physical and mental state, as indicated by biometric information such as heart rate and body temperature.

[1033] "Health Guidance" refers to specific advice or suggestions to help users improve or maintain their health.

[1034] "Notification" means a message or alert intended to convey information from the system to the user.

[1035] "Health products" refers to goods and services that help users improve or maintain their health.

[1036] "Anonymization" is the process of processing data so that it cannot be used to identify individuals.

[1037] An "external institution" is an external organization that uses a user's biometric information, such as a medical institution or pharmaceutical company.

[1038] "Location Information" means data about a user's geographic location.

[1039] "Abnormal behavior patterns" are data that show movements or situations that differ from normal behavior.

[1040] "Safety confirmation during a disaster" refers to the process of confirming the safety of users when a disaster such as an earthquake or typhoon occurs.

[1041] This invention is a system for managing a user's health status in real time and providing appropriate health guidance. The system mainly consists of a terminal that collects the user's biometric information, an edge device that analyzes the information, and an interface that sends notifications.

[1042] First, the system collects the user's biometric information through a mobile device such as a smartphone, including heart rate, steps taken, sleep patterns, body temperature, etc. The smartphone records the user's condition in real time using data from built-in sensors and external devices.

[1043] The collected biometric information is then securely transmitted to the edge device (server) using encrypted communication protocols (e.g., SSL or TLS). The server then analyzes the received data using advanced AI algorithms (e.g., TensorFlow or PyTorch). This allows the user's current health status to be monitored and future health risks to be predicted.

[1044] Based on the analysis results, the server generates a personalized health guide, supervised by an expert, including specific advice on exercise, diet, rest, etc. The advice is then encrypted and sent back to the smartphone, where it is displayed to the user as a push notification or in-app alert.

[1045] In addition, the server will suggest health products and services tailored to the user's health condition, including supplements, fitness programs, medical devices, etc. The suggestions will also be sent to the device, providing the user with the opportunity to receive appropriate products and services.

[1046] The collected biometric information is anonymized by the server and provided to external organizations such as medical institutions and pharmaceutical companies, which makes it possible to provide data for new health services and research while maintaining data security and privacy.

[1047] Smartphones also constantly collect the user's location information and send it to a server. The server analyzes the location information and promptly notifies the user if it detects abnormal behavior patterns or an emergency. This is particularly useful in the event of a disaster such as an earthquake or typhoon, as it allows for quick safety confirmation and automatically sends safety confirmation notifications to the user's family and friends.

[1048] As a concrete example, consider the case where User A starts jogging at 7:00 AM. The device measures the heart rate and number of steps in real time and sends them to the edge device. The server analyzes this data, generates advice on appropriate hydration timing and jogging pace, and notifies the device. User A receives this advice and continues jogging in a healthy manner.

[1049] Additionally, personalized advice can be generated by inputting prompts like the following into the generative AI model:

[1050] "Generate optimal health advice based on the following user situation: User A started jogging at 7 AM, their current heart rate is 150 bpm, and they have taken 1500 steps."

[1051] In this way, the system manages the user's health condition in real time and provides appropriate advice and product suggestions, thereby helping to improve the user's health and ensure safety.

[1052] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1053] Step 1:

[1054] The user launches the dedicated app and begins their daily activities. For example, to start jogging, they launch the app and press the start button.

[1055] Input: User operation (launching an app, pressing the start button)

[1056] Output: The app's sensors are activated and data collection begins.

[1057] Step 2:

[1058] The device (smartphone) collects biometric information such as heart rate, number of steps, sleep patterns, and body temperature in real time from built-in sensors and external devices. The sensors detect the user's physical movements and acquire the data.

[1059] Input: User's biometric information (heart rate, steps, sleep pattern, body temperature)

[1060] Output: A dataset of collected biometric information

[1061] Step 3:

[1062] The collected biometric information is securely transmitted to the edge device (server) using encrypted communication protocols (e.g., SSL or TLS). The device periodically divides the data into packets and transmits them to the server.

[1063] Input: Biometric dataset

[1064] Output: Encrypted data packet

[1065] Step 4:

[1066] The server stores the received biometric information in a database and analyzes it using advanced AI algorithms (such as TensorFlow and PyTorch). The server calculates heart rate fluctuations and step rhythms to check for abnormalities.

[1067] Input: Encrypted data packet

[1068] Output: Analysis results (heart rate fluctuations, step rhythm, presence or absence of abnormalities)

[1069] Step 5:

[1070] Based on the analysis results, the server evaluates the user's current health status and predicts future health risks, for example, using models to predict elevated blood pressure and abnormal heart rate.

[1071] Input: Analysis results (heart rate fluctuations, step rhythm, presence or absence of abnormalities)

[1072] Output: Health status assessment and health risk prediction

[1073] Step 6:

[1074] The server generates personalized health advice supervised by experts based on the health status assessment and health risk prediction results. Using a generative AI model, it creates the most appropriate advice for the user in text format.

[1075] Input: Health status assessment and health risk prediction

[1076] Output: Health advice text message

[1077] Step 7:

[1078] The server encrypts the generated health advice and sends it to the device, which then decrypts the received message and notifies the user.

[1079] Input: Health advice text message

[1080] Output: Encrypted health advice message

[1081] Step 8:

[1082] The device will provide users with health guidance via push notifications and in-app alerts, such as messages like "stay hydrated."

[1083] Input: Encrypted health advice message

[1084] Output: A notification message that is displayed to the user.

[1085] Step 9:

[1086] The server will suggest health products and services based on the user's health status, and this information will also be encrypted and sent to the device, where it will be displayed as a notification.

[1087] Input: Health status assessment and health risk prediction

[1088] Output: Encrypted health product suggestion message

[1089] Step 10:

[1090] The server anonymizes the biometric information and safely provides it to external organizations such as medical institutions and pharmaceutical companies. The information is then anonymized and stored in a database.

[1091] Input: Biometric dataset

[1092] Output: Anonymized dataset

[1093] Step 11:

[1094] The device constantly acquires the user's location information and periodically sends it to the server, which then monitors for abnormal behavior patterns and notifies the user if necessary.

[1095] Input: User's location

[1096] Output: A dataset of location information

[1097] Step 12:

[1098] When a disaster such as an earthquake or typhoon occurs, the device sends its location information to the server in real time, and the server quickly checks the user's safety and automatically notifies family and friends.

[1099] Input: Location information at the time of disaster

[1100] Output: Safety confirmation notification

[1101] (Application example 1)

[1102] 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."

[1103] In recent years, users have become increasingly interested in managing their health, creating a demand for systems that provide advice tailored to individual health conditions. However, existing systems only collect and analyze users' biometric information, making it difficult to optimally reflect it in their actual social lives and consumer behavior. Furthermore, they lack safety confirmation functions in the event of a disaster, delaying the provision of comprehensive health management systems. Therefore, a new system is needed that utilizes users' health information to support their behavior in the real world.

[1104] 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.

[1105] In this invention, the server includes means for collecting user biometric information, means for transmitting the collected biometric information to an edge server, means for analyzing the biometric information in the edge server and monitoring and predicting the health status, means for generating personalized health advice based on the analysis results and notifying the user, means for proposing health products, means for anonymizing the biometric information and selling it to an external organization, means for constantly monitoring the user's location information and detecting abnormal behavioral patterns, means for confirming the user's safety in the event of a disaster, and means for proposing products in a physical store based on the user's health information. This enables appropriate behavior and product suggestions in the real world based on the user's health information.

[1106] "User biometric information" refers to numerical data related to a person's physical condition, such as heart rate, number of steps, body temperature, and sleep patterns.

[1107] An "edge server" is a server that receives data sent by users in real time and performs analysis and predictions.

[1108] "Health monitoring and prediction tools" refers to algorithms and programs that analyze collected biometric information to determine current health status and predict future risks.

[1109] "Individualized health advice" is advice based on the analysis of a user's biometric information, providing suggestions for improving behaviors and habits that are optimal for each individual's health condition.

[1110] "Health product suggestion" is the process of recommending appropriate products and services to users according to their analyzed health status.

[1111] "Biometric de-identification" is the process of removing or modifying personal information from collected biometric data so that it cannot be used to identify individual users.

[1112] "External organizations" are third-party organizations, such as medical institutions and pharmaceutical companies, that conduct research or provide services using users' biometric information.

[1113] "Means for constantly monitoring location information" refers to technologies and devices that continuously obtain the user's current location and identify abnormal behavior or trends during disasters.

[1114] "Abnormal behavior" refers to behavior or movement patterns that are different from normal and are predicted to pose a risk to the user's health or safety.

[1115] "Means for checking safety during a disaster" is a system that uses the user's location information to confirm the user's safety when a disaster occurs and notifies family members and other relevant parties of that information.

[1116] The "means of making product suggestions within a physical store" is a system that analyzes a user's health information and makes real-time suggestions about products that are recommended for purchase within the store where the user is currently located.

[1117] To implement the present invention, a system is constructed and operated in the following manner.

[1118] System configuration

[1119] 1. Data collection and transmission

[1120] The user uses a device (smartphone) to collect biometric information. This smartphone is equipped with a heart rate sensor and an acceleration sensor to acquire biometric information such as heart rate, number of steps, sleep pattern, and body temperature. The collected data is sent to the edge server using an encrypted communication protocol (e.g., TLS / SSL).

[1121] 2. Data Analysis

[1122] The edge server analyzes the received data using AI algorithms (e.g., TensorFlow, PyTorch, etc.), which monitors the user's current health status and predicts future health risks. The analysis results are updated in real time.

[1123] 3. Advice and Notice

[1124] Based on the analysis results, the edge server generates individual health advice, which is then sent to the user's smartphone via push notification. The advice helps users improve their health behavior and lifestyle habits.

[1125] 4. Health product suggestions

[1126] The edge server selects products and services suited to the user's health condition, and the user receives real-time recommendations, including health foods and fitness products.

[1127] 5. Anonymization and sale of data

[1128] The biometric information will be anonymized and sold to external organizations (e.g., medical institutions, pharmaceutical companies, etc.) This process will use secure data anonymization techniques to ensure the protection of personal information.

[1129] 6. Location Monitoring and Anomaly Detection

[1130] The user's smartphone constantly collects location information and sends it to the edge server. The edge server analyzes this data and detects abnormal behavioral patterns. If an abnormality is detected, the user is immediately notified. In addition, in the event of a disaster, the user's safety is quickly confirmed based on the location information, and family and friends are automatically notified.

