system

Abstract

We provide the system. [Solution] A means of receiving the user's biometric information and lifestyle data, A means for generating personalized health guidance using an artificial intelligence module based on the aforementioned biometric information and lifestyle data, A means for transmitting the aforementioned health guidance to the user terminal, A means for generating and managing reminders based on the aforementioned health guidance, A means of recording user activity history and evaluating progress, Means for providing feedback based on the aforementioned progress, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, as the global aging progresses, preventing cognitive decline and promoting a healthy life have become important issues. However, it is difficult to provide specific and sustainable health guidance tailored to individual health conditions and lifestyles. In particular, due to the complexity and difficulty of understanding health information, many users cannot maintain motivation and often cannot continue desirable health habits. In view of this, there is a need to develop a system that can easily provide health guidance optimized for users and motivate and support them to maintain their health.

Means for Solving the Problems

[0005] This invention provides a system that acquires a user's biometric information and lifestyle data, and uses an artificial intelligence module to generate personalized health guidance based on this data. This system generates and manages reminders based on the health guidance, and further records the user's activity history to evaluate progress. It then provides feedback to the user based on this evaluation, and by including graphical progress visualization, it enhances the user's motivation and promotes the continuation of healthy lifestyle habits.

[0006] "Biometric information" refers to data about the user's physical condition, specifically including medical measurements such as heart rate, body temperature, and blood pressure.

[0007] "Lifestyle data" refers to information about a user's daily activities and habits, including data on diet, exercise levels, and sleep patterns.

[0008] A "generative artificial intelligence module" is a collection of computational models and algorithms for generating personalized output from user input data.

[0009] "Personalized health guidance" refers to improvement strategies and support information that are optimized for each user's unique condition and habits.

[0010] A "reminder" is a notification or alert designed to remind a user of a specific action, and is usually presented via a device at a specified time.

[0011] "Activity history" refers to a record of actions and behaviors performed by the user, including various behavioral data such as daily exercise, meals, and rest.

[0012] A "means of evaluating progress" is a mechanism that analyzes the user's activity history to measure the degree of achievement against set goals and calculates the results.

[0013] "Feedback" refers to evaluation results and recommendations provided to users by a system, serving as a guide for modifying or improving user behavior.

[0014] "Graphical visualization" means representing information and data in a visually understandable way, using forms such as graphs and diagrams. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), etc.

[0019] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disk (e.g., hard disk), or magnetic tape, etc.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] This invention is a lifestyle support system that enables users to establish healthy lifestyle habits and prevent dementia. This system links a server with the user's terminal and provides personalized advice and management functions tailored to the user's health condition.

[0037] Server Role

[0038] The server receives biometric information and lifestyle data transmitted by the user and records it in a database. Based on this data, a generative artificial intelligence module generates personalized health guidance for each user. The generated health guidance includes recommendations for exercise, diet, intellectual activity, and social participation tailored to the user's condition.

[0039] Furthermore, the server has a reminder management function that generates and sends reminders to the device based on specified times. These reminders support the user's behavior and help them maintain healthy habits.

[0040] Role of the user terminal

[0041] The user terminal receives data sent from the server and displays it for the user. The interface on the terminal is intuitive and designed to allow users to easily check advice and reminders. Based on this information, users can record their daily activities and send that data back to the server, enabling continuous health management.

[0042] User behavior

[0043] Users use their devices daily to follow advice and record their exercise and diet. They can also track their progress graphically through the device's display, allowing them to see their improvement and maintain motivation. User feedback is also incorporated into the system, enabling the provision of even more precise health guidance.

[0044] Specific example

[0045] Specifically, a user receives advice to perform recommended stretches in the morning and records their activity on their device. The server then evaluates the user's exercise habits based on this information and recommends the next action. In the afternoon, a reminder appears on the device, encouraging the user to do some light exercise. In this way, users receive personalized health guidance every day and are supported in putting it into practice.

[0046] This system is expected to enable and maintain a healthy lifestyle optimized for each individual user, thereby contributing to health promotion and dementia prevention.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] The user uses a device to input basic information about their health status and lifestyle. The device validates this data once and then sends it to the server.

[0050] Step 2:

[0051] The server receives data from users and stores it in the database. This creates a profile for each user.

[0052] Step 3:

[0053] The server uses AI to generate personalized health guidance based on user data. This includes recommendations regarding exercise and diet.

[0054] Step 4:

[0055] The server sends the generated health guidance to the terminal. The terminal displays this to the user and notifies them that new advice is available.

[0056] Step 5:

[0057] The user records their daily activities (e.g., exercise and meals) on their device. The device then sends this activity data to a server.

[0058] Step 6:

[0059] The server receives the user's activity history and records it in a database. The generating AI analyzes this data and evaluates the progress.

[0060] Step 7:

[0061] The server generates feedback for the user based on the evaluation results. The evaluation includes graphical visualizations and is sent to the terminal.

[0062] Step 8:

[0063] The device displays feedback to the user and provides comments to boost motivation. This helps users maintain healthy habits.

[0064] (Example 1)

[0065] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0066] In today's busy lifestyle, it is not easy for individual users to maintain healthy habits. In this context, there is a need for a system that provides comprehensive health management tailored to individual health conditions and encourages improvements in exercise, diet, and lifestyle. However, conventional technology has limitations in effectively utilizing individual user health data and providing personalized advice and reminders.

[0067] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0068] In this invention, the server includes means for collecting user health-related data, means for generating personalized health guidance using intelligent functions based on the health-related data, and means for transmitting the health guidance to the user's communication device. This enables the user to receive health guidance best suited to their lifestyle and to continuously improve their daily activities.

[0069] "Health-related data" refers to information about a user's biological state and lifestyle, and specifically includes data such as heart rate, steps taken, diet, and sleep duration.

[0070] In the field of computer science, "intelligent function" refers to programs and algorithms that analyze collected data and provide individualized guidance or predictions through learning.

[0071] "Personalized health guidance" refers to providing advice on exercise, diet, and lifestyle habits that are tailored to each user's individual health condition and goals, based on their health-related data.

[0072] "Communication equipment" refers to personal devices capable of sending and receiving information, and specifically includes smartphones, tablets, wearable devices, and other similar devices.

[0073] A "notification" is a means of conveying certain information to a user, and is primarily delivered via the user's communication device in the form of push notifications or alerts.

[0074] "Schedule information" refers to data that shows a user's schedule and plans, and includes information stored in calendar applications and schedule management systems.

[0075] "Visual display" refers to presenting information visually through the screens of digital devices, in the form of graphs, charts, text, and other similar formats.

[0076] This invention is a system that supports users in establishing and maintaining a healthy lifestyle. The system receives user data and provides personalized health guidance. As hardware, the communication devices used by the user include smartphones and wearable devices. These devices are used to collect health-related data such as heart rate, steps taken, diet, and sleep duration.

[0077] 1. Server Role

[0078] The server receives health-related data sent by users via a secure protocol (e.g., HTTPS) and records it in a database. A relational database management system (RDBMS) is used for data processing to efficiently manage the data. The data stored in the database is analyzed by a generative AI model. This model is built using machine learning frameworks such as TENSORFLOW® and PyTorch. This allows the server to create personalized health guidance for users and send it to their communication devices.

[0079] 2. The role of the terminal

[0080] The device receives health guidance and notifications sent from the server and presents them to the user through an intuitive interface. For example, it displays notifications on the smartphone's screen, allowing the user to immediately see what action to take. Based on this, the user records their daily activities in detail, and this data is sent back to the server.

[0081] 3. User behavior

[0082] Users perform activities based on advice provided via the device. For example, they might perform recommended stretches and record the results on the device. Furthermore, by viewing progress information graphically visualized on the device, they can monitor their health status and maintain motivation.

[0083] For example, users can make specific requests to the generating AI model using prompts such as, "Consider my recent exercise habits data and suggest a routine for tomorrow. I would like advice on appropriate exercise and diet to maximize health benefits." Through such interactions, users can be supported in maintaining their daily health and increase their motivation to improve it.

[0084] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0085] Step 1:

[0086] Users input health-related data using smartphones or wearable devices. This data includes steps taken, heart rate, diet, and sleep duration. The device receives this data and processes it to convert the input data into a standard format. The output is formatted data ready for transmission.

[0087] Step 2:

[0088] The terminal sends formatted health-related data to the server. The secure HTTPS protocol is used for communication. The server receives this data and records it in a database. Using the received data as input, the server outputs structured health data by storing it in the database.

[0089] Step 3:

[0090] The server starts data analysis using a generative AI model based on the recorded data. It uses user-specific health-related data stored in a database as input. The AI ​​model learns from past data and performs pattern recognition to generate personalized health guidance for each user. The generated health guidance information is obtained as output.

[0091] Step 4:

[0092] The server sends the generated health guidance information to the user's device. Furthermore, it creates a reminder based on the health guidance and sends it at the specified time. The health guidance information is used as input, and the timing of the reminder is determined simultaneously based on its content. The output is the health guidance and reminder displayed on the user's device.

[0093] Step 5:

[0094] Users follow the health guidance provided on their device, engaging in activities such as exercise and diet, and recording the results on the device again. Specific actions include performing recommended stretches and recording meal details. This generates new activity data as input, which is sent to the server, allowing for further personalized advice to be provided as output for future sessions.

[0095] Step 6:

[0096] The server receives new activity data from the user and records it in the database. Through this process, the server continuously collects and analyzes data, which is then used to generate health guidance in the next step. It processes activity data as input and lays the foundation for achieving improved health guidance as output.

[0097] (Application Example 1)

[0098] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0099] In modern society, individuals are expected to maintain healthy lifestyles and prevent cognitive decline amidst their busy daily lives. However, opportunities for health-promoting activities and intellectual stimulation are limited, especially while traveling, making regular health maintenance difficult. This problem needs to be solved, and personalized health guidance and intellectual stimulation must be provided effectively even while on the go.

[0100] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0101] In this invention, the server includes a device for receiving the user's biometric information and lifestyle activity data, a device for generating personalized health guidance using an intelligent module, and a device for transmitting the health guidance to the user's terminal. This makes it possible to provide guidance and intellectual stimulation for maintaining healthy lifestyle habits even to users who are on the go.

[0102] "Biometric information" refers to data about the user's body, such as heart rate, body temperature, and activity level.

[0103] "Lifestyle activity data" refers to information about a user's daily life, such as their diet, exercise, and sleep.

[0104] An "intelligent module" is an algorithm or program that generates personalized advice based on user data.

[0105] "Health guidance" refers to recommendations regarding exercise, diet, and lifestyle habits provided to maintain and improve the user's health.

[0106] "User terminal" refers to hardware such as a user's mobile device or in-car display used to receive health guidance.

[0107] "Notifications" are reminders and alerts sent to users periodically to encourage them to practice health guidance.

[0108] "Data for providing health guidance and intellectual stimulation during travel" refers to information that provides users with opportunities for exercise and learning while they are in transit.

[0109] A "display device" refers to a device such as a monitor or speaker that conveys the content of health guidance to the user visually and audibly.

[0110] To implement this invention, it is necessary to construct a system in which information is exchanged between a server, a terminal, and a user. The server is responsible for receiving biometric information and lifestyle activity data transmitted by the user and recording it in a database. Based on this recorded information, an intelligent module within the server uses a generated AI model to create personalized health guidance. The generated health guidance includes recommendations for exercise, diet, intellectual activities, and social participation.

[0111] The terminal receives health guidance transmitted from the server and displays it to the user. Examples of terminal use include mobile devices and in-car displays. The interface on the terminal is intuitive and designed so that users can easily check guidance content and reminders. Users can record their daily activities through the terminal and send them back to the server, enabling continuous health management.

[0112] Users engage in healthy activities by following the advice provided by the device in their daily lives. For example, if a user has been sitting for a long time, the device will issue a notification such as, "Let's do some stretching for about 5 minutes." Such notifications are generated by a server that analyzes the user's activity data and uses a generative AI model to present the most appropriate guidance. While on the move, prompts such as, "Here's a quiz about sightseeing spots along your current route," are displayed to stimulate intellectual activity. Specifically, the generative AI model is used with prompts such as, "Generate the next exercise suggestion based on the user's heart rate data."

