System

Abstract

A system is provided.SOLUTION: A system comprising means for collecting body data of each family member, means for storing the collected data in a database, means for analyzing the stored data using a generative AI model, means for providing healthcare advice based on the analysis result, means for visualizing the advice and the analysis result, means for notifying a terminal of the visualized information, and means for generating an alert when the collected data is interrupted for a certain period of time or an abnormal value is detected.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] As our society ages, managing the health of family members and checking the safety of those who live far away are becoming increasingly important issues. In particular, there is a need to collect basic data such as heart rate, steps, and sleep time in real time and provide health advice based on that data to extend healthy lifespans and enable people to spend long, meaningful time with their loved ones. However, current technology does not provide a system that can efficiently collect, analyze, and notify this data, or issue early alerts if an abnormality occurs. This can lead to inadequate health management for family members, which can result in a deterioration of their health and delays in emergency responses. [Means for solving the problem]

[0005] The present invention solves this problem by providing a system that includes: means for collecting body data of each family member; means for storing the collected data in a database; means for analyzing the stored data using a generative AI model; means for providing health management advice based on the analysis results; means for visualizing the advice and analysis results; means for notifying a terminal of the visualized information; and means for generating an alert if the collected data is interrupted for a certain period of time or if an abnormal value is detected. This system enables the collection of body data such as heart rate, number of steps, and sleep time in real time, analysis of the data using AI, and provision of appropriate health advice. Furthermore, if an abnormality is detected or data transmission is interrupted, an alert is issued immediately, allowing family members to respond quickly. This allows for efficient management of each individual's health status and maintenance of family peace of mind and health.

[0006] "Family" refers to multiple individuals who are bound together by shared emotional or legal ties, and is a concept that includes those related by blood or marriage.

[0007] "Body data" is a general term for data that indicates physiological information related to an individual's health status and daily activities, such as heart rate, number of steps, and sleep time.

[0008] "Means of collection" refers to the infrastructure and software / hardware systems for acquiring and storing physiological data via wearable devices and smartphones.

[0009] A "database" is an information storage system for systematically storing and managing various collected data.

[0010] "Generative AI model" is a collective term for artificial intelligence algorithms and software systems that analyze and evaluate health status based on collected data and generate advice.

[0011] "Means for analysis" refers to the methods and technologies used to analyze collected body data to assess health status and identify potential health risks.

[0012] "Health management advice" refers to specific recommendations and guidance for improving lifestyle habits and maintaining health that are provided based on the analysis results.

[0013] "Visualization means" refers to the infrastructure and technology for displaying analysis results and advice in a visually easy-to-understand format, such as graphs and charts.

[0014] "Notification means" refers to systems and protocols for transmitting analysis results, advice, and alerts to users' terminals and notifying them in real time.

[0015] "Alert" means a warning message or notification that is automatically generated and sent when collected data is lost for a certain period of time or when an abnormal value is detected. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] The present invention is a system for managing the health status of each family member in real time and providing necessary health advice. This system includes collecting body data by a terminal, storing and analyzing the data by a server, generating health advice, visualizing the results, and generating notifications and alerts.

[0038] Data collection and transmission

[0039] Terminal

[0040] Using a wearable device or smartphone worn by the user, physical data such as heart rate, number of steps, and sleep time are periodically collected, and this data is sent to a server via Bluetooth or Wi-Fi.

[0041] For example, if user A is wearing a smartwatch, the smartwatch measures his / her heart rate every minute and sends the data to his / her smartphone, which then uploads the data to a server at regular intervals, for example, every 30 minutes.

[0042] Data storage and initial evaluation

[0043] server

[0044] The server receives the body data sent from the device and stores it in a database. The database is managed separately for each user, ensuring accurate recording of individual data. As soon as the data is saved, it is compared with the previous data and an initial evaluation is performed. For example, it checks whether the number of steps taken today has increased compared to the previous day, and whether there are any abnormalities in the heart rate.

[0045] AI-powered detailed analysis and advice generation

[0046] server

[0047] The stored data is then analyzed in detail using a generative AI model. This AI analyzes trends (e.g., increasing or decreasing trends) based on data from the past week, and evaluates stress levels and fatigue levels based on heart rate and sleep patterns. Based on the analysis results, health management advice is automatically generated. For example, for User A, whose recent step count has not reached its goal, specific recommendations such as "We recommend that you walk a little more" are made.

[0048] Data visualization and notification

[0049] server

[0050] The analysis results are then visualized in graphs and charts, such as a graph showing daily step count changes or average heart rate trends. This visualized information is then sent to the device via push notification.

[0051] Terminal

[0052] The device receives the push notification and displays it within the application. By opening the smartphone app, users can visually check their health status. For example, they can view a graph visualizing the number of steps taken over the past week and understand their own exercise trends.

[0053] Alerts and safety confirmation

[0054] server

[0055] If the server detects abnormal values ​​in the heart rate or if data transmission is interrupted for a certain period of time, it automatically generates an alert and sends it to the user's device. For example, if user A's data has not been updated for more than 24 hours, a notification will be sent saying, "There has been no data update for a while. How are you?"

[0056] Gathering feedback

[0057] User

[0058] Users can provide feedback on the advice provided through the smartphone application, which is also sent to the server and stored in the database.

[0059] server

[0060] The server analyzes the received feedback and uses it to improve the generative AI model, which will provide more relevant and accurate health advice in the future.

[0061] According to the above aspects, the present invention manages the health status of family members in real time and provides necessary health advice, thereby extending healthy life expectancy and realizing an environment in which family members can live in peace of mind.

[0062] The processing flow will be explained below.

[0063] Step 1:

[0064] The device collects the user's body data, such as heart rate, steps, and sleep time, using a wearable device or a smartphone application. Data collection occurs at regular intervals (e.g., every minute or every second).

[0065] Step 2:

[0066] The device temporarily stores the collected body data, and a dedicated application on the smartphone manages the storage of this data to prevent data loss.

[0067] Step 3:

[0068] The device sends the temporarily stored data to the server at regular intervals (for example, every 30 minutes). Bluetooth or Wi-Fi is used as the communication protocol.

[0069] Step 4:

[0070] The server receives the body data sent from the device, stores it in a temporary storage area, and then transfers it to the database.

[0071] Step 5:

[0072] The server stores the data in a database, which categorizes and stores data for each user, including historical data.

[0073] Step 6:

[0074] The server performs an initial evaluation of the stored data, for example checking for variations compared to previous data and for abnormal values ​​(such as an extremely high or low heart rate).

[0075] Step 7:

[0076] After the initial assessment is complete, the server inputs the data into a generative AI model for further analysis. The generative AI model analyzes health trends and abnormal patterns based on data from the past week or month.

[0077] Step 8:

[0078] The server automatically generates health management advice based on the analysis results. For example, if you are not getting enough exercise, it will say, "We recommend walking 1,000 more steps per day," or if your stress level is high, it will say, "Make sure you take time to relax."

[0079] Step 9:

[0080] The server visualizes the generated advice and analysis results, using graphs and charts to organize and present the data in a format that users can easily view.

[0081] Step 10:

[0082] The server sends the visualized information to the device as push notifications, which include the latest advice and alerts about detected anomalies.

[0083] Step 11:

[0084] The device receives a push notification, which displays an alert or advice to the user, and the user can check the detailed information within the smartphone application.

[0085] Step 12:

[0086] If the server detects an abnormal value or a disruption in data transmission, it automatically generates an alert and sends a warning to the user's device, such as "Your heart rate is abnormally high. Please consult a doctor" or "We haven't seen any data updates in a while. How are you?"

[0087] Step 13:

[0088] The user submits feedback on the advice provided through the application, for example, by inputting feedback such as "This advice was very helpful" or "I would like more specific suggestions."

[0089] Step 14:

[0090] The server receives user feedback and stores it in a database. This feedback is then analyzed and used to improve the generative AI model, resulting in more accurate advice.

[0091] In this way, all steps work together to create a system that manages and improves the user's health in real time.

[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 becoming increasingly important to manage the health status of each family member in real time and provide necessary health advice. However, existing systems lack sufficient data collection, storage, and analysis, making it difficult to provide specific advice tailored to each individual's health status in real time. Furthermore, there are cases where responses to abnormal values ​​or interruptions in data transmission are delayed, which does not necessarily ensure user safety. Another issue is the lack of a mechanism for appropriately receiving user feedback and reflecting it in system improvements.

[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 a user's biometric data, means for storing the collected data in a database, means for analyzing the stored data using a generative AI model, means for providing health management advice based on the analysis results, means for visualizing the advice and analysis results, means for notifying a terminal of the visualized information, means for generating an alert when the collected data is interrupted for a certain period of time or when an abnormal value is detected, means for collecting feedback from the user, and means for analyzing the feedback and reflecting it in the generative AI model. This enables real-time monitoring of the health status of each family member and providing specific advice tailored to their individual health status. Furthermore, the system can quickly respond to abnormal values ​​or interruptions in data transmission, ensuring user safety. Furthermore, by incorporating user feedback into system improvements, more accurate advice can be provided.

[0097] "Biometric data" refers to data that indicates the user's health condition, such as heart rate, number of steps, and sleep time.

[0098] A "database" is a system for storing and managing collected biometric data.

[0099] A "generative AI model" refers to artificial intelligence that uses machine learning and deep learning techniques to analyze data and automatically generate health management advice.

[0100] "Health Management Advice" provides specific recommendations and advice to improve the user's health based on data analyzed by the generative AI model.

[0101] "Visualization" is a method of displaying data in a visual format, such as a graph or chart, so that it can be easily understood by users.

[0102] "Notification" is a means of conveying visualized information and alerts to a device.

[0103] An "alert" is a warning message that is automatically generated when an abnormality occurs in the user's health condition or system operation, such as an abnormal value or a disruption in data transmission.

[0104] "Feedback" refers to the user's opinions and evaluations of the health care advice provided.

[0105] "Analysis" refers to the detailed analysis of data and the extraction of meaningful information and trends from it.

[0106] The present invention is a system for managing the health status of each family member in real time and providing necessary health advice. This system includes the collection of biometric data by a terminal, the storage and analysis of the data by a server, the generation of health advice, the visualization of the results, and the generation of notifications and alerts.

[0107] Data collection and transmission

[0108] Terminal

[0109] Users periodically collect biometric data such as heart rate, number of steps, and sleep time using wearable devices such as smartwatches and smartphones, and this data is transmitted to a server via Bluetooth or Wi-Fi.

[0110] As a specific example, if user A is wearing a smartwatch, the smartwatch measures his / her heart rate every minute and sends the data to a smartphone, which then uploads the data to a server at regular intervals (e.g., every 30 minutes).

[0111] Data storage and initial evaluation

[0112] server

[0113] The server receives the biometric data sent from the device and stores it in a database. The database is managed separately for each user, so individual data is recorded accurately.

[0114] Once saved, an initial evaluation is performed by comparing the data with previous data. For example, it checks whether the number of steps taken today is higher than the number of steps taken yesterday, or whether there are any abnormalities in the heart rate. This processing can be done using SQL queries or the programming language Python.

[0115] AI-powered detailed analysis and advice generation

[0116] server

[0117] The server performs detailed analysis of the stored data using a generative AI model. This AI model analyzes trends (increases, decreases, etc.) based on data from the past week, and evaluates stress levels and fatigue levels based on heart rate and sleep patterns. Based on the analysis results, health management advice is automatically generated.

[0118] Specifically, the server runs an AI model using a machine learning library such as TensorFlow to perform analysis and provide specific recommendations, such as "User A, whose recent step count has not reached its goal, is advised to walk a little more."

[0119] Data visualization and notification

[0120] server

[0121] The server visualizes the analysis results in graphs and charts, such as a graph showing daily step count changes or average heart rate trends. This visualized information is sent to the device via push notification.

[0122] Terminal

[0123] The device receives the push notification and displays it within the application. By opening the smartphone app, users can visually check their health status. If an abnormality is detected, an alert is displayed immediately. For example, a message such as "You haven't updated your data in a while. How are you?" is sent.

[0124] Gathering feedback

[0125] User

[0126] Users can provide feedback on the advice provided through the smartphone application, for example by sending a rating such as "This advice was helpful."

[0127] server

[0128] The server receives user feedback, stores it in a database, and uses it to improve the generative AI model, which will provide better and more accurate health advice in the future.

[0129] Prompt Sentence Examples

[0130] As a concrete example, here is a prompt for data collection from User A, who is using a smartwatch:

[0131] "User A wears a smartwatch and records his / her heart rate every minute."

[0132] "Analyze user A's heart rate data from the past week and assess user A's stress level."

[0133] Based on the above detailed description, the present invention realizes a system for managing the health status of family members in real time and providing appropriate health advice to individual users.

[0134] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0135] Step 1:

[0136] Data collection

[0137] Terminal

[0138] The user wears a smartwatch or smartphone. The device periodically measures biometric data such as heart rate, steps taken, and sleep time. This data is sent to the smartphone via Bluetooth. The smartphone converts the collected data into JSON format and sends it to a server via Wi-Fi.

[0139] input

[0140] Biometric data from smartwatch (heart rate, steps, sleep time)

[0141] output

[0142] Data sent to the server (JSON format)

[0143] Specific actions

[0144] The smartwatch measures your heart rate every minute and sends the data to your smartphone via Bluetooth.

[0145] The smartphone converts the collected data into JSON format every 30 minutes and uploads it to the server via Wi-Fi.

[0146] Step 2:

[0147] Data storage and initial evaluation

[0148] server

[0149] The server receives the biometric data sent from the device and stores it in a database. The data is managed in separate tables for each user. As soon as it is stored, it is compared with the previous data and an initial evaluation is performed.

[0150] input

[0151] Biometric data sent from the device (JSON format)

[0152] output

[0153] Data stored in a database

[0154] Initial evaluation results

[0155] Specific actions

[0156] The server parses the received JSON data and inserts the data into each user's table.

[0157] Compare with previous data and use SQL queries to check for any abnormalities in heart rate or step count.

[0158] Step 3:

[0159] AI-powered detailed analysis and advice generation

[0160] server

[0161] The server performs detailed analysis of the stored data using a generative AI model. It analyzes trends (increases, decreases, etc.) based on data from the past week, and evaluates stress levels and fatigue levels based on heart rate and sleep patterns. Health management advice is automatically generated as needed.

[0162] input

[0163] User biometric data stored in a database

[0164] output

[0165] Health Management Advice

[0166] Specific actions

[0167] The server uses Python and TensorFlow to run AI models.

[0168] Data from the past week is input into the AI ​​model to evaluate stress levels and fatigue.

[0169] Generate health management advice such as, "User A, who has not reached his / her step goal recently, we recommend that you walk a little more."

[0170] Step 4:

[0171] Data visualization and notification

[0172] server

[0173] The server visualizes the analysis results in graphs and charts, and the visualized data is sent to the device via push notification.

[0174] input

[0175] Health management advice and detailed analysis results

[0176] output

[0177] Visualized graphs, charts, and push notifications

[0178] Specific actions

[0179] The server uses Matplotlib and D3.js to generate graphs and save them as image files.

[0180] The generated image files and text information are sent to the device using a push notification service (e.g., Firebase Cloud Messaging).

[0181] Terminal

[0182] The device receives the push notification and displays it within the application, allowing the user to check their health status by opening the app.

[0183] input

[0184] Push notifications sent from the server

[0185] output

[0186] Display information within the application

[0187] Specific actions

[0188] Parses notifications received by the application and displays them in a graphical interface.

[0189] Step 5:

[0190] Alerts and safety confirmation

[0191] server

[0192] If data transmission is interrupted for a certain period of time or if abnormal values ​​are detected in the heart rate or other data, the server automatically generates an alert and sends it to the user's device.

[0193] input

[0194] User biometric data stored in a database

[0195] output

[0196] Alert Notifications

[0197] Specific actions

[0198] The server uses a cron job to periodically check the database and send alert emails or push notifications if an abnormality is detected.

[0199] Step 6:

[0200] Gathering feedback

[0201] User

[0202] The user provides feedback on the advice provided through the application.

[0203] input

[0204] User feedback

[0205] output

[0206] Feedback data sent to the server

[0207] Specific actions

[0208] Users fill out and submit feedback in the in-app feedback form.

[0209] server

[0210] The server receives feedback from users and stores it in a database, which then reflects the feedback data in improving the generative AI model.

[0211] input

[0212] User feedback data

[0213] output

[0214] Updated AI model

[0215] Specific actions

[0216] The server stores the feedback data in text format and periodically adds it to the AI ​​model's training dataset.

[0217] (Application example 1)

[0218] 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."

[0219] Existing systems that manage the health status of each family member in real time and provide necessary health advice have limited ways for users to easily understand their own health status. Furthermore, there is a lack of means to provide health information in a way that is easily accessible to employees and customers working in physical stores and other environments. Furthermore, providing prompt and appropriate alerts in the event of an emergency is also a challenge. The present invention aims to solve these problems and support health management for users in physical store environments.

[0220] 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.

[0221] In this invention, the server includes a means for collecting body data of each family member in real time, a means for storing the collected data in a database, and a means for analyzing the stored data using a generative AI model, which enables the user's health condition to be visually grasped in real time and to provide appropriate advice and warnings promptly.

[0222] "Body data for each family member" refers to physical information such as each individual's heart rate, number of steps taken, and sleep time.

[0223] "Real-time collection means" refers to technologies and methods that use wearable devices and smartphones to continuously collect user-generated health data.

[0224] "Database" refers to a system for storing and managing collected body data, and a place for centrally managing data for each user.

[0225] "Generative AI model" refers to the artificial intelligence algorithm used to analyze collected data and generate health advice.

[0226] "Visualization means" refers to techniques and methods for displaying analysis results in a visually easy-to-understand format, such as graphs or charts.

[0227] "User terminal" refers to a device that is directly used by the user, such as a smartphone or smart glasses.

[0228] "Means for generating alerts" refers to technologies and methods for sending notifications to users in the event of an emergency, such as abnormal health data values ​​or disruptions to data transmission.

[0229] "Visual device" refers to a device used to provide information directly to the eye, such as smart glasses.

[0230] This system collects, analyzes, and visualizes the body data of each family member in real time, and provides necessary health advice and warnings. This system uses wearable devices, smartphones, a server, a generative AI model, a visual device, and a user terminal.

[0231] System Configuration

[0232] Wearable devices and smartphones

[0233] Users wear wearable devices (e.g., fitness trackers, smartwatches), which collect body data such as heart rate, steps taken, and sleep time. This data is sent in real time to a smartphone via Bluetooth or Wi-Fi, and the smartphone uploads the data to a server at regular intervals.

[0234] server

[0235] The server receives the body data sent from the smartphone and stores it in a database. The data stored in the database is then analyzed using a generative AI model. This generative AI model analyzes trends (increases, decreases, etc.) based on the user's data from the past week, and evaluates stress levels and fatigue levels based on heart rate and sleep patterns. Health management advice is automatically generated based on the analysis results.

[0236] Visibility and Notification

[0237] The server visualizes the analysis results in graphs and charts, and this visualized information is sent to the user's device via push notification. The user can then visually check the information using a visual device such as a smartphone or smart glasses.

[0238] Warning generation

[0239] The server automatically generates and sends a warning to the user's device if data transmission is interrupted for a certain period of time or if abnormal values ​​are detected in the health data. For example, if the user's heart rate suddenly increases, a warning message will appear on the visual device saying, "Your heart rate is increasing. Please sit down and take a break."

[0240] Gathering feedback

[0241] Users can provide feedback on the advice provided through a smartphone application. This feedback is also sent to the server and stored in a database. The server analyzes the received feedback and reflects it in improving the generative AI model.

[0242] Specific examples

[0243] For example, consider a scenario in which an employee working in a brick-and-mortar store is wearing smart glasses. These smart glasses monitor their heart rate in real time and send the data to a server. The server analyzes this data, and if overwork is detected, the smart glasses display the advice, "Fatigue detected. Please take a short break."

[0244] Prompt Sentence Examples

[0245] "Analyze the user's health data (heart rate, steps, sleep time) from the past seven days to assess stress levels and fatigue levels and generate appropriate health advice."

[0246] By establishing such a system, it is possible to effectively support health management for users in a physical store environment and provide a safe and comfortable environment.

[0247] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0248] Step 1:

[0249] Data Collection and Transmission

[0250] The user wears a wearable device that collects physical data such as heart rate, steps taken, and sleep time.

[0251] The terminal (smartphone) receives the collected data using Bluetooth or Wi-Fi.

[0252] Input: Body data sent from a wearable device.

[0253] Output: Body data stored on the device.

[0254] Step 2:

[0255] Uploading data

[0256] The device uploads the received body data to the server at regular intervals (e.g., every 30 minutes).

[0257] Input: Body data stored on the device.

[0258] Output: The body data sent to the server.

[0259] Step 3:

[0260] Data storage and initial evaluation

[0261] The server receives the body data sent from the terminal and stores it in a database.

[0262] The server compares the stored data with past data and performs an initial assessment, checking, for example, whether the number of steps has increased compared to the previous day or whether there are any abnormalities in the heart rate.

[0263] Input: Body data sent from the device.

[0264] Output: Data stored in a database and initial evaluation results.

[0265] Step 4:

[0266] AI-powered detailed analysis and advice generation

[0267] The server analyzes the data stored in the database using a generative AI model.

[0268] The server generates health advice according to the prompt sentence.

[0269] Example: "Analyze the user's health data (heart rate, steps, and sleep time) from the past 7 days to assess their stress level and fatigue level and generate appropriate health advice."

[0270] Input: Historical health data stored in a database.

[0271] Output: Health advice from a generative AI model.

[0272] Step 5:

[0273] Data Visualization and Notification

[0274] The server visualizes the generated health advice and analysis results in graphs and charts.

[0275] The server sends the visualized data to the user's device via push notification.

[0276] Input: Health advice and analysis results from generative AI models.

[0277] Output: Visualized data and notification messages.

[0278] Step 6:

[0279] Display by visual device

[0280] The terminal or visual device (smart glasses) displays the transmitted visualization data and health advice.

[0281] Input: Visualization data and health advice sent from the server.