[1131] 7. Product proposals in physical stores

[1132] The app will suggest products based on health information while users are in physical stores. These suggestions are optimized based on the user's current location within the store and sent to the user's smartphone. For example, a user with high stress levels will be recommended products with a relaxation effect.

[1133] Examples and prompts

[1134] Examples:

[1135] User A visits a physical store called "Health Shop." User A's smartphone collects real-time information such as heart rate, number of steps, and body temperature. The edge server analyzes this information and detects that User A's recent stress level has been high. Based on this, the server recommends relaxing herbal teas and stress-relieving products sold in the store. This information is displayed on User A's smartphone via push notification, allowing User A to purchase products based on that information.

[1136] Example prompt sentence:

[1137] "I want to create a smartphone app that collects heart rate, steps, and body temperature in real time, analyzes the user's health status, and recommends appropriate health products. If it detects that the user is feeling stressed, it should suggest products that are effective for relieving stress in a physical store."

[1138] This will enable appropriate actions and product suggestions in the real world based on the user's health information, improving the user's quality of life.

[1139] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1140] Step 1:

[1141] The user's smartphone collects biometric information such as heart rate, number of steps, and body temperature using the smartphone's built-in heart rate sensor and accelerometer. The input here is the user's real-time biometric information, and the output is the collected biometric data.

[1142] Step 2:

[1143] The device sends the collected biometric data to the edge server using an encrypted communication protocol (e.g., TLS / SSL). With this operation, the input is the data sent through the encryption protocol, and the output is the secure data transfer to the edge server.

[1144] Step 3:

[1145] The edge server analyzes the received biometric data using a generative AI model (e.g., TensorFlow or PyTorch). The input is the biometric data received by the edge server, and the output is the user's current health status and predicted future health risks.

[1146] Step 4:

[1147] The server generates individual health advice based on the analysis results. This advice generation is based on an algorithm supervised by experts. The input here is the health status analysis data, and the output is health advice to be notified to the user.

[1148] Step 5:

[1149] The device receives health advice via push notifications from the server. These notifications include specific behavioral guidelines and recommended products. The input is advice data based on the analysis results, and the output is a notification to the user.

[1150] Step 6:

[1151] The server suggests health products and related services that are appropriate for the user's health status using a real-time recommendation algorithm, whose input is the user's current health status data and whose output is a list of recommended health products.

[1152] Step 7:

[1153] The terminal displays recommended health products and services to support in-store activities. The input here is the recommended product information sent from the server, and the output is the product information displayed on the user's smartphone screen.

[1154] Step 8:

[1155] The server anonymizes the biometric data and sells it to external organizations. This process uses data anonymization tools and techniques. The input is biometric data and the output is anonymized data.

[1156] Step 9:

[1157] The device constantly monitors the user's location information and sends it to the server. This operation uses location information technologies such as GPS and Wi-Fi. The input is the user's location data, and the output is location information data sent to the edge server.

[1158] Step 10:

[1159] The server analyzes the received location data and detects abnormal behavioral patterns. In the event of a disaster, it also checks the user's safety and notifies relevant parties. The input is location data and disaster data, and the output is notifications of abnormal behavior and safety information.

[1160] This will enable appropriate actions and product suggestions in the real world based on the user's health information, improving the user's quality of life.

[1161] 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.

[1162] This invention is a system for managing a user's health status and providing appropriate health advice. By combining it with an emotion engine that recognizes the user's emotions, it is possible to provide optimal emotional advice. This system includes a device (smartphone) that collects the user's biometric information, an edge server, and an emotion engine.

[1163] System Overview

[1164] This system is configured as follows.

[1165] 1. Device (smartphone)

[1166] It collects biometric information such as the user's heart rate, steps, sleep patterns, and body temperature.

[1167] The biometric information is encrypted and sent to the edge server.

[1168] It uses a camera and microphone to analyze the user's facial expressions and voice and sends the data to an emotion engine.

[1169] 2. Edge Server

[1170] The biometric information sent from the terminal is received and stored in a database.

[1171] Using advanced AI algorithms, it analyzes biometric information in real time and monitors health conditions.

[1172] Based on the analysis results, the user's health risks are predicted.

[1173] It takes into account data from the emotion engine to generate personalized health advice.

[1174] Propose health products.

[1175] The collected health data is anonymized and securely sold to external organizations (medical institutions, pharmaceutical companies, etc.).

[1176] Monitors user location information and detects abnormal behavioral patterns.

[1177] In the event of a disaster, the system will check the safety of users and automatically notify them.

[1178] 3. Emotion Engine

[1179] Recognizes user emotions through facial expression recognition and voice analysis.

[1180] The recognized emotion information is sent to the edge server.

[1181] Customize health advice and product suggestions based on emotional information.

[1182] Specific examples

[1183] 1. Daily health management

[1184] User A starts jogging in the morning. The device measures the heart rate and number of steps and sends the data to the edge server.

[1185] The server analyzes the data and notifies the user when it is time to hydrate.

[1186] The emotion engine analyzes User A's facial expressions and voice, and if it senses that the user is feeling very tired, it will advise them to slow down their pace.

[1187] 2. Long-term sedentary monitoring

[1188] User B has been sitting in the office for a long time. The device detects this and sends a message to the server.

[1189] The server analyzes how long you have been sitting and notifies you to stretch periodically.

[1190] The emotion engine detects from User B's facial expression that he is losing concentration and advises him to take a short break.

[1191] 3. Sleep monitoring using emotion recognition

[1192] User C goes to bed at night. The device measures his sleep pattern and sends it to the server.

[1193] The server analyzes the quality of sleep and suggests a wake-up time that matches the next day's schedule.

[1194] The emotion engine analyzes facial expressions and tone of voice before bed, and if anxiety is detected, it suggests relaxing music and content.

[1195] 4. Safety confirmation including emotion analysis during disasters

[1196] An earthquake occurs and user D is in the affected area. The device sends location information to the server.

[1197] The server analyzes the safety of user D and automatically notifies family and friends.

[1198] The emotion engine analyzes user D's facial expressions and voice and provides support information to reduce stress as needed.

[1199] In this way, the system, which combines an emotion engine, can gain a deeper understanding of the user's health condition and provide optimal emotional support, thereby improving the user's quality of life.

[1200] The processing flow will be explained below.

[1201] Step 1:

[1202] The device uses sensors to continuously collect biometric information such as the user's heart rate, number of steps taken, sleep patterns, and body temperature.

[1203] Step 2:

[1204] The device uses a communication protocol (e.g., HTTPS) to encrypt the collected biometric information and send it to the edge server. The transmitted data is given a timestamp.

[1205] Step 3:

[1206] The device collects the user's facial expressions and voice using a camera and microphone, and processes the data to send it to the emotion engine.

[1207] Step 4:

[1208] The emotion engine analyzes facial and voice data sent from the device and recognizes the user's emotions in real time using AI algorithms.

[1209] Step 5:

[1210] The emotion engine sends the recognized emotion information to the edge server.

[1211] Step 6:

[1212] The server receives the biometric information sent from the device and stores it in a database. The database is in NoSQL format and has a structure suitable for real-time analysis.

[1213] Step 7:

[1214] The server then inputs the received biometric information into an AI algorithm for real-time analysis, which includes predictive models using past data.

[1215] Step 8:

[1216] The server also integrates emotional data from the emotion engine into the analysis results to assess the user's current health status and identify predicted health risks.

[1217] Step 9:

[1218] The server takes into account the analysis results and emotional information to generate personalized health advice for the user, which is optimized according to the user's emotional state.

[1219] Step 10:

[1220] The server sends the generated health advice to the device, which displays it as a pop-up notification or a voice message.

[1221] Step 11:

[1222] The device receives health advice from the server and notifies the user, who then checks the notification and follows the advice.

[1223] Step 12:

[1224] The server then recommends appropriate health products and services based on the user's health status, and also reflects their emotional information.

[1225] Step 13:

[1226] The device receives health product and service recommendations from the server and displays them to the user, including links to purchase products and gym discounts.

[1227] Step 14:

[1228] The server anonymizes the collected biometric information and prepares it for sale to external organizations (medical institutions, pharmaceutical companies, etc.). The sales data does not include any information that can identify individual users.

[1229] Step 15:

[1230] The device continuously collects the user's location information and sends it to a server, which is also encrypted.

[1231] Step 16:

[1232] The server analyzes location information in real time and detects abnormal behavior that deviates from normal patterns of behavior.

[1233] Step 17:

[1234] If the server detects any abnormal behavioral patterns, it will immediately alert the user and, if necessary, notify emergency contacts.

[1235] Step 18:

[1236] In the event of a disaster, the server will quickly check the safety of registered users based on their location information, and the results will be automatically sent to registered family and friends.

[1237] This system provides comprehensive support from daily health management to emergency response, improving the health and safety of users.

[1238] Example 2

[1239] 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."

[1240] In modern society, personal health and mental health management are becoming increasingly important. However, conventional health management systems are limited to collecting biometric information and simple data analysis, making it difficult to provide comprehensive health advice that takes into account the user's emotional state. Furthermore, issues such as the security of collected data, privacy protection, and rapid safety confirmation in the event of a disaster remain unresolved. Therefore, there is a need for a system that can simultaneously analyze a user's biometric information and emotional data to provide comprehensive health management and safety.

[1241] 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.

[1242] In this invention, the server includes means for receiving and analyzing the user's biometric information and monitoring and predicting the health condition, means for analyzing facial expressions and voice data using an emotion engine, and means for generating personalized health advice based on the analysis results and emotion information and notifying the user. This makes it possible to comprehensively analyze the user's biometric information and emotional state and provide personalized health advice.

[1243] "Biometric information" is data that indicates the user's physical condition, such as heart rate, number of steps, sleep pattern, and body temperature.

[1244] An "edge server" is a server located at the edge of a network for the purpose of collecting, analyzing, and storing data.

[1245] An "emotion engine" is software and algorithms that analyze facial expressions and voice to recognize a user's emotional state.

[1246] "Data analysis" is the process of applying statistical processing and machine learning algorithms to collected data to extract meaningful information.

[1247] "Health advice" is specific course of action or recommendations provided based on a user's health or emotional state.

[1248] "Health products" are goods and services that aim to maintain or improve the health of users.

[1249] "Anonymization" is the process of removing personally identifiable information from data, enhancing the protection of personal information.

[1250] An "external organization" is a corporation or organization outside the system, such as a medical institution or pharmaceutical company.

[1251] "Location information" is data that indicates a user's current geographic location and is collected using technologies such as GPS and Wi-Fi.