[0113] The technologies supporting this system include data processing using Python, AI model generation using TensorFlow, and the construction of the user interface for devices using React Native. SQLite is used as the database, and various analytical tools are utilized to track user behavior. In this way, users can receive health guidance regardless of location and achieve a richer lifestyle.

[0114] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0115] Step 1:

[0116] The server receives biometric and activity data from the user's device. This input includes heart rate, exercise levels, and dietary details. The server standardizes this data and records it in a database. A Python script is used to unify data in different formats for data standardization.

[0117] Step 2:

[0118] An intelligent module analyzes data recorded on the server and generates personalized health guidance using a generative AI model. Here, the generative AI model creates exercise and dietary recommendations tailored to the user's health condition based on the prompts it receives. This output is presented in the form of advice sent to the user.

[0119] Step 3:

[0120] The server sends the generated health guidance to the user's terminal. This process uses the TCP / IP protocol for data transfer. The output is displayed on the terminal in a format easily accessible to the user.

[0121] Step 4:

[0122] The device displays the received health guidance to the user. The input is health guidance data from the server, which is output on the device as an interactive interface using React Native. Icons and graphs are used to help the user easily understand the guidance content.

[0123] Step 5:

[0124] Users act based on the health guidance provided and input activity data into their devices. This data includes the duration and type of exercise, as well as the content of their meals. The user's activity history is then sent back to the server to serve as the basis for further analysis.

[0125] Step 6:

[0126] The server analyzes the user's new activity data and generates the next health guidance. This analysis incorporates a feedback loop for continuous improvement. The resulting new health guidance includes progress-based advice and new reminders.

[0127] Step 7:

[0128] The device receives new health guidance and reminders from the server and presents them to the user. When presented, it alerts the user, especially through voice and vibration, to prompt action. This process is repeated, creating a system that continuously supports the user's health.

[0129] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0130] This invention combines a system that provides personalized health guidance based on a user's health status and lifestyle data with an emotion engine that recognizes the user's emotional state. This system operates through a server and the user's terminal, providing the user with optimized health guidance and emotionally responsive feedback.

[0131] Server Role

[0132] The server estimates the user's emotional state using a sentiment engine in addition to the user's biometric and lifestyle data. This involves analyzing the user's input data and activity history, and using AI to recognize emotions. Based on the estimated emotional state, the server further adjusts personalized health guidance and generates guidance content that is appropriate for the user's emotions.

[0133] In addition, the server has a reminder management function that generates reminders based on the user's schedule and sends them to the device. This allows for notifications to be sent at the appropriate time according to the user's emotional state.

[0134] Role of the user terminal

[0135] The user terminal receives health guidance and emotion-sensitive feedback sent from the server and displays it to the user. The interface is flexible and emotionally responsive, providing information tailored to the user's state. Users record their activities through the terminal, and this data is sent to the server to help estimate future emotions.

[0136] User behavior

[0137] Users use their devices daily to record their activities and emotions. The feedback displayed on the device is tailored based on their emotional state, promoting motivation that takes their mood into consideration. The health guidance provided through the analysis of emotional states becomes more personalized and effective, further enhancing the health management of users.

[0138] Specific example

[0139] For example, if a user hasn't gotten enough sleep, the system's emotional engine recognizes this as fatigue or stress. Based on this, the server recommends stretching or meditation to help the user relax and communicates this through the device. At the same time, soothing colors and encouraging words are displayed on the device to help the user relax.

[0140] This system enables personalized health management tailored to the user's emotions, promoting improved health and better management of stress and mental state.

[0141] The following describes the processing flow.

[0142] Step 1:

[0143] Users use their devices to input health information, daily mood, and emotions. The devices collect this data and send it to a server.

[0144] Step 2:

[0145] The server receives biometric information, lifestyle data, and emotional state transmitted by the user and records them in a database.

[0146] Step 3:

[0147] The server uses an emotion engine to estimate the user's emotional state from the received data. This emotional data is estimated by analyzing the correlation between the user's previously recorded emotions and activity data.

[0148] Step 4:

[0149] The AI ​​generation module generates personalized health guidance based on the user's biometric information, lifestyle, and estimated emotional state. This guidance includes content that takes the user's mental state into consideration.

[0150] Step 5:

[0151] The server generates health guidance and sets reminders, sending data to the user's device to send notifications according to the user's schedule.

[0152] Step 6:

[0153] The device displays health guidance and reminder information received from the server to the user. This uses a visually conscious interface with emotionally responsive adjustments.

[0154] Step 7:

[0155] Users record activities in accordance with health guidance and submit feedback on their emotional state. This allows users to continuously engage in experiences that contribute to improving their own health.

[0156] Step 8:

[0157] The server continuously receives newly recorded activity and emotion data, updating the database. This data is then used for future emotion estimation and health guidance.

[0158] This entire process allows users to receive health guidance while also managing their emotions, resulting in a system that supports overall health improvement.

[0159] (Example 2)

[0160] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0161] Traditional health management systems provide health guidance based on users' biometric information and lifestyle data, but they lack guidance that takes psychological states into account, making it difficult to adequately support users' emotions and mental health. Furthermore, if reminder timing is not appropriate for the user's emotional state, it can potentially decrease motivation.

[0162] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0163] In this invention, the server includes means for predicting the user's psychological state, means for generating personalized health guidance, and means for transmitting the health guidance to the user's terminal. This makes it possible to provide optimal health guidance and reminders according to the user's emotional state.

[0164] "Biometric information" refers to measurable data about a user's body, including heart rate, body temperature, and activity level.

[0165] "Lifestyle data" refers to data that shows a user's daily actions and habits, including diet, exercise, and sleep duration.

[0166] "Psychological state" refers to a user's emotions and mental condition, and is evaluated based on factors such as stress levels and mood.

[0167] "Health guidance" refers to specific advice and recommendations provided to improve a user's health, including information on exercise, nutrition, and relaxation techniques.

[0168] "Inference methods" refer to algorithms and technologies used to analyze user data and evaluate their psychological and health status.

[0169] A "reminder" is a notification that prompts a user to take action at a specific time or in a specific situation.

[0170] "Activity history" refers to a record of a user's past activity data, which is used to analyze progress and behavioral patterns.

[0171] This invention is a system that provides personalized health guidance and feedback based on the user's physical and psychological state. The system consists of a server for data analysis and a terminal for user interaction.

[0172] The server aggregates biometric information and lifestyle data sent by the user and uses an AI model to infer the user's psychological state. This AI model is built using, for example, open-source machine learning libraries such as TensorFlow and PyTorch. The data received includes heart rate and activity levels from smartphones and wearable devices, as well as emotional data based on the user's daily self-assessment. Based on this data analysis, the server generates personalized health guidance and registers it as a reminder.

[0173] The device presents health guidance and feedback from the server to the user via a user interface. This interface is developed using HTML and JavaScript (registered trademark) technologies to ensure that users can comfortably receive information. Reminders and feedback are tailored to the user's mental state and schedule, and notifications are sent at the appropriate time.

[0174] Users record their daily activity data and mood through their devices. This data is later analyzed and sent to a server to further improve the accuracy of the user's psychological state estimation.

[0175] For example, if a user hasn't gotten enough sleep, the server's emotion engine evaluates this state as stress or fatigue. Based on this, the server generates health advice, such as "Try a 5-minute meditation to relax," and sends it to the device along with a user interface featuring gentle colors.

[0176] Examples of prompt messages include the following:

[0177] "A user recorded their sleep duration last night and found it was two hours shorter than average. Based on this data, how does the emotion engine evaluate the user's emotional state and suggest health guidance?"

[0178] This system enables health support that takes into maximum consideration the user's emotions and behavior, and is expected to improve both their psychological and physical health.

[0179] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0180] Step 1:

[0181] Users input activity data and emotional states via their devices. Specifically, they use smartphones or wearable devices to record biometric information such as heart rate, steps taken, and sleep duration, and also input self-assessments of their mood and stress levels for the day. This information is aggregated on the device.

[0182] Step 2:

[0183] The terminal converts the collected data into a standard format and sends it to the server using a security protocol. The data is formatted in JSON format and encrypted using SSL / TLS before transmission. The server receives and decrypts it.

[0184] Step 3:

[0185] The server inputs the received user biometric information and lifestyle data into an AI model for data analysis. Specifically, it uses an emotion estimation algorithm using Python (for example, a model built with TensorFlow) to estimate the user's current psychological state. The server outputs the user's emotional state as the analysis result.

[0186] Step 4:

[0187] The server generates personalized health guidance based on analyzed emotional states and biometric data. For example, it generates specific advice on recommended exercise, nutrition, and relaxation. The generated guidance is output as actions to take next and areas for improvement. The server optimizes this based on the user's profile.

[0188] Step 5:

[0189] The server sends the generated health guidance to the user's device. Simultaneously, it utilizes a scheduling management system to set reminders and coordinate notifications to the device at appropriate times. These reminders might be sent for scheduled meditation or exercise sessions, for example.

[0190] Step 6:

[0191] The device visually presents received health guidance to the user. It uses a flexible interface, for example, displaying recommendations against a light-colored background. It also features a function to play music in the background to promote relaxation.

[0192] Step 7:

[0193] Users follow the health guidance presented on the device and decide whether to incorporate it into their daily routines. They also input feedback into the device, and this data is used in the next analysis. Information based on the feedback is sent from the device to the server and reflected in the next health guidance and psychological state estimation.

[0194] (Application Example 2)

[0195] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0196] In current brick-and-mortar stores, it is difficult to provide personalized experiences tailored to each customer's health and emotional state. This can lead to customers not receiving services or products suitable for their health condition, potentially resulting in lower satisfaction. Furthermore, traditional systems are unable to effectively provide feedback based on customers' emotions and health status.

[0197] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0198] In this invention, the server includes means for receiving the user's biometric information and lifestyle data, means for generating personalized health guidance using an artificial intelligence module based on the biometric information and lifestyle data, and means for detecting the user's emotional state. This makes it possible to provide personalized health guidance and emotionally responsive feedback to customers in real time.

[0199] "Biometric information" refers to data that indicates the user's physical condition, including measurements such as heart rate, blood pressure, and body temperature.

[0200] "Lifestyle data" refers to information about a user's daily actions and habits, including diet, exercise, and sleep patterns.

[0201] A "generative artificial intelligence module" is a program with artificial intelligence capabilities that analyzes input data and generates personalized instruction content.

[0202] "Health guidance" refers to specific advice and guidance provided to improve or maintain the user's health.

[0203] "User visual devices" refer to devices used to present information to users visually, and include smart glasses and display devices.

[0204] A "notification" is a system message that informs a user of specific information, and functions as a reminder or alert.

[0205] "Activity history" refers to a record of a user's past actions and trends, and is used to analyze behavioral patterns.

[0206] "Emotional state" refers to the user's psychological and emotional condition, including joy, sadness, stress, etc.

[0207] "Recommended products and entertainment" refers to products and entertainment content that are suggested according to the individual needs of the user.

[0208] The system for implementing this invention provides a personalized experience based on the user's health and emotional state. The system consists of a server, smart glasses which are the user's visual device, and a module that manages the user's activity data.

[0209] The server receives biometric information and lifestyle data transmitted from the user and uses an AI model to generate health guidance based on this data. The generated health guidance is sent to the user's smart glasses and displayed as visual information. This includes suggestions for a healthy lifestyle and customized feedback tailored to the user's emotional state.

[0210] Smart glasses use built-in cameras and sensors to monitor the user's facial expressions and biometric data in real time and estimate their emotional state. An emotion recognition AI model is used for emotion recognition. The emotional data sent to the server is used to provide recommended products and entertainment.

[0211] Users record their daily activities through smart glasses, and this data is analyzed on a server. The analysis results will contribute to future health guidance and improvements in the accuracy of emotion recognition.

[0212] For example, if smart glasses detect stress in the user, the server sends suggestions to the smart glasses recommending relaxing products or calming music playlists. In this way, personalized services based on the user's health and emotional state are provided.