[0282] Output: Health information displayed on a visual device.

[0283] Step 7:

[0284] Alert Generation and Notification

[0285] The server generates an alert if data transmission is interrupted for a certain period of time or if an abnormal value is detected.

[0286] The server sends the generated alert to the user terminal.

[0287] Input: Data transmission disruption or abnormal values.

[0288] Output: The warning message sent to the user terminal.

[0289] Step 8:

[0290] Collecting and analyzing feedback

[0291] Users provide feedback on the health advice provided via a smartphone app.

[0292] The server stores the received feedback in a database and uses it to improve the generative AI model.

[0293] Input: User feedback.

[0294] Output: Feedback data and an improved generative AI model.

[0295] 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.

[0296] This invention is a system for managing the health status of each family member in real time and providing necessary health advice. This system not only collects and analyzes body data, generates advice, visualizes it, and generates notifications and alerts, but also combines it with an emotion engine to realize health management that takes into account the user's emotions.

[0297] Data collection and transmission

[0298] Terminal

[0299] Using a wearable device or smartphone application worn by the user, physical data such as heart rate, number of steps, and sleep time are periodically collected.Facial expressions and tone of voice are also collected using a camera and microphone, and this data is used to provide to the emotion engine.For example, a smartwatch measures the user's heart rate every minute, and a smartphone periodically scans the user's face to obtain facial expression data.

[0300] Data storage and initial evaluation

[0301] server

[0302] The system receives the body data and emotional data sent from the device and first stores them in a temporary storage area. It then transfers the data to a database, where it categorizes and manages the data for each user. Once the data is saved, it compares it with the previous data and performs an initial evaluation. For example, it checks whether the number of steps today has increased compared to the previous day, and whether there are any abnormalities in the heart rate.

[0303] Deep analysis with AI and sentiment engine

[0304] server

[0305] The stored data is then analyzed in detail using a generative AI model. This AI analyzes trends (e.g., increasing or decreasing trends) based on data from the past week, and assesses stress levels and fatigue levels based on heart rate and sleep patterns. An emotion engine also analyzes facial expressions and voice data to estimate the user's emotional state. For example, it can detect "happiness" from the user's facial expression and "tension" from the tone of their voice.

[0306] Advice generation and visualization

[0307] server

[0308] Health management advice is automatically generated based on the analysis results of the generative AI model and emotion engine. For example, if User A's recent step count has not reached its target, advice such as "We recommend you walk more" will be generated. If the emotional state is "high stress," advice such as "Try relaxation" will also be added. The analysis results and advice are then visualized in graphs and charts. For example, a graph of daily step count fluctuations, a graph of average heart rate trends, and a graph of emotional state fluctuations can be created.

[0309] Data Notifications and Alerts

[0310] server

[0311] The visualized information is sent to the device as a push notification. The notification includes the latest advice and alerts regarding abnormalities. For example, an alert may be sent saying, "Your recent average heart rate has been high. Please consult your doctor."

[0312] Terminal

[0313] Users will receive push notifications to display alerts and advice, and within the smartphone app, they can view detailed information, such as a graph of their heart rate over the past week and their emotional state at the same time.

[0314] Gathering feedback

[0315] User

[0316] Feedback on the advice provided is submitted via a smartphone application, for example, by inputting feedback such as "This advice was very helpful" or "The content is lacking."

[0317] server

[0318] It receives user feedback, stores it in a database, analyzes it, and uses it to improve the generative AI model and emotion engine, resulting in more accurate advice.

[0319] According to the above aspects, the present invention manages the health and emotional states of family members in real time and provides appropriate health advice, thereby extending healthy life expectancy and realizing an environment in which family members can live in peace of mind.

[0320] The processing flow will be explained below.

[0321] Step 1:

[0322] The device collects the user's heart rate, steps, and sleep time, using sensors in the wearable device or smartphone, as well as a camera to collect facial expression data and a microphone to collect audio data.

[0323] Step 2:

[0324] The device temporarily stores the data it collects, and a dedicated application on the smartphone manages this to prevent data loss.

[0325] Step 3:

[0326] The device sends collected data to the server at regular intervals (for example, every 30 minutes) using Bluetooth or Wi-Fi.

[0327] Step 4:

[0328] The server receives the data sent from the terminal, stores the received data in a temporary storage area, and then transfers it to the database.

[0329] Step 5:

[0330] The server stores the data in a database and categorizes and manages it for each user. After saving, it compares it with the previous data for an initial evaluation. For example, it checks whether the number of steps has increased compared to the previous day, and whether there are any abnormalities in the heart rate.

[0331] Step 6:

[0332] After the server completes the initial evaluation, it passes the data to a generative AI model for further analysis, which analyzes trends and abnormal patterns based on past data.

[0333] Step 7:

[0334] The server passes the collected facial expression and voice data to the emotion engine, which analyzes this data and estimates the user's emotional state. For example, it can detect "happiness" or "sadness" from facial expressions and "tension" or "calmness" from voice tone.

[0335] Step 8:

[0336] The server combines the analysis results from both the generative AI model and the emotion engine. Based on this, it generates specific health management advice. For example, if the user is not getting enough exercise, it generates advice such as "We recommend walking more." If the user's emotional state is determined to be "high stress," it adds the advice "Try relaxation techniques."

[0337] Step 9:

[0338] The server visualizes the analysis results and generated advice, including graphs of daily step count fluctuations, average heart rate trends, and emotional state fluctuations.

[0339] Step 10:

[0340] The server sends the visualized information to the device as a push notification. The notification includes the latest advice and alerts regarding abnormalities. For example, an alert may be sent saying, "Your recent average heart rate has been high. Please consult a doctor."

[0341] Step 11:

[0342] The device receives a push notification and displays an alert or advice to the user. The user can then view more detailed information within the smartphone application. For example, they can view graphs showing the number of steps taken over the past week and their emotional state.

[0343] Step 12:

[0344] If the server detects an abnormal value or a disruption in data transmission, it automatically generates an alert and sends a warning to the user's device, such as "Your heart rate is abnormally high. Please consult a doctor" or "We haven't seen any data updates in a while. How are you?"

[0345] Step 13:

[0346] The user sends feedback on the advice provided through the application, for example, by inputting feedback such as "This advice was very helpful" or "The content is difficult to understand."

[0347] Step 14:

[0348] The server receives user feedback, stores it in a database, and analyzes it to improve the generative AI model and emotion engine, resulting in more accurate advice in the future.

[0349] In this way, the present invention is a system that can comprehensively manage the health and emotional states of family members and provide appropriate health advice.

[0350] Example 2

[0351] 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."

[0352] While current health management systems place emphasis on collecting and analyzing biometric data, they lack the ability to take into account the user's emotional state. This prevents them from properly managing the impact of stress and emotional states on health. They also lack a mechanism to effectively incorporate user feedback and improve the accuracy of advice. The objective of this invention is to solve these problems and provide a more comprehensive health management system.

[0353] 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.

[0354] In this invention, the server includes means for collecting biometric data and emotional data of each family member, means for storing the collected data in a database, means for analyzing the stored data using a generative AI model and an emotion engine, means for providing health management advice based on the analysis results, means for visualizing the advice and analysis results, means for notifying a terminal of the visualized information, means for generating an alert when the collected data is discontinued for a certain period of time or when an abnormal value is detected, and means for collecting user feedback and reflecting it in improving the generative AI model and the emotion engine. This enables comprehensive health management including emotional states and realizes the provision of more accurate advice.

[0355] "Biometric data" refers to data that indicates the state of the body, such as heart rate, number of steps, and sleep time.

[0356] "Emotion data" is data that indicates the user's emotional state, such as facial expressions and vocal tones.

[0357] A "database" is a system for storing, classifying, and managing collected data.

[0358] A "generative AI model" is an artificial intelligence algorithm that analyzes collected data and generates health management advice.

[0359] An "emotion engine" is a system that analyzes facial expressions and vocal tones to estimate a user's emotional state.

[0360] "Health management advice" refers to suggestions for maintaining or improving health based on collected data and the results of its analysis.

[0361] "Visualization" refers to the presentation of data and its analytical results in a visual format such as a graph or chart.

[0362] "Push notification" is a system that sends information from a server to a device in real time.

[0363] "Feedback" refers to the act of a user sending an evaluation or opinion regarding the advice provided.

[0364] An "outlier" is a value in the collected data that is outside the normal range.

[0365] This invention is a system for managing the health and emotional state of each family member in real time and providing necessary health advice. The operation and configuration of this system will be specifically described below.

[0366] Data collection and transmission

[0367] Terminal

[0368] Biometric data such as heart rate, number of steps, and sleep time are periodically collected using wearable devices or smartphone applications worn by the user. Cameras and microphones are also used to collect facial expressions and tone of voice, which are then treated as emotion data. For example, a smartwatch measures the user's heart rate every minute, and a smartphone periodically scans the user's face to obtain facial expression data.

[0369] Hardware and software examples

[0370] Wearable devices: Apple Watch, Fitbit

[0371] Smartphone applications: iOS applications, Android applications

[0372] Camera and microphone: Built-in smartphone

[0373] Data storage and initial evaluation

[0374] server

[0375] The system receives body and emotional data sent from the device and stores it in a temporary storage area. It then transfers the data to a database, where it categorizes and manages the data for each user. Once the data is saved, it performs an initial evaluation by comparing it with the previous data. For example, it checks whether the number of steps taken today has increased compared to the previous day, and whether there are any abnormalities in the heart rate.

[0376] Hardware and software examples

[0377] Server: Cloud server, database management system (e.g. MySQL, PostgreSQL)

[0378] Detailed analysis

[0379] server

[0380] The stored data is then analyzed in detail using a generative AI model. This AI analyzes trends (e.g., increasing or decreasing trends) based on data from the past week, and assesses stress levels and fatigue levels based on heart rate and sleep patterns. An emotion engine also analyzes facial expressions and voice data to estimate the user's emotional state. For example, it can detect "happiness" from the user's facial expression and "tension" from the tone of their voice.

[0381] Hardware and software examples

[0382] AI model: TensorFlow-based generative AI model

[0383] Sentiment Engine: IBM Watson Sentiment Analysis API

[0384] Advice generation and visualization

[0385] server

[0386] Health management advice is automatically generated based on the analysis results of the generative AI model and emotion engine. For example, if a user's recent step count has not reached their goal, the system will generate the advice "We recommend you walk more." If the user's emotional state is "high stress," the system will also add the advice "Try relaxation." These results are then visualized in graphs and charts. For example, it creates a graph of daily step count fluctuations, average heart rate trends, and emotional state fluctuations.

[0387] Hardware and software examples

[0388] Graph and chart generation: Matplotlib, D3.js

[0389] Data Notifications and Alerts

[0390] server

[0391] The visualized information is sent to the device as a push notification. The notification includes the latest advice and alerts regarding abnormalities. For example, an alert may be sent saying, "Your recent average heart rate has been high. Please consult your doctor."

[0392] Terminal

[0393] Users will receive push notifications to display alerts and advice, and within the smartphone app, they can view detailed information, such as a graph of their heart rate over the past week and their emotional state at the same time.

[0394] Hardware and software examples

[0395] Push notifications: Firebase Cloud Messaging, Apple Push Notification Service

[0396] Gathering feedback

[0397] User

[0398] Feedback on the advice provided is submitted via a smartphone application, for example, by inputting feedback such as "This advice was very helpful" or "The content is lacking."

[0399] server

[0400] It receives user feedback, stores it in a database, analyzes it, and uses it to improve the generative AI model and emotion engine, resulting in more accurate advice.

[0401] (Example of a prompt)

[0402] "Explain how to assess a user's current stress level and generate appropriate health advice based on step count and heart rate data from the past week."

[0403] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0404] Step 1:

[0405] Data collection

[0406] The device uses a wearable device or smartphone application to periodically collect biometric data such as the user's heart rate, number of steps, and sleep time. It also uses a camera and microphone to collect facial expressions and tone of voice, which are treated as emotion data. The inputs are the user's biometric data, facial expressions, and tone of voice. The output is the various collected data. Specifically, the smartwatch records the heart rate every minute, and the smartphone activates the camera every hour to scan the user's face and obtain facial expression data.

[0407] Step 2:

[0408] Data transmission

[0409] The data collected by the device is sent to the server via wireless communication (Bluetooth or Wi-Fi). The collected data is the input. The data sent to the server is obtained as the output. In concrete terms, the smartwatch transfers data to the smartphone via Bluetooth, and the smartphone sends the data to the server via Wi-Fi.

[0410] Step 3:

[0411] Data storage and initial evaluation

[0412] The server stores the transmitted data in a temporary storage area and then transfers it to the database. The data is classified and managed for each user, and after storage is complete, it is compared with the previous data for an initial evaluation. The input is the data sent to the server. The output is the classified and managed data and the results of the initial evaluation. Specifically, the server stores the new data in the database, compares it with past data, and checks for any outliers.

[0413] Step 4:

[0414] Detailed analysis

[0415] The server performs detailed analysis of the stored data using a generative AI model and emotion engine. Trends are analyzed based on data from the past week, and stress levels and fatigue levels are assessed based on heart rate and sleep patterns. The emotion engine also analyzes facial expressions and voice data to estimate the user's emotional state. The input is data stored in the database. The output is the analysis results. Specifically, the AI ​​model analyzes heart rate, step count, and sleep data, and the emotion engine estimates the user's emotional state from facial expressions and tone of voice.

[0416] Step 5:

[0417] Advice generation and visualization

[0418] The server automatically generates health management advice based on the analysis results of the generative AI model and emotion engine. The advice and analysis results are visualized in graphs and charts. The input is the analysis results. The output is the generated advice and visualized graphs and charts. Specifically, the generative AI model generates advice such as "Your recent step count has not reached your goal. Walk more," and the emotion engine adds advice such as "Try relaxation." A graph is generated based on this.

[0419] Step 6:

[0420] Notifications and Alerts

[0421] The server sends the visualized information to the device as a push notification, and also sends an alert if an abnormality is detected. The inputs are the generated advice and visualized data. The output is the push notification and alert sent to the device. Specifically, the server generates an alert saying, "Your recent average heart rate has been high. Please consult a doctor," and sends it to the device.

[0422] The device receives the push notification and displays it to the user. The input is a push notification from the server. The output is an alert or advice that is displayed to the user. The specific behavior is that the smartphone displays the push notification, and the user checks for more information in the app.

[0423] Step 7:

[0424] Gathering feedback

[0425] The user sends feedback on the advice provided through a smartphone application. The input is the user's feedback. The output is the feedback sent to the server. The specific operation is that the user inputs feedback such as "This advice was very helpful" into the smartphone app and sends it.

[0426] The server receives feedback from users and stores it in a database. The input is the feedback sent by the user. The output is the feedback stored in the database. Specifically, the server stores the feedback data and uses it to improve the generative AI model and emotion engine.

[0427] (Application example 2)

[0428] 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."

[0429] Conventional health management systems were able to collect and analyze the health data of individual family members and provide the results of their analysis, but they did not support acquiring customers' health and emotional data in real time in physical stores and providing appropriate advice or product and service recommendations based on that data. This made it difficult to grasp customers' health conditions and provide individualized support in physical stores. As a result, it was not possible to provide appropriate services according to the customer's health condition, which led to the issue of not being able to improve customer satisfaction or achieve appropriate health management.

[0430] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting body data of each family member, means for storing the collected data in a database, means for analyzing the stored data using a generative AI model, means for providing health management advice based on the analysis results, means for visualizing the advice and analysis results, means for notifying a terminal of the visualized information, and means for generating an alert when the collected data is discontinued for a certain period of time or when an abnormal value is detected. In addition, it also includes means for collecting body data and emotional data of customers in a physical store and displaying them in real time on an augmented reality device worn by a staff member, means for recommending appropriate products and services based on the body data and emotional data of the customer, and means for notifying the staff member of the augmented reality device of the recommendation. This makes it possible to recommend optimal advice, products, and services based on the customer's health and emotional state, even in a physical store.

[0431] "Means of collecting physical data from each family member" refers to technology for recording the health status of each family member using wearable devices, smartphones, etc.

[0432] "Means for storing data in a database" refers to a system or method for organizing, recording, and managing collected data in a certain manner.

[0433] A "generative AI model" refers to an artificial intelligence model that uses machine learning algorithms to analyze data and predict relevant patterns and trends.

[0434] "Means for providing health management advice" refers to technologies for providing users with health advice and recommendations based on the collected data and the analysis results of the AI ​​model.

[0435] "Visualization means" refers to the technology of displaying data and analysis results in a visually easy-to-understand format such as graphs and charts.

[0436] "Means for notifying the device" refers to technology for sending the visualized information or alerts to the user's device and notifying them.

[0437] "Means for generating alerts" refers to technology that generates and notifies when an abnormality is detected based on collected data or when data is lost for a certain period of time.

[0438] "Means of collecting customer physical and emotional data within physical stores" refers to technology that monitors customer health and emotions using devices installed within the store or wearable devices worn by staff.

[0439] An "augmented reality device" is a device that can overlay digital information onto the real world, and when worn by staff, it allows them to display customer data in real time.

[0440] "Means for recommending appropriate products and services" refers to technology that suggests products and services that are best suited to a customer based on collected health and emotional data of the customer.

[0441] This invention provides a system for managing a customer's health and emotional state in real time in a physical store and recommending appropriate products and services. The system includes: means for collecting body data of each family member; means for storing the collected data in a database; means for analyzing the data using a generative AI model; means for providing health management advice based on the analysis results; means for visualizing the advice and analysis results; means for notifying a terminal; and means for generating alerts. The system further includes means for collecting body data and emotional data of customers in the physical store and displaying the data in real time on an augmented reality device worn by staff; means for recommending appropriate products and services based on the customer data; and means for notifying the augmented reality device of the recommendations.

[0442] Program Generation

[0443] The core program of this system operates by including the following elements:

[0444] Data collection and transmission

[0445] User device:

[0446] Using a wearable device (e.g., a smartwatch) and a smartphone worn by the user, physical data such as heart rate, number of steps, and sleep time are periodically collected.Facial expression and voice data are also collected using a camera and microphone, and used as data to determine emotional state.

[0447] Data storage and initial evaluation

[0448] server:

[0449] Body data and emotional data sent from the device are received and stored in a temporary storage area.The data is then transferred to a database where it is classified and managed for each user.Once the data has been saved, it is compared with the previous data and an initial evaluation is performed.

[0450] Deep analysis with AI and sentiment engine

[0451] server:

[0452] The stored data is analyzed in detail using a generative AI model (e.g., Decision Tree Classifier). The analysis identifies trends based on data from the past week and evaluates health and emotional status. In addition, an emotion engine (e.g., Emotion Engine) analyzes facial expressions and voice data to estimate the customer's emotional state.

[0453] Advice generation and visualization

[0454] server:

[0455] Based on the analysis results, health management advice and product recommendations are generated, which are visualized in graphs and charts and displayed in real time on an augmented reality device (e.g., smart glasses) worn by staff.

[0456] Data Notifications and Alerts

[0457] server:

[0458] The visualized information is sent as push notifications to staff members' augmented reality devices and customers' smartphones, and an alert is generated if abnormal data is detected or if data is lost for a certain period of time.

[0459] Specific examples

[0460] For example, consider a customer using a treadmill in a fitness club. Here's how the system works:

[0461] 1. The smartwatch worn by the customer collects heart rate data.

[0462] 2. Facial recognition cameras collect customer facial expression data.

[0463] 3. The collected data is sent to the server for storage and initial evaluation.

[0464] 4. Generative AI models and emotion engines analyze the data and assess the customer's exercise intensity and emotional state.

[0465] 5. The staff member's smart glasses will display advice such as "Please slow down a bit" and emotional analysis results such as "You appear tired" in real time.

[0466] Prompt Sentence Examples

[0467] While using the treadmill, the customer's heart rate is high and their facial expression looks tired. Please suggest a healthcare app feature that will provide appropriate advice.

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

[0469] Step 1:

[0470] Data collection and transmission

[0471] Input: Wearable device and smartphone worn by the user

[0472] Specific operation and output: Using a wearable device (e.g., a smartwatch) and a smartphone, the system periodically collects physical data such as the user's heart rate, number of steps, and sleep time. Furthermore, the smartphone's camera and microphone are used to collect facial expression data and voice data, which are then used to determine the user's emotional state. The collected data is then sent from the device to a server.

[0473] Step 2:

[0474] Data storage and initial evaluation

[0475] Input: Body data and emotional data sent from the device

[0476] Specific operations and output: The server receives the body data and emotional data sent from the device and stores them in a temporary storage area. It then transfers the data to a database where it categorizes and manages the data for each user. The stored data is compared with previous data for an initial evaluation. For example, the collected heart rate and step count are compared with the data from the previous day to evaluate any abnormalities or fluctuations. The results of the initial evaluation are sent to the next step for further analysis.

[0477] Step 3:

[0478] Deep analysis with AI and sentiment engine

[0479] Input: Stored body and emotional data

[0480] Specific operation and output: The server performs detailed analysis of the stored data using a generative AI model (e.g., DecisionTreeClassifier). Data from the past week is used to identify trends in heart rate and step count and assess health status. An emotion engine (e.g., EmotionEngine) also analyzes facial expressions and voice data to estimate the user's emotional state. The analysis results are stored for use in the next step.

[0481] Step 4:

[0482] Advice generation and visualization

[0483] Input: Results of detailed analysis (evaluation of health and emotional state)

[0484] Specific operation and output: The server generates health management advice based on the analysis results of the generative AI model and emotion engine. For example, if the user's step count has not reached the target, the advice "We recommend you walk more" is generated. Furthermore, if the emotional state is determined to be "high stress," the advice "Try relaxation" is added. The generated advice and analysis results are visualized and displayed as graphs and charts.

[0485] Step 5:

[0486] Data Notifications and Alerts

[0487] Input: Visualized advice and analysis results

[0488] Specific operation and output: The server sends the visualized information as a push notification to a device (e.g., a smartphone tag or an augmented reality device worn by a staff member). For example, if a customer shows a high heart rate, an alert such as "Please slow down a little" will be displayed on the augmented reality device of the staff member. If abnormal data is detected, a warning message will also be provided to prompt appropriate action.