[1252] "Abnormal behavioral patterns" refer to user activity or behavior that is significantly different from normal behavior and may pose a danger or health risk.

[1253] "Safety confirmation" is a process for checking whether users are safe in the event of a disaster, and notifying them if necessary.

[1254] MODE FOR CARRYING OUT THE INVENTION

[1255] This invention is a system for managing a user's health status and providing appropriate health advice. By combining it with an emotion engine that recognizes the user's emotions, it is possible to provide optimal emotional advice. The system includes a device (smartphone) that collects the user's biometric information, an edge server, and an emotion engine.

[1256] System Overview

[1257] This system is configured as follows.

[1258] 1. Device (smartphone)

[1259] Dedicated sensors and apps are used to collect biometric information such as the user's heart rate, number of steps, sleep patterns, body temperature, etc. Examples include heart rate measurement apps, pedometer apps, and sleep trackers.

[1260] The biometric information is encrypted and sent to the edge server using advanced encryption methods such as AES-256.

[1261] It uses a camera and microphone to analyze the user's facial expressions and voice and sends the data to an emotion engine.

[1262] 2. Edge Server

[1263] The biometric information sent from the device is received and stored in a database, typically an SQL database or NoSQL database.

[1264] It uses advanced AI algorithms to analyze biometric information and monitor health conditions in real time, leveraging machine learning libraries such as TensorFlow and PyTorch.

[1265] Based on the analysis results, the app predicts the user's health risks. For example, if their heart rate is abnormally high or their walking pattern is different from normal, the app notifies the user of the risk via push notification or email.

[1266] Taking into account data from the emotion engine, personalized health advice is generated and communicated to the user, including specific courses of action and recommendations.

[1267] Propose health products, such as supplements to help recover from fatigue or relaxation products.

[1268] We anonymize collected health data and sell it securely to external organizations (medical institutions, pharmaceutical companies, etc.) We use pseudonymization and data masking techniques to prevent individuals from being identified.

[1269] It monitors your location and detects unusual patterns of activity, which can help detect prolonged inactivity or unusual movement patterns.

[1270] In the event of a disaster, the app will check the user's safety and automatically notify them. When an earthquake or other disaster occurs, it will analyze the user's safety based on location information and notify family and friends.

[1271] 3. Emotion Engine

[1272] It recognizes the user's emotions through facial expression recognition and voice analysis. For example, it uses image processing libraries such as OpenCV to analyze facial expressions and evaluate stress and fatigue levels.

[1273] The recognized emotion information is sent to the edge server and used to generate health advice.

[1274] Personalize health advice and product recommendations based on emotional information, for example, suggesting relaxation techniques if the user is tired.

[1275] This system makes it possible to comprehensively analyze a user's biometric and emotional data and provide personalized health advice.

[1276] Examples of specific examples and prompts

[1277] 1. Daily health management

[1278] The device measures user A's heart rate and sends it to the edge server.

[1279] The server analyzes the measurement data and notifies the user when it is time to hydrate.

[1280] The emotion engine analyzes user A's facial expressions and, if it determines that the user is feeling very tired, notifies them to slow down.

[1281] Example prompt sentence:

[1282] Generate specific health advice based on the user's biometric information. Input heart rate and step count data while jogging, and create prompts based on that data to advise the optimal time to hydrate.

[1283] 2. Long-term sedentary monitoring

[1284] The device detects the amount of time that user B has been sitting and sends this information to the edge server.

[1285] The server analyzes this data and periodically notifies you to stretch.

[1286] The emotion engine analyzes user B's facial expression data and, if it determines that their concentration is declining, notifies them to take a short break.

[1287] Example prompt sentence:

[1288] Generate specific advice for users who sit for long periods in the office, encouraging them to stretch regularly. Furthermore, create prompts based on facial expression data to suggest taking a break if concentration is declining.

[1289] 3. Sleep monitoring using emotion recognition

[1290] The device measures user C's sleep pattern and sends it to the edge server.

[1291] The server analyzes sleep data and suggests a wake-up time that fits the next day's schedule.

[1292] The emotion engine analyzes user C's facial expressions and voice before bed, and if it detects anxiety, it recommends relaxing music.

[1293] Example prompt sentence:

[1294] Analyze the quality of your sleep based on your sleep patterns and suggest the optimal time to wake up based on your schedule for the next day. Furthermore, create prompts that suggest relaxation methods based on your facial expressions and tone of voice before going to bed.

[1295] 4. Safety confirmation including emotion analysis during disasters

[1296] An earthquake occurs, and the device sends user D's location information to the edge server.

[1297] The server automatically analyzes user D's safety and notifies family and friends.

[1298] The emotion engine analyzes user D's facial expressions and voice and provides support information to reduce stress.

[1299] Example prompt sentence:

[1300] Explain a program that checks the user's safety during a disaster and automatically notifies family and friends. Furthermore, analyze the user's emotional data and create prompts that provide support information to reduce stress.

[1301] This system can monitor the user's health and emotional state in real time and provide appropriate support, which can significantly improve the user's quality of life.

[1302] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1303] Step 1:

[1304] The device collects the user's biometric information. Specifically, it obtains heart rate, number of steps, sleep patterns, body temperature, etc. using dedicated sensors and apps (e.g., heart rate measurement apps, pedometer apps, sleep trackers). The input is the user's biometric data, and the output is the collected biometric information.

[1305] Step 2:

[1306] The device encrypts the biometric information collected. Advanced encryption methods such as AES-256 are used to securely protect the data. The input is the collected biometric information, and the output is the encrypted biometric information.

[1307] Step 3:

[1308] The device sends encrypted biometric information to the edge server. HTTPS or other communication protocols are used to ensure secure data transfer. The input is encrypted biometric information, and the output is the biometric information sent to the server.

[1309] Step 4:

[1310] The server receives the encrypted biometric information sent from the terminal and decrypts it. The input is the encrypted biometric information, and the output is the decrypted biometric information.

[1311] Step 5:

[1312] The server stores the decoded biometric information in a database. SQL databases and NoSQL databases are used to efficiently manage data. The input is the decoded biometric information, and the output is stored in the database.

[1313] Step 6:

[1314] The server analyzes the stored biometric information in real time. Using machine learning libraries such as TensorFlow and PyTorch, an AI model analyzes the data. The input is the stored biometric information, and the output is the analysis results. The analysis results are used to assess the user's health status and risk.

[1315] Step 7:

[1316] The server predicts health risks based on the analysis results. If the heart rate is abnormally high or the walking pattern is different from normal, the server evaluates the risk and notifies the user. The input is the analysis results, and the output is a risk notification message.

[1317] Step 8:

[1318] The device collects the user's facial expressions and voice and sends the data to the emotion engine. The input is the user's facial and voice data, and the output is the data sent to the emotion engine.

[1319] Step 9:

[1320] The emotion engine performs facial expression recognition and voice analysis. It uses image processing libraries such as OpenCV to analyze facial expressions and evaluate the user's emotional state and stress level. The input is facial expression and voice data, and the output is the emotion analysis results.

[1321] Step 10:

[1322] The server generates personalized health advice based on the results of emotion analysis and notifies the user. The advice, including specific guidelines and recommendations, is generated and sent to the user via push notification or email. The input is the emotion analysis results and the analysis results of biometric information, and the output is a health advice message.

[1323] Step 11:

[1324] The server proposes health products. It proposes appropriate health products (e.g., supplements, relaxation goods) by taking into account the user's biometric information and emotional state. The input is the biometric information and the results of emotional analysis, and the output is a product proposal message.

[1325] Step 12:

[1326] The health data collected by the server is anonymized. Pseudonymization and data masking techniques are used to prevent individuals from being identified. The input is decoded biometric information, and the output is anonymized data.

[1327] Step 13:

[1328] The server sells the anonymized data to external organizations. The data is securely provided to medical institutions, pharmaceutical companies, etc. The input is anonymized data, and the output is data provided to external organizations.

[1329] Step 14:

[1330] The server monitors the user's location information and detects abnormal behavior patterns. It detects cases of prolonged inactivity or unusual movement patterns and notifies the user. The input is the user's location information, and the output is abnormal behavior detection and notification.

[1331] Step 15:

[1332] The server checks the user's safety in the event of a disaster. When a disaster such as an earthquake occurs, it analyzes the user's safety based on location information and automatically notifies family and friends. It also utilizes an emotion engine to provide support information as needed. The input is location information and emotion data at the time of the disaster, and the output is a safety confirmation and notification message.

[1333] (Application example 2)

[1334] 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."

[1335] Conventional healthcare systems were able to monitor a user's health status and provide advice, but they did not address health management that took into account the user's emotional state or provide lifestyle suggestions based on that. Furthermore, there was no system that could recommend and deliver optimal foods based on the user's health and emotional state. This left users without a way to easily achieve an appropriate diet that matched their own health and emotional state.

[1336] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting the user's biometric information, means for transmitting the collected biometric information to the edge server, means for analyzing the biometric information in the edge server and monitoring and predicting the health status, means for generating personalized health advice based on the analysis results and notifying the user, means for proposing health products, means for anonymizing the biometric information and selling it to external organizations, means for constantly monitoring the user's location information and detecting abnormal behavioral patterns, means for confirming the user's safety in the event of a disaster, means for proposing optimal foods based on the user's health status and emotions, and means for ordering the foods based on the suggestions. This enables the recommendation and delivery of optimal foods according to the user's health status and emotional state.

[1337] "Means for collecting user biometric information" refers to devices or systems that collect health-related data of users, such as heart rate, body temperature, number of steps, and sleep patterns.

[1338] The "means for transmitting biometric information to the edge server" refers to a device or system that transmits collected health-related data of a user to the edge server.

[1339] "Means for analyzing biometric information on an edge server and monitoring and predicting health status" refers to a device or system that analyzes transmitted biometric information, monitors the user's health status in real time, and predicts future health risks.

[1340] "Means for generating individual health advice based on the analysis results and notifying the user" refers to a device or system that generates health advice tailored to each individual user based on the results of analysis performed by the edge server and notifies the user of this advice.

[1341] The "means for proposing health products" refers to a device or system that proposes appropriate health-related products based on the user's health condition and analysis results.

[1342] "Means for anonymizing biometric information and selling it to external organizations" refers to devices or systems that convert collected biometric information into a form that makes it impossible to identify individual users and then sell it to external institutions or organizations.

[1343] "Means for constantly monitoring a user's location information and detecting abnormal behavioral patterns" refers to a device or system that constantly monitors a user's location information and detects behavioral patterns that differ from normal.