[0213] An example of a prompt is as follows: "We want to develop a system that analyzes the emotional state of customers and suggests relaxation methods using an emotion engine. Please provide specific examples using a generative AI model for an application that can recognize a customer's specific health or emotional state and provide appropriate feedback and suggestions accordingly."

[0214] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0215] Step 1:

[0216] The server continuously receives biometric information and lifestyle data from users. This data includes heart rate, blood pressure, and dietary records. The entered data is stored in a database and passed to the generating AI model.

[0217] Step 2:

[0218] The server uses a generative AI model to analyze received biometric and lifestyle data, generating personalized health guidance for the user. This analysis applies a data processing algorithm based on the input information to generate optimal health advice. The generated health guidance is then ready to be transmitted to the user's visual device.

[0219] Step 3:

[0220] The smart glasses integrated into the device capture the user's facial expressions and biometric data in real time. The data obtained from the camera and sensors is pre-processed within the smart glasses before being sent to the server.

[0221] Step 4:

[0222] The server analyzes facial expression data transmitted from the smart glasses using an emotion recognition AI model to estimate the user's emotional state. This process involves data calculations based on facial changes and other biometric indicators, and assigns emotion labels. This creates the user's emotional profile.

[0223] Step 5:

[0224] The server selects recommended products and entertainment based on the analysis of the user's emotional state. It filters appropriate candidates from the product database and creates a list to send to the user's smart glasses.

[0225] Step 6:

[0226] The user's smart glasses display health guidance and entertainment information sent from a server on a visual display, providing feedback through audio and video. This feedback is delivered in an interactive format that takes into account the user's emotional state.

[0227] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0228] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0229] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0230] [Second Embodiment]

[0231] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0232] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0233] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0234] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0235] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0236] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0237] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0238] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0239] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0240] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0241] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0242] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0243] This invention is a lifestyle support system that enables users to establish healthy lifestyle habits and prevent dementia. This system links a server with the user's terminal and provides personalized advice and management functions tailored to the user's health condition.

[0244] Server Role

[0245] The server receives biometric information and lifestyle data transmitted by the user and records it in a database. Based on this data, a generative artificial intelligence module generates personalized health guidance for each user. The generated health guidance includes recommendations for exercise, diet, intellectual activity, and social participation tailored to the user's condition.

[0246] Furthermore, the server has a reminder management function that generates and sends reminders to the device based on specified times. These reminders support the user's behavior and help them maintain healthy habits.

[0247] Role of the user terminal

[0248] The user terminal receives data sent from the server and displays it for the user. The interface on the terminal is intuitive and designed to allow users to easily check advice and reminders. Based on this information, users can record their daily activities and send that data back to the server, enabling continuous health management.

[0249] User behavior

[0250] Users use their devices daily to follow advice and record their exercise and diet. They can also track their progress graphically through the device's display, allowing them to see their improvement and maintain motivation. User feedback is also incorporated into the system, enabling the provision of even more precise health guidance.

[0251] Specific example

[0252] Specifically, a user receives advice to perform recommended stretches in the morning and records their activity on their device. The server then evaluates the user's exercise habits based on this information and recommends the next action. In the afternoon, a reminder appears on the device, encouraging the user to do some light exercise. In this way, users receive personalized health guidance every day and are supported in putting it into practice.

[0253] This system is expected to enable and maintain a healthy lifestyle optimized for each individual user, thereby contributing to health promotion and dementia prevention.

[0254] The following describes the processing flow.

[0255] Step 1:

[0256] The user uses a device to input basic information about their health status and lifestyle. The device validates this data once and then sends it to the server.

[0257] Step 2:

[0258] The server receives data from users and stores it in the database. This creates a profile for each user.

[0259] Step 3:

[0260] The server uses AI to generate personalized health guidance based on user data. This includes recommendations regarding exercise and diet.

[0261] Step 4:

[0262] The server sends the generated health guidance to the terminal. The terminal displays this to the user and notifies them that new advice is available.

[0263] Step 5:

[0264] The user records their daily activities (e.g., exercise and meals) on their device. The device then sends this activity data to a server.

[0265] Step 6:

[0266] The server receives the user's activity history and records it in a database. The generating AI analyzes this data and evaluates the progress.

[0267] Step 7:

[0268] The server generates feedback for the user based on the evaluation results. The evaluation includes graphical visualizations and is sent to the terminal.

[0269] Step 8:

[0270] The device displays feedback to the user and provides comments to boost motivation. This helps users maintain healthy habits.

[0271] (Example 1)

[0272] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0273] In today's busy lifestyle, it is not easy for individual users to maintain healthy habits. In this context, there is a need for a system that provides comprehensive health management tailored to individual health conditions and encourages improvements in exercise, diet, and lifestyle. However, conventional technology has limitations in effectively utilizing individual user health data and providing personalized advice and reminders.

[0274] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0275] In this invention, the server includes means for collecting user health-related data, means for generating personalized health guidance using intelligent functions based on the health-related data, and means for transmitting the health guidance to the user's communication device. This enables the user to receive health guidance best suited to their lifestyle and to continuously improve their daily activities.

[0276] "Health-related data" refers to information about a user's biological state and lifestyle, and specifically includes data such as heart rate, steps taken, diet, and sleep duration.

[0277] In the field of computer science, "intelligent function" refers to programs and algorithms that analyze collected data and provide individualized guidance or predictions through learning.

[0278] "Personalized health guidance" refers to providing advice on exercise, diet, and lifestyle habits that are tailored to each user's individual health condition and goals, based on their health-related data.

[0279] "Communication equipment" refers to personal devices capable of sending and receiving information, and specifically includes smartphones, tablets, wearable devices, and other similar devices.

[0280] A "notification" is a means of conveying certain information to a user, and is primarily delivered via the user's communication device in the form of push notifications or alerts.

[0281] "Schedule information" refers to data that shows a user's schedule and plans, and includes information stored in calendar applications and schedule management systems.

[0282] "Visual display" refers to presenting information visually through the screens of digital devices, in the form of graphs, charts, text, and other similar formats.

[0283] This invention is a system that supports users in establishing and maintaining a healthy lifestyle. The system receives user data and provides personalized health guidance. As hardware, the communication devices used by the user include smartphones and wearable devices. These devices are used to collect health-related data such as heart rate, steps taken, diet, and sleep duration.

[0284] 1. Role of the Server

[0285] The server receives health-related data sent from users through a secure protocol (e.g., HTTPS) and records it in a database. For data processing, a relational database management system (RDBMS) is used to efficiently manage the data. The data stored in the database is analyzed by a generative AI model. This model is built using machine learning frameworks such as TensorFlow and PyTorch. As a result, the server creates health guidance optimized for the user and sends it to the communication device.

[0286] 2. Role of the Terminal

[0287] The terminal receives health guidance and notifications sent from the server and presents them to the user through an intuitive interface. For example, it displays notifications on the smartphone's display so that the user can quickly confirm the actions. Based on this, the user records their daily activities in detail, and that data is sent back to the server again.

[0288] 3. User Actions

[0289] The user performs activities according to the advice provided using the terminal. For example, it is to perform the recommended stretches and record the results on the terminal. Also, by looking at the progress information graphically visualized by the terminal, the user can check their health status and maintain motivation.

[0290] As a specific example, the user can also make a specific request to the generative AI model using a prompt sentence such as "Propose tomorrow's routine considering my recent exercise habit data. I want appropriate exercise and diet advice to maximize the health effect." Through such interactions, the user can be supported in maintaining their daily health and enhance their motivation for improvement.

[0291] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0292] Step 1:

[0293] Users input health-related data using smartphones or wearable devices. This data includes steps taken, heart rate, diet, and sleep duration. The device receives this data and processes it to convert the input data into a standard format. The output is formatted data ready for transmission.

[0294] Step 2:

[0295] The terminal sends formatted health-related data to the server. The secure HTTPS protocol is used for communication. The server receives this data and records it in a database. Using the received data as input, the server outputs structured health data by storing it in the database.

[0296] Step 3:

[0297] The server starts data analysis using a generative AI model based on the recorded data. It uses user-specific health-related data stored in a database as input. The AI ​​model learns from past data and performs pattern recognition to generate personalized health guidance for each user. The generated health guidance information is obtained as output.

[0298] Step 4:

[0299] The server sends the generated health guidance information to the user's device. Furthermore, it creates a reminder based on the health guidance and sends it at the specified time. The health guidance information is used as input, and the timing of the reminder is determined simultaneously based on its content. The output is the health guidance and reminder displayed on the user's device.

[0300] Step 5:

[0301] The user follows the health guidance provided by the terminal, performs exercises, diets, etc., and records the results on the terminal again. Specific actions include performing the recommended stretches and recording the diet content. This generates new activity data as input, and by sending it to the server, it becomes possible to obtain more individualized advice for subsequent times as output.

[0302] Step 6:

[0303] The server receives the new activity data from the user again and records it in the database. Through this process, the server continuously collects and analyzes data, which is utilized for generating health guidance in the next step. It processes the activity data as input to prepare a foundation for realizing improved health guidance as output.

[0304] (Application Example 1)

[0305] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0306] In modern society, individuals are required to maintain healthy lifestyle habits in their busy daily lives and prevent cognitive decline. However, especially during movement, the opportunities to engage in activities for health and receive intellectual stimulation are limited, making regular health maintenance difficult. It is necessary to solve this problem and effectively provide individualized health guidance and intellectual stimulation even during movement.

[0307] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0308] In this invention, the server includes a device for receiving the user's biometric information and lifestyle activity data, a device for generating personalized health guidance using an intelligent module, and a device for transmitting the health guidance to the user's terminal. This makes it possible to provide guidance and intellectual stimulation for maintaining healthy lifestyle habits even to users who are on the go.

[0309] "Biometric information" refers to data about the user's body, such as heart rate, body temperature, and activity level.

[0310] "Lifestyle activity data" refers to information about a user's daily life, such as their diet, exercise, and sleep.

[0311] An "intelligent module" is an algorithm or program that generates personalized advice based on user data.

[0312] "Health guidance" refers to recommendations regarding exercise, diet, and lifestyle habits provided to maintain and improve the user's health.

[0313] "User terminal" refers to hardware such as a user's mobile device or in-car display used to receive health guidance.

[0314] "Notifications" are reminders and alerts sent to users periodically to encourage them to practice health guidance.

[0315] "Data for providing health guidance and intellectual stimulation during travel" refers to information that provides users with opportunities for exercise and learning while they are in transit.

[0316] A "display device" refers to a device such as a monitor or speaker that conveys the content of health guidance to the user visually and audibly.

[0317] To implement this invention, it is necessary to construct a system in which information is exchanged between a server, a terminal, and a user. The server is responsible for receiving biometric information and lifestyle activity data transmitted by the user and recording it in a database. Based on this recorded information, an intelligent module within the server uses a generated AI model to create personalized health guidance. The generated health guidance includes recommendations for exercise, diet, intellectual activities, and social participation.

[0318] The terminal receives health guidance transmitted from the server and displays it to the user. Examples of terminal use include mobile devices and in-car displays. The interface on the terminal is intuitive and designed so that users can easily check guidance content and reminders. Users can record their daily activities through the terminal and send them back to the server, enabling continuous health management.

[0319] Users engage in healthy activities by following the advice provided by the device in their daily lives. For example, if a user has been sitting for a long time, the device will issue a notification such as, "Let's do some stretching for about 5 minutes." Such notifications are generated by a server that analyzes the user's activity data and uses a generative AI model to present the most appropriate guidance. While on the move, prompts such as, "Here's a quiz about sightseeing spots along your current route," are displayed to stimulate intellectual activity. Specifically, the generative AI model is used with prompts such as, "Generate the next exercise suggestion based on the user's heart rate data."

[0320] The technologies supporting this system include data processing using Python, AI model generation using TensorFlow, and the construction of the user interface for devices using React Native. SQLite is used as the database, and various analytical tools are utilized to track user behavior. In this way, users can receive health guidance regardless of location and achieve a richer lifestyle.

[0321] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0322] Step 1:

[0323] The server receives biometric and activity data from the user's device. This input includes heart rate, exercise levels, and dietary details. The server standardizes this data and records it in a database. A Python script is used to unify data in different formats for data standardization.