[0489] Step 6:

[0490] Collecting and using feedback

[0491] Input: Feedback from users or staff

[0492] Specific operation and output: The user or store staff sends feedback on the advice or recommendation provided via the terminal. The server receives this feedback and stores it in a database. The stored feedback is analyzed and reflected in improvements to the generative AI model and emotion engine, resulting in more accurate advice being provided.

[0493] 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.

[0494] 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.

[0495] 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.

[0496] [Second embodiment]

[0497] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0498] 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.

[0499] 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).

[0500] 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.

[0501] 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.

[0502] 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).

[0503] 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.

[0504] 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.

[0505] 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.

[0506] 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.

[0507] 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.

[0508] 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."

[0509] The present invention is a system for managing the health status of each family member in real time and providing necessary health advice. This system includes collecting body data by a terminal, storing and analyzing the data by a server, generating health advice, visualizing the results, and generating notifications and alerts.

[0510] Data collection and transmission

[0511] Terminal

[0512] Using a wearable device or smartphone worn by the user, physical data such as heart rate, number of steps, and sleep time are periodically collected, and this data is sent to a server via Bluetooth or Wi-Fi.

[0513] For example, if user A is wearing a smartwatch, the smartwatch measures his / her heart rate every minute and sends the data to his / her smartphone, which then uploads the data to a server at regular intervals, for example, every 30 minutes.

[0514] Data storage and initial evaluation

[0515] server

[0516] The server receives the body data sent from the device and stores it in a database. The database is managed separately for each user, ensuring accurate recording of individual data. As soon as the data is saved, it is compared with the previous data and an initial evaluation is performed. For example, it checks whether the number of steps taken today has increased compared to the previous day, and whether there are any abnormalities in the heart rate.

[0517] AI-powered detailed analysis and advice generation

[0518] server

[0519] The stored data is then analyzed in detail using a generative AI model. This AI analyzes trends (e.g., increasing or decreasing trends) based on data from the past week, and evaluates stress levels and fatigue levels based on heart rate and sleep patterns. Based on the analysis results, health management advice is automatically generated. For example, for User A, whose recent step count has not reached its goal, specific recommendations such as "We recommend that you walk a little more" are made.

[0520] Data visualization and notification

[0521] server

[0522] The analysis results are then visualized in graphs and charts, such as a graph showing daily step count changes or average heart rate trends. This visualized information is then sent to the device via push notification.

[0523] Terminal

[0524] The device receives the push notification and displays it within the application. By opening the smartphone app, users can visually check their health status. For example, they can view a graph visualizing the number of steps taken over the past week and understand their own exercise trends.

[0525] Alerts and safety confirmation

[0526] server

[0527] If the server detects abnormal values ​​in the heart rate or if data transmission is interrupted for a certain period of time, it automatically generates an alert and sends it to the user's device. For example, if user A's data has not been updated for more than 24 hours, a notification will be sent saying, "There has been no data update for a while. How are you?"

[0528] Gathering feedback

[0529] User

[0530] Users can provide feedback on the advice provided through the smartphone application, which is also sent to the server and stored in the database.

[0531] server

[0532] The server analyzes the received feedback and uses it to improve the generative AI model, which will provide more relevant and accurate health advice in the future.

[0533] According to the above aspects, the present invention manages the health status of family members in real time and provides necessary health advice, thereby extending healthy life expectancy and realizing an environment in which family members can live in peace of mind.

[0534] The processing flow will be explained below.

[0535] Step 1:

[0536] The device collects the user's body data, such as heart rate, steps, and sleep time, using a wearable device or a smartphone application. Data collection occurs at regular intervals (e.g., every minute or every second).

[0537] Step 2:

[0538] The device temporarily stores the collected body data, and a dedicated application on the smartphone manages the storage of this data to prevent data loss.

[0539] Step 3:

[0540] The device sends the temporarily stored data to the server at regular intervals (for example, every 30 minutes). Bluetooth or Wi-Fi is used as the communication protocol.

[0541] Step 4:

[0542] The server receives the body data sent from the device, stores it in a temporary storage area, and then transfers it to the database.

[0543] Step 5:

[0544] The server stores the data in a database, which categorizes and stores data for each user, including historical data.

[0545] Step 6:

[0546] The server performs an initial evaluation of the stored data, for example checking for variations compared to previous data and for abnormal values ​​(such as an extremely high or low heart rate).

[0547] Step 7:

[0548] After the initial assessment is complete, the server inputs the data into a generative AI model for further analysis. The generative AI model analyzes health trends and abnormal patterns based on data from the past week or month.

[0549] Step 8:

[0550] The server automatically generates health management advice based on the analysis results. For example, if you are not getting enough exercise, it will say, "We recommend walking 1,000 more steps per day," or if your stress level is high, it will say, "Make sure you take time to relax."

[0551] Step 9:

[0552] The server visualizes the generated advice and analysis results, using graphs and charts to organize and present the data in a format that users can easily view.

[0553] Step 10:

[0554] The server sends the visualized information to the device as push notifications, which include the latest advice and alerts about detected anomalies.

[0555] Step 11:

[0556] The device receives a push notification, which displays an alert or advice to the user, and the user can check the detailed information within the smartphone application.

[0557] Step 12:

[0558] If the server detects an abnormal value or a disruption in data transmission, it automatically generates an alert and sends a warning to the user's device, such as "Your heart rate is abnormally high. Please consult a doctor" or "We haven't seen any data updates in a while. How are you?"

[0559] Step 13:

[0560] The user submits feedback on the advice provided through the application, for example, by inputting feedback such as "This advice was very helpful" or "I would like more specific suggestions."

[0561] Step 14:

[0562] The server receives user feedback and stores it in a database. This feedback is then analyzed and used to improve the generative AI model, resulting in more accurate advice.

[0563] In this way, all steps work together to create a system that manages and improves the user's health in real time.

[0564] Example 1

[0565] 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."

[0566] In modern society, it is becoming increasingly important to manage the health status of each family member in real time and provide necessary health advice. However, existing systems lack sufficient data collection, storage, and analysis, making it difficult to provide specific advice tailored to each individual's health status in real time. Furthermore, there are cases where responses to abnormal values ​​or interruptions in data transmission are delayed, which does not necessarily ensure user safety. Another issue is the lack of a mechanism for appropriately receiving user feedback and reflecting it in system improvements.

[0567] 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.

[0568] In this invention, the server includes means for collecting a user's biometric data, means for storing the collected data in a database, means for analyzing the stored data using a generative AI model, means for providing health management advice based on the analysis results, means for visualizing the advice and analysis results, means for notifying a terminal of the visualized information, means for generating an alert when the collected data is interrupted for a certain period of time or when an abnormal value is detected, means for collecting feedback from the user, and means for analyzing the feedback and reflecting it in the generative AI model. This enables real-time monitoring of the health status of each family member and providing specific advice tailored to their individual health status. Furthermore, the system can quickly respond to abnormal values ​​or interruptions in data transmission, ensuring user safety. Furthermore, by incorporating user feedback into system improvements, more accurate advice can be provided.

[0569] "Biometric data" refers to data that indicates the user's health condition, such as heart rate, number of steps, and sleep time.

[0570] A "database" is a system for storing and managing collected biometric data.

[0571] A "generative AI model" refers to artificial intelligence that uses machine learning and deep learning techniques to analyze data and automatically generate health management advice.

[0572] "Health Management Advice" provides specific recommendations and advice to improve the user's health based on data analyzed by the generative AI model.

[0573] "Visualization" is a method of displaying data in a visual format, such as a graph or chart, so that it can be easily understood by users.

[0574] "Notification" is a means of conveying visualized information and alerts to a device.

[0575] An "alert" is a warning message that is automatically generated when an abnormality occurs in the user's health condition or system operation, such as an abnormal value or a disruption in data transmission.

[0576] "Feedback" refers to the user's opinions and evaluations of the health care advice provided.

[0577] "Analysis" refers to the detailed analysis of data and the extraction of meaningful information and trends from it.

[0578] The present invention is a system for managing the health status of each family member in real time and providing necessary health advice. This system includes the collection of biometric data by a terminal, the storage and analysis of the data by a server, the generation of health advice, the visualization of the results, and the generation of notifications and alerts.

[0579] Data collection and transmission

[0580] Terminal

[0581] Users periodically collect biometric data such as heart rate, number of steps, and sleep time using wearable devices such as smartwatches and smartphones, and this data is transmitted to a server via Bluetooth or Wi-Fi.

[0582] As a specific example, if user A is wearing a smartwatch, the smartwatch measures his / her heart rate every minute and sends the data to a smartphone, which then uploads the data to a server at regular intervals (e.g., every 30 minutes).

[0583] Data storage and initial evaluation

[0584] server

[0585] The server receives the biometric data sent from the device and stores it in a database. The database is managed separately for each user, so individual data is recorded accurately.

[0586] Once saved, an initial evaluation is performed by comparing the data with previous data. For example, it checks whether the number of steps taken today is higher than the number of steps taken yesterday, or whether there are any abnormalities in the heart rate. This processing can be done using SQL queries or the programming language Python.

[0587] AI-powered detailed analysis and advice generation

[0588] server

[0589] The server performs detailed analysis of the stored data using a generative AI model. This AI model analyzes trends (increases, decreases, etc.) based on data from the past week, and evaluates stress levels and fatigue levels based on heart rate and sleep patterns. Based on the analysis results, health management advice is automatically generated.

[0590] Specifically, the server runs an AI model using a machine learning library such as TensorFlow to perform analysis and provide specific recommendations, such as "User A, whose recent step count has not reached its goal, is advised to walk a little more."

[0591] Data visualization and notification

[0592] server

[0593] The server visualizes the analysis results in graphs and charts, such as a graph showing daily step count changes or average heart rate trends. This visualized information is sent to the device via push notification.

[0594] Terminal

[0595] The device receives the push notification and displays it within the application. By opening the smartphone app, users can visually check their health status. If an abnormality is detected, an alert is displayed immediately. For example, a message such as "You haven't updated your data in a while. How are you?" is sent.

[0596] Gathering feedback

[0597] User

[0598] Users can provide feedback on the advice provided through the smartphone application, for example by sending a rating such as "This advice was helpful."

[0599] server

[0600] The server receives user feedback, stores it in a database, and uses it to improve the generative AI model, which will provide better and more accurate health advice in the future.

[0601] Prompt Sentence Examples

[0602] As a concrete example, here is a prompt for data collection from User A, who is using a smartwatch:

[0603] "User A wears a smartwatch and records his / her heart rate every minute."

[0604] "Analyze user A's heart rate data from the past week and assess user A's stress level."

[0605] Based on the above detailed description, the present invention realizes a system for managing the health status of family members in real time and providing appropriate health advice to individual users.

[0606] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0607] Step 1:

[0608] Data collection

[0609] Terminal

[0610] The user wears a smartwatch or smartphone. The device periodically measures biometric data such as heart rate, steps taken, and sleep time. This data is sent to the smartphone via Bluetooth. The smartphone converts the collected data into JSON format and sends it to a server via Wi-Fi.

[0611] input

[0612] Biometric data from smartwatch (heart rate, steps, sleep time)

[0613] output

[0614] Data sent to the server (JSON format)

[0615] Specific actions

[0616] The smartwatch measures your heart rate every minute and sends the data to your smartphone via Bluetooth.

[0617] The smartphone converts the collected data into JSON format every 30 minutes and uploads it to the server via Wi-Fi.

[0618] Step 2:

[0619] Data storage and initial evaluation

[0620] server

[0621] The server receives the biometric data sent from the device and stores it in a database. The data is managed in separate tables for each user. As soon as it is stored, it is compared with the previous data and an initial evaluation is performed.

[0622] input

[0623] Biometric data sent from the device (JSON format)

[0624] output

[0625] Data stored in a database

[0626] Initial evaluation results

[0627] Specific actions

[0628] The server parses the received JSON data and inserts the data into each user's table.

[0629] Compare with previous data and use SQL queries to check for any abnormalities in heart rate or step count.

[0630] Step 3:

[0631] AI-powered detailed analysis and advice generation

[0632] server

[0633] The server performs detailed analysis of the stored data using a generative AI model. It analyzes trends (increases, decreases, etc.) based on data from the past week, and evaluates stress levels and fatigue levels based on heart rate and sleep patterns. Health management advice is automatically generated as needed.

[0634] input

[0635] User biometric data stored in a database

[0636] output

[0637] Health Management Advice

[0638] Specific actions

[0639] The server uses Python and TensorFlow to run AI models.

[0640] Data from the past week is input into the AI ​​model to evaluate stress levels and fatigue.

[0641] Generate health management advice such as, "User A, who has not reached his / her step goal recently, we recommend that you walk a little more."

[0642] Step 4:

[0643] Data visualization and notification

[0644] server

[0645] The server visualizes the analysis results in graphs and charts, and the visualized data is sent to the device via push notification.

[0646] input

[0647] Health management advice and detailed analysis results

[0648] output

[0649] Visualized graphs, charts, and push notifications

[0650] Specific actions

[0651] The server uses Matplotlib and D3.js to generate graphs and save them as image files.

[0652] The generated image files and text information are sent to the device using a push notification service (e.g., Firebase Cloud Messaging).

[0653] Terminal

[0654] The device receives the push notification and displays it within the application, allowing the user to check their health status by opening the app.

[0655] input

[0656] Push notifications sent from the server

[0657] output

[0658] Display information within the application

[0659] Specific actions

[0660] Parses notifications received by the application and displays them in a graphical interface.

[0661] Step 5:

[0662] Alerts and safety confirmation

[0663] server

[0664] If data transmission is interrupted for a certain period of time or if abnormal values ​​are detected in the heart rate or other data, the server automatically generates an alert and sends it to the user's device.

[0665] input

[0666] User biometric data stored in a database

[0667] output

[0668] Alert Notifications

[0669] Specific actions

[0670] The server uses a cron job to periodically check the database and send alert emails or push notifications if an abnormality is detected.

[0671] Step 6:

[0672] Gathering feedback

[0673] User

[0674] The user provides feedback on the advice provided through the application.

[0675] input

[0676] User feedback

[0677] output

[0678] Feedback data sent to the server

[0679] Specific actions

[0680] Users fill out and submit feedback in the in-app feedback form.

[0681] server

[0682] The server receives feedback from users and stores it in a database, which then reflects the feedback data in improving the generative AI model.

[0683] input

[0684] User feedback data

[0685] output

[0686] Updated AI model

[0687] Specific actions

[0688] The server stores the feedback data in text format and periodically adds it to the AI ​​model's training dataset.

[0689] (Application example 1)

[0690] 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."

[0691] Existing systems that manage the health status of each family member in real time and provide necessary health advice have limited ways for users to easily understand their own health status. Furthermore, there is a lack of means to provide health information in a way that is easily accessible to employees and customers working in physical stores and other environments. Furthermore, providing prompt and appropriate alerts in the event of an emergency is also a challenge. The present invention aims to solve these problems and support health management for users in physical store environments.

[0692] 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.

[0693] In this invention, the server includes a means for collecting body data of each family member in real time, a means for storing the collected data in a database, and a means for analyzing the stored data using a generative AI model, which enables the user's health condition to be visually grasped in real time and to provide appropriate advice and warnings promptly.

[0694] "Body data for each family member" refers to physical information such as each individual's heart rate, number of steps taken, and sleep time.

[0695] "Real-time collection means" refers to technologies and methods that use wearable devices and smartphones to continuously collect user-generated health data.

[0696] "Database" refers to a system for storing and managing collected body data, and a place for centrally managing data for each user.

[0697] "Generative AI model" refers to the artificial intelligence algorithm used to analyze collected data and generate health advice.

[0698] "Visualization means" refers to techniques and methods for displaying analysis results in a visually easy-to-understand format, such as graphs or charts.

[0699] "User terminal" refers to a device that is directly used by the user, such as a smartphone or smart glasses.

[0700] "Means for generating alerts" refers to technologies and methods for sending notifications to users in the event of an emergency, such as abnormal health data values ​​or disruptions to data transmission.

[0701] "Visual device" refers to a device used to provide information directly to the eye, such as smart glasses.

[0702] This system collects, analyzes, and visualizes the body data of each family member in real time, and provides necessary health advice and warnings. This system uses wearable devices, smartphones, a server, a generative AI model, a visual device, and a user terminal.

[0703] System Configuration

[0704] Wearable devices and smartphones

[0705] Users wear wearable devices (e.g., fitness trackers, smartwatches), which collect body data such as heart rate, steps taken, and sleep time. This data is sent in real time to a smartphone via Bluetooth or Wi-Fi, and the smartphone uploads the data to a server at regular intervals.

[0706] server

[0707] The server receives the body data sent from the smartphone and stores it in a database. The data stored in the database is then analyzed using a generative AI model. This generative AI model analyzes trends (increases, decreases, etc.) based on the user's data from the past week, and evaluates stress levels and fatigue levels based on heart rate and sleep patterns. Health management advice is automatically generated based on the analysis results.

[0708] Visibility and Notification

[0709] The server visualizes the analysis results in graphs and charts, and this visualized information is sent to the user's device via push notification. The user can then visually check the information using a visual device such as a smartphone or smart glasses.

[0710] Warning generation

[0711] The server automatically generates and sends a warning to the user's device if data transmission is interrupted for a certain period of time or if abnormal values ​​are detected in the health data. For example, if the user's heart rate suddenly increases, a warning message will appear on the visual device saying, "Your heart rate is increasing. Please sit down and take a break."

[0712] Gathering feedback

[0713] Users can provide feedback on the advice provided through a smartphone application. This feedback is also sent to the server and stored in a database. The server analyzes the received feedback and reflects it in improving the generative AI model.

[0714] Specific examples

[0715] For example, consider a scenario in which an employee working in a brick-and-mortar store is wearing smart glasses. These smart glasses monitor their heart rate in real time and send the data to a server. The server analyzes this data, and if overwork is detected, the smart glasses display the advice, "Fatigue detected. Please take a short break."

[0716] Prompt Sentence Examples

[0717] "Analyze the user's health data (heart rate, steps, sleep time) from the past seven days to assess stress levels and fatigue levels and generate appropriate health advice."

[0718] By establishing such a system, it is possible to effectively support health management for users in a physical store environment and provide a safe and comfortable environment.

[0719] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0720] Step 1:

[0721] Data Collection and Transmission

[0722] The user wears a wearable device that collects physical data such as heart rate, steps taken, and sleep time.

[0723] The terminal (smartphone) receives the collected data using Bluetooth or Wi-Fi.

[0724] Input: Body data sent from a wearable device.

[0725] Output: Body data stored on the device.

[0726] Step 2:

[0727] Uploading data

[0728] The device uploads the received body data to the server at regular intervals (e.g., every 30 minutes).

[0729] Input: Body data stored on the device.

[0730] Output: The body data sent to the server.

[0731] Step 3:

[0732] Data storage and initial evaluation

[0733] The server receives the body data sent from the terminal and stores it in a database.

[0734] The server compares the stored data with past data and performs an initial assessment, checking, for example, whether the number of steps has increased compared to the previous day or whether there are any abnormalities in the heart rate.

[0735] Input: Body data sent from the device.

[0736] Output: Data stored in a database and initial evaluation results.

[0737] Step 4:

[0738] AI-powered detailed analysis and advice generation

[0739] The server analyzes the data stored in the database using a generative AI model.

[0740] The server generates health advice according to the prompt sentence.

[0741] Example: "Analyze the user's health data (heart rate, steps, and sleep time) from the past 7 days to assess their stress level and fatigue level and generate appropriate health advice."

[0742] Input: Historical health data stored in a database.

[0743] Output: Health advice from a generative AI model.

[0744] Step 5:

[0745] Data Visualization and Notification

[0746] The server visualizes the generated health advice and analysis results in graphs and charts.

[0747] The server sends the visualized data to the user's device via push notification.

[0748] Input: Health advice and analysis results from generative AI models.

[0749] Output: Visualized data and notification messages.

[0750] Step 6:

[0751] Display by visual device

[0752] The terminal or visual device (smart glasses) displays the transmitted visualization data and health advice.

[0753] Input: Visualization data and health advice sent from the server.

[0754] Output: Health information displayed on a visual device.

[0755] Step 7:

[0756] Alert Generation and Notification

[0757] The server generates an alert if data transmission is interrupted for a certain period of time or if an abnormal value is detected.

[0758] The server sends the generated alert to the user terminal.

[0759] Input: Data transmission disruption or abnormal values.

[0760] Output: The warning message sent to the user terminal.

[0761] Step 8:

[0762] Collecting and analyzing feedback

[0763] Users provide feedback on the health advice provided via a smartphone app.

[0764] The server stores the received feedback in a database and uses it to improve the generative AI model.

[0765] Input: User feedback.

[0766] Output: Feedback data and an improved generative AI model.

[0767] 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.

[0768] This invention is a system for managing the health status of each family member in real time and providing necessary health advice. This system not only collects and analyzes body data, generates advice, visualizes it, and generates notifications and alerts, but also combines it with an emotion engine to realize health management that takes into account the user's emotions.

[0769] Data collection and transmission

[0770] Terminal

[0771] Using a wearable device or smartphone application worn by the user, physical data such as heart rate, number of steps, and sleep time are periodically collected.Facial expressions and tone of voice are also collected using a camera and microphone, and this data is used to provide to the emotion engine.For example, a smartwatch measures the user's heart rate every minute, and a smartphone periodically scans the user's face to obtain facial expression data.

[0772] Data storage and initial evaluation

[0773] server

[0774] The system receives the body data and emotional data sent from the device and first stores them in a temporary storage area. It then transfers the data to a database, where it categorizes and manages the data for each user. Once the data is saved, it compares it with the previous data and performs an initial evaluation. For example, it checks whether the number of steps today has increased compared to the previous day, and whether there are any abnormalities in the heart rate.

[0775] Deep analysis with AI and sentiment engine

[0776] server

[0777] The stored data is then analyzed in detail using a generative AI model. This AI analyzes trends (e.g., increasing or decreasing trends) based on data from the past week, and assesses stress levels and fatigue levels based on heart rate and sleep patterns. An emotion engine also analyzes facial expressions and voice data to estimate the user's emotional state. For example, it can detect "happiness" from the user's facial expression and "tension" from the tone of their voice.