[1344] "Means for confirming safety during a disaster" refers to devices or systems that confirm the safety of users when a disaster occurs and automatically notify them of necessary information.

[1345] The "means for suggesting optimal foods based on the user's health condition and emotions" refers to a device or system that suggests optimal foods to a user based on the user's health condition and emotional data.

[1346] A "means for ordering food based on the suggestions" is a device or system that allows a user to order the suggested food.

[1347] This invention is a system that takes into account the user's health condition and emotions, and proposes and delivers the most suitable foods. This system not only collects and analyzes the user's biometric and emotional data to provide health advice, but also proposes the most suitable foods based on the user's health and emotional state, enabling the user to order food based on these suggestions, creating a new service.

[1348] The system consists of the following:

[1349] 1. Device (smartphone)

[1350] Biometric data collection: Using smartphone sensors, biometric data such as the user's heart rate, body temperature, steps taken, and sleep patterns are collected.

[1351] Emotion data collection: The camera and microphone are used to analyze the user's facial expressions and voice, and the data is sent to the emotion engine.

[1352] Data transmission: The collected biometric information and emotion data are encrypted and sent to the edge server.

[1353] 2. Edge Server

[1354] Data reception and storage: Receives biometric and emotional data sent from the device and stores it in a database.

[1355] Real-time analysis: Advanced AI algorithms are used to analyze biometric and emotional data, and the results are used to monitor health conditions and predict health risks.

[1356] Advice generation: Based on the analysis results, individual health advice is generated and notified to the user.

[1357] Product Suggestion: Recommend appropriate health products based on health advice.

[1358] Data anonymization: Health data collected is anonymized so that it can be securely provided to external organizations.

[1359] Location monitoring: Constantly monitors user location information to detect abnormal behavioral patterns. Confirm safety in the event of a disaster.

[1360] Food Suggestion and Ordering: Recommends the best foods based on the user's health and emotional state, and allows the user to order food based on the suggestions.

[1361] Hardware and software used

[1362] Hardware: Smartphones, edge servers

[1363] Software: Biometric information collection app, emotion recognition engine, data analysis AI, database management system, communication protocol (encryption)

[1364] Specific examples

[1365] When a user returns home from work, their smartphone detects their fatigue based on their heart rate, body temperature, and facial expression. The edge server analyzes this data and sends the user a notification suggesting a sushi set with green tea for relaxation. If the user agrees to the suggestion, the system sends an order to the nearest restaurant and arranges for food delivery.

[1366] Example prompts for generative AI models

[1367] Input data: Heart rate = 80, Body temperature = 36.5°C, Sleep pattern = irregular, Facial expression = fatigue, Tone of voice = stress

[1368] Output data: Suggested food = Sushi set with relaxing green tea

[1369] This allows users to easily order the best foods for their health and emotional state.

[1370] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1371] Step 1:

[1372] Data collection

[1373] The device (smartphone) collects the user's biometric information. Specifically, it obtains data such as heart rate, body temperature, number of steps, and sleep patterns from sensors. It also uses the device's camera and microphone to analyze the user's facial expressions and voice and generate emotional data.

[1374] Input: Sensor, Camera, Microphone

[1375] Output: Heart rate, body temperature, steps, sleep patterns, facial expression data, voice data

[1376] Step 2:

[1377] Data transmission

[1378] The biometric and emotional data collected by the device is encrypted and sent to the edge server via a highly secure communication protocol (e.g., HTTPS).

[1379] Input: Heart rate, body temperature, steps, sleep pattern, facial expression data, voice data

[1380] Output: Encrypted data packet

[1381] Step 3:

[1382] Data reception and storage

[1383] The server receives the transmitted data and stores it in a database. Specifically, it uses a database management system (DBMS) to record the transmitted biometric and emotional data in an appropriate format.

[1384] Input: Encrypted data packet

[1385] Output: Biometric and emotional data stored in a database

[1386] Step 4:

[1387] Real-time analytics

[1388] The server analyzes the stored data in real time, using advanced AI algorithms to analyze biometric and emotional data to assess health and emotional status, and predict health risks based on the results.

[1389] Input: Biometric and emotional data stored in a database

[1390] Output: Health status assessment, emotional status assessment, health risk prediction

[1391] Step 5:

[1392] Advice Generation

[1393] The server generates personalized health advice based on the analysis results, specifically using a generative AI model to create health advice tailored to the user.

[1394] Input: Health status assessment, emotional status assessment, health risk prediction

[1395] Output: personalized health advice

[1396] Step 6:

[1397] notification

[1398] The server notifies the device of the generated health advice, specifically by sending the advice directly to the user via mobile notifications or email.

[1399] Input: personalized health advice

[1400] Output: User notification

[1401] Step 7:

[1402] food suggestions

[1403] The server will suggest the best food based on the user's health and emotional state, and will create a list of food delivery options based on the results of data analysis.

[1404] Input: Health status assessment, emotional status assessment

[1405] Output: Food suggestion list

[1406] Step 8:

[1407] Food ordering

[1408] The user selects the suggested food items and confirms the order. Specifically, the user confirms the food selection on the device and sends the order to the nearest restaurant.

[1409] Input: Food suggestion list

[1410] Output: Restaurant order data

[1411] Step 9:

[1412] Delivery arrangements

[1413] The restaurant receives the order and arranges for the food to be delivered. Specifically, the restaurant that received the order uses a delivery service to deliver the food to the user.

[1414] Input: Restaurant order data

[1415] Output: Food delivered to the user

[1416] In this way, the system can utilize the user's health and emotional information to provide optimal health advice and food suggestions, thereby improving the user's quality of life.

[1417] 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.

[1418] 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.

[1419] 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.

[1420] [Fourth embodiment]

[1421] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1422] 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.

[1423] 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).

[1424] 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.

[1425] 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.

[1426] 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).

[1427] 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.

[1428] 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.

[1429] 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.

[1430] 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.

[1431] 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.

[1432] 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.

[1433] 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."

[1434] This invention is a system that manages a user's health condition in real time and provides appropriate health advice. This system consists of a device (smartphone) that collects the user's biometric information, a server (edge ​​server) that analyzes and predicts that information, and an interface that notifies the user.

[1435] System Overview

[1436] This system first collects the user's biometric information. This is performed by a device (smartphone), which continuously collects biometric information such as heart rate, number of steps, sleep patterns, and body temperature. This data is then sent to a server (edge ​​server) using an encrypted communication protocol.

[1437] The edge server is equipped with advanced AI algorithms that analyze the received biometric information in real time. This analysis allows the user's current health status to be monitored and future health risks to be predicted. Based on the analysis results, personalized health advice supervised by experts is generated and communicated to the user.

[1438] The server also suggests health products, selecting appropriate products and services based on the user's health condition and notifying the device. It also has the ability to anonymize the collected health data and safely sell it to external organizations (medical institutions, pharmaceutical companies, etc.). This allows the data to be used for research and development of new health services while maintaining its security.

[1439] Location monitoring is also a very important function. The device constantly obtains the user's location information and sends it to a server. The server analyzes this data and detects abnormal behavioral patterns. If an abnormality is detected, the user is immediately notified. In addition, in the event of a disaster, the location information can be used to quickly check the user's safety and automatically notify family and friends.

[1440] Specific examples

[1441] 1. Daily health management

[1442] User A starts jogging at 7:00 a.m. The device measures the heart rate and number of steps in real time and sends the data to the edge server.

[1443] The server analyzes the received data, generates advice on the appropriate timing for hydration and jogging pace, and notifies the device.

[1444] User A receives the notification and can continue jogging in a healthy manner based on the advice.

[1445] 2. Long-term sedentary monitoring

[1446] User B works in an office while sitting for a long time. The terminal measures the time spent sitting and sends the data to the server.

[1447] The server detects that the user has been sitting still for a long period of time and generates and sends a notification to the device to encourage the user to stand up and stretch periodically.

[1448] User B receives the notification and stretches for health reasons.

[1449] 3. Sleep monitoring

[1450] User C goes to bed and the device measures his / her sleep patterns. The data is sent to the server for analysis.

[1451] The server analyzes the time spent in deep sleep and the quality of sleep, generates advice to improve performance the next day, and notifies the device.

[1452] User C receives the advice and takes action to improve their sleep quality.

[1453] 4. Confirming safety during disasters

[1454] When a disaster such as an earthquake occurs, user D is in the affected area.

[1455] The device sends its location information to a server, which then verifies the user's location in the disaster area.

[1456] The server checks the safety of user D and automatically notifies family and friends.

[1457] In this way, this system manages the user's health in real time and helps improve their health through appropriate advice and product suggestions.

[1458] The processing flow will be explained below.

[1459] Step 1:

[1460] The device continuously collects the user's biometric information (heart rate, number of steps, sleep patterns, body temperature, etc.) using sensors.

[1461] Step 2:

[1462] The device uses a communication protocol (e.g., HTTPS) to encrypt the collected biometric information and send it to the edge server. The transmitted data is given a timestamp.

[1463] Step 3:

[1464] The server stores the received data in a database in NoSQL format, with a structure suitable for real-time analysis.

[1465] Step 4:

[1466] The server feeds the data into AI algorithms for real-time analysis, including predictive models using historical data.

[1467] Step 5:

[1468] The server evaluates the user's current health status based on the analysis results and identifies predicted health risks.

[1469] Step 6:

[1470] The server generates health advice that has been supervised by experts and transmits the advice to the terminal.

[1471] Step 7:

[1472] The device will notify the user of the health advice it receives, which will be displayed as a pop-up message and / or an audio alert.

[1473] Step 8:

[1474] The server then recommends appropriate health products and services based on the user's health status, and the recommendations are sent to the device.

[1475] Step 9:

[1476] The device displays suggestions for health products and services to users, including links to purchase products and gym discounts.

[1477] Step 10:

[1478] The server anonymizes the collected biometric information and prepares it for sale to external organizations (medical institutions, pharmaceutical companies, etc.). The sales data does not include any information that can identify individual users.

[1479] Step 11:

[1480] The device continuously collects the user's location information and sends it to a server, which is also encrypted.

[1481] Step 12:

[1482] The server analyzes location information in real time and detects abnormal behavior that deviates from normal patterns of behavior.

[1483] Step 13:

[1484] If the server detects any abnormal behavioral patterns, it will immediately alert the user and, if necessary, notify emergency contacts.

[1485] Step 14:

[1486] In the event of a disaster, the server will quickly check the safety of registered users based on their location information, and the results will be automatically sent to registered family and friends.