[0324] Step 2:

[0325] An intelligent module analyzes data recorded on the server and generates personalized health guidance using a generative AI model. Here, the generative AI model creates exercise and dietary recommendations tailored to the user's health condition based on the prompts it receives. This output is presented in the form of advice sent to the user.

[0326] Step 3:

[0327] The server sends the generated health guidance to the user's terminal. This process uses the TCP / IP protocol for data transfer. The output is displayed on the terminal in a format easily accessible to the user.

[0328] Step 4:

[0329] The device displays the received health guidance to the user. The input is health guidance data from the server, which is output on the device as an interactive interface using React Native. Icons and graphs are used to help the user easily understand the guidance content.

[0330] Step 5:

[0331] Users act based on the health guidance provided and input activity data into their devices. This data includes the duration and type of exercise, as well as the content of their meals. The user's activity history is then sent back to the server to serve as the basis for further analysis.

[0332] Step 6:

[0333] The server analyzes the user's new activity data and generates the next health guidance. This analysis incorporates a feedback loop for continuous improvement. The resulting new health guidance includes progress-based advice and new reminders.

[0334] Step 7:

[0335] The device receives new health guidance and reminders from the server and presents them to the user. When presented, it alerts the user, especially through voice and vibration, to prompt action. This process is repeated, creating a system that continuously supports the user's health.

[0336] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0337] This invention combines a system that provides personalized health guidance based on a user's health status and lifestyle data with an emotion engine that recognizes the user's emotional state. This system operates through a server and the user's terminal, providing the user with optimized health guidance and emotionally responsive feedback.

[0338] Server Role

[0339] The server estimates the user's emotional state using a sentiment engine in addition to the user's biometric and lifestyle data. This involves analyzing the user's input data and activity history, and using AI to recognize emotions. Based on the estimated emotional state, the server further adjusts personalized health guidance and generates guidance content that is appropriate for the user's emotions.

[0340] In addition, the server has a reminder management function that generates reminders based on the user's schedule and sends them to the device. This allows for notifications to be sent at the appropriate time according to the user's emotional state.

[0341] Role of the user terminal

[0342] The user terminal receives health guidance and emotion-sensitive feedback sent from the server and displays it to the user. The interface is flexible and emotionally responsive, providing information tailored to the user's state. Users record their activities through the terminal, and this data is sent to the server to help estimate future emotions.

[0343] User behavior

[0344] Users use their devices daily to record their activities and emotions. The feedback displayed on the device is tailored based on their emotional state, promoting motivation that takes their mood into consideration. The health guidance provided through the analysis of emotional states becomes more personalized and effective, further enhancing the health management of users.

[0345] Specific example

[0346] For example, if a user hasn't gotten enough sleep, the system's emotional engine recognizes this as fatigue or stress. Based on this, the server recommends stretching or meditation to help the user relax and communicates this through the device. At the same time, soothing colors and encouraging words are displayed on the device to help the user relax.

[0347] This system enables personalized health management tailored to the user's emotions, promoting improved health and better management of stress and mental state.

[0348] The following describes the processing flow.

[0349] Step 1:

[0350] Users use their devices to input health information, daily mood, and emotions. The devices collect this data and send it to a server.

[0351] Step 2:

[0352] The server receives biometric information, lifestyle data, and emotional state transmitted by the user and records them in a database.

[0353] Step 3:

[0354] The server uses an emotion engine to estimate the user's emotional state from the received data. This emotional data is estimated by analyzing the correlation between the user's previously recorded emotions and activity data.

[0355] Step 4:

[0356] The AI ​​generation module generates personalized health guidance based on the user's biometric information, lifestyle, and estimated emotional state. This guidance includes content that takes the user's mental state into consideration.

[0357] Step 5:

[0358] The server generates health guidance and sets reminders, sending data to the user's device to send notifications according to the user's schedule.

[0359] Step 6:

[0360] The device displays health guidance and reminder information received from the server to the user. This uses a visually conscious interface with emotionally responsive adjustments.

[0361] Step 7:

[0362] Users record activities in accordance with health guidance and submit feedback on their emotional state. This allows users to continuously engage in experiences that contribute to improving their own health.

[0363] Step 8:

[0364] The server continuously receives newly recorded activity and emotion data, updating the database. This data is then used for future emotion estimation and health guidance.

[0365] This entire process allows users to receive health guidance while also managing their emotions, resulting in a system that supports overall health improvement.

[0366] (Example 2)

[0367] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0368] Traditional health management systems provide health guidance based on users' biometric information and lifestyle data, but they lack guidance that takes psychological states into account, making it difficult to adequately support users' emotions and mental health. Furthermore, if reminder timing is not appropriate for the user's emotional state, it can potentially decrease motivation.

[0369] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0370] In this invention, the server includes means for predicting the user's psychological state, means for generating personalized health guidance, and means for transmitting the health guidance to the user's terminal. This makes it possible to provide optimal health guidance and reminders according to the user's emotional state.

[0371] "Biometric information" refers to measurable data about a user's body, including heart rate, body temperature, and activity level.

[0372] "Lifestyle data" refers to data that shows a user's daily actions and habits, including diet, exercise, and sleep duration.

[0373] "Psychological state" refers to a user's emotions and mental condition, and is evaluated based on factors such as stress levels and mood.

[0374] "Health guidance" refers to specific advice and recommendations provided to improve a user's health, including information on exercise, nutrition, and relaxation techniques.

[0375] "Inference methods" refer to algorithms and technologies used to analyze user data and evaluate their psychological and health status.

[0376] A "reminder" is a notification that prompts a user to take action at a specific time or in a specific situation.

[0377] "Activity history" refers to a record of a user's past activity data, which is used to analyze progress and behavioral patterns.

[0378] This invention is a system that provides personalized health guidance and feedback based on the user's physical and psychological state. The system consists of a server for data analysis and a terminal for user interaction.

[0379] The server aggregates biometric information and lifestyle data sent by the user and uses an AI model to infer the user's psychological state. This AI model is built using, for example, open-source machine learning libraries such as TensorFlow and PyTorch. The data received includes heart rate and activity levels from smartphones and wearable devices, as well as emotional data based on the user's daily self-assessment. Based on this data analysis, the server generates personalized health guidance and registers it as a reminder.

[0380] The device presents health guidance and feedback from the server to the user via a user interface. This interface is developed using HTML and JavaScript technologies to ensure that users can comfortably receive information. Reminders and feedback are tailored to the user's mental state and schedule, and notifications are sent at the appropriate time.

[0381] Users record their daily activity data and mood through their devices. This data is later analyzed and sent to a server to further improve the accuracy of the user's psychological state estimation.

[0382] For example, if a user hasn't gotten enough sleep, the server's emotion engine evaluates this state as stress or fatigue. Based on this, the server generates health advice, such as "Try a 5-minute meditation to relax," and sends it to the device along with a user interface featuring gentle colors.

[0383] Examples of prompt messages include the following:

[0384] "A user recorded their sleep duration last night and found it was two hours shorter than average. Based on this data, how does the emotion engine evaluate the user's emotional state and suggest health guidance?"

[0385] This system enables health support that takes into maximum consideration the user's emotions and behavior, and is expected to improve both their psychological and physical health.

[0386] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0387] Step 1:

[0388] Users input activity data and emotional states via their devices. Specifically, they use smartphones or wearable devices to record biometric information such as heart rate, steps taken, and sleep duration, and also input self-assessments of their mood and stress levels for the day. This information is aggregated on the device.

[0389] Step 2:

[0390] The terminal converts the collected data into a standard format and sends it to the server using a security protocol. The data is formatted in JSON format and encrypted using SSL / TLS before transmission. The server receives and decrypts it.

[0391] Step 3:

[0392] The server inputs the received user biometric information and lifestyle data into an AI model for data analysis. Specifically, it uses an emotion estimation algorithm using Python (for example, a model built with TensorFlow) to estimate the user's current psychological state. The server outputs the user's emotional state as the analysis result.

[0393] Step 4:

[0394] The server generates personalized health guidance based on analyzed emotional states and biometric data. For example, it generates specific advice on recommended exercise, nutrition, and relaxation. The generated guidance is output as actions to take next and areas for improvement. The server optimizes this based on the user's profile.

[0395] Step 5:

[0396] The server sends the generated health guidance to the user's device. Simultaneously, it utilizes a scheduling management system to set reminders and coordinate notifications to the device at appropriate times. These reminders might be sent for scheduled meditation or exercise sessions, for example.

[0397] Step 6:

[0398] The device visually presents received health guidance to the user. It uses a flexible interface, for example, displaying recommendations against a light-colored background. It also features a function to play music in the background to promote relaxation.

[0399] Step 7:

[0400] Users follow the health guidance presented on the device and decide whether to incorporate it into their daily routines. They also input feedback into the device, and this data is used in the next analysis. Information based on the feedback is sent from the device to the server and reflected in the next health guidance and psychological state estimation.

[0401] (Application Example 2)

[0402] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0403] In current brick-and-mortar stores, it is difficult to provide personalized experiences tailored to each customer's health and emotional state. This can lead to customers not receiving services or products suitable for their health condition, potentially resulting in lower satisfaction. Furthermore, traditional systems are unable to effectively provide feedback based on customers' emotions and health status.

[0404] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0405] In this invention, the server includes means for receiving the user's biometric information and lifestyle data, means for generating personalized health guidance using an artificial intelligence module based on the biometric information and lifestyle data, and means for detecting the user's emotional state. This makes it possible to provide personalized health guidance and emotionally responsive feedback to customers in real time.

[0406] "Biometric information" refers to data that indicates the user's physical condition, including measurements such as heart rate, blood pressure, and body temperature.

[0407] "Lifestyle data" refers to information about a user's daily actions and habits, including diet, exercise, and sleep patterns.

[0408] A "generative artificial intelligence module" is a program with artificial intelligence capabilities that analyzes input data and generates personalized instruction content.

[0409] "Health guidance" refers to specific advice and guidance provided to improve or maintain the user's health.

[0410] "User visual devices" refer to devices used to present information to users visually, and include smart glasses and display devices.

[0411] A "notification" is a system message that informs a user of specific information, and functions as a reminder or alert.

[0412] "Activity history" refers to a record of a user's past actions and trends, and is used to analyze behavioral patterns.

[0413] "Emotional state" refers to the user's psychological and emotional condition, including joy, sadness, stress, etc.

[0414] "Recommended products and entertainment" refers to products and entertainment content that are suggested according to the individual needs of the user.

[0415] The system for implementing this invention provides a personalized experience based on the user's health and emotional state. The system consists of a server, smart glasses which are the user's visual device, and a module that manages the user's activity data.

[0416] The server receives biometric information and lifestyle data transmitted from the user and uses an AI model to generate health guidance based on this data. The generated health guidance is sent to the user's smart glasses and displayed as visual information. This includes suggestions for a healthy lifestyle and customized feedback tailored to the user's emotional state.

[0417] Smart glasses use built-in cameras and sensors to monitor the user's facial expressions and biometric data in real time and estimate their emotional state. An emotion recognition AI model is used for emotion recognition. The emotional data sent to the server is used to provide recommended products and entertainment.

[0418] Users record their daily activities through smart glasses, and this data is analyzed on a server. The analysis results will contribute to future health guidance and improvements in the accuracy of emotion recognition.

[0419] For example, if smart glasses detect stress in the user, the server sends suggestions to the smart glasses recommending relaxing products or calming music playlists. In this way, personalized services based on the user's health and emotional state are provided.

[0420] An example of a prompt is as follows: "We want to develop a system that analyzes the emotional state of customers and suggests relaxation methods using an emotion engine. Please provide specific examples using a generative AI model for an application that can recognize a customer's specific health or emotional state and provide appropriate feedback and suggestions accordingly."

[0421] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0422] Step 1:

[0423] The server continuously receives biometric information and lifestyle data from users. This data includes heart rate, blood pressure, and dietary records. The entered data is stored in a database and passed to the generating AI model.

[0424] Step 2:

[0425] The server uses a generative AI model to analyze received biometric and lifestyle data, generating personalized health guidance for the user. This analysis applies a data processing algorithm based on the input information to generate optimal health advice. The generated health guidance is then ready to be transmitted to the user's visual device.