[0778] Advice generation and visualization

[0779] server

[0780] Health management advice is automatically generated based on the analysis results of the generative AI model and emotion engine. For example, if User A's recent step count has not reached its target, advice such as "We recommend you walk more" will be generated. If the emotional state is "high stress," advice such as "Try relaxation" will also be added. The analysis results and advice are then visualized in graphs and charts. For example, a graph of daily step count fluctuations, a graph of average heart rate trends, and a graph of emotional state fluctuations can be created.

[0781] Data Notifications and Alerts

[0782] server

[0783] The visualized information is sent to the device as a push notification. The notification includes the latest advice and alerts regarding abnormalities. For example, an alert may be sent saying, "Your recent average heart rate has been high. Please consult your doctor."

[0784] Terminal

[0785] Users will receive push notifications to display alerts and advice, and within the smartphone app, they can view detailed information, such as a graph of their heart rate over the past week and their emotional state at the same time.

[0786] Gathering feedback

[0787] User

[0788] Feedback on the advice provided is submitted via a smartphone application, for example, by inputting feedback such as "This advice was very helpful" or "The content is lacking."

[0789] server

[0790] It receives user feedback, stores it in a database, analyzes it, and uses it to improve the generative AI model and emotion engine, resulting in more accurate advice.

[0791] According to the above aspects, the present invention manages the health and emotional states of family members in real time and provides appropriate health advice, thereby extending healthy life expectancy and realizing an environment in which family members can live in peace of mind.

[0792] The processing flow will be explained below.

[0793] Step 1:

[0794] The device collects the user's heart rate, steps, and sleep time, using sensors in the wearable device or smartphone, as well as a camera to collect facial expression data and a microphone to collect audio data.

[0795] Step 2:

[0796] The device temporarily stores the data it collects, and a dedicated application on the smartphone manages this to prevent data loss.

[0797] Step 3:

[0798] The device sends collected data to the server at regular intervals (for example, every 30 minutes) using Bluetooth or Wi-Fi.

[0799] Step 4:

[0800] The server receives the data sent from the terminal, stores the received data in a temporary storage area, and then transfers it to the database.

[0801] Step 5:

[0802] The server stores the data in a database and categorizes and manages it for each user. After saving, it compares it with the previous data for an initial evaluation. For example, it checks whether the number of steps has increased compared to the previous day, and whether there are any abnormalities in the heart rate.

[0803] Step 6:

[0804] After the server completes the initial evaluation, it passes the data to a generative AI model for further analysis, which analyzes trends and abnormal patterns based on past data.

[0805] Step 7:

[0806] The server passes the collected facial expression and voice data to the emotion engine, which analyzes this data and estimates the user's emotional state. For example, it can detect "happiness" or "sadness" from facial expressions and "tension" or "calmness" from voice tone.

[0807] Step 8:

[0808] The server combines the analysis results from both the generative AI model and the emotion engine. Based on this, it generates specific health management advice. For example, if the user is not getting enough exercise, it generates advice such as "We recommend walking more." If the user's emotional state is determined to be "high stress," it adds the advice "Try relaxation techniques."

[0809] Step 9:

[0810] The server visualizes the analysis results and generated advice, including graphs of daily step count fluctuations, average heart rate trends, and emotional state fluctuations.

[0811] Step 10:

[0812] The server sends the visualized information to the device as a push notification. The notification includes the latest advice and alerts regarding abnormalities. For example, an alert may be sent saying, "Your recent average heart rate has been high. Please consult a doctor."

[0813] Step 11:

[0814] The device receives a push notification and displays an alert or advice to the user. The user can then view more detailed information within the smartphone application. For example, they can view graphs showing the number of steps taken over the past week and their emotional state.

[0815] Step 12:

[0816] If the server detects an abnormal value or a disruption in data transmission, it automatically generates an alert and sends a warning to the user's device, such as "Your heart rate is abnormally high. Please consult a doctor" or "We haven't seen any data updates in a while. How are you?"

[0817] Step 13:

[0818] The user sends feedback on the advice provided through the application, for example, by inputting feedback such as "This advice was very helpful" or "The content is difficult to understand."

[0819] Step 14:

[0820] The server receives user feedback, stores it in a database, and analyzes it to improve the generative AI model and emotion engine, resulting in more accurate advice in the future.

[0821] In this way, the present invention is a system that can comprehensively manage the health and emotional states of family members and provide appropriate health advice.

[0822] Example 2

[0823] 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."

[0824] While current health management systems place emphasis on collecting and analyzing biometric data, they lack the ability to take into account the user's emotional state. This prevents them from properly managing the impact of stress and emotional states on health. They also lack a mechanism to effectively incorporate user feedback and improve the accuracy of advice. The objective of this invention is to solve these problems and provide a more comprehensive health management system.

[0825] 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.

[0826] In this invention, the server includes means for collecting biometric data and emotional data of each family member, means for storing the collected data in a database, means for analyzing the stored data using a generative AI model and an emotion engine, means for providing health management advice based on the analysis results, means for visualizing the advice and analysis results, means for notifying a terminal of the visualized information, means for generating an alert when the collected data is discontinued for a certain period of time or when an abnormal value is detected, and means for collecting user feedback and reflecting it in improving the generative AI model and the emotion engine. This enables comprehensive health management including emotional states and realizes the provision of more accurate advice.

[0827] "Biometric data" refers to data that indicates the state of the body, such as heart rate, number of steps, and sleep time.

[0828] "Emotion data" is data that indicates the user's emotional state, such as facial expressions and vocal tones.

[0829] A "database" is a system for storing, classifying, and managing collected data.

[0830] A "generative AI model" is an artificial intelligence algorithm that analyzes collected data and generates health management advice.

[0831] An "emotion engine" is a system that analyzes facial expressions and vocal tones to estimate a user's emotional state.

[0832] "Health management advice" refers to suggestions for maintaining or improving health based on collected data and the results of its analysis.

[0833] "Visualization" refers to the presentation of data and its analytical results in a visual format such as a graph or chart.

[0834] "Push notification" is a system that sends information from a server to a device in real time.

[0835] "Feedback" refers to the act of a user sending an evaluation or opinion regarding the advice provided.

[0836] An "outlier" is a value in the collected data that is outside the normal range.

[0837] This invention is a system for managing the health and emotional state of each family member in real time and providing necessary health advice. The operation and configuration of this system will be specifically described below.

[0838] Data collection and transmission

[0839] Terminal

[0840] Biometric data such as heart rate, number of steps, and sleep time are periodically collected using wearable devices or smartphone applications worn by the user. Cameras and microphones are also used to collect facial expressions and tone of voice, which are then treated as emotion data. For example, a smartwatch measures the user's heart rate every minute, and a smartphone periodically scans the user's face to obtain facial expression data.

[0841] Hardware and software examples

[0842] Wearable devices: Apple Watch, Fitbit

[0843] Smartphone applications: iOS applications, Android applications

[0844] Camera and microphone: Built-in smartphone

[0845] Data storage and initial evaluation

[0846] server

[0847] The system receives body and emotional data sent from the device and stores it in a temporary storage area. It then transfers the data to a database, where it categorizes and manages the data for each user. Once the data is saved, it performs an initial evaluation by comparing it with the previous data. For example, it checks whether the number of steps taken today has increased compared to the previous day, and whether there are any abnormalities in the heart rate.

[0848] Hardware and software examples

[0849] Server: Cloud server, database management system (e.g. MySQL, PostgreSQL)

[0850] Detailed analysis

[0851] server

[0852] The stored data is then analyzed in detail using a generative AI model. This AI analyzes trends (e.g., increasing or decreasing trends) based on data from the past week, and assesses stress levels and fatigue levels based on heart rate and sleep patterns. An emotion engine also analyzes facial expressions and voice data to estimate the user's emotional state. For example, it can detect "happiness" from the user's facial expression and "tension" from the tone of their voice.

[0853] Hardware and software examples

[0854] AI model: TensorFlow-based generative AI model

[0855] Sentiment Engine: IBM Watson Sentiment Analysis API

[0856] Advice generation and visualization

[0857] server

[0858] Health management advice is automatically generated based on the analysis results of the generative AI model and emotion engine. For example, if a user's recent step count has not reached their goal, the system will generate the advice "We recommend you walk more." If the user's emotional state is "high stress," the system will also add the advice "Try relaxation." These results are then visualized in graphs and charts. For example, it creates a graph of daily step count fluctuations, average heart rate trends, and emotional state fluctuations.

[0859] Hardware and software examples

[0860] Graph and chart generation: Matplotlib, D3.js

[0861] Data Notifications and Alerts

[0862] server

[0863] The visualized information is sent to the device as a push notification. The notification includes the latest advice and alerts regarding abnormalities. For example, an alert may be sent saying, "Your recent average heart rate has been high. Please consult your doctor."

[0864] Terminal

[0865] Users will receive push notifications to display alerts and advice, and within the smartphone app, they can view detailed information, such as a graph of their heart rate over the past week and their emotional state at the same time.

[0866] Hardware and software examples

[0867] Push notifications: Firebase Cloud Messaging, Apple Push Notification Service

[0868] Gathering feedback

[0869] User

[0870] Feedback on the advice provided is submitted via a smartphone application, for example, by inputting feedback such as "This advice was very helpful" or "The content is lacking."

[0871] server

[0872] It receives user feedback, stores it in a database, analyzes it, and uses it to improve the generative AI model and emotion engine, resulting in more accurate advice.

[0873] (Example of a prompt)

[0874] "Explain how to assess a user's current stress level and generate appropriate health advice based on step count and heart rate data from the past week."

[0875] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0876] Step 1:

[0877] Data collection

[0878] The device uses a wearable device or smartphone application to periodically collect biometric data such as the user's heart rate, number of steps, and sleep time. It also uses a camera and microphone to collect facial expressions and tone of voice, which are treated as emotion data. The inputs are the user's biometric data, facial expressions, and tone of voice. The output is the various collected data. Specifically, the smartwatch records the heart rate every minute, and the smartphone activates the camera every hour to scan the user's face and obtain facial expression data.

[0879] Step 2:

[0880] Data transmission

[0881] The data collected by the device is sent to the server via wireless communication (Bluetooth or Wi-Fi). The collected data is the input. The data sent to the server is obtained as the output. In concrete terms, the smartwatch transfers data to the smartphone via Bluetooth, and the smartphone sends the data to the server via Wi-Fi.

[0882] Step 3:

[0883] Data storage and initial evaluation

[0884] The server stores the transmitted data in a temporary storage area and then transfers it to the database. The data is classified and managed for each user, and after storage is complete, it is compared with the previous data for an initial evaluation. The input is the data sent to the server. The output is the classified and managed data and the results of the initial evaluation. Specifically, the server stores the new data in the database, compares it with past data, and checks for any outliers.

[0885] Step 4:

[0886] Detailed analysis

[0887] The server performs detailed analysis of the stored data using a generative AI model and emotion engine. Trends are analyzed based on data from the past week, and stress levels and fatigue levels are assessed based on heart rate and sleep patterns. The emotion engine also analyzes facial expressions and voice data to estimate the user's emotional state. The input is data stored in the database. The output is the analysis results. Specifically, the AI ​​model analyzes heart rate, step count, and sleep data, and the emotion engine estimates the user's emotional state from facial expressions and tone of voice.

[0888] Step 5:

[0889] Advice generation and visualization

[0890] The server automatically generates health management advice based on the analysis results of the generative AI model and emotion engine. The advice and analysis results are visualized in graphs and charts. The input is the analysis results. The output is the generated advice and visualized graphs and charts. Specifically, the generative AI model generates advice such as "Your recent step count has not reached your goal. Walk more," and the emotion engine adds advice such as "Try relaxation." A graph is generated based on this.

[0891] Step 6:

[0892] Notifications and Alerts

[0893] The server sends the visualized information to the device as a push notification, and also sends an alert if an abnormality is detected. The inputs are the generated advice and visualized data. The output is the push notification and alert sent to the device. Specifically, the server generates an alert saying, "Your recent average heart rate has been high. Please consult a doctor," and sends it to the device.

[0894] The device receives the push notification and displays it to the user. The input is a push notification from the server. The output is an alert or advice that is displayed to the user. The specific behavior is that the smartphone displays the push notification, and the user checks for more information in the app.

[0895] Step 7:

[0896] Gathering feedback

[0897] The user sends feedback on the advice provided through a smartphone application. The input is the user's feedback. The output is the feedback sent to the server. The specific operation is that the user inputs feedback such as "This advice was very helpful" into the smartphone app and sends it.

[0898] The server receives feedback from users and stores it in a database. The input is the feedback sent by the user. The output is the feedback stored in the database. Specifically, the server stores the feedback data and uses it to improve the generative AI model and emotion engine.

[0899] (Application example 2)

[0900] 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."

[0901] Conventional health management systems were able to collect and analyze the health data of individual family members and provide the results of their analysis, but they did not support acquiring customers' health and emotional data in real time in physical stores and providing appropriate advice or product and service recommendations based on that data. This made it difficult to grasp customers' health conditions and provide individualized support in physical stores. As a result, it was not possible to provide appropriate services according to the customer's health condition, which led to the issue of not being able to improve customer satisfaction or achieve appropriate health management.

[0902] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting body data of each family member, means for storing the collected data in a database, means for analyzing the stored data using a generative AI model, means for providing health management advice based on the analysis results, means for visualizing the advice and analysis results, means for notifying a terminal of the visualized information, and means for generating an alert when the collected data is discontinued for a certain period of time or when an abnormal value is detected. In addition, it also includes means for collecting body data and emotional data of customers in a physical store and displaying them in real time on an augmented reality device worn by a staff member, means for recommending appropriate products and services based on the body data and emotional data of the customer, and means for notifying the staff member of the augmented reality device of the recommendation. This makes it possible to recommend optimal advice, products, and services based on the customer's health and emotional state, even in a physical store.

[0903] "Means of collecting physical data from each family member" refers to technology for recording the health status of each family member using wearable devices, smartphones, etc.

[0904] "Means for storing data in a database" refers to a system or method for organizing, recording, and managing collected data in a certain manner.

[0905] A "generative AI model" refers to an artificial intelligence model that uses machine learning algorithms to analyze data and predict relevant patterns and trends.

[0906] "Means for providing health management advice" refers to technologies for providing users with health advice and recommendations based on the collected data and the analysis results of the AI ​​model.

[0907] "Visualization means" refers to the technology of displaying data and analysis results in a visually easy-to-understand format such as graphs and charts.

[0908] "Means for notifying the device" refers to technology for sending the visualized information or alerts to the user's device and notifying them.

[0909] "Means for generating alerts" refers to technology that generates and notifies when an abnormality is detected based on collected data or when data is lost for a certain period of time.

[0910] "Means of collecting customer physical and emotional data within physical stores" refers to technology that monitors customer health and emotions using devices installed within the store or wearable devices worn by staff.

[0911] An "augmented reality device" is a device that can overlay digital information onto the real world, and when worn by staff, it allows them to display customer data in real time.

[0912] "Means for recommending appropriate products and services" refers to technology that suggests products and services that are best suited to a customer based on collected health and emotional data of the customer.

[0913] This invention provides a system for managing a customer's health and emotional state in real time in a physical store and recommending appropriate products and services. The system includes: means for collecting body data of each family member; means for storing the collected data in a database; means for analyzing the data using a generative AI model; means for providing health management advice based on the analysis results; means for visualizing the advice and analysis results; means for notifying a terminal; and means for generating alerts. The system further includes means for collecting body data and emotional data of customers in the physical store and displaying the data in real time on an augmented reality device worn by staff; means for recommending appropriate products and services based on the customer data; and means for notifying the augmented reality device of the recommendations.

[0914] Program Generation

[0915] The core program of this system operates by including the following elements:

[0916] Data collection and transmission

[0917] User device:

[0918] Using a wearable device (e.g., a smartwatch) and a smartphone worn by the user, physical data such as heart rate, number of steps, and sleep time are periodically collected.Facial expression and voice data are also collected using a camera and microphone, and used as data to determine emotional state.

[0919] Data storage and initial evaluation

[0920] server:

[0921] Body data and emotional data sent from the device are received and stored in a temporary storage area.The data is then transferred to a database where it is classified and managed for each user.Once the data has been saved, it is compared with the previous data and an initial evaluation is performed.

[0922] Deep analysis with AI and sentiment engine

[0923] server:

[0924] The stored data is analyzed in detail using a generative AI model (e.g., Decision Tree Classifier). The analysis identifies trends based on data from the past week and evaluates health and emotional status. In addition, an emotion engine (e.g., Emotion Engine) analyzes facial expressions and voice data to estimate the customer's emotional state.

[0925] Advice generation and visualization

[0926] server:

[0927] Based on the analysis results, health management advice and product recommendations are generated, which are visualized in graphs and charts and displayed in real time on an augmented reality device (e.g., smart glasses) worn by staff.

[0928] Data Notifications and Alerts

[0929] server:

[0930] The visualized information is sent as push notifications to staff members' augmented reality devices and customers' smartphones, and an alert is generated if abnormal data is detected or if data is lost for a certain period of time.

[0931] Specific examples

[0932] For example, consider a customer using a treadmill in a fitness club. Here's how the system works:

[0933] 1. The smartwatch worn by the customer collects heart rate data.

[0934] 2. Facial recognition cameras collect customer facial expression data.

[0935] 3. The collected data is sent to the server for storage and initial evaluation.

[0936] 4. Generative AI models and emotion engines analyze the data and assess the customer's exercise intensity and emotional state.

[0937] 5. The staff member's smart glasses will display advice such as "Please slow down a bit" and emotional analysis results such as "You appear tired" in real time.

[0938] Prompt Sentence Examples

[0939] While using the treadmill, the customer's heart rate is high and their facial expression looks tired. Please suggest a healthcare app feature that will provide appropriate advice.

[0940] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0941] Step 1:

[0942] Data collection and transmission

[0943] Input: Wearable device and smartphone worn by the user

[0944] Specific operation and output: Using a wearable device (e.g., a smartwatch) and a smartphone, the system periodically collects physical data such as the user's heart rate, number of steps, and sleep time. Furthermore, the smartphone's camera and microphone are used to collect facial expression data and voice data, which are then used to determine the user's emotional state. The collected data is then sent from the device to a server.

[0945] Step 2:

[0946] Data storage and initial evaluation

[0947] Input: Body data and emotional data sent from the device

[0948] Specific operations and output: The server receives the body data and emotional data sent from the device and stores them in a temporary storage area. It then transfers the data to a database where it categorizes and manages the data for each user. The stored data is compared with previous data for an initial evaluation. For example, the collected heart rate and step count are compared with the data from the previous day to evaluate any abnormalities or fluctuations. The results of the initial evaluation are sent to the next step for further analysis.

[0949] Step 3:

[0950] Deep analysis with AI and sentiment engine

[0951] Input: Stored body and emotional data

[0952] Specific operation and output: The server performs detailed analysis of the stored data using a generative AI model (e.g., DecisionTreeClassifier). Data from the past week is used to identify trends in heart rate and step count and assess health status. An emotion engine (e.g., EmotionEngine) also analyzes facial expressions and voice data to estimate the user's emotional state. The analysis results are stored for use in the next step.

[0953] Step 4:

[0954] Advice generation and visualization

[0955] Input: Results of detailed analysis (evaluation of health and emotional state)

[0956] Specific operation and output: The server generates health management advice based on the analysis results of the generative AI model and emotion engine. For example, if the user's step count has not reached the target, the advice "We recommend you walk more" is generated. Furthermore, if the emotional state is determined to be "high stress," the advice "Try relaxation" is added. The generated advice and analysis results are visualized and displayed as graphs and charts.

[0957] Step 5:

[0958] Data Notifications and Alerts

[0959] Input: Visualized advice and analysis results

[0960] Specific operation and output: The server sends the visualized information as a push notification to a device (e.g., a smartphone tag or an augmented reality device worn by a staff member). For example, if a customer shows a high heart rate, an alert such as "Please slow down a little" will be displayed on the augmented reality device of the staff member. If abnormal data is detected, a warning message will also be provided to prompt appropriate action.

[0961] Step 6:

[0962] Collecting and using feedback

[0963] Input: Feedback from users or staff

[0964] Specific operation and output: The user or store staff sends feedback on the advice or recommendation provided via the terminal. The server receives this feedback and stores it in a database. The stored feedback is analyzed and reflected in improvements to the generative AI model and emotion engine, resulting in more accurate advice being provided.

[0965] 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.

[0966] 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.

[0967] 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.

[0968] [Third embodiment]

[0969] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0970] 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.

[0971] 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).

[0972] 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.

[0973] 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.

[0974] 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).

[0975] 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.

[0976] 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.

[0977] 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.

[0978] 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.

[0979] 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.

[0980] 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."

[0981] The present invention is a system for managing the health status of each family member in real time and providing necessary health advice. This system includes collecting body data by a terminal, storing and analyzing the data by a server, generating health advice, visualizing the results, and generating notifications and alerts.

[0982] Data collection and transmission

[0983] Terminal

[0984] Using a wearable device or smartphone worn by the user, physical data such as heart rate, number of steps, and sleep time are periodically collected, and this data is sent to a server via Bluetooth or Wi-Fi.

[0985] For example, if user A is wearing a smartwatch, the smartwatch measures his / her heart rate every minute and sends the data to his / her smartphone, which then uploads the data to a server at regular intervals, for example, every 30 minutes.

[0986] Data storage and initial evaluation

[0987] server

[0988] The server receives the body data sent from the device and stores it in a database. The database is managed separately for each user, ensuring accurate recording of individual data. As soon as the data is saved, it is compared with the previous data and an initial evaluation is performed. For example, it checks whether the number of steps taken today has increased compared to the previous day, and whether there are any abnormalities in the heart rate.