[1487] Through these steps, the system can manage users' health status in real time, provide appropriate health advice, and respond quickly in emergencies.

[1488] Example 1

[1489] 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."

[1490] In modern society, it is important to manage a user's health status in real time and provide appropriate individualized health guidance. However, current systems have difficulty integrating multiple functions, such as collecting and analyzing biometric information, generating health guidance, and confirming safety in the event of a disaster. Therefore, there is a need to efficiently manage both the user's health and confirm their safety. Furthermore, security and privacy protection of biometric information are also major issues, and a system for utilizing data while satisfying these needs is required.

[1491] 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.

[1492] In this invention, the server includes means for collecting user biometric information, means for transmitting the collected biometric information to an edge device, means for analyzing the biometric information at the edge device and monitoring and predicting the health status, means for generating individual health advice based on the analysis results and notifying the user, means for suggesting health products, means for anonymizing the biometric information and providing it to an external organization, means for constantly monitoring the user's location information and detecting abnormal movement patterns, and means for confirming the user's safety in the event of a disaster. This enables the secure collection and analysis of the user's biometric information, real-time health management, and rapid safety confirmation in the event of a disaster.

[1493] "User" means an individual who uses the system to manage their own health status.

[1494] "Biometric information" refers to data about the body, such as heart rate, number of steps taken, sleep patterns, and body temperature.

[1495] A "means" is a method or device used to achieve a particular purpose.

[1496] "Edge devices" refers to multiple devices, typically including servers, that process and analyze collected data.

[1497] "Analysis" is the process of examining collected biometric information in detail and finding meaning.

[1498] "Health" refers to a user's physical and mental state, as indicated by biometric information such as heart rate and body temperature.

[1499] "Health Guidance" refers to specific advice or suggestions to help users improve or maintain their health.

[1500] "Notification" means a message or alert intended to convey information from the system to the user.

[1501] "Health products" refers to goods and services that help users improve or maintain their health.

[1502] "Anonymization" is the process of processing data so that it cannot be used to identify individuals.

[1503] An "external institution" is an external organization that uses a user's biometric information, such as a medical institution or pharmaceutical company.

[1504] "Location Information" means data about a user's geographic location.

[1505] "Abnormal behavior patterns" are data that show movements or situations that differ from normal behavior.

[1506] "Safety confirmation during a disaster" refers to the process of confirming the safety of users when a disaster such as an earthquake or typhoon occurs.

[1507] This invention is a system for managing a user's health status in real time and providing appropriate health guidance. The system mainly consists of a terminal that collects the user's biometric information, an edge device that analyzes the information, and an interface that sends notifications.

[1508] First, the system collects the user's biometric information through a mobile device such as a smartphone, including heart rate, steps taken, sleep patterns, body temperature, etc. The smartphone records the user's condition in real time using data from built-in sensors and external devices.

[1509] The collected biometric information is then securely transmitted to the edge device (server) using encrypted communication protocols (e.g., SSL or TLS). The server then analyzes the received data using advanced AI algorithms (e.g., TensorFlow or PyTorch). This allows the user's current health status to be monitored and future health risks to be predicted.

[1510] Based on the analysis results, the server generates a personalized health guide, supervised by an expert, including specific advice on exercise, diet, rest, etc. The advice is then encrypted and sent back to the smartphone, where it is displayed to the user as a push notification or in-app alert.

[1511] In addition, the server will suggest health products and services tailored to the user's health condition, including supplements, fitness programs, medical devices, etc. The suggestions will also be sent to the device, providing the user with the opportunity to receive appropriate products and services.

[1512] The collected biometric information is anonymized by the server and provided to external organizations such as medical institutions and pharmaceutical companies, which makes it possible to provide data for new health services and research while maintaining data security and privacy.

[1513] Smartphones also constantly collect the user's location information and send it to a server. The server analyzes the location information and promptly notifies the user if it detects abnormal behavior patterns or an emergency. This is particularly useful in the event of a disaster such as an earthquake or typhoon, as it allows for quick safety confirmation and automatically sends safety confirmation notifications to the user's family and friends.

[1514] As a concrete example, consider the case where User A starts jogging at 7:00 AM. The device measures the heart rate and number of steps in real time and sends them to the edge device. The server analyzes this data, generates advice on appropriate hydration timing and jogging pace, and notifies the device. User A receives this advice and continues jogging in a healthy manner.

[1515] Additionally, personalized advice can be generated by inputting prompts like the following into the generative AI model:

[1516] "Generate optimal health advice based on the following user situation: User A started jogging at 7 AM, their current heart rate is 150 bpm, and they have taken 1500 steps."

[1517] In this way, the system manages the user's health condition in real time and provides appropriate advice and product suggestions, thereby helping to improve the user's health and ensure safety.

[1518] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1519] Step 1:

[1520] The user launches the dedicated app and begins their daily activities. For example, to start jogging, they launch the app and press the start button.

[1521] Input: User operation (launching an app, pressing the start button)

[1522] Output: The app's sensors are activated and data collection begins.

[1523] Step 2:

[1524] The device (smartphone) collects biometric information such as heart rate, number of steps, sleep patterns, and body temperature in real time from built-in sensors and external devices. The sensors detect the user's physical movements and acquire the data.

[1525] Input: User's biometric information (heart rate, steps, sleep pattern, body temperature)

[1526] Output: A dataset of collected biometric information

[1527] Step 3:

[1528] The collected biometric information is securely transmitted to the edge device (server) using encrypted communication protocols (e.g., SSL or TLS). The device periodically divides the data into packets and transmits them to the server.

[1529] Input: Biometric dataset

[1530] Output: Encrypted data packet

[1531] Step 4:

[1532] The server stores the received biometric information in a database and analyzes it using advanced AI algorithms (such as TensorFlow and PyTorch). The server calculates heart rate fluctuations and step rhythms to check for abnormalities.

[1533] Input: Encrypted data packet

[1534] Output: Analysis results (heart rate fluctuations, step rhythm, presence or absence of abnormalities)

[1535] Step 5:

[1536] Based on the analysis results, the server evaluates the user's current health status and predicts future health risks, for example, using models to predict elevated blood pressure and abnormal heart rate.

[1537] Input: Analysis results (heart rate fluctuations, step rhythm, presence or absence of abnormalities)

[1538] Output: Health status assessment and health risk prediction

[1539] Step 6:

[1540] The server generates personalized health advice supervised by experts based on the health status assessment and health risk prediction results. Using a generative AI model, it creates the most appropriate advice for the user in text format.

[1541] Input: Health status assessment and health risk prediction

[1542] Output: Health advice text message

[1543] Step 7:

[1544] The server encrypts the generated health advice and sends it to the device, which then decrypts the received message and notifies the user.

[1545] Input: Health advice text message

[1546] Output: Encrypted health advice message

[1547] Step 8:

[1548] The device will provide users with health guidance via push notifications and in-app alerts, such as messages like "stay hydrated."

[1549] Input: Encrypted health advice message

[1550] Output: A notification message that is displayed to the user.

[1551] Step 9:

[1552] The server will suggest health products and services based on the user's health status, and this information will also be encrypted and sent to the device, where it will be displayed as a notification.

[1553] Input: Health status assessment and health risk prediction

[1554] Output: Encrypted health product suggestion message

[1555] Step 10:

[1556] The server anonymizes the biometric information and safely provides it to external organizations such as medical institutions and pharmaceutical companies. The information is then anonymized and stored in a database.

[1557] Input: Biometric dataset

[1558] Output: Anonymized dataset

[1559] Step 11:

[1560] The device constantly acquires the user's location information and periodically sends it to the server, which then monitors for abnormal behavior patterns and notifies the user if necessary.

[1561] Input: User's location

[1562] Output: A dataset of location information

[1563] Step 12:

[1564] When a disaster such as an earthquake or typhoon occurs, the device sends its location information to the server in real time, and the server quickly checks the user's safety and automatically notifies family and friends.

[1565] Input: Location information at the time of disaster

[1566] Output: Safety confirmation notification

[1567] (Application example 1)

[1568] 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."

[1569] In recent years, users have become increasingly interested in managing their health, creating a demand for systems that provide advice tailored to individual health conditions. However, existing systems only collect and analyze users' biometric information, making it difficult to optimally reflect it in their actual social lives and consumer behavior. Furthermore, they lack safety confirmation functions in the event of a disaster, delaying the provision of comprehensive health management systems. Therefore, a new system is needed that utilizes users' health information to support their behavior in the real world.

[1570] 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.

[1571] In this invention, the server includes means for collecting user biometric information, means for transmitting the collected biometric information to an edge server, means for analyzing the biometric information in the edge server and monitoring and predicting the health status, means for generating personalized health advice based on the analysis results and notifying the user, means for proposing health products, means for anonymizing the biometric information and selling it to an external organization, means for constantly monitoring the user's location information and detecting abnormal behavioral patterns, means for confirming the user's safety in the event of a disaster, and means for proposing products in a physical store based on the user's health information. This enables appropriate behavior and product suggestions in the real world based on the user's health information.

[1572] "User biometric information" refers to numerical data related to a person's physical condition, such as heart rate, number of steps, body temperature, and sleep patterns.

[1573] An "edge server" is a server that receives data sent by users in real time and performs analysis and predictions.

[1574] "Health monitoring and prediction tools" refers to algorithms and programs that analyze collected biometric information to determine current health status and predict future risks.

[1575] "Individualized health advice" is advice based on the analysis of a user's biometric information, providing suggestions for improving behaviors and habits that are optimal for each individual's health condition.

[1576] "Health product suggestion" is the process of recommending appropriate products and services to users according to their analyzed health status.

[1577] "Biometric de-identification" is the process of removing or modifying personal information from collected biometric data so that it cannot be used to identify individual users.

[1578] "External organizations" are third-party organizations, such as medical institutions and pharmaceutical companies, that conduct research or provide services using users' biometric information.

[1579] "Means for constantly monitoring location information" refers to technologies and devices that continuously obtain the user's current location and identify abnormal behavior or trends during disasters.

[1580] "Abnormal behavior" refers to behavior or movement patterns that are different from normal and are predicted to pose a risk to the user's health or safety.

[1581] "Means for checking safety during a disaster" is a system that uses the user's location information to confirm the user's safety when a disaster occurs and notifies family members and other relevant parties of that information.

[1582] The "means of making product suggestions within a physical store" is a system that analyzes a user's health information and makes real-time suggestions about products that are recommended for purchase within the store where the user is currently located.

[1583] To implement the present invention, a system is constructed and operated in the following manner.