[0426] Step 3:

[0427] The smart glasses integrated into the device capture the user's facial expressions and biometric data in real time. The data obtained from the camera and sensors is pre-processed within the smart glasses before being sent to the server.

[0428] Step 4:

[0429] The server analyzes facial expression data transmitted from the smart glasses using an emotion recognition AI model to estimate the user's emotional state. This process involves data calculations based on facial changes and other biometric indicators, and assigns emotion labels. This creates the user's emotional profile.

[0430] Step 5:

[0431] The server selects recommended products and entertainment based on the analysis of the user's emotional state. It filters appropriate candidates from the product database and creates a list to send to the user's smart glasses.

[0432] Step 6:

[0433] The user's smart glasses display health guidance and entertainment information sent from a server on a visual display, providing feedback through audio and video. This feedback is delivered in an interactive format that takes into account the user's emotional state.

[0434] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0435] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0436] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0437] [Third Embodiment]

[0438] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0439] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0440] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0441] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0442] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0443] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0444] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0445] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0446] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0447] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0448] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0449] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0450] This invention is a lifestyle support system that enables users to establish healthy lifestyle habits and prevent dementia. This system links a server with the user's terminal and provides personalized advice and management functions tailored to the user's health condition.

[0451] Server Role

[0452] The server receives biometric information and lifestyle data transmitted by the user and records it in a database. Based on this data, a generative artificial intelligence module generates personalized health guidance for each user. The generated health guidance includes recommendations for exercise, diet, intellectual activity, and social participation tailored to the user's condition.

[0453] Furthermore, the server has a reminder management function that generates and sends reminders to the device based on specified times. These reminders support the user's behavior and help them maintain healthy habits.

[0454] Role of the user terminal

[0455] The user terminal receives data sent from the server and displays it for the user. The interface on the terminal is intuitive and designed to allow users to easily check advice and reminders. Based on this information, users can record their daily activities and send that data back to the server, enabling continuous health management.

[0456] User behavior

[0457] Users use their devices daily to follow advice and record their exercise and diet. They can also track their progress graphically through the device's display, allowing them to see their improvement and maintain motivation. User feedback is also incorporated into the system, enabling the provision of even more precise health guidance.

[0458] Specific example

[0459] Specifically, a user receives advice to perform recommended stretches in the morning and records their activity on their device. The server then evaluates the user's exercise habits based on this information and recommends the next action. In the afternoon, a reminder appears on the device, encouraging the user to do some light exercise. In this way, users receive personalized health guidance every day and are supported in putting it into practice.

[0460] This system is expected to enable and maintain a healthy lifestyle optimized for each individual user, thereby contributing to health promotion and dementia prevention.

[0461] The following describes the processing flow.

[0462] Step 1:

[0463] The user uses a device to input basic information about their health status and lifestyle. The device validates this data once and then sends it to the server.

[0464] Step 2:

[0465] The server receives data from users and stores it in the database. This creates a profile for each user.

[0466] Step 3:

[0467] The server uses AI to generate personalized health guidance based on user data. This includes recommendations regarding exercise and diet.

[0468] Step 4:

[0469] The server sends the generated health guidance to the terminal. The terminal displays this to the user and notifies them that new advice is available.

[0470] Step 5:

[0471] The user records their daily activities (e.g., exercise and meals) on their device. The device then sends this activity data to a server.

[0472] Step 6:

[0473] The server receives the user's activity history and records it in a database. The generating AI analyzes this data and evaluates the progress.

[0474] Step 7:

[0475] The server generates feedback for the user based on the evaluation results. The evaluation includes graphical visualizations and is sent to the terminal.

[0476] Step 8:

[0477] The device displays feedback to the user and provides comments to boost motivation. This helps users maintain healthy habits.

[0478] (Example 1)

[0479] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0480] In today's busy lifestyle, it is not easy for individual users to maintain healthy habits. In this context, there is a need for a system that provides comprehensive health management tailored to individual health conditions and encourages improvements in exercise, diet, and lifestyle. However, conventional technology has limitations in effectively utilizing individual user health data and providing personalized advice and reminders.

[0481] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0482] In this invention, the server includes means for collecting user health-related data, means for generating personalized health guidance using intelligent functions based on the health-related data, and means for transmitting the health guidance to the user's communication device. This enables the user to receive health guidance best suited to their lifestyle and to continuously improve their daily activities.

[0483] "Health-related data" refers to information about a user's biological state and lifestyle, and specifically includes data such as heart rate, steps taken, diet, and sleep duration.

[0484] In the field of computer science, "intelligent function" refers to programs and algorithms that analyze collected data and provide individualized guidance or predictions through learning.

[0485] "Personalized health guidance" refers to providing advice on exercise, diet, and lifestyle habits that are tailored to each user's individual health condition and goals, based on their health-related data.

[0486] "Communication equipment" refers to personal devices capable of sending and receiving information, and specifically includes smartphones, tablets, wearable devices, and other similar devices.

[0487] A "notification" is a means of conveying certain information to a user, and is primarily delivered via the user's communication device in the form of push notifications or alerts.

[0488] "Schedule information" refers to data that shows a user's schedule and plans, and includes information stored in calendar applications and schedule management systems.

[0489] "Visual display" refers to presenting information visually through the screens of digital devices, in the form of graphs, charts, text, and other similar formats.

[0490] This invention is a system that supports users in establishing and maintaining a healthy lifestyle. The system receives user data and provides personalized health guidance. As hardware, the communication devices used by the user include smartphones and wearable devices. These devices are used to collect health-related data such as heart rate, steps taken, diet, and sleep duration.

[0491] 1. Server Role

[0492] The server receives health-related data sent by users via a secure protocol (e.g., HTTPS) and records it in a database. A relational database management system (RDBMS) is used for data processing to efficiently manage the data. The data stored in the database is analyzed by a generative AI model. This model is built using machine learning frameworks such as TensorFlow and PyTorch. This allows the server to create personalized health guidance for users and send it to their communication devices.

[0493] 2. The role of the terminal

[0494] The device receives health guidance and notifications sent from the server and presents them to the user through an intuitive interface. For example, it displays notifications on the smartphone's screen, allowing the user to immediately see what action to take. Based on this, the user records their daily activities in detail, and this data is sent back to the server.

[0495] 3. User behavior

[0496] Users perform activities based on advice provided via the device. For example, they might perform recommended stretches and record the results on the device. Furthermore, by viewing progress information graphically visualized on the device, they can monitor their health status and maintain motivation.

[0497] For example, users can make specific requests to the generating AI model using prompts such as, "Consider my recent exercise habits data and suggest a routine for tomorrow. I would like advice on appropriate exercise and diet to maximize health benefits." Through such interactions, users can be supported in maintaining their daily health and increase their motivation to improve it.

[0498] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0499] Step 1:

[0500] Users input health-related data using smartphones or wearable devices. This data includes steps taken, heart rate, diet, and sleep duration. The device receives this data and processes it to convert the input data into a standard format. The output is formatted data ready for transmission.

[0501] Step 2:

[0502] The terminal sends formatted health-related data to the server. The secure HTTPS protocol is used for communication. The server receives this data and records it in a database. Using the received data as input, the server outputs structured health data by storing it in the database.

[0503] Step 3:

[0504] The server starts data analysis using a generative AI model based on the recorded data. It uses user-specific health-related data stored in a database as input. The AI ​​model learns from past data and performs pattern recognition to generate personalized health guidance for each user. The generated health guidance information is obtained as output.

[0505] Step 4:

[0506] The server sends the generated health guidance information to the user's device. Furthermore, it creates a reminder based on the health guidance and sends it at the specified time. The health guidance information is used as input, and the timing of the reminder is determined simultaneously based on its content. The output is the health guidance and reminder displayed on the user's device.

[0507] Step 5:

[0508] Users follow the health guidance provided on their device, engaging in activities such as exercise and diet, and recording the results on the device again. Specific actions include performing recommended stretches and recording meal details. This generates new activity data as input, which is sent to the server, allowing for further personalized advice to be provided as output for future sessions.

[0509] Step 6:

[0510] The server receives new activity data from the user and records it in the database. Through this process, the server continuously collects and analyzes data, which is then used to generate health guidance in the next step. It processes activity data as input and lays the foundation for achieving improved health guidance as output.

[0511] (Application Example 1)

[0512] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0513] In modern society, individuals are expected to maintain healthy lifestyles and prevent cognitive decline amidst their busy daily lives. However, opportunities for health-promoting activities and intellectual stimulation are limited, especially while traveling, making regular health maintenance difficult. This problem needs to be solved, and personalized health guidance and intellectual stimulation must be provided effectively even while on the go.

[0514] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0515] In this invention, the server includes a device for receiving the user's biometric information and lifestyle activity data, a device for generating personalized health guidance using an intelligent module, and a device for transmitting the health guidance to the user's terminal. This makes it possible to provide guidance and intellectual stimulation for maintaining healthy lifestyle habits even to users who are on the go.

[0516] "Biometric information" refers to data about the user's body, such as heart rate, body temperature, and activity level.

[0517] "Lifestyle activity data" refers to information about a user's daily life, such as their diet, exercise, and sleep.

[0518] An "intelligent module" is an algorithm or program that generates personalized advice based on user data.

[0519] "Health guidance" refers to recommendations regarding exercise, diet, and lifestyle habits provided to maintain and improve the user's health.

[0520] "User terminal" refers to hardware such as a user's mobile device or in-car display used to receive health guidance.

[0521] "Notifications" are reminders and alerts sent to users periodically to encourage them to practice health guidance.

[0522] "Data for providing health guidance and intellectual stimulation during travel" refers to information that provides users with opportunities for exercise and learning while they are in transit.

[0523] A "display device" refers to a device such as a monitor or speaker that conveys the content of health guidance to the user visually and audibly.

[0524] To implement this invention, it is necessary to construct a system in which information is exchanged between a server, a terminal, and a user. The server is responsible for receiving biometric information and lifestyle activity data transmitted by the user and recording it in a database. Based on this recorded information, an intelligent module within the server uses a generated AI model to create personalized health guidance. The generated health guidance includes recommendations for exercise, diet, intellectual activities, and social participation.

[0525] The terminal receives health guidance transmitted from the server and displays it to the user. Examples of terminal use include mobile devices and in-car displays. The interface on the terminal is intuitive and designed so that users can easily check guidance content and reminders. Users can record their daily activities through the terminal and send them back to the server, enabling continuous health management.

[0526] Users engage in healthy activities by following the advice provided by the device in their daily lives. For example, if a user has been sitting for a long time, the device will issue a notification such as, "Let's do some stretching for about 5 minutes." Such notifications are generated by a server that analyzes the user's activity data and uses a generative AI model to present the most appropriate guidance. While on the move, prompts such as, "Here's a quiz about sightseeing spots along your current route," are displayed to stimulate intellectual activity. Specifically, the generative AI model is used with prompts such as, "Generate the next exercise suggestion based on the user's heart rate data."

[0527] The technologies supporting this system include data processing using Python, AI model generation using TensorFlow, and the construction of the user interface for devices using React Native. SQLite is used as the database, and various analytical tools are utilized to track user behavior. In this way, users can receive health guidance regardless of location and achieve a richer lifestyle.

[0528] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0529] Step 1:

[0530] The server receives biometric and activity data from the user's device. This input includes heart rate, exercise levels, and dietary details. The server standardizes this data and records it in a database. A Python script is used to unify data in different formats for data standardization.

[0531] Step 2:

[0532] An intelligent module analyzes data recorded on the server and generates personalized health guidance using a generative AI model. Here, the generative AI model creates exercise and dietary recommendations tailored to the user's health condition based on the prompts it receives. This output is presented in the form of advice sent to the user.

[0533] Step 3:

[0534] The server sends the generated health guidance to the user's terminal. This process uses the TCP / IP protocol for data transfer. The output is displayed on the terminal in a format easily accessible to the user.

[0535] Step 4:

[0536] The device displays the received health guidance to the user. The input is health guidance data from the server, which is output on the device as an interactive interface using React Native. Icons and graphs are used to help the user easily understand the guidance content.