[0989] AI-powered detailed analysis and advice generation

[0990] server

[0991] The stored data is then analyzed in detail using a generative AI model. This AI analyzes trends (e.g., increasing or decreasing trends) based on data from the past week, and evaluates stress levels and fatigue levels based on heart rate and sleep patterns. Based on the analysis results, health management advice is automatically generated. For example, for User A, whose recent step count has not reached its goal, specific recommendations such as "We recommend that you walk a little more" are made.

[0992] Data visualization and notification

[0993] server

[0994] The analysis results are then visualized in graphs and charts, such as a graph showing daily step count changes or average heart rate trends. This visualized information is then sent to the device via push notification.

[0995] Terminal

[0996] The device receives the push notification and displays it within the application. By opening the smartphone app, users can visually check their health status. For example, they can view a graph visualizing the number of steps taken over the past week and understand their own exercise trends.

[0997] Alerts and safety confirmation

[0998] server

[0999] If the server detects abnormal values ​​in the heart rate or if data transmission is interrupted for a certain period of time, it automatically generates an alert and sends it to the user's device. For example, if user A's data has not been updated for more than 24 hours, a notification will be sent saying, "There has been no data update for a while. How are you?"

[1000] Gathering feedback

[1001] User

[1002] Users can provide feedback on the advice provided through the smartphone application, which is also sent to the server and stored in the database.

[1003] server

[1004] The server analyzes the received feedback and uses it to improve the generative AI model, which will provide more relevant and accurate health advice in the future.

[1005] According to the above aspects, the present invention manages the health status of family members in real time and provides necessary health advice, thereby extending healthy life expectancy and realizing an environment in which family members can live in peace of mind.

[1006] The processing flow will be explained below.

[1007] Step 1:

[1008] The device collects the user's body data, such as heart rate, steps, and sleep time, using a wearable device or a smartphone application. Data collection occurs at regular intervals (e.g., every minute or every second).

[1009] Step 2:

[1010] The device temporarily stores the collected body data, and a dedicated application on the smartphone manages the storage of this data to prevent data loss.

[1011] Step 3:

[1012] The device sends the temporarily stored data to the server at regular intervals (for example, every 30 minutes). Bluetooth or Wi-Fi is used as the communication protocol.

[1013] Step 4:

[1014] The server receives the body data sent from the device, stores it in a temporary storage area, and then transfers it to the database.

[1015] Step 5:

[1016] The server stores the data in a database, which categorizes and stores data for each user, including historical data.

[1017] Step 6:

[1018] The server performs an initial evaluation of the stored data, for example checking for variations compared to previous data and for abnormal values ​​(such as an extremely high or low heart rate).

[1019] Step 7:

[1020] After the initial assessment is complete, the server inputs the data into a generative AI model for further analysis. The generative AI model analyzes health trends and abnormal patterns based on data from the past week or month.

[1021] Step 8:

[1022] The server automatically generates health management advice based on the analysis results. For example, if you are not getting enough exercise, it will say, "We recommend walking 1,000 more steps per day," or if your stress level is high, it will say, "Make sure you take time to relax."

[1023] Step 9:

[1024] The server visualizes the generated advice and analysis results, using graphs and charts to organize and present the data in a format that users can easily view.

[1025] Step 10:

[1026] The server sends the visualized information to the device as push notifications, which include the latest advice and alerts about detected anomalies.

[1027] Step 11:

[1028] The device receives a push notification, which displays an alert or advice to the user, and the user can check the detailed information within the smartphone application.

[1029] Step 12:

[1030] If the server detects an abnormal value or a disruption in data transmission, it automatically generates an alert and sends a warning to the user's device, such as "Your heart rate is abnormally high. Please consult a doctor" or "We haven't seen any data updates in a while. How are you?"

[1031] Step 13:

[1032] The user submits feedback on the advice provided through the application, for example, by inputting feedback such as "This advice was very helpful" or "I would like more specific suggestions."

[1033] Step 14:

[1034] The server receives user feedback and stores it in a database. This feedback is then analyzed and used to improve the generative AI model, resulting in more accurate advice.

[1035] In this way, all steps work together to create a system that manages and improves the user's health in real time.

[1036] Example 1

[1037] 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."

[1038] In modern society, it is becoming increasingly important to manage the health status of each family member in real time and provide necessary health advice. However, existing systems lack sufficient data collection, storage, and analysis, making it difficult to provide specific advice tailored to each individual's health status in real time. Furthermore, there are cases where responses to abnormal values ​​or interruptions in data transmission are delayed, which does not necessarily ensure user safety. Another issue is the lack of a mechanism for appropriately receiving user feedback and reflecting it in system improvements.

[1039] 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.

[1040] In this invention, the server includes means for collecting a user's biometric data, means for storing the collected data in a database, means for analyzing the stored data using a generative AI model, means for providing health management advice based on the analysis results, means for visualizing the advice and analysis results, means for notifying a terminal of the visualized information, means for generating an alert when the collected data is interrupted for a certain period of time or when an abnormal value is detected, means for collecting feedback from the user, and means for analyzing the feedback and reflecting it in the generative AI model. This enables real-time monitoring of the health status of each family member and providing specific advice tailored to their individual health status. Furthermore, the system can quickly respond to abnormal values ​​or interruptions in data transmission, ensuring user safety. Furthermore, by incorporating user feedback into system improvements, more accurate advice can be provided.

[1041] "Biometric data" refers to data that indicates the user's health condition, such as heart rate, number of steps, and sleep time.

[1042] A "database" is a system for storing and managing collected biometric data.

[1043] A "generative AI model" refers to artificial intelligence that uses machine learning and deep learning techniques to analyze data and automatically generate health management advice.

[1044] "Health Management Advice" provides specific recommendations and advice to improve the user's health based on data analyzed by the generative AI model.

[1045] "Visualization" is a method of displaying data in a visual format, such as a graph or chart, so that it can be easily understood by users.

[1046] "Notification" is a means of conveying visualized information and alerts to a device.

[1047] An "alert" is a warning message that is automatically generated when an abnormality occurs in the user's health condition or system operation, such as an abnormal value or a disruption in data transmission.

[1048] "Feedback" refers to the user's opinions and evaluations of the health care advice provided.

[1049] "Analysis" refers to the detailed analysis of data and the extraction of meaningful information and trends from it.

[1050] The present invention is a system for managing the health status of each family member in real time and providing necessary health advice. This system includes the collection of biometric data by a terminal, the storage and analysis of the data by a server, the generation of health advice, the visualization of the results, and the generation of notifications and alerts.

[1051] Data collection and transmission

[1052] Terminal

[1053] Users periodically collect biometric data such as heart rate, number of steps, and sleep time using wearable devices such as smartwatches and smartphones, and this data is transmitted to a server via Bluetooth or Wi-Fi.

[1054] As a specific example, if user A is wearing a smartwatch, the smartwatch measures his / her heart rate every minute and sends the data to a smartphone, which then uploads the data to a server at regular intervals (e.g., every 30 minutes).

[1055] Data storage and initial evaluation

[1056] server

[1057] The server receives the biometric data sent from the device and stores it in a database. The database is managed separately for each user, so individual data is recorded accurately.

[1058] Once saved, an initial evaluation is performed by comparing the data with previous data. For example, it checks whether the number of steps taken today is higher than the number of steps taken yesterday, or whether there are any abnormalities in the heart rate. This processing can be done using SQL queries or the programming language Python.

[1059] AI-powered detailed analysis and advice generation

[1060] server

[1061] The server performs detailed analysis of the stored data using a generative AI model. This AI model analyzes trends (increases, decreases, etc.) based on data from the past week, and evaluates stress levels and fatigue levels based on heart rate and sleep patterns. Based on the analysis results, health management advice is automatically generated.

[1062] Specifically, the server runs an AI model using a machine learning library such as TensorFlow to perform analysis and provide specific recommendations, such as "User A, whose recent step count has not reached its goal, is advised to walk a little more."

[1063] Data visualization and notification

[1064] server

[1065] The server visualizes the analysis results in graphs and charts, such as a graph showing daily step count changes or average heart rate trends. This visualized information is sent to the device via push notification.

[1066] Terminal

[1067] The device receives the push notification and displays it within the application. By opening the smartphone app, users can visually check their health status. If an abnormality is detected, an alert is displayed immediately. For example, a message such as "You haven't updated your data in a while. How are you?" is sent.

[1068] Gathering feedback

[1069] User

[1070] Users can provide feedback on the advice provided through the smartphone application, for example by sending a rating such as "This advice was helpful."

[1071] server

[1072] The server receives user feedback, stores it in a database, and uses it to improve the generative AI model, which will provide better and more accurate health advice in the future.

[1073] Prompt Sentence Examples

[1074] As a concrete example, here is a prompt for data collection from User A, who is using a smartwatch:

[1075] "User A wears a smartwatch and records his / her heart rate every minute."

[1076] "Analyze user A's heart rate data from the past week and assess user A's stress level."

[1077] Based on the above detailed description, the present invention realizes a system for managing the health status of family members in real time and providing appropriate health advice to individual users.

[1078] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1079] Step 1:

[1080] Data collection

[1081] Terminal

[1082] The user wears a smartwatch or smartphone. The device periodically measures biometric data such as heart rate, steps taken, and sleep time. This data is sent to the smartphone via Bluetooth. The smartphone converts the collected data into JSON format and sends it to a server via Wi-Fi.

[1083] input

[1084] Biometric data from smartwatch (heart rate, steps, sleep time)

[1085] output

[1086] Data sent to the server (JSON format)

[1087] Specific actions

[1088] The smartwatch measures your heart rate every minute and sends the data to your smartphone via Bluetooth.

[1089] The smartphone converts the collected data into JSON format every 30 minutes and uploads it to the server via Wi-Fi.

[1090] Step 2:

[1091] Data storage and initial evaluation

[1092] server

[1093] The server receives the biometric data sent from the device and stores it in a database. The data is managed in separate tables for each user. As soon as it is stored, it is compared with the previous data and an initial evaluation is performed.

[1094] input

[1095] Biometric data sent from the device (JSON format)

[1096] output

[1097] Data stored in a database

[1098] Initial evaluation results

[1099] Specific actions

[1100] The server parses the received JSON data and inserts the data into each user's table.

[1101] Compare with previous data and use SQL queries to check for any abnormalities in heart rate or step count.

[1102] Step 3:

[1103] AI-powered detailed analysis and advice generation

[1104] server

[1105] The server performs detailed analysis of the stored data using a generative AI model. It analyzes trends (increases, decreases, etc.) based on data from the past week, and evaluates stress levels and fatigue levels based on heart rate and sleep patterns. Health management advice is automatically generated as needed.

[1106] input

[1107] User biometric data stored in a database

[1108] output

[1109] Health Management Advice

[1110] Specific actions

[1111] The server uses Python and TensorFlow to run AI models.

[1112] Data from the past week is input into the AI ​​model to evaluate stress levels and fatigue.

[1113] Generate health management advice such as, "User A, who has not reached his / her step goal recently, we recommend that you walk a little more."

[1114] Step 4:

[1115] Data visualization and notification

[1116] server

[1117] The server visualizes the analysis results in graphs and charts, and the visualized data is sent to the device via push notification.

[1118] input

[1119] Health management advice and detailed analysis results

[1120] output

[1121] Visualized graphs, charts, and push notifications

[1122] Specific actions

[1123] The server uses Matplotlib and D3.js to generate graphs and save them as image files.

[1124] The generated image files and text information are sent to the device using a push notification service (e.g., Firebase Cloud Messaging).

[1125] Terminal

[1126] The device receives the push notification and displays it within the application, allowing the user to check their health status by opening the app.

[1127] input

[1128] Push notifications sent from the server

[1129] output

[1130] Display information within the application

[1131] Specific actions

[1132] Parses notifications received by the application and displays them in a graphical interface.

[1133] Step 5:

[1134] Alerts and safety confirmation

[1135] server

[1136] If data transmission is interrupted for a certain period of time or if abnormal values ​​are detected in the heart rate or other data, the server automatically generates an alert and sends it to the user's device.

[1137] input

[1138] User biometric data stored in a database

[1139] output

[1140] Alert Notifications

[1141] Specific actions

[1142] The server uses a cron job to periodically check the database and send alert emails or push notifications if an abnormality is detected.

[1143] Step 6:

[1144] Gathering feedback

[1145] User

[1146] The user provides feedback on the advice provided through the application.

[1147] input

[1148] User feedback

[1149] output

[1150] Feedback data sent to the server

[1151] Specific actions

[1152] Users fill out and submit feedback in the in-app feedback form.

[1153] server

[1154] The server receives feedback from users and stores it in a database, which then reflects the feedback data in improving the generative AI model.

[1155] input

[1156] User feedback data

[1157] output

[1158] Updated AI model

[1159] Specific actions

[1160] The server stores the feedback data in text format and periodically adds it to the AI ​​model's training dataset.

[1161] (Application example 1)

[1162] 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."

[1163] Existing systems that manage the health status of each family member in real time and provide necessary health advice have limited ways for users to easily understand their own health status. Furthermore, there is a lack of means to provide health information in a way that is easily accessible to employees and customers working in physical stores and other environments. Furthermore, providing prompt and appropriate alerts in the event of an emergency is also a challenge. The present invention aims to solve these problems and support health management for users in physical store environments.

[1164] 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.

[1165] In this invention, the server includes a means for collecting body data of each family member in real time, a means for storing the collected data in a database, and a means for analyzing the stored data using a generative AI model, which enables the user's health condition to be visually grasped in real time and to provide appropriate advice and warnings promptly.

[1166] "Body data for each family member" refers to physical information such as each individual's heart rate, number of steps taken, and sleep time.

[1167] "Real-time collection means" refers to technologies and methods that use wearable devices and smartphones to continuously collect user-generated health data.

[1168] "Database" refers to a system for storing and managing collected body data, and a place for centrally managing data for each user.

[1169] "Generative AI model" refers to the artificial intelligence algorithm used to analyze collected data and generate health advice.

[1170] "Visualization means" refers to techniques and methods for displaying analysis results in a visually easy-to-understand format, such as graphs or charts.

[1171] "User terminal" refers to a device that is directly used by the user, such as a smartphone or smart glasses.

[1172] "Means for generating alerts" refers to technologies and methods for sending notifications to users in the event of an emergency, such as abnormal health data values ​​or disruptions to data transmission.

[1173] "Visual device" refers to a device used to provide information directly to the eye, such as smart glasses.

[1174] This system collects, analyzes, and visualizes the body data of each family member in real time, and provides necessary health advice and warnings. This system uses wearable devices, smartphones, a server, a generative AI model, a visual device, and a user terminal.

[1175] System Configuration

[1176] Wearable devices and smartphones

[1177] Users wear wearable devices (e.g., fitness trackers, smartwatches), which collect body data such as heart rate, steps taken, and sleep time. This data is sent in real time to a smartphone via Bluetooth or Wi-Fi, and the smartphone uploads the data to a server at regular intervals.

[1178] server

[1179] The server receives the body data sent from the smartphone and stores it in a database. The data stored in the database is then analyzed using a generative AI model. This generative AI model analyzes trends (increases, decreases, etc.) based on the user's data from the past week, and evaluates stress levels and fatigue levels based on heart rate and sleep patterns. Health management advice is automatically generated based on the analysis results.

[1180] Visibility and Notification

[1181] The server visualizes the analysis results in graphs and charts, and this visualized information is sent to the user's device via push notification. The user can then visually check the information using a visual device such as a smartphone or smart glasses.

[1182] Warning generation

[1183] The server automatically generates and sends a warning to the user's device if data transmission is interrupted for a certain period of time or if abnormal values ​​are detected in the health data. For example, if the user's heart rate suddenly increases, a warning message will appear on the visual device saying, "Your heart rate is increasing. Please sit down and take a break."

[1184] Gathering feedback

[1185] Users can provide feedback on the advice provided through a smartphone application. This feedback is also sent to the server and stored in a database. The server analyzes the received feedback and reflects it in improving the generative AI model.

[1186] Specific examples

[1187] For example, consider a scenario in which an employee working in a brick-and-mortar store is wearing smart glasses. These smart glasses monitor their heart rate in real time and send the data to a server. The server analyzes this data, and if overwork is detected, the smart glasses display the advice, "Fatigue detected. Please take a short break."

[1188] Prompt Sentence Examples

[1189] "Analyze the user's health data (heart rate, steps, sleep time) from the past seven days to assess stress levels and fatigue levels and generate appropriate health advice."

[1190] By establishing such a system, it is possible to effectively support health management for users in a physical store environment and provide a safe and comfortable environment.

[1191] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1192] Step 1:

[1193] Data Collection and Transmission

[1194] The user wears a wearable device that collects physical data such as heart rate, steps taken, and sleep time.

[1195] The terminal (smartphone) receives the collected data using Bluetooth or Wi-Fi.

[1196] Input: Body data sent from a wearable device.

[1197] Output: Body data stored on the device.

[1198] Step 2:

[1199] Uploading data

[1200] The device uploads the received body data to the server at regular intervals (e.g., every 30 minutes).

[1201] Input: Body data stored on the device.

[1202] Output: The body data sent to the server.

[1203] Step 3:

[1204] Data storage and initial evaluation

[1205] The server receives the body data sent from the terminal and stores it in a database.

[1206] The server compares the stored data with past data and performs an initial assessment, checking, for example, whether the number of steps has increased compared to the previous day or whether there are any abnormalities in the heart rate.

[1207] Input: Body data sent from the device.

[1208] Output: Data stored in a database and initial evaluation results.

[1209] Step 4:

[1210] AI-powered detailed analysis and advice generation

[1211] The server analyzes the data stored in the database using a generative AI model.

[1212] The server generates health advice according to the prompt sentence.

[1213] Example: "Analyze the user's health data (heart rate, steps, and sleep time) from the past 7 days to assess their stress level and fatigue level and generate appropriate health advice."

[1214] Input: Historical health data stored in a database.

[1215] Output: Health advice from a generative AI model.

[1216] Step 5:

[1217] Data Visualization and Notification

[1218] The server visualizes the generated health advice and analysis results in graphs and charts.

[1219] The server sends the visualized data to the user's device via push notification.

[1220] Input: Health advice and analysis results from generative AI models.

[1221] Output: Visualized data and notification messages.

[1222] Step 6:

[1223] Display by visual device

[1224] The terminal or visual device (smart glasses) displays the transmitted visualization data and health advice.

[1225] Input: Visualization data and health advice sent from the server.

[1226] Output: Health information displayed on a visual device.

[1227] Step 7:

[1228] Alert Generation and Notification

[1229] The server generates an alert if data transmission is interrupted for a certain period of time or if an abnormal value is detected.

[1230] The server sends the generated alert to the user terminal.

[1231] Input: Data transmission disruption or abnormal values.

[1232] Output: The warning message sent to the user terminal.

[1233] Step 8:

[1234] Collecting and analyzing feedback

[1235] Users provide feedback on the health advice provided via a smartphone app.

[1236] The server stores the received feedback in a database and uses it to improve the generative AI model.

[1237] Input: User feedback.

[1238] Output: Feedback data and an improved generative AI model.

[1239] 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.

[1240] This invention is a system for managing the health status of each family member in real time and providing necessary health advice. This system not only collects and analyzes body data, generates advice, visualizes it, and generates notifications and alerts, but also combines it with an emotion engine to realize health management that takes into account the user's emotions.

[1241] Data collection and transmission

[1242] Terminal

[1243] Using a wearable device or smartphone application worn by the user, physical data such as heart rate, number of steps, and sleep time are periodically collected.Facial expressions and tone of voice are also collected using a camera and microphone, and this data is used to provide to the emotion engine.For example, a smartwatch measures the user's heart rate every minute, and a smartphone periodically scans the user's face to obtain facial expression data.

[1244] Data storage and initial evaluation

[1245] server

[1246] The system receives the body data and emotional data sent from the device and first stores them in a temporary storage area. It then transfers the data to a database, where it categorizes and manages the data for each user. Once the data is saved, it compares it with the previous data and performs an initial evaluation. For example, it checks whether the number of steps today has increased compared to the previous day, and whether there are any abnormalities in the heart rate.

[1247] Deep analysis with AI and sentiment engine

[1248] server

[1249] The stored data is then analyzed in detail using a generative AI model. This AI analyzes trends (e.g., increasing or decreasing trends) based on data from the past week, and assesses stress levels and fatigue levels based on heart rate and sleep patterns. An emotion engine also analyzes facial expressions and voice data to estimate the user's emotional state. For example, it can detect "happiness" from the user's facial expression and "tension" from the tone of their voice.

[1250] Advice generation and visualization

[1251] server

[1252] Health management advice is automatically generated based on the analysis results of the generative AI model and emotion engine. For example, if User A's recent step count has not reached its target, advice such as "We recommend you walk more" will be generated. If the emotional state is "high stress," advice such as "Try relaxation" will also be added. The analysis results and advice are then visualized in graphs and charts. For example, a graph of daily step count fluctuations, a graph of average heart rate trends, and a graph of emotional state fluctuations can be created.

[1253] Data Notifications and Alerts

[1254] server

[1255] The visualized information is sent to the device as a push notification. The notification includes the latest advice and alerts regarding abnormalities. For example, an alert may be sent saying, "Your recent average heart rate has been high. Please consult your doctor."

[1256] Terminal

[1257] Users will receive push notifications to display alerts and advice, and within the smartphone app, they can view detailed information, such as a graph of their heart rate over the past week and their emotional state at the same time.

[1258] Gathering feedback

[1259] User

[1260] Feedback on the advice provided is submitted via a smartphone application, for example, by inputting feedback such as "This advice was very helpful" or "The content is lacking."

[1261] server

[1262] It receives user feedback, stores it in a database, analyzes it, and uses it to improve the generative AI model and emotion engine, resulting in more accurate advice.

[1263] According to the above aspects, the present invention manages the health and emotional states of family members in real time and provides appropriate health advice, thereby extending healthy life expectancy and realizing an environment in which family members can live in peace of mind.

[1264] The processing flow will be explained below.

[1265] Step 1:

[1266] The device collects the user's heart rate, steps, and sleep time, using sensors in the wearable device or smartphone, as well as a camera to collect facial expression data and a microphone to collect audio data.

[1267] Step 2:

[1268] The device temporarily stores the data it collects, and a dedicated application on the smartphone manages this to prevent data loss.