[1584] System configuration

[1585] 1. Data collection and transmission

[1586] The user uses a device (smartphone) to collect biometric information. This smartphone is equipped with a heart rate sensor and an acceleration sensor to acquire biometric information such as heart rate, number of steps, sleep pattern, and body temperature. The collected data is sent to the edge server using an encrypted communication protocol (e.g., TLS / SSL).

[1587] 2. Data Analysis

[1588] The edge server analyzes the received data using AI algorithms (e.g., TensorFlow, PyTorch, etc.), which monitors the user's current health status and predicts future health risks. The analysis results are updated in real time.

[1589] 3. Advice and Notice

[1590] Based on the analysis results, the edge server generates individual health advice, which is then sent to the user's smartphone via push notification. The advice helps users improve their health behavior and lifestyle habits.

[1591] 4. Health product suggestions

[1592] The edge server selects products and services suited to the user's health condition, and the user receives real-time recommendations, including health foods and fitness products.

[1593] 5. Anonymization and sale of data

[1594] The biometric information will be anonymized and sold to external organizations (e.g., medical institutions, pharmaceutical companies, etc.) This process will use secure data anonymization techniques to ensure the protection of personal information.

[1595] 6. Location Monitoring and Anomaly Detection

[1596] The user's smartphone constantly collects location information and sends it to the edge server. The edge server analyzes this data and detects abnormal behavioral patterns. If an abnormality is detected, the user is immediately notified. In addition, in the event of a disaster, the user's safety is quickly confirmed based on the location information, and family and friends are automatically notified.

[1597] 7. Product proposals in physical stores

[1598] The app will suggest products based on health information while users are in physical stores. These suggestions are optimized based on the user's current location within the store and sent to the user's smartphone. For example, a user with high stress levels will be recommended products with a relaxation effect.

[1599] Examples and prompts

[1600] Examples:

[1601] User A visits a physical store called "Health Shop." User A's smartphone collects real-time information such as heart rate, number of steps, and body temperature. The edge server analyzes this information and detects that User A's recent stress level has been high. Based on this, the server recommends relaxing herbal teas and stress-relieving products sold in the store. This information is displayed on User A's smartphone via push notification, allowing User A to purchase products based on that information.

[1602] Example prompt sentence:

[1603] "I want to create a smartphone app that collects heart rate, steps, and body temperature in real time, analyzes the user's health status, and recommends appropriate health products. If it detects that the user is feeling stressed, it should suggest products that are effective for relieving stress in a physical store."

[1604] This will enable appropriate actions and product suggestions in the real world based on the user's health information, improving the user's quality of life.

[1605] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1606] Step 1:

[1607] The user's smartphone collects biometric information such as heart rate, number of steps, and body temperature using the smartphone's built-in heart rate sensor and accelerometer. The input here is the user's real-time biometric information, and the output is the collected biometric data.

[1608] Step 2:

[1609] The device sends the collected biometric data to the edge server using an encrypted communication protocol (e.g., TLS / SSL). With this operation, the input is the data sent through the encryption protocol, and the output is the secure data transfer to the edge server.

[1610] Step 3:

[1611] The edge server analyzes the received biometric data using a generative AI model (e.g., TensorFlow or PyTorch). The input is the biometric data received by the edge server, and the output is the user's current health status and predicted future health risks.

[1612] Step 4:

[1613] The server generates individual health advice based on the analysis results. This advice generation is based on an algorithm supervised by experts. The input here is the health status analysis data, and the output is health advice to be notified to the user.

[1614] Step 5:

[1615] The device receives health advice via push notifications from the server. These notifications include specific behavioral guidelines and recommended products. The input is advice data based on the analysis results, and the output is a notification to the user.

[1616] Step 6:

[1617] The server suggests health products and related services that are appropriate for the user's health status using a real-time recommendation algorithm, whose input is the user's current health status data and whose output is a list of recommended health products.

[1618] Step 7:

[1619] The terminal displays recommended health products and services to support in-store activities. The input here is the recommended product information sent from the server, and the output is the product information displayed on the user's smartphone screen.

[1620] Step 8:

[1621] The server anonymizes the biometric data and sells it to external organizations. This process uses data anonymization tools and techniques. The input is biometric data and the output is anonymized data.

[1622] Step 9:

[1623] The device constantly monitors the user's location information and sends it to the server. This operation uses location information technologies such as GPS and Wi-Fi. The input is the user's location data, and the output is location information data sent to the edge server.

[1624] Step 10:

[1625] The server analyzes the received location data and detects abnormal behavioral patterns. In the event of a disaster, it also checks the user's safety and notifies relevant parties. The input is location data and disaster data, and the output is notifications of abnormal behavior and safety information.

[1626] This will enable appropriate actions and product suggestions in the real world based on the user's health information, improving the user's quality of life.

[1627] 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.

[1628] This invention is a system for managing a user's health status and providing appropriate health advice. By combining it with an emotion engine that recognizes the user's emotions, it is possible to provide optimal emotional advice. This system includes a device (smartphone) that collects the user's biometric information, an edge server, and an emotion engine.

[1629] System Overview

[1630] This system is configured as follows.

[1631] 1. Device (smartphone)

[1632] It collects biometric information such as the user's heart rate, steps, sleep patterns, and body temperature.

[1633] The biometric information is encrypted and sent to the edge server.

[1634] It uses a camera and microphone to analyze the user's facial expressions and voice and sends the data to an emotion engine.

[1635] 2. Edge Server

[1636] The biometric information sent from the terminal is received and stored in a database.

[1637] Using advanced AI algorithms, it analyzes biometric information in real time and monitors health conditions.

[1638] Based on the analysis results, the user's health risks are predicted.

[1639] It takes into account data from the emotion engine to generate personalized health advice.

[1640] Propose health products.

[1641] The collected health data is anonymized and securely sold to external organizations (medical institutions, pharmaceutical companies, etc.).

[1642] Monitors user location information and detects abnormal behavioral patterns.

[1643] In the event of a disaster, the system will check the safety of users and automatically notify them.

[1644] 3. Emotion Engine

[1645] Recognizes user emotions through facial expression recognition and voice analysis.

[1646] The recognized emotion information is sent to the edge server.

[1647] Customize health advice and product suggestions based on emotional information.

[1648] Specific examples

[1649] 1. Daily health management

[1650] User A starts jogging in the morning. The device measures the heart rate and number of steps and sends the data to the edge server.

[1651] The server analyzes the data and notifies the user when it is time to hydrate.

[1652] The emotion engine analyzes User A's facial expressions and voice, and if it senses that the user is feeling very tired, it will advise them to slow down their pace.

[1653] 2. Long-term sedentary monitoring

[1654] User B has been sitting in the office for a long time. The device detects this and sends a message to the server.

[1655] The server analyzes how long you have been sitting and notifies you to stretch periodically.

[1656] The emotion engine detects from User B's facial expression that he is losing concentration and advises him to take a short break.

[1657] 3. Sleep monitoring using emotion recognition

[1658] User C goes to bed at night. The device measures his sleep pattern and sends it to the server.

[1659] The server analyzes the quality of sleep and suggests a wake-up time that matches the next day's schedule.

[1660] The emotion engine analyzes facial expressions and tone of voice before bed, and if anxiety is detected, it suggests relaxing music and content.

[1661] 4. Safety confirmation including emotion analysis during disasters

[1662] An earthquake occurs and user D is in the affected area. The device sends location information to the server.

[1663] The server analyzes the safety of user D and automatically notifies family and friends.

[1664] The emotion engine analyzes user D's facial expressions and voice and provides support information to reduce stress as needed.

[1665] In this way, the system, which combines an emotion engine, can gain a deeper understanding of the user's health condition and provide optimal emotional support, thereby improving the user's quality of life.

[1666] The processing flow will be explained below.

[1667] Step 1:

[1668] The device uses sensors to continuously collect biometric information such as the user's heart rate, number of steps taken, sleep patterns, and body temperature.

[1669] Step 2:

[1670] The device uses a communication protocol (e.g., HTTPS) to encrypt the collected biometric information and send it to the edge server. The transmitted data is given a timestamp.

[1671] Step 3:

[1672] The device collects the user's facial expressions and voice using a camera and microphone, and processes the data to send it to the emotion engine.

[1673] Step 4:

[1674] The emotion engine analyzes facial and voice data sent from the device and recognizes the user's emotions in real time using AI algorithms.

[1675] Step 5:

[1676] The emotion engine sends the recognized emotion information to the edge server.

[1677] Step 6:

[1678] The server receives the biometric information sent from the device and stores it in a database. The database is in NoSQL format and has a structure suitable for real-time analysis.

[1679] Step 7:

[1680] The server then inputs the received biometric information into an AI algorithm for real-time analysis, which includes predictive models using past data.

[1681] Step 8:

[1682] The server also integrates emotional data from the emotion engine into the analysis results to assess the user's current health status and identify predicted health risks.

[1683] Step 9:

[1684] The server takes into account the analysis results and emotional information to generate personalized health advice for the user, which is optimized according to the user's emotional state.

[1685] Step 10:

[1686] The server sends the generated health advice to the device, which displays it as a pop-up notification or a voice message.

[1687] Step 11:

[1688] The device receives health advice from the server and notifies the user, who then checks the notification and follows the advice.

[1689] Step 12:

[1690] The server then recommends appropriate health products and services based on the user's health status, and also reflects their emotional information.

[1691] Step 13:

[1692] The device receives health product and service recommendations from the server and displays them to the user, including links to purchase products and gym discounts.

[1693] Step 14:

[1694] The server anonymizes the collected biometric information and prepares it for sale to external organizations (medical institutions, pharmaceutical companies, etc.). The sales data does not include any information that can identify individual users.

[1695] Step 15:

[1696] The device continuously collects the user's location information and sends it to a server, which is also encrypted.

[1697] Step 16:

[1698] The server analyzes location information in real time and detects abnormal behavior that deviates from normal patterns of behavior.

[1699] Step 17:

[1700] If the server detects any abnormal behavioral patterns, it will immediately alert the user and, if necessary, notify emergency contacts.

[1701] Step 18:

[1702] In the event of a disaster, the server will quickly check the safety of registered users based on their location information, and the results will be automatically sent to registered family and friends.

[1703] This system provides comprehensive support from daily health management to emergency response, improving the health and safety of users.

[1704] Example 2

[1705] 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."