[0537] Step 5:

[0538] Users act based on the health guidance provided and input activity data into their devices. This data includes the duration and type of exercise, as well as the content of their meals. The user's activity history is then sent back to the server to serve as the basis for further analysis.

[0539] Step 6:

[0540] The server analyzes the user's new activity data and generates the next health guidance. This analysis incorporates a feedback loop for continuous improvement. The resulting new health guidance includes progress-based advice and new reminders.

[0541] Step 7:

[0542] The device receives new health guidance and reminders from the server and presents them to the user. When presented, it alerts the user, especially through voice and vibration, to prompt action. This process is repeated, creating a system that continuously supports the user's health.

[0543] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0544] This invention combines a system that provides personalized health guidance based on a user's health status and lifestyle data with an emotion engine that recognizes the user's emotional state. This system operates through a server and the user's terminal, providing the user with optimized health guidance and emotionally responsive feedback.

[0545] Server Role

[0546] The server estimates the user's emotional state using a sentiment engine in addition to the user's biometric and lifestyle data. This involves analyzing the user's input data and activity history, and using AI to recognize emotions. Based on the estimated emotional state, the server further adjusts personalized health guidance and generates guidance content that is appropriate for the user's emotions.

[0547] In addition, the server has a reminder management function that generates reminders based on the user's schedule and sends them to the device. This allows for notifications to be sent at the appropriate time according to the user's emotional state.

[0548] Role of the user terminal

[0549] The user terminal receives health guidance and emotion-sensitive feedback sent from the server and displays it to the user. The interface is flexible and emotionally responsive, providing information tailored to the user's state. Users record their activities through the terminal, and this data is sent to the server to help estimate future emotions.

[0550] User behavior

[0551] Users use their devices daily to record their activities and emotions. The feedback displayed on the device is tailored based on their emotional state, promoting motivation that takes their mood into consideration. The health guidance provided through the analysis of emotional states becomes more personalized and effective, further enhancing the health management of users.

[0552] Specific example

[0553] For example, if a user hasn't gotten enough sleep, the system's emotional engine recognizes this as fatigue or stress. Based on this, the server recommends stretching or meditation to help the user relax and communicates this through the device. At the same time, soothing colors and encouraging words are displayed on the device to help the user relax.

[0554] This system enables personalized health management tailored to the user's emotions, promoting improved health and better management of stress and mental state.

[0555] The following describes the processing flow.

[0556] Step 1:

[0557] Users use their devices to input health information, daily mood, and emotions. The devices collect this data and send it to a server.

[0558] Step 2:

[0559] The server receives biometric information, lifestyle data, and emotional state transmitted by the user and records them in a database.

[0560] Step 3:

[0561] The server uses an emotion engine to estimate the user's emotional state from the received data. This emotional data is estimated by analyzing the correlation between the user's previously recorded emotions and activity data.

[0562] Step 4:

[0563] The AI ​​generation module generates personalized health guidance based on the user's biometric information, lifestyle, and estimated emotional state. This guidance includes content that takes the user's mental state into consideration.

[0564] Step 5:

[0565] The server generates health guidance and sets reminders, sending data to the user's device to send notifications according to the user's schedule.

[0566] Step 6:

[0567] The device displays health guidance and reminder information received from the server to the user. This uses a visually conscious interface with emotionally responsive adjustments.

[0568] Step 7:

[0569] Users record activities in accordance with health guidance and submit feedback on their emotional state. This allows users to continuously engage in experiences that contribute to improving their own health.

[0570] Step 8:

[0571] The server continuously receives newly recorded activity and emotion data, updating the database. This data is then used for future emotion estimation and health guidance.

[0572] This entire process allows users to receive health guidance while also managing their emotions, resulting in a system that supports overall health improvement.

[0573] (Example 2)

[0574] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0575] Traditional health management systems provide health guidance based on users' biometric information and lifestyle data, but they lack guidance that takes psychological states into account, making it difficult to adequately support users' emotions and mental health. Furthermore, if reminder timing is not appropriate for the user's emotional state, it can potentially decrease motivation.

[0576] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0577] In this invention, the server includes means for predicting the user's psychological state, means for generating personalized health guidance, and means for transmitting the health guidance to the user's terminal. This makes it possible to provide optimal health guidance and reminders according to the user's emotional state.

[0578] "Biometric information" refers to measurable data about a user's body, including heart rate, body temperature, and activity level.

[0579] "Lifestyle data" refers to data that shows a user's daily actions and habits, including diet, exercise, and sleep duration.

[0580] "Psychological state" refers to a user's emotions and mental condition, and is evaluated based on factors such as stress levels and mood.

[0581] "Health guidance" refers to specific advice and recommendations provided to improve a user's health, including information on exercise, nutrition, and relaxation techniques.

[0582] "Inference methods" refer to algorithms and technologies used to analyze user data and evaluate their psychological and health status.

[0583] A "reminder" is a notification that prompts a user to take action at a specific time or in a specific situation.

[0584] "Activity history" refers to a record of a user's past activity data, which is used to analyze progress and behavioral patterns.

[0585] This invention is a system that provides personalized health guidance and feedback based on the user's physical and psychological state. The system consists of a server for data analysis and a terminal for user interaction.

[0586] The server aggregates biometric information and lifestyle data sent by the user and uses an AI model to infer the user's psychological state. This AI model is built using, for example, open-source machine learning libraries such as TensorFlow and PyTorch. The data received includes heart rate and activity levels from smartphones and wearable devices, as well as emotional data based on the user's daily self-assessment. Based on this data analysis, the server generates personalized health guidance and registers it as a reminder.

[0587] The device presents health guidance and feedback from the server to the user via a user interface. This interface is developed using HTML and JavaScript technologies to ensure that users can comfortably receive information. Reminders and feedback are tailored to the user's mental state and schedule, and notifications are sent at the appropriate time.

[0588] Users record their daily activity data and mood through their devices. This data is later analyzed and sent to a server to further improve the accuracy of the user's psychological state estimation.

[0589] For example, if a user hasn't gotten enough sleep, the server's emotion engine evaluates this state as stress or fatigue. Based on this, the server generates health advice, such as "Try a 5-minute meditation to relax," and sends it to the device along with a user interface featuring gentle colors.

[0590] Examples of prompt messages include the following:

[0591] "A user recorded their sleep duration last night and found it was two hours shorter than average. Based on this data, how does the emotion engine evaluate the user's emotional state and suggest health guidance?"

[0592] This system enables health support that takes into maximum consideration the user's emotions and behavior, and is expected to improve both their psychological and physical health.

[0593] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0594] Step 1:

[0595] Users input activity data and emotional states via their devices. Specifically, they use smartphones or wearable devices to record biometric information such as heart rate, steps taken, and sleep duration, and also input self-assessments of their mood and stress levels for the day. This information is aggregated on the device.

[0596] Step 2:

[0597] The terminal converts the collected data into a standard format and sends it to the server using a security protocol. The data is formatted in JSON format and encrypted using SSL / TLS before transmission. The server receives and decrypts it.

[0598] Step 3:

[0599] The server inputs the received user biometric information and lifestyle data into an AI model for data analysis. Specifically, it uses an emotion estimation algorithm using Python (for example, a model built with TensorFlow) to estimate the user's current psychological state. The server outputs the user's emotional state as the analysis result.

[0600] Step 4:

[0601] The server generates personalized health guidance based on analyzed emotional states and biometric data. For example, it generates specific advice on recommended exercise, nutrition, and relaxation. The generated guidance is output as actions to take next and areas for improvement. The server optimizes this based on the user's profile.

[0602] Step 5:

[0603] The server sends the generated health guidance to the user's device. Simultaneously, it utilizes a scheduling management system to set reminders and coordinate notifications to the device at appropriate times. These reminders might be sent for scheduled meditation or exercise sessions, for example.

[0604] Step 6:

[0605] The device visually presents received health guidance to the user. It uses a flexible interface, for example, displaying recommendations against a light-colored background. It also features a function to play music in the background to promote relaxation.

[0606] Step 7:

[0607] Users follow the health guidance presented on the device and decide whether to incorporate it into their daily routines. They also input feedback into the device, and this data is used in the next analysis. Information based on the feedback is sent from the device to the server and reflected in the next health guidance and psychological state estimation.

[0608] (Application Example 2)

[0609] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0610] In current brick-and-mortar stores, it is difficult to provide personalized experiences tailored to each customer's health and emotional state. This can lead to customers not receiving services or products suitable for their health condition, potentially resulting in lower satisfaction. Furthermore, traditional systems are unable to effectively provide feedback based on customers' emotions and health status.

[0611] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0612] In this invention, the server includes means for receiving the user's biometric information and lifestyle data, means for generating personalized health guidance using an artificial intelligence module based on the biometric information and lifestyle data, and means for detecting the user's emotional state. This makes it possible to provide personalized health guidance and emotionally responsive feedback to customers in real time.

[0613] "Biometric information" refers to data that indicates the user's physical condition, including measurements such as heart rate, blood pressure, and body temperature.

[0614] "Lifestyle data" refers to information about a user's daily actions and habits, including diet, exercise, and sleep patterns.

[0615] A "generative artificial intelligence module" is a program with artificial intelligence capabilities that analyzes input data and generates personalized instruction content.

[0616] "Health guidance" refers to specific advice and guidance provided to improve or maintain the user's health.

[0617] "User visual devices" refer to devices used to present information to users visually, and include smart glasses and display devices.

[0618] A "notification" is a system message that informs a user of specific information, and functions as a reminder or alert.

[0619] "Activity history" refers to a record of a user's past actions and trends, and is used to analyze behavioral patterns.

[0620] "Emotional state" refers to the user's psychological and emotional condition, including joy, sadness, stress, etc.

[0621] "Recommended products and entertainment" refers to products and entertainment content that are suggested according to the individual needs of the user.

[0622] The system for implementing this invention provides a personalized experience based on the user's health and emotional state. The system consists of a server, smart glasses which are the user's visual device, and a module that manages the user's activity data.

[0623] The server receives biometric information and lifestyle data transmitted from the user and uses an AI model to generate health guidance based on this data. The generated health guidance is sent to the user's smart glasses and displayed as visual information. This includes suggestions for a healthy lifestyle and customized feedback tailored to the user's emotional state.

[0624] Smart glasses use built-in cameras and sensors to monitor the user's facial expressions and biometric data in real time and estimate their emotional state. An emotion recognition AI model is used for emotion recognition. The emotional data sent to the server is used to provide recommended products and entertainment.

[0625] Users record their daily activities through smart glasses, and this data is analyzed on a server. The analysis results will contribute to future health guidance and improvements in the accuracy of emotion recognition.

[0626] For example, if smart glasses detect stress in the user, the server sends suggestions to the smart glasses recommending relaxing products or calming music playlists. In this way, personalized services based on the user's health and emotional state are provided.

[0627] An example of a prompt is as follows: "We want to develop a system that analyzes the emotional state of customers and suggests relaxation methods using an emotion engine. Please provide specific examples using a generative AI model for an application that can recognize a customer's specific health or emotional state and provide appropriate feedback and suggestions accordingly."

[0628] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0629] Step 1:

[0630] The server continuously receives biometric information and lifestyle data from users. This data includes heart rate, blood pressure, and dietary records. The entered data is stored in a database and passed to the generating AI model.

[0631] Step 2:

[0632] The server uses a generative AI model to analyze received biometric and lifestyle data, generating personalized health guidance for the user. This analysis applies a data processing algorithm based on the input information to generate optimal health advice. The generated health guidance is then ready to be transmitted to the user's visual device.

[0633] Step 3:

[0634] The smart glasses integrated into the device capture the user's facial expressions and biometric data in real time. The data obtained from the camera and sensors is pre-processed within the smart glasses before being sent to the server.

[0635] Step 4:

[0636] The server analyzes facial expression data transmitted from the smart glasses using an emotion recognition AI model to estimate the user's emotional state. This process involves data calculations based on facial changes and other biometric indicators, and assigns emotion labels. This creates the user's emotional profile.