[1269] Step 3:

[1270] The device sends collected data to the server at regular intervals (for example, every 30 minutes) using Bluetooth or Wi-Fi.

[1271] Step 4:

[1272] The server receives the data sent from the terminal, stores the received data in a temporary storage area, and then transfers it to the database.

[1273] Step 5:

[1274] The server stores the data in a database and categorizes and manages it for each user. After saving, it compares it with the previous data for an initial evaluation. For example, it checks whether the number of steps has increased compared to the previous day, and whether there are any abnormalities in the heart rate.

[1275] Step 6:

[1276] After the server completes the initial evaluation, it passes the data to a generative AI model for further analysis, which analyzes trends and abnormal patterns based on past data.

[1277] Step 7:

[1278] The server passes the collected facial expression and voice data to the emotion engine, which analyzes this data and estimates the user's emotional state. For example, it can detect "happiness" or "sadness" from facial expressions and "tension" or "calmness" from voice tone.

[1279] Step 8:

[1280] The server combines the analysis results from both the generative AI model and the emotion engine. Based on this, it generates specific health management advice. For example, if the user is not getting enough exercise, it generates advice such as "We recommend walking more." If the user's emotional state is determined to be "high stress," it adds the advice "Try relaxation techniques."

[1281] Step 9:

[1282] The server visualizes the analysis results and generated advice, including graphs of daily step count fluctuations, average heart rate trends, and emotional state fluctuations.

[1283] Step 10:

[1284] The server sends the visualized information to the device as a push notification. The notification includes the latest advice and alerts regarding abnormalities. For example, an alert may be sent saying, "Your recent average heart rate has been high. Please consult a doctor."

[1285] Step 11:

[1286] The device receives a push notification and displays an alert or advice to the user. The user can then view more detailed information within the smartphone application. For example, they can view graphs showing the number of steps taken over the past week and their emotional state.

[1287] Step 12:

[1288] If the server detects an abnormal value or a disruption in data transmission, it automatically generates an alert and sends a warning to the user's device, such as "Your heart rate is abnormally high. Please consult a doctor" or "We haven't seen any data updates in a while. How are you?"

[1289] Step 13:

[1290] The user sends feedback on the advice provided through the application, for example, by inputting feedback such as "This advice was very helpful" or "The content is difficult to understand."

[1291] Step 14:

[1292] The server receives user feedback, stores it in a database, and analyzes it to improve the generative AI model and emotion engine, resulting in more accurate advice in the future.

[1293] In this way, the present invention is a system that can comprehensively manage the health and emotional states of family members and provide appropriate health advice.

[1294] Example 2

[1295] 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."

[1296] While current health management systems place emphasis on collecting and analyzing biometric data, they lack the ability to take into account the user's emotional state. This prevents them from properly managing the impact of stress and emotional states on health. They also lack a mechanism to effectively incorporate user feedback and improve the accuracy of advice. The objective of this invention is to solve these problems and provide a more comprehensive health management system.

[1297] 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.

[1298] In this invention, the server includes means for collecting biometric data and emotional data of each family member, means for storing the collected data in a database, means for analyzing the stored data using a generative AI model and an emotion engine, means for providing health management advice based on the analysis results, means for visualizing the advice and analysis results, means for notifying a terminal of the visualized information, means for generating an alert when the collected data is discontinued for a certain period of time or when an abnormal value is detected, and means for collecting user feedback and reflecting it in improving the generative AI model and the emotion engine. This enables comprehensive health management including emotional states and realizes the provision of more accurate advice.

[1299] "Biometric data" refers to data that indicates the state of the body, such as heart rate, number of steps, and sleep time.

[1300] "Emotion data" is data that indicates the user's emotional state, such as facial expressions and vocal tones.

[1301] A "database" is a system for storing, classifying, and managing collected data.

[1302] A "generative AI model" is an artificial intelligence algorithm that analyzes collected data and generates health management advice.

[1303] An "emotion engine" is a system that analyzes facial expressions and vocal tones to estimate a user's emotional state.

[1304] "Health management advice" refers to suggestions for maintaining or improving health based on collected data and the results of its analysis.

[1305] "Visualization" refers to the presentation of data and its analytical results in a visual format such as a graph or chart.

[1306] "Push notification" is a system that sends information from a server to a device in real time.

[1307] "Feedback" refers to the act of a user sending an evaluation or opinion regarding the advice provided.

[1308] An "outlier" is a value in the collected data that is outside the normal range.

[1309] This invention is a system for managing the health and emotional state of each family member in real time and providing necessary health advice. The operation and configuration of this system will be specifically described below.

[1310] Data collection and transmission

[1311] Terminal

[1312] Biometric data such as heart rate, number of steps, and sleep time are periodically collected using wearable devices or smartphone applications worn by the user. Cameras and microphones are also used to collect facial expressions and tone of voice, which are then treated as emotion data. For example, a smartwatch measures the user's heart rate every minute, and a smartphone periodically scans the user's face to obtain facial expression data.

[1313] Hardware and software examples

[1314] Wearable devices: Apple Watch, Fitbit

[1315] Smartphone applications: iOS applications, Android applications

[1316] Camera and microphone: Built-in smartphone

[1317] Data storage and initial evaluation

[1318] server

[1319] The system receives body and emotional data sent from the device and stores it in a temporary storage area. It then transfers the data to a database, where it categorizes and manages the data for each user. Once the data is saved, it performs an initial evaluation by comparing it with the previous data. For example, it checks whether the number of steps taken today has increased compared to the previous day, and whether there are any abnormalities in the heart rate.

[1320] Hardware and software examples

[1321] Server: Cloud server, database management system (e.g. MySQL, PostgreSQL)

[1322] Detailed analysis

[1323] server

[1324] The stored data is then analyzed in detail using a generative AI model. This AI analyzes trends (e.g., increasing or decreasing trends) based on data from the past week, and assesses stress levels and fatigue levels based on heart rate and sleep patterns. An emotion engine also analyzes facial expressions and voice data to estimate the user's emotional state. For example, it can detect "happiness" from the user's facial expression and "tension" from the tone of their voice.

[1325] Hardware and software examples

[1326] AI model: TensorFlow-based generative AI model

[1327] Sentiment Engine: IBM Watson Sentiment Analysis API

[1328] Advice generation and visualization

[1329] server

[1330] Health management advice is automatically generated based on the analysis results of the generative AI model and emotion engine. For example, if a user's recent step count has not reached their goal, the system will generate the advice "We recommend you walk more." If the user's emotional state is "high stress," the system will also add the advice "Try relaxation." These results are then visualized in graphs and charts. For example, it creates a graph of daily step count fluctuations, average heart rate trends, and emotional state fluctuations.

[1331] Hardware and software examples

[1332] Graph and chart generation: Matplotlib, D3.js

[1333] Data Notifications and Alerts

[1334] server

[1335] The visualized information is sent to the device as a push notification. The notification includes the latest advice and alerts regarding abnormalities. For example, an alert may be sent saying, "Your recent average heart rate has been high. Please consult your doctor."

[1336] Terminal

[1337] Users will receive push notifications to display alerts and advice, and within the smartphone app, they can view detailed information, such as a graph of their heart rate over the past week and their emotional state at the same time.

[1338] Hardware and software examples

[1339] Push notifications: Firebase Cloud Messaging, Apple Push Notification Service

[1340] Gathering feedback

[1341] User

[1342] Feedback on the advice provided is submitted via a smartphone application, for example, by inputting feedback such as "This advice was very helpful" or "The content is lacking."

[1343] server

[1344] It receives user feedback, stores it in a database, analyzes it, and uses it to improve the generative AI model and emotion engine, resulting in more accurate advice.

[1345] (Example of a prompt)

[1346] "Explain how to assess a user's current stress level and generate appropriate health advice based on step count and heart rate data from the past week."

[1347] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1348] Step 1:

[1349] Data collection

[1350] The device uses a wearable device or smartphone application to periodically collect biometric data such as the user's heart rate, number of steps, and sleep time. It also uses a camera and microphone to collect facial expressions and tone of voice, which are treated as emotion data. The inputs are the user's biometric data, facial expressions, and tone of voice. The output is the various collected data. Specifically, the smartwatch records the heart rate every minute, and the smartphone activates the camera every hour to scan the user's face and obtain facial expression data.

[1351] Step 2:

[1352] Data transmission

[1353] The data collected by the device is sent to the server via wireless communication (Bluetooth or Wi-Fi). The collected data is the input. The data sent to the server is obtained as the output. In concrete terms, the smartwatch transfers data to the smartphone via Bluetooth, and the smartphone sends the data to the server via Wi-Fi.

[1354] Step 3:

[1355] Data storage and initial evaluation

[1356] The server stores the transmitted data in a temporary storage area and then transfers it to the database. The data is classified and managed for each user, and after storage is complete, it is compared with the previous data for an initial evaluation. The input is the data sent to the server. The output is the classified and managed data and the results of the initial evaluation. Specifically, the server stores the new data in the database, compares it with past data, and checks for any outliers.

[1357] Step 4:

[1358] Detailed analysis

[1359] The server performs detailed analysis of the stored data using a generative AI model and emotion engine. Trends are analyzed based on data from the past week, and stress levels and fatigue levels are assessed based on heart rate and sleep patterns. The emotion engine also analyzes facial expressions and voice data to estimate the user's emotional state. The input is data stored in the database. The output is the analysis results. Specifically, the AI ​​model analyzes heart rate, step count, and sleep data, and the emotion engine estimates the user's emotional state from facial expressions and tone of voice.

[1360] Step 5:

[1361] Advice generation and visualization

[1362] The server automatically generates health management advice based on the analysis results of the generative AI model and emotion engine. The advice and analysis results are visualized in graphs and charts. The input is the analysis results. The output is the generated advice and visualized graphs and charts. Specifically, the generative AI model generates advice such as "Your recent step count has not reached your goal. Walk more," and the emotion engine adds advice such as "Try relaxation." A graph is generated based on this.

[1363] Step 6:

[1364] Notifications and Alerts

[1365] The server sends the visualized information to the device as a push notification, and also sends an alert if an abnormality is detected. The inputs are the generated advice and visualized data. The output is the push notification and alert sent to the device. Specifically, the server generates an alert saying, "Your recent average heart rate has been high. Please consult a doctor," and sends it to the device.

[1366] The device receives the push notification and displays it to the user. The input is a push notification from the server. The output is an alert or advice that is displayed to the user. The specific behavior is that the smartphone displays the push notification, and the user checks for more information in the app.

[1367] Step 7:

[1368] Gathering feedback

[1369] The user sends feedback on the advice provided through a smartphone application. The input is the user's feedback. The output is the feedback sent to the server. The specific operation is that the user inputs feedback such as "This advice was very helpful" into the smartphone app and sends it.

[1370] The server receives feedback from users and stores it in a database. The input is the feedback sent by the user. The output is the feedback stored in the database. Specifically, the server stores the feedback data and uses it to improve the generative AI model and emotion engine.

[1371] (Application example 2)

[1372] 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."

[1373] Conventional health management systems were able to collect and analyze the health data of individual family members and provide the results of their analysis, but they did not support acquiring customers' health and emotional data in real time in physical stores and providing appropriate advice or product and service recommendations based on that data. This made it difficult to grasp customers' health conditions and provide individualized support in physical stores. As a result, it was not possible to provide appropriate services according to the customer's health condition, which led to the issue of not being able to improve customer satisfaction or achieve appropriate health management.

[1374] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting body data of each family member, means for storing the collected data in a database, means for analyzing the stored data using a generative AI model, means for providing health management advice based on the analysis results, means for visualizing the advice and analysis results, means for notifying a terminal of the visualized information, and means for generating an alert when the collected data is discontinued for a certain period of time or when an abnormal value is detected. In addition, it also includes means for collecting body data and emotional data of customers in a physical store and displaying them in real time on an augmented reality device worn by a staff member, means for recommending appropriate products and services based on the body data and emotional data of the customer, and means for notifying the staff member of the augmented reality device of the recommendation. This makes it possible to recommend optimal advice, products, and services based on the customer's health and emotional state, even in a physical store.

[1375] "Means of collecting physical data from each family member" refers to technology for recording the health status of each family member using wearable devices, smartphones, etc.

[1376] "Means for storing data in a database" refers to a system or method for organizing, recording, and managing collected data in a certain manner.

[1377] A "generative AI model" refers to an artificial intelligence model that uses machine learning algorithms to analyze data and predict relevant patterns and trends.

[1378] "Means for providing health management advice" refers to technologies for providing users with health advice and recommendations based on the collected data and the analysis results of the AI ​​model.

[1379] "Visualization means" refers to the technology of displaying data and analysis results in a visually easy-to-understand format such as graphs and charts.

[1380] "Means for notifying the device" refers to technology for sending the visualized information or alerts to the user's device and notifying them.

[1381] "Means for generating alerts" refers to technology that generates and notifies when an abnormality is detected based on collected data or when data is lost for a certain period of time.

[1382] "Means of collecting customer physical and emotional data within physical stores" refers to technology that monitors customer health and emotions using devices installed within the store or wearable devices worn by staff.

[1383] An "augmented reality device" is a device that can overlay digital information onto the real world, and when worn by staff, it allows them to display customer data in real time.

[1384] "Means for recommending appropriate products and services" refers to technology that suggests products and services that are best suited to a customer based on collected health and emotional data of the customer.

[1385] This invention provides a system for managing a customer's health and emotional state in real time in a physical store and recommending appropriate products and services. The system includes: means for collecting body data of each family member; means for storing the collected data in a database; means for analyzing the data using a generative AI model; means for providing health management advice based on the analysis results; means for visualizing the advice and analysis results; means for notifying a terminal; and means for generating alerts. The system further includes means for collecting body data and emotional data of customers in the physical store and displaying the data in real time on an augmented reality device worn by staff; means for recommending appropriate products and services based on the customer data; and means for notifying the augmented reality device of the recommendations.

[1386] Program Generation

[1387] The core program of this system operates by including the following elements:

[1388] Data collection and transmission

[1389] User device:

[1390] Using a wearable device (e.g., a smartwatch) and a smartphone worn by the user, physical data such as heart rate, number of steps, and sleep time are periodically collected.Facial expression and voice data are also collected using a camera and microphone, and used as data to determine emotional state.

[1391] Data storage and initial evaluation

[1392] server:

[1393] Body data and emotional data sent from the device are received and stored in a temporary storage area.The data is then transferred to a database where it is classified and managed for each user.Once the data has been saved, it is compared with the previous data and an initial evaluation is performed.

[1394] Deep analysis with AI and sentiment engine

[1395] server:

[1396] The stored data is analyzed in detail using a generative AI model (e.g., Decision Tree Classifier). The analysis identifies trends based on data from the past week and evaluates health and emotional status. In addition, an emotion engine (e.g., Emotion Engine) analyzes facial expressions and voice data to estimate the customer's emotional state.

[1397] Advice generation and visualization

[1398] server:

[1399] Based on the analysis results, health management advice and product recommendations are generated, which are visualized in graphs and charts and displayed in real time on an augmented reality device (e.g., smart glasses) worn by staff.

[1400] Data Notifications and Alerts

[1401] server:

[1402] The visualized information is sent as push notifications to staff members' augmented reality devices and customers' smartphones, and an alert is generated if abnormal data is detected or if data is lost for a certain period of time.

[1403] Specific examples

[1404] For example, consider a customer using a treadmill in a fitness club. Here's how the system works:

[1405] 1. The smartwatch worn by the customer collects heart rate data.

[1406] 2. Facial recognition cameras collect customer facial expression data.

[1407] 3. The collected data is sent to the server for storage and initial evaluation.

[1408] 4. Generative AI models and emotion engines analyze the data and assess the customer's exercise intensity and emotional state.

[1409] 5. The staff member's smart glasses will display advice such as "Please slow down a bit" and emotional analysis results such as "You appear tired" in real time.

[1410] Prompt Sentence Examples

[1411] While using the treadmill, the customer's heart rate is high and their facial expression looks tired. Please suggest a healthcare app feature that will provide appropriate advice.

[1412] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1413] Step 1:

[1414] Data collection and transmission

[1415] Input: Wearable device and smartphone worn by the user

[1416] Specific operation and output: Using a wearable device (e.g., a smartwatch) and a smartphone, the system periodically collects physical data such as the user's heart rate, number of steps, and sleep time. Furthermore, the smartphone's camera and microphone are used to collect facial expression data and voice data, which are then used to determine the user's emotional state. The collected data is then sent from the device to a server.

[1417] Step 2:

[1418] Data storage and initial evaluation

[1419] Input: Body data and emotional data sent from the device

[1420] Specific operations and output: The server receives the body data and emotional data sent from the device and stores them in a temporary storage area. It then transfers the data to a database where it categorizes and manages the data for each user. The stored data is compared with previous data for an initial evaluation. For example, the collected heart rate and step count are compared with the data from the previous day to evaluate any abnormalities or fluctuations. The results of the initial evaluation are sent to the next step for further analysis.

[1421] Step 3:

[1422] Deep analysis with AI and sentiment engine

[1423] Input: Stored body and emotional data

[1424] Specific operation and output: The server performs detailed analysis of the stored data using a generative AI model (e.g., DecisionTreeClassifier). Data from the past week is used to identify trends in heart rate and step count and assess health status. An emotion engine (e.g., EmotionEngine) also analyzes facial expressions and voice data to estimate the user's emotional state. The analysis results are stored for use in the next step.

[1425] Step 4:

[1426] Advice generation and visualization

[1427] Input: Results of detailed analysis (evaluation of health and emotional state)

[1428] Specific operation and output: The server generates health management advice based on the analysis results of the generative AI model and emotion engine. For example, if the user's step count has not reached the target, the advice "We recommend you walk more" is generated. Furthermore, if the emotional state is determined to be "high stress," the advice "Try relaxation" is added. The generated advice and analysis results are visualized and displayed as graphs and charts.

[1429] Step 5:

[1430] Data Notifications and Alerts

[1431] Input: Visualized advice and analysis results

[1432] Specific operation and output: The server sends the visualized information as a push notification to a device (e.g., a smartphone tag or an augmented reality device worn by a staff member). For example, if a customer shows a high heart rate, an alert such as "Please slow down a little" will be displayed on the augmented reality device of the staff member. If abnormal data is detected, a warning message will also be provided to prompt appropriate action.

[1433] Step 6:

[1434] Collecting and using feedback

[1435] Input: Feedback from users or staff

[1436] Specific operation and output: The user or store staff sends feedback on the advice or recommendation provided via the terminal. The server receives this feedback and stores it in a database. The stored feedback is analyzed and reflected in improvements to the generative AI model and emotion engine, resulting in more accurate advice being provided.

[1437] 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.

[1438] 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.

[1439] 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.

[1440] [Fourth embodiment]

[1441] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1442] 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.

[1443] 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).

[1444] 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.

[1445] 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.

[1446] 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).

[1447] 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.

[1448] 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.

[1449] 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.

[1450] 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.

[1451] 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.

[1452] 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.

[1453] 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."

[1454] The present invention is a system for managing the health status of each family member in real time and providing necessary health advice. This system includes collecting body data by a terminal, storing and analyzing the data by a server, generating health advice, visualizing the results, and generating notifications and alerts.

[1455] Data collection and transmission

[1456] Terminal

[1457] Using a wearable device or smartphone worn by the user, physical data such as heart rate, number of steps, and sleep time are periodically collected, and this data is sent to a server via Bluetooth or Wi-Fi.

[1458] For example, if user A is wearing a smartwatch, the smartwatch measures his / her heart rate every minute and sends the data to his / her smartphone, which then uploads the data to a server at regular intervals, for example, every 30 minutes.

[1459] Data storage and initial evaluation

[1460] server

[1461] The server receives the body data sent from the device and stores it in a database. The database is managed separately for each user, ensuring accurate recording of individual data. As soon as the data is saved, it is compared with the previous data and an initial evaluation is performed. For example, it checks whether the number of steps taken today has increased compared to the previous day, and whether there are any abnormalities in the heart rate.

[1462] AI-powered detailed analysis and advice generation

[1463] server

[1464] The stored data is then analyzed in detail using a generative AI model. This AI analyzes trends (e.g., increasing or decreasing trends) based on data from the past week, and evaluates stress levels and fatigue levels based on heart rate and sleep patterns. Based on the analysis results, health management advice is automatically generated. For example, for User A, whose recent step count has not reached its goal, specific recommendations such as "We recommend that you walk a little more" are made.

[1465] Data visualization and notification

[1466] server

[1467] The analysis results are then visualized in graphs and charts, such as a graph showing daily step count changes or average heart rate trends. This visualized information is then sent to the device via push notification.

[1468] Terminal

[1469] The device receives the push notification and displays it within the application. By opening the smartphone app, users can visually check their health status. For example, they can view a graph visualizing the number of steps taken over the past week and understand their own exercise trends.

[1470] Alerts and safety confirmation

[1471] server

[1472] If the server detects abnormal values ​​in the heart rate or if data transmission is interrupted for a certain period of time, it automatically generates an alert and sends it to the user's device. For example, if user A's data has not been updated for more than 24 hours, a notification will be sent saying, "There has been no data update for a while. How are you?"

[1473] Gathering feedback

[1474] User

[1475] Users can provide feedback on the advice provided through the smartphone application, which is also sent to the server and stored in the database.

[1476] server

[1477] The server analyzes the received feedback and uses it to improve the generative AI model, which will provide more relevant and accurate health advice in the future.

[1478] According to the above aspects, the present invention manages the health status of family members in real time and provides necessary health advice, thereby extending healthy life expectancy and realizing an environment in which family members can live in peace of mind.

[1479] The processing flow will be explained below.

[1480] Step 1:

[1481] The device collects the user's body data, such as heart rate, steps, and sleep time, using a wearable device or a smartphone application. Data collection occurs at regular intervals (e.g., every minute or every second).

[1482] Step 2:

[1483] The device temporarily stores the collected body data, and a dedicated application on the smartphone manages the storage of this data to prevent data loss.