[1706] In modern society, personal health and mental health management are becoming increasingly important. However, conventional health management systems are limited to collecting biometric information and simple data analysis, making it difficult to provide comprehensive health advice that takes into account the user's emotional state. Furthermore, issues such as the security of collected data, privacy protection, and rapid safety confirmation in the event of a disaster remain unresolved. Therefore, there is a need for a system that can simultaneously analyze a user's biometric information and emotional data to provide comprehensive health management and safety.

[1707] 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.

[1708] In this invention, the server includes means for receiving and analyzing the user's biometric information and monitoring and predicting the health condition, means for analyzing facial expressions and voice data using an emotion engine, and means for generating personalized health advice based on the analysis results and emotion information and notifying the user. This makes it possible to comprehensively analyze the user's biometric information and emotional state and provide personalized health advice.

[1709] "Biometric information" is data that indicates the user's physical condition, such as heart rate, number of steps, sleep pattern, and body temperature.

[1710] An "edge server" is a server located at the edge of a network for the purpose of collecting, analyzing, and storing data.

[1711] An "emotion engine" is software and algorithms that analyze facial expressions and voice to recognize a user's emotional state.

[1712] "Data analysis" is the process of applying statistical processing and machine learning algorithms to collected data to extract meaningful information.

[1713] "Health advice" is specific course of action or recommendations provided based on a user's health or emotional state.

[1714] "Health products" are goods and services that aim to maintain or improve the health of users.

[1715] "Anonymization" is the process of removing personally identifiable information from data, enhancing the protection of personal information.

[1716] An "external organization" is a corporation or organization outside the system, such as a medical institution or pharmaceutical company.

[1717] "Location information" is data that indicates a user's current geographic location and is collected using technologies such as GPS and Wi-Fi.

[1718] "Abnormal behavioral patterns" refer to user activity or behavior that is significantly different from normal behavior and may pose a danger or health risk.

[1719] "Safety confirmation" is a process for checking whether users are safe in the event of a disaster, and notifying them if necessary.

[1720] MODE FOR CARRYING OUT THE INVENTION

[1721] This invention is a system for managing a user's health status and providing appropriate health advice. By combining it with an emotion engine that recognizes the user's emotions, it is possible to provide optimal emotional advice. The system includes a device (smartphone) that collects the user's biometric information, an edge server, and an emotion engine.

[1722] System Overview

[1723] This system is configured as follows.

[1724] 1. Device (smartphone)

[1725] Dedicated sensors and apps are used to collect biometric information such as the user's heart rate, number of steps, sleep patterns, body temperature, etc. Examples include heart rate measurement apps, pedometer apps, and sleep trackers.

[1726] The biometric information is encrypted and sent to the edge server using advanced encryption methods such as AES-256.

[1727] It uses a camera and microphone to analyze the user's facial expressions and voice and sends the data to an emotion engine.

[1728] 2. Edge Server

[1729] The biometric information sent from the device is received and stored in a database, typically an SQL database or NoSQL database.

[1730] It uses advanced AI algorithms to analyze biometric information and monitor health conditions in real time, leveraging machine learning libraries such as TensorFlow and PyTorch.

[1731] Based on the analysis results, the app predicts the user's health risks. For example, if their heart rate is abnormally high or their walking pattern is different from normal, the app notifies the user of the risk via push notification or email.

[1732] Taking into account data from the emotion engine, personalized health advice is generated and communicated to the user, including specific courses of action and recommendations.

[1733] Propose health products, such as supplements to help recover from fatigue or relaxation products.

[1734] We anonymize collected health data and sell it securely to external organizations (medical institutions, pharmaceutical companies, etc.) We use pseudonymization and data masking techniques to prevent individuals from being identified.

[1735] It monitors your location and detects unusual patterns of activity, which can help detect prolonged inactivity or unusual movement patterns.

[1736] In the event of a disaster, the app will check the user's safety and automatically notify them. When an earthquake or other disaster occurs, it will analyze the user's safety based on location information and notify family and friends.

[1737] 3. Emotion Engine

[1738] It recognizes the user's emotions through facial expression recognition and voice analysis. For example, it uses image processing libraries such as OpenCV to analyze facial expressions and evaluate stress and fatigue levels.

[1739] The recognized emotion information is sent to the edge server and used to generate health advice.

[1740] Personalize health advice and product recommendations based on emotional information, for example, suggesting relaxation techniques if the user is tired.

[1741] This system makes it possible to comprehensively analyze a user's biometric and emotional data and provide personalized health advice.

[1742] Examples of specific examples and prompts

[1743] 1. Daily health management

[1744] The device measures user A's heart rate and sends it to the edge server.

[1745] The server analyzes the measurement data and notifies the user when it is time to hydrate.

[1746] The emotion engine analyzes user A's facial expressions and, if it determines that the user is feeling very tired, notifies them to slow down.

[1747] Example prompt sentence:

[1748] Generate specific health advice based on the user's biometric information. Input heart rate and step count data while jogging, and create prompts based on that data to advise the optimal time to hydrate.

[1749] 2. Long-term sedentary monitoring

[1750] The device detects the amount of time that user B has been sitting and sends this information to the edge server.

[1751] The server analyzes this data and periodically notifies you to stretch.

[1752] The emotion engine analyzes user B's facial expression data and, if it determines that their concentration is declining, notifies them to take a short break.

[1753] Example prompt sentence:

[1754] Generate specific advice for users who sit for long periods in the office, encouraging them to stretch regularly. Furthermore, create prompts based on facial expression data to suggest taking a break if concentration is declining.

[1755] 3. Sleep monitoring using emotion recognition

[1756] The device measures user C's sleep pattern and sends it to the edge server.

[1757] The server analyzes sleep data and suggests a wake-up time that fits the next day's schedule.

[1758] The emotion engine analyzes user C's facial expressions and voice before bed, and if it detects anxiety, it recommends relaxing music.

[1759] Example prompt sentence:

[1760] Analyze the quality of your sleep based on your sleep patterns and suggest the optimal time to wake up based on your schedule for the next day. Furthermore, create prompts that suggest relaxation methods based on your facial expressions and tone of voice before going to bed.

[1761] 4. Safety confirmation including emotion analysis during disasters

[1762] An earthquake occurs, and the device sends user D's location information to the edge server.

[1763] The server automatically analyzes user D's safety and notifies family and friends.

[1764] The emotion engine analyzes user D's facial expressions and voice and provides support information to reduce stress.

[1765] Example prompt sentence:

[1766] Explain a program that checks the user's safety during a disaster and automatically notifies family and friends. Furthermore, analyze the user's emotional data and create prompts that provide support information to reduce stress.

[1767] This system can monitor the user's health and emotional state in real time and provide appropriate support, which can significantly improve the user's quality of life.

[1768] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1769] Step 1:

[1770] The device collects the user's biometric information. Specifically, it obtains heart rate, number of steps, sleep patterns, body temperature, etc. using dedicated sensors and apps (e.g., heart rate measurement apps, pedometer apps, sleep trackers). The input is the user's biometric data, and the output is the collected biometric information.

[1771] Step 2:

[1772] The device encrypts the biometric information collected. Advanced encryption methods such as AES-256 are used to securely protect the data. The input is the collected biometric information, and the output is the encrypted biometric information.

[1773] Step 3:

[1774] The device sends encrypted biometric information to the edge server. HTTPS or other communication protocols are used to ensure secure data transfer. The input is encrypted biometric information, and the output is the biometric information sent to the server.

[1775] Step 4:

[1776] The server receives the encrypted biometric information sent from the terminal and decrypts it. The input is the encrypted biometric information, and the output is the decrypted biometric information.

[1777] Step 5:

[1778] The server stores the decoded biometric information in a database. SQL databases and NoSQL databases are used to efficiently manage data. The input is the decoded biometric information, and the output is stored in the database.

[1779] Step 6:

[1780] The server analyzes the stored biometric information in real time. Using machine learning libraries such as TensorFlow and PyTorch, an AI model analyzes the data. The input is the stored biometric information, and the output is the analysis results. The analysis results are used to assess the user's health status and risk.

[1781] Step 7:

[1782] The server predicts health risks based on the analysis results. If the heart rate is abnormally high or the walking pattern is different from normal, the server evaluates the risk and notifies the user. The input is the analysis results, and the output is a risk notification message.

[1783] Step 8:

[1784] The device collects the user's facial expressions and voice and sends the data to the emotion engine. The input is the user's facial and voice data, and the output is the data sent to the emotion engine.

[1785] Step 9:

[1786] The emotion engine performs facial expression recognition and voice analysis. It uses image processing libraries such as OpenCV to analyze facial expressions and evaluate the user's emotional state and stress level. The input is facial expression and voice data, and the output is the emotion analysis results.

[1787] Step 10:

[1788] The server generates personalized health advice based on the results of emotion analysis and notifies the user. The advice, including specific guidelines and recommendations, is generated and sent to the user via push notification or email. The input is the emotion analysis results and the analysis results of biometric information, and the output is a health advice message.

[1789] Step 11:

[1790] The server proposes health products. It proposes appropriate health products (e.g., supplements, relaxation goods) by taking into account the user's biometric information and emotional state. The input is the biometric information and the results of emotional analysis, and the output is a product proposal message.

[1791] Step 12:

[1792] The health data collected by the server is anonymized. Pseudonymization and data masking techniques are used to prevent individuals from being identified. The input is decoded biometric information, and the output is anonymized data.

[1793] Step 13:

[1794] The server sells the anonymized data to external organizations. The data is securely provided to medical institutions, pharmaceutical companies, etc. The input is anonymized data, and the output is data provided to external organizations.

[1795] Step 14:

[1796] The server monitors the user's location information and detects abnormal behavior patterns. It detects cases of prolonged inactivity or unusual movement patterns and notifies the user. The input is the user's location information, and the output is abnormal behavior detection and notification.

[1797] Step 15:

[1798] The server checks the user's safety in the event of a disaster. When a disaster such as an earthquake occurs, it analyzes the user's safety based on location information and automatically notifies family and friends. It also utilizes an emotion engine to provide support information as needed. The input is location information and emotion data at the time of the disaster, and the output is a safety confirmation and notification message.

[1799] (Application example 2)

[1800] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1801] Conventional healthcare systems were able to monitor a user's health status and provide advice, but they did not address health management that took into account the user's emotional state or provide lifestyle suggestions based on that. Furthermore, there was no system that could recommend and deliver optimal foods based on the user's health and emotional state. This left users without a way to easily achieve an appropriate diet that matched their own health and emotional state.