[0637] Step 5:

[0638] The server selects recommended products and entertainment based on the analysis of the user's emotional state. It filters appropriate candidates from the product database and creates a list to send to the user's smart glasses.

[0639] Step 6:

[0640] The user's smart glasses display health guidance and entertainment information sent from a server on a visual display, providing feedback through audio and video. This feedback is delivered in an interactive format that takes into account the user's emotional state.

[0641] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0642] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0643] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0644] [Fourth Embodiment]

[0645] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0646] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0647] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0648] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0649] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0650] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0651] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0652] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0653] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0654] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0655] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0656] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0657] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0658] This invention is a lifestyle support system that enables users to establish healthy lifestyle habits and prevent dementia. This system links a server with the user's terminal and provides personalized advice and management functions tailored to the user's health condition.

[0659] Server Role

[0660] The server receives biometric information and lifestyle data transmitted by the user and records it in a database. Based on this data, a generative artificial intelligence module generates personalized health guidance for each user. The generated health guidance includes recommendations for exercise, diet, intellectual activity, and social participation tailored to the user's condition.

[0661] Furthermore, the server has a reminder management function that generates and sends reminders to the device based on specified times. These reminders support the user's behavior and help them maintain healthy habits.

[0662] Role of the user terminal

[0663] The user terminal receives data sent from the server and displays it for the user. The interface on the terminal is intuitive and designed to allow users to easily check advice and reminders. Based on this information, users can record their daily activities and send that data back to the server, enabling continuous health management.

[0664] User behavior

[0665] Users use their devices daily to follow advice and record their exercise and diet. They can also track their progress graphically through the device's display, allowing them to see their improvement and maintain motivation. User feedback is also incorporated into the system, enabling the provision of even more precise health guidance.

[0666] Specific example

[0667] Specifically, a user receives advice to perform recommended stretches in the morning and records their activity on their device. The server then evaluates the user's exercise habits based on this information and recommends the next action. In the afternoon, a reminder appears on the device, encouraging the user to do some light exercise. In this way, users receive personalized health guidance every day and are supported in putting it into practice.

[0668] This system is expected to enable and maintain a healthy lifestyle optimized for each individual user, thereby contributing to health promotion and dementia prevention.

[0669] The following describes the processing flow.

[0670] Step 1:

[0671] The user uses a device to input basic information about their health status and lifestyle. The device validates this data once and then sends it to the server.

[0672] Step 2:

[0673] The server receives data from users and stores it in the database. This creates a profile for each user.

[0674] Step 3:

[0675] The server uses AI to generate personalized health guidance based on user data. This includes recommendations regarding exercise and diet.

[0676] Step 4:

[0677] The server sends the generated health guidance to the terminal. The terminal displays this to the user and notifies them that new advice is available.

[0678] Step 5:

[0679] The user records their daily activities (e.g., exercise and meals) on their device. The device then sends this activity data to a server.

[0680] Step 6:

[0681] The server receives the user's activity history and records it in a database. The generating AI analyzes this data and evaluates the progress.

[0682] Step 7:

[0683] The server generates feedback for the user based on the evaluation results. The evaluation includes graphical visualizations and is sent to the terminal.

[0684] Step 8:

[0685] The device displays feedback to the user and provides comments to boost motivation. This helps users maintain healthy habits.

[0686] (Example 1)

[0687] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0688] In today's busy lifestyle, it is not easy for individual users to maintain healthy habits. In this context, there is a need for a system that provides comprehensive health management tailored to individual health conditions and encourages improvements in exercise, diet, and lifestyle. However, conventional technology has limitations in effectively utilizing individual user health data and providing personalized advice and reminders.

[0689] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0690] In this invention, the server includes means for collecting user health-related data, means for generating personalized health guidance using intelligent functions based on the health-related data, and means for transmitting the health guidance to the user's communication device. This enables the user to receive health guidance best suited to their lifestyle and to continuously improve their daily activities.

[0691] "Health-related data" refers to information about a user's biological state and lifestyle, and specifically includes data such as heart rate, steps taken, diet, and sleep duration.

[0692] In the field of computer science, "intelligent function" refers to programs and algorithms that analyze collected data and provide individualized guidance or predictions through learning.

[0693] "Personalized health guidance" refers to providing advice on exercise, diet, and lifestyle habits that are tailored to each user's individual health condition and goals, based on their health-related data.

[0694] "Communication equipment" refers to personal devices capable of sending and receiving information, and specifically includes smartphones, tablets, wearable devices, and other similar devices.

[0695] A "notification" is a means of conveying certain information to a user, and is primarily delivered via the user's communication device in the form of push notifications or alerts.

[0696] "Schedule information" refers to data that shows a user's schedule and plans, and includes information stored in calendar applications and schedule management systems.

[0697] "Visual display" refers to presenting information visually through the screens of digital devices, in the form of graphs, charts, text, and other similar formats.

[0698] This invention is a system that supports users in establishing and maintaining a healthy lifestyle. The system receives user data and provides personalized health guidance. As hardware, the communication devices used by the user include smartphones and wearable devices. These devices are used to collect health-related data such as heart rate, steps taken, diet, and sleep duration.

[0699] 1. Server Role

[0700] The server receives health-related data sent by users via a secure protocol (e.g., HTTPS) and records it in a database. A relational database management system (RDBMS) is used for data processing to efficiently manage the data. The data stored in the database is analyzed by a generative AI model. This model is built using machine learning frameworks such as TensorFlow and PyTorch. This allows the server to create personalized health guidance for users and send it to their communication devices.

[0701] 2. The role of the terminal

[0702] The device receives health guidance and notifications sent from the server and presents them to the user through an intuitive interface. For example, it displays notifications on the smartphone's screen, allowing the user to immediately see what action to take. Based on this, the user records their daily activities in detail, and this data is sent back to the server.

[0703] 3. User behavior

[0704] Users perform activities based on advice provided via the device. For example, they might perform recommended stretches and record the results on the device. Furthermore, by viewing progress information graphically visualized on the device, they can monitor their health status and maintain motivation.

[0705] For example, users can make specific requests to the generating AI model using prompts such as, "Consider my recent exercise habits data and suggest a routine for tomorrow. I would like advice on appropriate exercise and diet to maximize health benefits." Through such interactions, users can be supported in maintaining their daily health and increase their motivation to improve it.

[0706] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0707] Step 1:

[0708] Users input health-related data using smartphones or wearable devices. This data includes steps taken, heart rate, diet, and sleep duration. The device receives this data and processes it to convert the input data into a standard format. The output is formatted data ready for transmission.

[0709] Step 2:

[0710] The terminal sends formatted health-related data to the server. The secure HTTPS protocol is used for communication. The server receives this data and records it in a database. Using the received data as input, the server outputs structured health data by storing it in the database.

[0711] Step 3:

[0712] The server starts data analysis using a generative AI model based on the recorded data. It uses user-specific health-related data stored in a database as input. The AI ​​model learns from past data and performs pattern recognition to generate personalized health guidance for each user. The generated health guidance information is obtained as output.

[0713] Step 4:

[0714] The server sends the generated health guidance information to the user's device. Furthermore, it creates a reminder based on the health guidance and sends it at the specified time. The health guidance information is used as input, and the timing of the reminder is determined simultaneously based on its content. The output is the health guidance and reminder displayed on the user's device.

[0715] Step 5:

[0716] Users follow the health guidance provided on their device, engaging in activities such as exercise and diet, and recording the results on the device again. Specific actions include performing recommended stretches and recording meal details. This generates new activity data as input, which is sent to the server, allowing for further personalized advice to be provided as output for future sessions.

[0717] Step 6:

[0718] The server receives new activity data from the user and records it in the database. Through this process, the server continuously collects and analyzes data, which is then used to generate health guidance in the next step. It processes activity data as input and lays the foundation for achieving improved health guidance as output.

[0719] (Application Example 1)

[0720] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0721] In modern society, individuals are expected to maintain healthy lifestyles and prevent cognitive decline amidst their busy daily lives. However, opportunities for health-promoting activities and intellectual stimulation are limited, especially while traveling, making regular health maintenance difficult. This problem needs to be solved, and personalized health guidance and intellectual stimulation must be provided effectively even while on the go.

[0722] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0723] In this invention, the server includes a device for receiving the user's biometric information and lifestyle activity data, a device for generating personalized health guidance using an intelligent module, and a device for transmitting the health guidance to the user's terminal. This makes it possible to provide guidance and intellectual stimulation for maintaining healthy lifestyle habits even to users who are on the go.

[0724] "Biometric information" refers to data about the user's body, such as heart rate, body temperature, and activity level.

[0725] "Lifestyle activity data" refers to information about a user's daily life, such as their diet, exercise, and sleep.

[0726] An "intelligent module" is an algorithm or program that generates personalized advice based on user data.

[0727] "Health guidance" refers to recommendations regarding exercise, diet, and lifestyle habits provided to maintain and improve the user's health.

[0728] "User terminal" refers to hardware such as a user's mobile device or in-car display used to receive health guidance.

[0729] "Notifications" are reminders and alerts sent to users periodically to encourage them to practice health guidance.

[0730] "Data for providing health guidance and intellectual stimulation during travel" refers to information that provides users with opportunities for exercise and learning while they are in transit.

[0731] A "display device" refers to a device such as a monitor or speaker that conveys the content of health guidance to the user visually and audibly.

[0732] To implement this invention, it is necessary to construct a system in which information is exchanged between a server, a terminal, and a user. The server is responsible for receiving biometric information and lifestyle activity data transmitted by the user and recording it in a database. Based on this recorded information, an intelligent module within the server uses a generated AI model to create personalized health guidance. The generated health guidance includes recommendations for exercise, diet, intellectual activities, and social participation.

[0733] The terminal receives health guidance transmitted from the server and displays it to the user. Examples of terminal use include mobile devices and in-car displays. The interface on the terminal is intuitive and designed so that users can easily check guidance content and reminders. Users can record their daily activities through the terminal and send them back to the server, enabling continuous health management.

[0734] Users engage in healthy activities by following the advice provided by the device in their daily lives. For example, if a user has been sitting for a long time, the device will issue a notification such as, "Let's do some stretching for about 5 minutes." Such notifications are generated by a server that analyzes the user's activity data and uses a generative AI model to present the most appropriate guidance. While on the move, prompts such as, "Here's a quiz about sightseeing spots along your current route," are displayed to stimulate intellectual activity. Specifically, the generative AI model is used with prompts such as, "Generate the next exercise suggestion based on the user's heart rate data."

[0735] The technologies supporting this system include data processing using Python, AI model generation using TensorFlow, and the construction of the user interface for devices using React Native. SQLite is used as the database, and various analytical tools are utilized to track user behavior. In this way, users can receive health guidance regardless of location and achieve a richer lifestyle.

[0736] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0737] Step 1:

[0738] The server receives biometric and activity data from the user's device. This input includes heart rate, exercise levels, and dietary details. The server standardizes this data and records it in a database. A Python script is used to unify data in different formats for data standardization.

[0739] Step 2:

[0740] An intelligent module analyzes data recorded on the server and generates personalized health guidance using a generative AI model. Here, the generative AI model creates exercise and dietary recommendations tailored to the user's health condition based on the prompts it receives. This output is presented in the form of advice sent to the user.

[0741] Step 3:

[0742] The server sends the generated health guidance to the user's terminal. This process uses the TCP / IP protocol for data transfer. The output is displayed on the terminal in a format easily accessible to the user.

[0743] Step 4:

[0744] The device displays the received health guidance to the user. The input is health guidance data from the server, which is output on the device as an interactive interface using React Native. Icons and graphs are used to help the user easily understand the guidance content.

[0745] Step 5:

[0746] Users act based on the health guidance provided and input activity data into their devices. This data includes the duration and type of exercise, as well as the content of their meals. The user's activity history is then sent back to the server to serve as the basis for further analysis.

[0747] Step 6:

[0748] The server analyzes the user's new activity data and generates the next health guidance. This analysis incorporates a feedback loop for continuous improvement. The resulting new health guidance includes progress-based advice and new reminders.

[0749] Step 7:

[0750] The device receives new health guidance and reminders from the server and presents them to the user. When presented, it alerts the user, especially through voice and vibration, to prompt action. This process is repeated, creating a system that continuously supports the user's health.