[1484] Step 3:

[1485] The device sends the temporarily stored data to the server at regular intervals (for example, every 30 minutes). Bluetooth or Wi-Fi is used as the communication protocol.

[1486] Step 4:

[1487] The server receives the body data sent from the device, stores it in a temporary storage area, and then transfers it to the database.

[1488] Step 5:

[1489] The server stores the data in a database, which categorizes and stores data for each user, including historical data.

[1490] Step 6:

[1491] The server performs an initial evaluation of the stored data, for example checking for variations compared to previous data and for abnormal values ​​(such as an extremely high or low heart rate).

[1492] Step 7:

[1493] After the initial assessment is complete, the server inputs the data into a generative AI model for further analysis. The generative AI model analyzes health trends and abnormal patterns based on data from the past week or month.

[1494] Step 8:

[1495] The server automatically generates health management advice based on the analysis results. For example, if you are not getting enough exercise, it will say, "We recommend walking 1,000 more steps per day," or if your stress level is high, it will say, "Make sure you take time to relax."

[1496] Step 9:

[1497] The server visualizes the generated advice and analysis results, using graphs and charts to organize and present the data in a format that users can easily view.

[1498] Step 10:

[1499] The server sends the visualized information to the device as push notifications, which include the latest advice and alerts about detected anomalies.

[1500] Step 11:

[1501] The device receives a push notification, which displays an alert or advice to the user, and the user can check the detailed information within the smartphone application.

[1502] Step 12:

[1503] If the server detects an abnormal value or a disruption in data transmission, it automatically generates an alert and sends a warning to the user's device, such as "Your heart rate is abnormally high. Please consult a doctor" or "We haven't seen any data updates in a while. How are you?"

[1504] Step 13:

[1505] The user submits feedback on the advice provided through the application, for example, by inputting feedback such as "This advice was very helpful" or "I would like more specific suggestions."

[1506] Step 14:

[1507] The server receives user feedback and stores it in a database. This feedback is then analyzed and used to improve the generative AI model, resulting in more accurate advice.

[1508] In this way, all steps work together to create a system that manages and improves the user's health in real time.

[1509] Example 1

[1510] 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."

[1511] In modern society, it is becoming increasingly important to manage the health status of each family member in real time and provide necessary health advice. However, existing systems lack sufficient data collection, storage, and analysis, making it difficult to provide specific advice tailored to each individual's health status in real time. Furthermore, there are cases where responses to abnormal values ​​or interruptions in data transmission are delayed, which does not necessarily ensure user safety. Another issue is the lack of a mechanism for appropriately receiving user feedback and reflecting it in system improvements.

[1512] 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.

[1513] In this invention, the server includes means for collecting a user's biometric data, means for storing the collected data in a database, means for analyzing the stored data using a generative AI model, means for providing health management advice based on the analysis results, means for visualizing the advice and analysis results, means for notifying a terminal of the visualized information, means for generating an alert when the collected data is interrupted for a certain period of time or when an abnormal value is detected, means for collecting feedback from the user, and means for analyzing the feedback and reflecting it in the generative AI model. This enables real-time monitoring of the health status of each family member and providing specific advice tailored to their individual health status. Furthermore, the system can quickly respond to abnormal values ​​or interruptions in data transmission, ensuring user safety. Furthermore, by incorporating user feedback into system improvements, more accurate advice can be provided.

[1514] "Biometric data" refers to data that indicates the user's health condition, such as heart rate, number of steps, and sleep time.

[1515] A "database" is a system for storing and managing collected biometric data.

[1516] A "generative AI model" refers to artificial intelligence that uses machine learning and deep learning techniques to analyze data and automatically generate health management advice.

[1517] "Health Management Advice" provides specific recommendations and advice to improve the user's health based on data analyzed by the generative AI model.

[1518] "Visualization" is a method of displaying data in a visual format, such as a graph or chart, so that it can be easily understood by users.

[1519] "Notification" is a means of conveying visualized information and alerts to a device.

[1520] An "alert" is a warning message that is automatically generated when an abnormality occurs in the user's health condition or system operation, such as an abnormal value or a disruption in data transmission.

[1521] "Feedback" refers to the user's opinions and evaluations of the health care advice provided.

[1522] "Analysis" refers to the detailed analysis of data and the extraction of meaningful information and trends from it.

[1523] The present invention is a system for managing the health status of each family member in real time and providing necessary health advice. This system includes the collection of biometric data by a terminal, the storage and analysis of the data by a server, the generation of health advice, the visualization of the results, and the generation of notifications and alerts.

[1524] Data collection and transmission

[1525] Terminal

[1526] Users periodically collect biometric data such as heart rate, number of steps, and sleep time using wearable devices such as smartwatches and smartphones, and this data is transmitted to a server via Bluetooth or Wi-Fi.

[1527] As a specific example, if user A is wearing a smartwatch, the smartwatch measures his / her heart rate every minute and sends the data to a smartphone, which then uploads the data to a server at regular intervals (e.g., every 30 minutes).

[1528] Data storage and initial evaluation

[1529] server

[1530] The server receives the biometric data sent from the device and stores it in a database. The database is managed separately for each user, so individual data is recorded accurately.

[1531] Once saved, an initial evaluation is performed by comparing the data with previous data. For example, it checks whether the number of steps taken today is higher than the number of steps taken yesterday, or whether there are any abnormalities in the heart rate. This processing can be done using SQL queries or the programming language Python.

[1532] AI-powered detailed analysis and advice generation

[1533] server

[1534] The server performs detailed analysis of the stored data using a generative AI model. This AI model analyzes trends (increases, decreases, etc.) based on data from the past week, and evaluates stress levels and fatigue levels based on heart rate and sleep patterns. Based on the analysis results, health management advice is automatically generated.

[1535] Specifically, the server runs an AI model using a machine learning library such as TensorFlow to perform analysis and provide specific recommendations, such as "User A, whose recent step count has not reached its goal, is advised to walk a little more."

[1536] Data visualization and notification

[1537] server

[1538] The server visualizes the analysis results in graphs and charts, such as a graph showing daily step count changes or average heart rate trends. This visualized information is sent to the device via push notification.

[1539] Terminal

[1540] The device receives the push notification and displays it within the application. By opening the smartphone app, users can visually check their health status. If an abnormality is detected, an alert is displayed immediately. For example, a message such as "You haven't updated your data in a while. How are you?" is sent.

[1541] Gathering feedback

[1542] User

[1543] Users can provide feedback on the advice provided through the smartphone application, for example by sending a rating such as "This advice was helpful."

[1544] server

[1545] The server receives user feedback, stores it in a database, and uses it to improve the generative AI model, which will provide better and more accurate health advice in the future.

[1546] Prompt Sentence Examples

[1547] As a concrete example, here is a prompt for data collection from User A, who is using a smartwatch:

[1548] "User A wears a smartwatch and records his / her heart rate every minute."

[1549] "Analyze user A's heart rate data from the past week and assess user A's stress level."

[1550] Based on the above detailed description, the present invention realizes a system for managing the health status of family members in real time and providing appropriate health advice to individual users.

[1551] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1552] Step 1:

[1553] Data collection

[1554] Terminal

[1555] The user wears a smartwatch or smartphone. The device periodically measures biometric data such as heart rate, steps taken, and sleep time. This data is sent to the smartphone via Bluetooth. The smartphone converts the collected data into JSON format and sends it to a server via Wi-Fi.

[1556] input

[1557] Biometric data from smartwatch (heart rate, steps, sleep time)

[1558] output

[1559] Data sent to the server (JSON format)

[1560] Specific actions

[1561] The smartwatch measures your heart rate every minute and sends the data to your smartphone via Bluetooth.

[1562] The smartphone converts the collected data into JSON format every 30 minutes and uploads it to the server via Wi-Fi.

[1563] Step 2:

[1564] Data storage and initial evaluation

[1565] server

[1566] The server receives the biometric data sent from the device and stores it in a database. The data is managed in separate tables for each user. As soon as it is stored, it is compared with the previous data and an initial evaluation is performed.

[1567] input

[1568] Biometric data sent from the device (JSON format)

[1569] output

[1570] Data stored in a database

[1571] Initial evaluation results

[1572] Specific actions

[1573] The server parses the received JSON data and inserts the data into each user's table.

[1574] Compare with previous data and use SQL queries to check for any abnormalities in heart rate or step count.

[1575] Step 3:

[1576] AI-powered detailed analysis and advice generation

[1577] server

[1578] The server performs detailed analysis of the stored data using a generative AI model. It analyzes trends (increases, decreases, etc.) based on data from the past week, and evaluates stress levels and fatigue levels based on heart rate and sleep patterns. Health management advice is automatically generated as needed.

[1579] input

[1580] User biometric data stored in a database

[1581] output

[1582] Health Management Advice

[1583] Specific actions

[1584] The server uses Python and TensorFlow to run AI models.

[1585] Data from the past week is input into the AI ​​model to evaluate stress levels and fatigue.

[1586] Generate health management advice such as, "User A, who has not reached his / her step goal recently, we recommend that you walk a little more."

[1587] Step 4:

[1588] Data visualization and notification

[1589] server

[1590] The server visualizes the analysis results in graphs and charts, and the visualized data is sent to the device via push notification.

[1591] input

[1592] Health management advice and detailed analysis results

[1593] output

[1594] Visualized graphs, charts, and push notifications

[1595] Specific actions

[1596] The server uses Matplotlib and D3.js to generate graphs and save them as image files.

[1597] The generated image files and text information are sent to the device using a push notification service (e.g., Firebase Cloud Messaging).

[1598] Terminal

[1599] The device receives the push notification and displays it within the application, allowing the user to check their health status by opening the app.

[1600] input

[1601] Push notifications sent from the server

[1602] output

[1603] Display information within the application

[1604] Specific actions

[1605] Parses notifications received by the application and displays them in a graphical interface.

[1606] Step 5:

[1607] Alerts and safety confirmation

[1608] server

[1609] If data transmission is interrupted for a certain period of time or if abnormal values ​​are detected in the heart rate or other data, the server automatically generates an alert and sends it to the user's device.

[1610] input

[1611] User biometric data stored in a database

[1612] output

[1613] Alert Notifications

[1614] Specific actions

[1615] The server uses a cron job to periodically check the database and send alert emails or push notifications if an abnormality is detected.

[1616] Step 6:

[1617] Gathering feedback

[1618] User

[1619] The user provides feedback on the advice provided through the application.

[1620] input

[1621] User feedback

[1622] output

[1623] Feedback data sent to the server

[1624] Specific actions

[1625] Users fill out and submit feedback in the in-app feedback form.

[1626] server

[1627] The server receives feedback from users and stores it in a database, which then reflects the feedback data in improving the generative AI model.

[1628] input

[1629] User feedback data

[1630] output

[1631] Updated AI model

[1632] Specific actions

[1633] The server stores the feedback data in text format and periodically adds it to the AI ​​model's training dataset.

[1634] (Application example 1)

[1635] 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."

[1636] Existing systems that manage the health status of each family member in real time and provide necessary health advice have limited ways for users to easily understand their own health status. Furthermore, there is a lack of means to provide health information in a way that is easily accessible to employees and customers working in physical stores and other environments. Furthermore, providing prompt and appropriate alerts in the event of an emergency is also a challenge. The present invention aims to solve these problems and support health management for users in physical store environments.

[1637] 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.

[1638] In this invention, the server includes a means for collecting body data of each family member in real time, a means for storing the collected data in a database, and a means for analyzing the stored data using a generative AI model, which enables the user's health condition to be visually grasped in real time and to provide appropriate advice and warnings promptly.

[1639] "Body data for each family member" refers to physical information such as each individual's heart rate, number of steps taken, and sleep time.

[1640] "Real-time collection means" refers to technologies and methods that use wearable devices and smartphones to continuously collect user-generated health data.

[1641] "Database" refers to a system for storing and managing collected body data, and a place for centrally managing data for each user.

[1642] "Generative AI model" refers to the artificial intelligence algorithm used to analyze collected data and generate health advice.

[1643] "Visualization means" refers to techniques and methods for displaying analysis results in a visually easy-to-understand format, such as graphs or charts.

[1644] "User terminal" refers to a device that is directly used by the user, such as a smartphone or smart glasses.

[1645] "Means for generating alerts" refers to technologies and methods for sending notifications to users in the event of an emergency, such as abnormal health data values ​​or disruptions to data transmission.

[1646] "Visual device" refers to a device used to provide information directly to the eye, such as smart glasses.

[1647] This system collects, analyzes, and visualizes the body data of each family member in real time, and provides necessary health advice and warnings. This system uses wearable devices, smartphones, a server, a generative AI model, a visual device, and a user terminal.

[1648] System Configuration

[1649] Wearable devices and smartphones

[1650] Users wear wearable devices (e.g., fitness trackers, smartwatches), which collect body data such as heart rate, steps taken, and sleep time. This data is sent in real time to a smartphone via Bluetooth or Wi-Fi, and the smartphone uploads the data to a server at regular intervals.

[1651] server

[1652] The server receives the body data sent from the smartphone and stores it in a database. The data stored in the database is then analyzed using a generative AI model. This generative AI model analyzes trends (increases, decreases, etc.) based on the user's data from the past week, and evaluates stress levels and fatigue levels based on heart rate and sleep patterns. Health management advice is automatically generated based on the analysis results.

[1653] Visibility and Notification

[1654] The server visualizes the analysis results in graphs and charts, and this visualized information is sent to the user's device via push notification. The user can then visually check the information using a visual device such as a smartphone or smart glasses.

[1655] Warning generation

[1656] The server automatically generates and sends a warning to the user's device if data transmission is interrupted for a certain period of time or if abnormal values ​​are detected in the health data. For example, if the user's heart rate suddenly increases, a warning message will appear on the visual device saying, "Your heart rate is increasing. Please sit down and take a break."

[1657] Gathering feedback

[1658] Users can provide feedback on the advice provided through a smartphone application. This feedback is also sent to the server and stored in a database. The server analyzes the received feedback and reflects it in improving the generative AI model.

[1659] Specific examples

[1660] For example, consider a scenario in which an employee working in a brick-and-mortar store is wearing smart glasses. These smart glasses monitor their heart rate in real time and send the data to a server. The server analyzes this data, and if overwork is detected, the smart glasses display the advice, "Fatigue detected. Please take a short break."

[1661] Prompt Sentence Examples

[1662] "Analyze the user's health data (heart rate, steps, sleep time) from the past seven days to assess stress levels and fatigue levels and generate appropriate health advice."

[1663] By establishing such a system, it is possible to effectively support health management for users in a physical store environment and provide a safe and comfortable environment.

[1664] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1665] Step 1:

[1666] Data Collection and Transmission

[1667] The user wears a wearable device that collects physical data such as heart rate, steps taken, and sleep time.

[1668] The terminal (smartphone) receives the collected data using Bluetooth or Wi-Fi.

[1669] Input: Body data sent from a wearable device.

[1670] Output: Body data stored on the device.

[1671] Step 2:

[1672] Uploading data

[1673] The device uploads the received body data to the server at regular intervals (e.g., every 30 minutes).

[1674] Input: Body data stored on the device.

[1675] Output: The body data sent to the server.

[1676] Step 3:

[1677] Data storage and initial evaluation

[1678] The server receives the body data sent from the terminal and stores it in a database.

[1679] The server compares the stored data with past data and performs an initial assessment, checking, for example, whether the number of steps has increased compared to the previous day or whether there are any abnormalities in the heart rate.

[1680] Input: Body data sent from the device.

[1681] Output: Data stored in a database and initial evaluation results.

[1682] Step 4:

[1683] AI-powered detailed analysis and advice generation

[1684] The server analyzes the data stored in the database using a generative AI model.

[1685] The server generates health advice according to the prompt sentence.

[1686] Example: "Analyze the user's health data (heart rate, steps, and sleep time) from the past 7 days to assess their stress level and fatigue level and generate appropriate health advice."

[1687] Input: Historical health data stored in a database.

[1688] Output: Health advice from a generative AI model.

[1689] Step 5:

[1690] Data Visualization and Notification

[1691] The server visualizes the generated health advice and analysis results in graphs and charts.

[1692] The server sends the visualized data to the user's device via push notification.

[1693] Input: Health advice and analysis results from generative AI models.

[1694] Output: Visualized data and notification messages.

[1695] Step 6:

[1696] Display by visual device

[1697] The terminal or visual device (smart glasses) displays the transmitted visualization data and health advice.

[1698] Input: Visualization data and health advice sent from the server.

[1699] Output: Health information displayed on a visual device.

[1700] Step 7:

[1701] Alert Generation and Notification

[1702] The server generates an alert if data transmission is interrupted for a certain period of time or if an abnormal value is detected.

[1703] The server sends the generated alert to the user terminal.

[1704] Input: Data transmission disruption or abnormal values.

[1705] Output: The warning message sent to the user terminal.

[1706] Step 8:

[1707] Collecting and analyzing feedback

[1708] Users provide feedback on the health advice provided via a smartphone app.

[1709] The server stores the received feedback in a database and uses it to improve the generative AI model.

[1710] Input: User feedback.

[1711] Output: Feedback data and an improved generative AI model.

[1712] 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.

[1713] This invention is a system for managing the health status of each family member in real time and providing necessary health advice. This system not only collects and analyzes body data, generates advice, visualizes it, and generates notifications and alerts, but also combines it with an emotion engine to realize health management that takes into account the user's emotions.

[1714] Data collection and transmission

[1715] Terminal

[1716] Using a wearable device or smartphone application worn by the user, physical data such as heart rate, number of steps, and sleep time are periodically collected.Facial expressions and tone of voice are also collected using a camera and microphone, and this data is used to provide to the emotion engine.For example, a smartwatch measures the user's heart rate every minute, and a smartphone periodically scans the user's face to obtain facial expression data.

[1717] Data storage and initial evaluation

[1718] server

[1719] The system receives the body data and emotional data sent from the device and first stores them in a temporary storage area. It then transfers the data to a database, where it categorizes and manages the data for each user. Once the data is saved, it compares it with the previous data and performs an initial evaluation. For example, it checks whether the number of steps today has increased compared to the previous day, and whether there are any abnormalities in the heart rate.

[1720] Deep analysis with AI and sentiment engine

[1721] server

[1722] The stored data is then analyzed in detail using a generative AI model. This AI analyzes trends (e.g., increasing or decreasing trends) based on data from the past week, and assesses stress levels and fatigue levels based on heart rate and sleep patterns. An emotion engine also analyzes facial expressions and voice data to estimate the user's emotional state. For example, it can detect "happiness" from the user's facial expression and "tension" from the tone of their voice.

[1723] Advice generation and visualization

[1724] server

[1725] Health management advice is automatically generated based on the analysis results of the generative AI model and emotion engine. For example, if User A's recent step count has not reached its target, advice such as "We recommend you walk more" will be generated. If the emotional state is "high stress," advice such as "Try relaxation" will also be added. The analysis results and advice are then visualized in graphs and charts. For example, a graph of daily step count fluctuations, a graph of average heart rate trends, and a graph of emotional state fluctuations can be created.

[1726] Data Notifications and Alerts

[1727] server

[1728] The visualized information is sent to the device as a push notification. The notification includes the latest advice and alerts regarding abnormalities. For example, an alert may be sent saying, "Your recent average heart rate has been high. Please consult your doctor."

[1729] Terminal

[1730] Users will receive push notifications to display alerts and advice, and within the smartphone app, they can view detailed information, such as a graph of their heart rate over the past week and their emotional state at the same time.

[1731] Gathering feedback

[1732] User

[1733] Feedback on the advice provided is submitted via a smartphone application, for example, by inputting feedback such as "This advice was very helpful" or "The content is lacking."

[1734] server

[1735] It receives user feedback, stores it in a database, analyzes it, and uses it to improve the generative AI model and emotion engine, resulting in more accurate advice.

[1736] According to the above aspects, the present invention manages the health and emotional states of family members in real time and provides appropriate health advice, thereby extending healthy life expectancy and realizing an environment in which family members can live in peace of mind.

[1737] The processing flow will be explained below.

[1738] Step 1:

[1739] The device collects the user's heart rate, steps, and sleep time, using sensors in the wearable device or smartphone, as well as a camera to collect facial expression data and a microphone to collect audio data.

[1740] Step 2:

[1741] The device temporarily stores the data it collects, and a dedicated application on the smartphone manages this to prevent data loss.

[1742] Step 3:

[1743] The device sends collected data to the server at regular intervals (for example, every 30 minutes) using Bluetooth or Wi-Fi.

[1744] Step 4:

[1745] The server receives the data sent from the terminal, stores the received data in a temporary storage area, and then transfers it to the database.

[1746] Step 5:

[1747] The server stores the data in a database and categorizes and manages it for each user. After saving, it compares it with the previous data for an initial evaluation. For example, it checks whether the number of steps has increased compared to the previous day, and whether there are any abnormalities in the heart rate.

[1748] Step 6:

[1749] After the server completes the initial evaluation, it passes the data to a generative AI model for further analysis, which analyzes trends and abnormal patterns based on past data.

[1750] Step 7:

[1751] The server passes the collected facial expression and voice data to the emotion engine, which analyzes this data and estimates the user's emotional state. For example, it can detect "happiness" or "sadness" from facial expressions and "tension" or "calmness" from voice tone.

[1752] Step 8:

[1753] The server combines the analysis results from both the generative AI model and the emotion engine. Based on this, it generates specific health management advice. For example, if the user is not getting enough exercise, it generates advice such as "We recommend walking more." If the user's emotional state is determined to be "high stress," it adds the advice "Try relaxation techniques."

[1754] Step 9:

[1755] The server visualizes the analysis results and generated advice, including graphs of daily step count fluctuations, average heart rate trends, and emotional state fluctuations.

[1756] Step 10:

[1757] The server sends the visualized information to the device as a push notification. The notification includes the latest advice and alerts regarding abnormalities. For example, an alert may be sent saying, "Your recent average heart rate has been high. Please consult a doctor."

[1758] Step 11:

[1759] The device receives a push notification and displays an alert or advice to the user. The user can then view more detailed information within the smartphone application. For example, they can view graphs showing the number of steps taken over the past week and their emotional state.