[1802] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting the user's biometric information, means for transmitting the collected biometric information to the edge server, means for analyzing the biometric information in the edge server and monitoring and predicting the health status, means for generating personalized health advice based on the analysis results and notifying the user, means for proposing health products, means for anonymizing the biometric information and selling it to external organizations, means for constantly monitoring the user's location information and detecting abnormal behavioral patterns, means for confirming the user's safety in the event of a disaster, means for proposing optimal foods based on the user's health status and emotions, and means for ordering the foods based on the suggestions. This enables the recommendation and delivery of optimal foods according to the user's health status and emotional state.

[1803] "Means for collecting user biometric information" refers to devices or systems that collect health-related data of users, such as heart rate, body temperature, number of steps, and sleep patterns.

[1804] The "means for transmitting biometric information to the edge server" refers to a device or system that transmits collected health-related data of a user to the edge server.

[1805] "Means for analyzing biometric information on an edge server and monitoring and predicting health status" refers to a device or system that analyzes transmitted biometric information, monitors the user's health status in real time, and predicts future health risks.

[1806] "Means for generating individual health advice based on the analysis results and notifying the user" refers to a device or system that generates health advice tailored to each individual user based on the results of analysis performed by the edge server and notifies the user of this advice.

[1807] The "means for proposing health products" refers to a device or system that proposes appropriate health-related products based on the user's health condition and analysis results.

[1808] "Means for anonymizing biometric information and selling it to external organizations" refers to devices or systems that convert collected biometric information into a form that makes it impossible to identify individual users and then sell it to external institutions or organizations.

[1809] "Means for constantly monitoring a user's location information and detecting abnormal behavioral patterns" refers to a device or system that constantly monitors a user's location information and detects behavioral patterns that differ from normal.

[1810] "Means for confirming safety during a disaster" refers to devices or systems that confirm the safety of users when a disaster occurs and automatically notify them of necessary information.

[1811] The "means for suggesting optimal foods based on the user's health condition and emotions" refers to a device or system that suggests optimal foods to a user based on the user's health condition and emotional data.

[1812] A "means for ordering food based on the suggestions" is a device or system that allows a user to order the suggested food.

[1813] This invention is a system that takes into account the user's health condition and emotions, and proposes and delivers the most suitable foods. This system not only collects and analyzes the user's biometric and emotional data to provide health advice, but also proposes the most suitable foods based on the user's health and emotional state, enabling the user to order food based on these suggestions, creating a new service.

[1814] The system consists of the following:

[1815] 1. Device (smartphone)

[1816] Biometric data collection: Using smartphone sensors, biometric data such as the user's heart rate, body temperature, steps taken, and sleep patterns are collected.

[1817] Emotion data collection: The camera and microphone are used to analyze the user's facial expressions and voice, and the data is sent to the emotion engine.

[1818] Data transmission: The collected biometric information and emotion data are encrypted and sent to the edge server.

[1819] 2. Edge Server

[1820] Data reception and storage: Receives biometric and emotional data sent from the device and stores it in a database.

[1821] Real-time analysis: Advanced AI algorithms are used to analyze biometric and emotional data, and the results are used to monitor health conditions and predict health risks.

[1822] Advice generation: Based on the analysis results, individual health advice is generated and notified to the user.

[1823] Product Suggestion: Recommend appropriate health products based on health advice.

[1824] Data anonymization: Health data collected is anonymized so that it can be securely provided to external organizations.

[1825] Location monitoring: Constantly monitors user location information to detect abnormal behavioral patterns. Confirm safety in the event of a disaster.

[1826] Food Suggestion and Ordering: Recommends the best foods based on the user's health and emotional state, and allows the user to order food based on the suggestions.

[1827] Hardware and software used

[1828] Hardware: Smartphones, edge servers

[1829] Software: Biometric information collection app, emotion recognition engine, data analysis AI, database management system, communication protocol (encryption)

[1830] Specific examples

[1831] When a user returns home from work, their smartphone detects their fatigue based on their heart rate, body temperature, and facial expression. The edge server analyzes this data and sends the user a notification suggesting a sushi set with green tea for relaxation. If the user agrees to the suggestion, the system sends an order to the nearest restaurant and arranges for food delivery.

[1832] Example prompts for generative AI models

[1833] Input data: Heart rate = 80, Body temperature = 36.5°C, Sleep pattern = irregular, Facial expression = fatigue, Tone of voice = stress

[1834] Output data: Suggested food = Sushi set with relaxing green tea

[1835] This allows users to easily order the best foods for their health and emotional state.

[1836] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1837] Step 1:

[1838] Data collection

[1839] The device (smartphone) collects the user's biometric information. Specifically, it obtains data such as heart rate, body temperature, number of steps, and sleep patterns from sensors. It also uses the device's camera and microphone to analyze the user's facial expressions and voice and generate emotional data.

[1840] Input: Sensor, Camera, Microphone

[1841] Output: Heart rate, body temperature, steps, sleep patterns, facial expression data, voice data

[1842] Step 2:

[1843] Data transmission

[1844] The biometric and emotional data collected by the device is encrypted and sent to the edge server via a highly secure communication protocol (e.g., HTTPS).

[1845] Input: Heart rate, body temperature, steps, sleep pattern, facial expression data, voice data

[1846] Output: Encrypted data packet

[1847] Step 3:

[1848] Data reception and storage

[1849] The server receives the transmitted data and stores it in a database. Specifically, it uses a database management system (DBMS) to record the transmitted biometric and emotional data in an appropriate format.

[1850] Input: Encrypted data packet

[1851] Output: Biometric and emotional data stored in a database

[1852] Step 4:

[1853] Real-time analytics

[1854] The server analyzes the stored data in real time, using advanced AI algorithms to analyze biometric and emotional data to assess health and emotional status, and predict health risks based on the results.

[1855] Input: Biometric and emotional data stored in a database

[1856] Output: Health status assessment, emotional status assessment, health risk prediction

[1857] Step 5:

[1858] Advice Generation

[1859] The server generates personalized health advice based on the analysis results, specifically using a generative AI model to create health advice tailored to the user.

[1860] Input: Health status assessment, emotional status assessment, health risk prediction

[1861] Output: personalized health advice

[1862] Step 6:

[1863] notification

[1864] The server notifies the device of the generated health advice, specifically by sending the advice directly to the user via mobile notifications or email.

[1865] Input: personalized health advice

[1866] Output: User notification

[1867] Step 7:

[1868] food suggestions

[1869] The server will suggest the best food based on the user's health and emotional state, and will create a list of food delivery options based on the results of data analysis.

[1870] Input: Health status assessment, emotional status assessment

[1871] Output: Food suggestion list

[1872] Step 8:

[1873] Food ordering

[1874] The user selects the suggested food items and confirms the order. Specifically, the user confirms the food selection on the device and sends the order to the nearest restaurant.

[1875] Input: Food suggestion list

[1876] Output: Restaurant order data

[1877] Step 9:

[1878] Delivery arrangements

[1879] The restaurant receives the order and arranges for the food to be delivered. Specifically, the restaurant that received the order uses a delivery service to deliver the food to the user.

[1880] Input: Restaurant order data

[1881] Output: Food delivered to the user

[1882] In this way, the system can utilize the user's health and emotional information to provide optimal health advice and food suggestions, thereby improving the user's quality of life.

[1883] 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.

[1884] 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.

[1885] 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.

[1886] 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.

[1887] 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.

[1888] 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.

[1889] 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).

[1890] 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.

[1891] 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."

[1892] 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.

[1893] 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).

[1894] 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.

[1895] 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.

[1896] 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.

[1897] 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.

[1898] 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.

[1899] 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.

[1900] 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.

[1901] 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.

[1902] 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.

[1903] 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.

[1904] The following is further disclosed regarding the above embodiment.

[1905] (Claim 1)

[1906] a means for collecting biometric information of a user;

[1907] means for transmitting the collected biometric information to an edge server;

[1908] A means for analyzing biometric information on an edge server and monitoring and predicting health conditions;

[1909] A means for generating and notifying the user of personalized health advice based on the analysis results;

[1910] a means for making health product recommendations;

[1911] means of anonymizing biometric information and selling it to external organizations;

[1912] A system that constantly monitors users' location information, detects abnormal behavioral patterns, and checks their safety in the event of a disaster.

[1913] (Claim 2)

[1914] 2. The system according to claim 1, wherein the means for collecting the user's biometric information uses a smartphone.

[1915] (Claim 3)

[1916] 10. The system of claim 1, wherein the means for transmitting the collected biometric information to the edge server uses an encrypted communication protocol.

[1917] "Example 1"

[1918] (Claim 1)

[1919] a means for collecting biometric information of a user;

[1920] means for transmitting the collected biometric information to an edge device;

[1921] A means for analyzing biometric information using edge devices to monitor and predict health conditions;

[1922] A means for generating individual health guidance based on the analysis results and notifying the user;

[1923] a means for making health product recommendations;

[1924] A means of anonymizing biometric information and providing it to an external organization;

[1925] A system that constantly monitors the user's location information, detects abnormal behavior patterns, and checks the user's safety in the event of a disaster.

[1926] (Claim 2)

[1927] 2. The system according to claim 1, wherein the means for collecting the user's biometric information uses a portable information terminal.

[1928] (Claim 3)

[1929] 10. The system of claim 1, wherein the means for transmitting the collected biometric information to the edge device uses an encrypted communication protocol.

[1930] "Application Example 1"

[1931] (Claim 1)

[1932] a means for collecting biometric information of a user;

[1933] means for transmitting the collected biometric information to an edge server;

[1934] A means for analyzing biometric information on an edge server and monitoring and predicting health conditions;

[1935] A means for generating and notifying the user of personalized health advice based on the analysis results;

[1936] a means for making health product recommendations;

[1937] means of anonymizing biometric information and selling it to external organizations;

[1938] A means for constantly monitoring user location information to detect abnormal behavior patterns and a means for confirming safety in the event of a disaster;

[1939] A system that includes a means for making product suggestions in physical stores based on the user's health information. 【...

Claims

1. a means for collecting biometric information of a user; means for transmitting the collected biometric information to an edge server; A means for analyzing biometric information on an edge server and monitoring and predicting health conditions; A means for generating and notifying the user of personalized health advice based on the analysis results; a means for making health product recommendations; means of anonymizing biometric information and selling it to external organizations; A system that constantly monitors users' location information, detects abnormal behavioral patterns, and checks their safety in the event of a disaster.

2. 2. The system according to claim 1, wherein the means for collecting biometric information of the user uses a smartphone.

3. 10. The system of claim 1, wherein the means for transmitting collected biometric information to an edge server uses an encrypted communication protocol.

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