[1760] Step 12:

[1761] If the server detects an abnormal value or a disruption in data transmission, it automatically generates an alert and sends a warning to the user's device, such as "Your heart rate is abnormally high. Please consult a doctor" or "We haven't seen any data updates in a while. How are you?"

[1762] Step 13:

[1763] The user sends feedback on the advice provided through the application, for example, by inputting feedback such as "This advice was very helpful" or "The content is difficult to understand."

[1764] Step 14:

[1765] The server receives user feedback, stores it in a database, and analyzes it to improve the generative AI model and emotion engine, resulting in more accurate advice in the future.

[1766] In this way, the present invention is a system that can comprehensively manage the health and emotional states of family members and provide appropriate health advice.

[1767] Example 2

[1768] 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."

[1769] While current health management systems place emphasis on collecting and analyzing biometric data, they lack the ability to take into account the user's emotional state. This prevents them from properly managing the impact of stress and emotional states on health. They also lack a mechanism to effectively incorporate user feedback and improve the accuracy of advice. The objective of this invention is to solve these problems and provide a more comprehensive health management system.

[1770] 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.

[1771] In this invention, the server includes means for collecting biometric data and emotional data of each family member, means for storing the collected data in a database, means for analyzing the stored data using a generative AI model and an emotion engine, means for providing health management advice based on the analysis results, means for visualizing the advice and analysis results, means for notifying a terminal of the visualized information, means for generating an alert when the collected data is discontinued for a certain period of time or when an abnormal value is detected, and means for collecting user feedback and reflecting it in improving the generative AI model and the emotion engine. This enables comprehensive health management including emotional states and realizes the provision of more accurate advice.

[1772] "Biometric data" refers to data that indicates the state of the body, such as heart rate, number of steps, and sleep time.

[1773] "Emotion data" is data that indicates the user's emotional state, such as facial expressions and vocal tones.

[1774] A "database" is a system for storing, classifying, and managing collected data.

[1775] A "generative AI model" is an artificial intelligence algorithm that analyzes collected data and generates health management advice.

[1776] An "emotion engine" is a system that analyzes facial expressions and vocal tones to estimate a user's emotional state.

[1777] "Health management advice" refers to suggestions for maintaining or improving health based on collected data and the results of its analysis.

[1778] "Visualization" refers to the presentation of data and its analytical results in a visual format such as a graph or chart.

[1779] "Push notification" is a system that sends information from a server to a device in real time.

[1780] "Feedback" refers to the act of a user sending an evaluation or opinion regarding the advice provided.

[1781] An "outlier" is a value in the collected data that is outside the normal range.

[1782] This invention is a system for managing the health and emotional state of each family member in real time and providing necessary health advice. The operation and configuration of this system will be specifically described below.

[1783] Data collection and transmission

[1784] Terminal

[1785] Biometric data such as heart rate, number of steps, and sleep time are periodically collected using wearable devices or smartphone applications worn by the user. Cameras and microphones are also used to collect facial expressions and tone of voice, which are then treated as emotion data. For example, a smartwatch measures the user's heart rate every minute, and a smartphone periodically scans the user's face to obtain facial expression data.

[1786] Hardware and software examples

[1787] Wearable devices: Apple Watch, Fitbit

[1788] Smartphone applications: iOS applications, Android applications

[1789] Camera and microphone: Built-in smartphone

[1790] Data storage and initial evaluation

[1791] server

[1792] The system receives body and emotional data sent from the device and stores it in a temporary storage area. It then transfers the data to a database, where it categorizes and manages the data for each user. Once the data is saved, it performs an initial evaluation by comparing it with the previous data. For example, it checks whether the number of steps taken today has increased compared to the previous day, and whether there are any abnormalities in the heart rate.

[1793] Hardware and software examples

[1794] Server: Cloud server, database management system (e.g. MySQL, PostgreSQL)

[1795] Detailed analysis

[1796] server

[1797] The stored data is then analyzed in detail using a generative AI model. This AI analyzes trends (e.g., increasing or decreasing trends) based on data from the past week, and assesses stress levels and fatigue levels based on heart rate and sleep patterns. An emotion engine also analyzes facial expressions and voice data to estimate the user's emotional state. For example, it can detect "happiness" from the user's facial expression and "tension" from the tone of their voice.

[1798] Hardware and software examples

[1799] AI model: TensorFlow-based generative AI model

[1800] Sentiment Engine: IBM Watson Sentiment Analysis API

[1801] Advice generation and visualization

[1802] server

[1803] Health management advice is automatically generated based on the analysis results of the generative AI model and emotion engine. For example, if a user's recent step count has not reached their goal, the system will generate the advice "We recommend you walk more." If the user's emotional state is "high stress," the system will also add the advice "Try relaxation." These results are then visualized in graphs and charts. For example, it creates a graph of daily step count fluctuations, average heart rate trends, and emotional state fluctuations.

[1804] Hardware and software examples

[1805] Graph and chart generation: Matplotlib, D3.js

[1806] Data Notifications and Alerts

[1807] server

[1808] The visualized information is sent to the device as a push notification. The notification includes the latest advice and alerts regarding abnormalities. For example, an alert may be sent saying, "Your recent average heart rate has been high. Please consult your doctor."

[1809] Terminal

[1810] Users will receive push notifications to display alerts and advice, and within the smartphone app, they can view detailed information, such as a graph of their heart rate over the past week and their emotional state at the same time.

[1811] Hardware and software examples

[1812] Push notifications: Firebase Cloud Messaging, Apple Push Notification Service

[1813] Gathering feedback

[1814] User

[1815] Feedback on the advice provided is submitted via a smartphone application, for example, by inputting feedback such as "This advice was very helpful" or "The content is lacking."

[1816] server

[1817] It receives user feedback, stores it in a database, analyzes it, and uses it to improve the generative AI model and emotion engine, resulting in more accurate advice.

[1818] (Example of a prompt)

[1819] "Explain how to assess a user's current stress level and generate appropriate health advice based on step count and heart rate data from the past week."

[1820] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1821] Step 1:

[1822] Data collection

[1823] The device uses a wearable device or smartphone application to periodically collect biometric data such as the user's heart rate, number of steps, and sleep time. It also uses a camera and microphone to collect facial expressions and tone of voice, which are treated as emotion data. The inputs are the user's biometric data, facial expressions, and tone of voice. The output is the various collected data. Specifically, the smartwatch records the heart rate every minute, and the smartphone activates the camera every hour to scan the user's face and obtain facial expression data.

[1824] Step 2:

[1825] Data transmission

[1826] The data collected by the device is sent to the server via wireless communication (Bluetooth or Wi-Fi). The collected data is the input. The data sent to the server is obtained as the output. In concrete terms, the smartwatch transfers data to the smartphone via Bluetooth, and the smartphone sends the data to the server via Wi-Fi.

[1827] Step 3:

[1828] Data storage and initial evaluation

[1829] The server stores the transmitted data in a temporary storage area and then transfers it to the database. The data is classified and managed for each user, and after storage is complete, it is compared with the previous data for an initial evaluation. The input is the data sent to the server. The output is the classified and managed data and the results of the initial evaluation. Specifically, the server stores the new data in the database, compares it with past data, and checks for any outliers.

[1830] Step 4:

[1831] Detailed analysis

[1832] The server performs detailed analysis of the stored data using a generative AI model and emotion engine. Trends are analyzed based on data from the past week, and stress levels and fatigue levels are assessed based on heart rate and sleep patterns. The emotion engine also analyzes facial expressions and voice data to estimate the user's emotional state. The input is data stored in the database. The output is the analysis results. Specifically, the AI ​​model analyzes heart rate, step count, and sleep data, and the emotion engine estimates the user's emotional state from facial expressions and tone of voice.

[1833] Step 5:

[1834] Advice generation and visualization

[1835] The server automatically generates health management advice based on the analysis results of the generative AI model and emotion engine. The advice and analysis results are visualized in graphs and charts. The input is the analysis results. The output is the generated advice and visualized graphs and charts. Specifically, the generative AI model generates advice such as "Your recent step count has not reached your goal. Walk more," and the emotion engine adds advice such as "Try relaxation." A graph is generated based on this.

[1836] Step 6:

[1837] Notifications and Alerts

[1838] The server sends the visualized information to the device as a push notification, and also sends an alert if an abnormality is detected. The inputs are the generated advice and visualized data. The output is the push notification and alert sent to the device. Specifically, the server generates an alert saying, "Your recent average heart rate has been high. Please consult a doctor," and sends it to the device.

[1839] The device receives the push notification and displays it to the user. The input is a push notification from the server. The output is an alert or advice that is displayed to the user. The specific behavior is that the smartphone displays the push notification, and the user checks for more information in the app.

[1840] Step 7:

[1841] Gathering feedback

[1842] The user sends feedback on the advice provided through a smartphone application. The input is the user's feedback. The output is the feedback sent to the server. The specific operation is that the user inputs feedback such as "This advice was very helpful" into the smartphone app and sends it.

[1843] The server receives feedback from users and stores it in a database. The input is the feedback sent by the user. The output is the feedback stored in the database. Specifically, the server stores the feedback data and uses it to improve the generative AI model and emotion engine.

[1844] (Application example 2)

[1845] 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."

[1846] Conventional health management systems were able to collect and analyze the health data of individual family members and provide the results of their analysis, but they did not support acquiring customers' health and emotional data in real time in physical stores and providing appropriate advice or product and service recommendations based on that data. This made it difficult to grasp customers' health conditions and provide individualized support in physical stores. As a result, it was not possible to provide appropriate services according to the customer's health condition, which led to the issue of not being able to improve customer satisfaction or achieve appropriate health management.

[1847] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting body data of each family member, means for storing the collected data in a database, means for analyzing the stored data using a generative AI model, means for providing health management advice based on the analysis results, means for visualizing the advice and analysis results, means for notifying a terminal of the visualized information, and means for generating an alert when the collected data is discontinued for a certain period of time or when an abnormal value is detected. In addition, it also includes means for collecting body data and emotional data of customers in a physical store and displaying them in real time on an augmented reality device worn by a staff member, means for recommending appropriate products and services based on the body data and emotional data of the customer, and means for notifying the staff member of the augmented reality device of the recommendation. This makes it possible to recommend optimal advice, products, and services based on the customer's health and emotional state, even in a physical store.

[1848] "Means of collecting physical data from each family member" refers to technology for recording the health status of each family member using wearable devices, smartphones, etc.

[1849] "Means for storing data in a database" refers to a system or method for organizing, recording, and managing collected data in a certain manner.

[1850] A "generative AI model" refers to an artificial intelligence model that uses machine learning algorithms to analyze data and predict relevant patterns and trends.

[1851] "Means for providing health management advice" refers to technologies for providing users with health advice and recommendations based on the collected data and the analysis results of the AI ​​model.

[1852] "Visualization means" refers to the technology of displaying data and analysis results in a visually easy-to-understand format such as graphs and charts.

[1853] "Means for notifying the device" refers to technology for sending the visualized information or alerts to the user's device and notifying them.

[1854] "Means for generating alerts" refers to technology that generates and notifies when an abnormality is detected based on collected data or when data is lost for a certain period of time.

[1855] "Means of collecting customer physical and emotional data within physical stores" refers to technology that monitors customer health and emotions using devices installed within the store or wearable devices worn by staff.

[1856] An "augmented reality device" is a device that can overlay digital information onto the real world, and when worn by staff, it allows them to display customer data in real time.

[1857] "Means for recommending appropriate products and services" refers to technology that suggests products and services that are best suited to a customer based on collected health and emotional data of the customer.

[1858] This invention provides a system for managing a customer's health and emotional state in real time in a physical store and recommending appropriate products and services. The system includes: means for collecting body data of each family member; means for storing the collected data in a database; means for analyzing the data using a generative AI model; means for providing health management advice based on the analysis results; means for visualizing the advice and analysis results; means for notifying a terminal; and means for generating alerts. The system further includes means for collecting body data and emotional data of customers in the physical store and displaying the data in real time on an augmented reality device worn by staff; means for recommending appropriate products and services based on the customer data; and means for notifying the augmented reality device of the recommendations.

[1859] Program Generation

[1860] The core program of this system operates by including the following elements:

[1861] Data collection and transmission

[1862] User device:

[1863] Using a wearable device (e.g., a smartwatch) and a smartphone worn by the user, physical data such as heart rate, number of steps, and sleep time are periodically collected.Facial expression and voice data are also collected using a camera and microphone, and used as data to determine emotional state.

[1864] Data storage and initial evaluation

[1865] server:

[1866] Body data and emotional data sent from the device are received and stored in a temporary storage area.The data is then transferred to a database where it is classified and managed for each user.Once the data has been saved, it is compared with the previous data and an initial evaluation is performed.

[1867] Deep analysis with AI and sentiment engine

[1868] server:

[1869] The stored data is analyzed in detail using a generative AI model (e.g., Decision Tree Classifier). The analysis identifies trends based on data from the past week and evaluates health and emotional status. In addition, an emotion engine (e.g., Emotion Engine) analyzes facial expressions and voice data to estimate the customer's emotional state.

[1870] Advice generation and visualization

[1871] server:

[1872] Based on the analysis results, health management advice and product recommendations are generated, which are visualized in graphs and charts and displayed in real time on an augmented reality device (e.g., smart glasses) worn by staff.

[1873] Data Notifications and Alerts

[1874] server:

[1875] The visualized information is sent as push notifications to staff members' augmented reality devices and customers' smartphones, and an alert is generated if abnormal data is detected or if data is lost for a certain period of time.

[1876] Specific examples

[1877] For example, consider a customer using a treadmill in a fitness club. Here's how the system works:

[1878] 1. The smartwatch worn by the customer collects heart rate data.

[1879] 2. Facial recognition cameras collect customer facial expression data.

[1880] 3. The collected data is sent to the server for storage and initial evaluation.

[1881] 4. Generative AI models and emotion engines analyze the data and assess the customer's exercise intensity and emotional state.

[1882] 5. The staff member's smart glasses will display advice such as "Please slow down a bit" and emotional analysis results such as "You appear tired" in real time.

[1883] Prompt Sentence Examples

[1884] While using the treadmill, the customer's heart rate is high and their facial expression looks tired. Please suggest a healthcare app feature that will provide appropriate advice.

[1885] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1886] Step 1:

[1887] Data collection and transmission

[1888] Input: Wearable device and smartphone worn by the user

[1889] Specific operation and output: Using a wearable device (e.g., a smartwatch) and a smartphone, the system periodically collects physical data such as the user's heart rate, number of steps, and sleep time. Furthermore, the smartphone's camera and microphone are used to collect facial expression data and voice data, which are then used to determine the user's emotional state. The collected data is then sent from the device to a server.

[1890] Step 2:

[1891] Data storage and initial evaluation

[1892] Input: Body data and emotional data sent from the device

[1893] Specific operations and output: The server receives the body data and emotional data sent from the device and stores them in a temporary storage area. It then transfers the data to a database where it categorizes and manages the data for each user. The stored data is compared with previous data for an initial evaluation. For example, the collected heart rate and step count are compared with the data from the previous day to evaluate any abnormalities or fluctuations. The results of the initial evaluation are sent to the next step for further analysis.

[1894] Step 3:

[1895] Deep analysis with AI and sentiment engine

[1896] Input: Stored body and emotional data

[1897] Specific operation and output: The server performs detailed analysis of the stored data using a generative AI model (e.g., DecisionTreeClassifier). Data from the past week is used to identify trends in heart rate and step count and assess health status. An emotion engine (e.g., EmotionEngine) also analyzes facial expressions and voice data to estimate the user's emotional state. The analysis results are stored for use in the next step.

[1898] Step 4:

[1899] Advice generation and visualization

[1900] Input: Results of detailed analysis (evaluation of health and emotional state)

[1901] Specific operation and output: The server generates health management advice based on the analysis results of the generative AI model and emotion engine. For example, if the user's step count has not reached the target, the advice "We recommend you walk more" is generated. Furthermore, if the emotional state is determined to be "high stress," the advice "Try relaxation" is added. The generated advice and analysis results are visualized and displayed as graphs and charts.

[1902] Step 5:

[1903] Data Notifications and Alerts

[1904] Input: Visualized advice and analysis results

[1905] Specific operation and output: The server sends the visualized information as a push notification to a device (e.g., a smartphone tag or an augmented reality device worn by a staff member). For example, if a customer shows a high heart rate, an alert such as "Please slow down a little" will be displayed on the augmented reality device of the staff member. If abnormal data is detected, a warning message will also be provided to prompt appropriate action.

[1906] Step 6:

[1907] Collecting and using feedback

[1908] Input: Feedback from users or staff

[1909] Specific operation and output: The user or store staff sends feedback on the advice or recommendation provided via the terminal. The server receives this feedback and stores it in a database. The stored feedback is analyzed and reflected in improvements to the generative AI model and emotion engine, resulting in more accurate advice being provided.

[1910] 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.

[1911] 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.

[1912] 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.

[1913] 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.

[1914] 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.

[1915] 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.

[1916] 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).

[1917] 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, motorcycles, and other devices, 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.

[1918] 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."

[1919] 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.

[1920] 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).

[1921] 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.

[1922] 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.

[1923] 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.

[1924] 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.

[1925] 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.

[1926] 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.

[1927] 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.

[1928] 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.

[1929] 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.

[1930] 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.

[1931] The following is further disclosed regarding the above embodiment.

[1932] (Claim 1)

[1933] a means for collecting physical data for each family member;

[1934] means for storing the collected data in a database;

[1935] means for analyzing the stored data using a generative AI model;

[1936] means for providing health care advice based on the analysis results;

[1937] means for visualizing the advice and analysis results;

[1938] means for notifying a terminal of the visualized information;

[1939] The system includes means for generating an alert when the collected data is lost for a certain period of time or when an abnormal value is detected.

[1940] (Claim 2)

[1941] 10. The system of claim 1, wherein the collected data includes heart rate, number of steps, and sleep time.

[1942] (Claim 3)

[1943] 2. The system according to claim 1, further comprising means for assessing stress levels and fatigue levels based on the collected data.

[1944] "Example 1"

[1945] (Claim 1)

[1946] means for collecting biometric data of a user;

[1947] means for storing the collected data in a database;

[1948] means for analyzing the stored data using a generative AI model;

[1949] means for providing health care advice based on the analysis results;

[1950] means for visualizing the advice and analysis results;

[1951] means for notifying a terminal of the visualized information;

[1952] means for generating an alert when the collected data is lost for a certain period of time or when an abnormal value is detected;

[1953] a means for collecting feedback from users;

[1954] A system including means for analyzing the feedback and incorporating it into a generative AI model.

[1955] (Claim 2)

[1956] 10. The system of claim 1, wherein the collected data includes heart rate, number of steps, and sleep time.

[1957] (Claim 3)

[1958] 2. The system according to claim 1, further comprising means for assessing stress levels and fatigue levels based on the collected data.

[1959] "Application Example 1"

[1960] (Claim 1)

[1961] A means of collecting real-time physical data for each family member,

[1962] means for storing the collected data in a database;

[1963] means for analyzing the stored data using a generative AI model;

[1964] means for providing health care advice based on the analysis results;

[1965] means for visualizing the advice and analysis results;

[1966] means for notifying a user terminal of the visualized information;

[1967] means for generating an alert when the collected data is lost for a certain period of time or when an abnormal value is detected;

[1968] The system includes means for displaying the visualized information and advice on a visual device worn by a user.

[1969] (Claim 2)

[1970] 10. The system of claim 1, wherein the collected data includes heart rate, number of steps, and sleep time.

[1971] (Claim 3)

[1972] 2. The system according to claim 1, further comprising means for assessing stress levels and fatigue levels based on the collected data.

[1973] "Example 2: Combining Emotion Engines"

[1974] (Claim 1)

[1975] means for collecting biometric and emotional data for each family member;

[1976] means for storing the collected data in a database;

[1977] means for analyzing the stored data using a generative AI model and an emotion engine;

[1978] means for providing health care advice based on the analysis results;

[1979] means for visualizing the advice and analysis results;

[1980] means for notifying a terminal of the visualized information;

[1981] means for generating an alert when the collected data is lost for a certain period of time or when an abnormal value is detected;

[1982] A system that includes a means to collect user feedback and incorporate it into improvements to the generative AI model and emotion engine.

[1983] (Claim 2)

[1984] 10. The system of claim 1, wherein the collected data includes heart rate, number of steps, sleep time, and facial expressions and voice tones.

[1985] (Claim 3)

[1986] 2. The system according to claim 1, further comprising means for assessing stress levels and fatigue levels based on the collected data.

[1987] "Application example 2 when combining emotion engines"

[1988] (Claim 1)

[1989] a means for collecting physical data for each family member;

[1990] means for storing the collected data in a database;

[1991] means for analyzing the stored data using a generative AI model;

[1992] means for providing health care advice based on the analysis results;

[1993] means for visualizing the advice and analysis results;

[1994] means for notifying a terminal of the visualized information;

[1995] In addition to a means for generating an alert when the collected data is lost for a certain period of time or when an abnormal value is detected,

[1996] A means of collecting customer body data and emotional data in a physical store and displaying it in real time on an augmented reality device worn by staff;

[1997] means for recommending appropriate products and services based on the body data and emotional data of the customer;

[1998] The system includes means for communicating the recommendation to an augmented reality device of a staff member.

[1999] (Claim 2)

[2000] 10. The system of claim 1, wherein the collected data includes heart rate, number of steps, sleep time, and emotional state.

[2001] (Claim 3)

[2002] 2. The system according to claim 1, further comprising means for evaluating stress levels and fatigue levels based on the collected data and providing advice in real time. [Explanation of symbols]

[2003] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for collecting physical data for each family member; means for storing the collected data in a database; means for analyzing the stored data using a generative AI model; means for providing health care advice based on the analysis results; a means for visualizing the advice and analysis results; means for notifying a terminal of the visualized information; The system includes means for generating an alert when the collected data is lost for a certain period of time or when an abnormal value is detected.

2. 10. The system of claim 1, wherein the collected data includes heart rate, number of steps, and sleep time.

3. 2. The system according to claim 1, further comprising means for assessing stress levels and fatigue levels based on the collected data.

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