[0751] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0752] This invention combines a system that provides personalized health guidance based on a user's health status and lifestyle data with an emotion engine that recognizes the user's emotional state. This system operates through a server and the user's terminal, providing the user with optimized health guidance and emotionally responsive feedback.

[0753] Server Role

[0754] The server estimates the user's emotional state using a sentiment engine in addition to the user's biometric and lifestyle data. This involves analyzing the user's input data and activity history, and using AI to recognize emotions. Based on the estimated emotional state, the server further adjusts personalized health guidance and generates guidance content that is appropriate for the user's emotions.

[0755] In addition, the server has a reminder management function that generates reminders based on the user's schedule and sends them to the device. This allows for notifications to be sent at the appropriate time according to the user's emotional state.

[0756] Role of the user terminal

[0757] The user terminal receives health guidance and emotion-sensitive feedback sent from the server and displays it to the user. The interface is flexible and emotionally responsive, providing information tailored to the user's state. Users record their activities through the terminal, and this data is sent to the server to help estimate future emotions.

[0758] User behavior

[0759] Users use their devices daily to record their activities and emotions. The feedback displayed on the device is tailored based on their emotional state, promoting motivation that takes their mood into consideration. The health guidance provided through the analysis of emotional states becomes more personalized and effective, further enhancing the health management of users.

[0760] Specific example

[0761] For example, if a user hasn't gotten enough sleep, the system's emotional engine recognizes this as fatigue or stress. Based on this, the server recommends stretching or meditation to help the user relax and communicates this through the device. At the same time, soothing colors and encouraging words are displayed on the device to help the user relax.

[0762] This system enables personalized health management tailored to the user's emotions, promoting improved health and better management of stress and mental state.

[0763] The following describes the processing flow.

[0764] Step 1:

[0765] Users use their devices to input health information, daily mood, and emotions. The devices collect this data and send it to a server.

[0766] Step 2:

[0767] The server receives biometric information, lifestyle data, and emotional state transmitted by the user and records them in a database.

[0768] Step 3:

[0769] The server uses an emotion engine to estimate the user's emotional state from the received data. This emotional data is estimated by analyzing the correlation between the user's previously recorded emotions and activity data.

[0770] Step 4:

[0771] The AI ​​generation module generates personalized health guidance based on the user's biometric information, lifestyle, and estimated emotional state. This guidance includes content that takes the user's mental state into consideration.

[0772] Step 5:

[0773] The server generates health guidance and sets reminders, sending data to the user's device to send notifications according to the user's schedule.

[0774] Step 6:

[0775] The device displays health guidance and reminder information received from the server to the user. This uses a visually conscious interface with emotionally responsive adjustments.

[0776] Step 7:

[0777] Users record activities in accordance with health guidance and submit feedback on their emotional state. This allows users to continuously engage in experiences that contribute to improving their own health.

[0778] Step 8:

[0779] The server continuously receives newly recorded activity and emotion data, updating the database. This data is then used for future emotion estimation and health guidance.

[0780] This entire process allows users to receive health guidance while also managing their emotions, resulting in a system that supports overall health improvement.

[0781] (Example 2)

[0782] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0783] Traditional health management systems provide health guidance based on users' biometric information and lifestyle data, but they lack guidance that takes psychological states into account, making it difficult to adequately support users' emotions and mental health. Furthermore, if reminder timing is not appropriate for the user's emotional state, it can potentially decrease motivation.

[0784] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0785] In this invention, the server includes means for predicting the user's psychological state, means for generating personalized health guidance, and means for transmitting the health guidance to the user's terminal. This makes it possible to provide optimal health guidance and reminders according to the user's emotional state.

[0786] "Biometric information" refers to measurable data about a user's body, including heart rate, body temperature, and activity level.

[0787] "Lifestyle data" refers to data that shows a user's daily actions and habits, including diet, exercise, and sleep duration.

[0788] "Psychological state" refers to a user's emotions and mental condition, and is evaluated based on factors such as stress levels and mood.

[0789] "Health guidance" refers to specific advice and recommendations provided to improve a user's health, including information on exercise, nutrition, and relaxation techniques.

[0790] "Inference methods" refer to algorithms and technologies used to analyze user data and evaluate their psychological and health status.

[0791] A "reminder" is a notification that prompts a user to take action at a specific time or in a specific situation.

[0792] "Activity history" refers to a record of a user's past activity data, which is used to analyze progress and behavioral patterns.

[0793] This invention is a system that provides personalized health guidance and feedback based on the user's physical and psychological state. The system consists of a server for data analysis and a terminal for user interaction.

[0794] The server aggregates biometric information and lifestyle data sent by the user and uses an AI model to infer the user's psychological state. This AI model is built using, for example, open-source machine learning libraries such as TensorFlow and PyTorch. The data received includes heart rate and activity levels from smartphones and wearable devices, as well as emotional data based on the user's daily self-assessment. Based on this data analysis, the server generates personalized health guidance and registers it as a reminder.

[0795] The device presents health guidance and feedback from the server to the user via a user interface. This interface is developed using HTML and JavaScript technologies to ensure that users can comfortably receive information. Reminders and feedback are tailored to the user's mental state and schedule, and notifications are sent at the appropriate time.

[0796] Users record their daily activity data and mood through their devices. This data is later analyzed and sent to a server to further improve the accuracy of the user's psychological state estimation.

[0797] For example, if a user hasn't gotten enough sleep, the server's emotion engine evaluates this state as stress or fatigue. Based on this, the server generates health advice, such as "Try a 5-minute meditation to relax," and sends it to the device along with a user interface featuring gentle colors.

[0798] Examples of prompt messages include the following:

[0799] "A user recorded their sleep duration last night and found it was two hours shorter than average. Based on this data, how does the emotion engine evaluate the user's emotional state and suggest health guidance?"

[0800] This system enables health support that takes into maximum consideration the user's emotions and behavior, and is expected to improve both their psychological and physical health.

[0801] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0802] Step 1:

[0803] Users input activity data and emotional states via their devices. Specifically, they use smartphones or wearable devices to record biometric information such as heart rate, steps taken, and sleep duration, and also input self-assessments of their mood and stress levels for the day. This information is aggregated on the device.

[0804] Step 2:

[0805] The terminal converts the collected data into a standard format and sends it to the server using a security protocol. The data is formatted in JSON format and encrypted using SSL / TLS before transmission. The server receives and decrypts it.

[0806] Step 3:

[0807] The server inputs the received user biometric information and lifestyle data into an AI model for data analysis. Specifically, it uses an emotion estimation algorithm using Python (for example, a model built with TensorFlow) to estimate the user's current psychological state. The server outputs the user's emotional state as the analysis result.

[0808] Step 4:

[0809] The server generates personalized health guidance based on analyzed emotional states and biometric data. For example, it generates specific advice on recommended exercise, nutrition, and relaxation. The generated guidance is output as actions to take next and areas for improvement. The server optimizes this based on the user's profile.

[0810] Step 5:

[0811] The server sends the generated health guidance to the user's device. Simultaneously, it utilizes a scheduling management system to set reminders and coordinate notifications to the device at appropriate times. These reminders might be sent for scheduled meditation or exercise sessions, for example.

[0812] Step 6:

[0813] The device visually presents received health guidance to the user. It uses a flexible interface, for example, displaying recommendations against a light-colored background. It also features a function to play music in the background to promote relaxation.

[0814] Step 7:

[0815] Users follow the health guidance presented on the device and decide whether to incorporate it into their daily routines. They also input feedback into the device, and this data is used in the next analysis. Information based on the feedback is sent from the device to the server and reflected in the next health guidance and psychological state estimation.

[0816] (Application Example 2)

[0817] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0818] In current brick-and-mortar stores, it is difficult to provide personalized experiences tailored to each customer's health and emotional state. This can lead to customers not receiving services or products suitable for their health condition, potentially resulting in lower satisfaction. Furthermore, traditional systems are unable to effectively provide feedback based on customers' emotions and health status.

[0819] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0820] In this invention, the server includes means for receiving the user's biometric information and lifestyle data, means for generating personalized health guidance using an artificial intelligence module based on the biometric information and lifestyle data, and means for detecting the user's emotional state. This makes it possible to provide personalized health guidance and emotionally responsive feedback to customers in real time.

[0821] "Biometric information" refers to data that indicates the user's physical condition, including measurements such as heart rate, blood pressure, and body temperature.

[0822] "Lifestyle data" refers to information about a user's daily actions and habits, including diet, exercise, and sleep patterns.

[0823] A "generative artificial intelligence module" is a program with artificial intelligence capabilities that analyzes input data and generates personalized instruction content.

[0824] "Health guidance" refers to specific advice and guidance provided to improve or maintain the user's health.

[0825] "User visual devices" refer to devices used to present information to users visually, and include smart glasses and display devices.

[0826] A "notification" is a system message that informs a user of specific information, and functions as a reminder or alert.

[0827] "Activity history" refers to a record of a user's past actions and trends, and is used to analyze behavioral patterns.

[0828] "Emotional state" refers to the user's psychological and emotional condition, including joy, sadness, stress, etc.

[0829] "Recommended products and entertainment" refers to products and entertainment content that are suggested according to the individual needs of the user.

[0830] The system for implementing this invention provides a personalized experience based on the user's health and emotional state. The system consists of a server, smart glasses which are the user's visual device, and a module that manages the user's activity data.

[0831] The server receives biometric information and lifestyle data transmitted from the user and uses an AI model to generate health guidance based on this data. The generated health guidance is sent to the user's smart glasses and displayed as visual information. This includes suggestions for a healthy lifestyle and customized feedback tailored to the user's emotional state.

[0832] Smart glasses use built-in cameras and sensors to monitor the user's facial expressions and biometric data in real time and estimate their emotional state. An emotion recognition AI model is used for emotion recognition. The emotional data sent to the server is used to provide recommended products and entertainment.

[0833] Users record their daily activities through smart glasses, and this data is analyzed on a server. The analysis results will contribute to future health guidance and improvements in the accuracy of emotion recognition.

[0834] For example, if smart glasses detect stress in the user, the server sends suggestions to the smart glasses recommending relaxing products or calming music playlists. In this way, personalized services based on the user's health and emotional state are provided.

[0835] An example of a prompt is as follows: "We want to develop a system that analyzes the emotional state of customers and suggests relaxation methods using an emotion engine. Please provide specific examples using a generative AI model for an application that can recognize a customer's specific health or emotional state and provide appropriate feedback and suggestions accordingly."

[0836] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0837] Step 1:

[0838] The server continuously receives biometric information and lifestyle data from users. This data includes heart rate, blood pressure, and dietary records. The entered data is stored in a database and passed to the generating AI model.

[0839] Step 2:

[0840] The server uses a generative AI model to analyze received biometric and lifestyle data, generating personalized health guidance for the user. This analysis applies a data processing algorithm based on the input information to generate optimal health advice. The generated health guidance is then ready to be transmitted to the user's visual device.

[0841] Step 3:

[0842] The smart glasses integrated into the device capture the user's facial expressions and biometric data in real time. The data obtained from the camera and sensors is pre-processed within the smart glasses before being sent to the server.

[0843] Step 4:

[0844] The server analyzes facial expression data transmitted from the smart glasses using an emotion recognition AI model to estimate the user's emotional state. This process involves data calculations based on facial changes and other biometric indicators, and assigns emotion labels. This creates the user's emotional profile.

[0845] Step 5:

[0846] The server selects recommended products and entertainment based on the analysis of the user's emotional state. It filters appropriate candidates from the product database and creates a list to send to the user's smart glasses.

[0847] Step 6:

[0848] The user's smart glasses display health guidance and entertainment information sent from a server on a visual display, providing feedback through audio and video. This feedback is delivered in an interactive format that takes into account the user's emotional state.

[0849] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0850] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0851] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0852] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0853] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0854] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0855] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0856] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0857] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0858] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0859] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0860] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0861] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0862] 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.

[0863] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0864] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0865] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0866] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0867] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0868] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0869] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0870] The following is further disclosed regarding the embodiments described above.

[0871] (Claim 1)

[0872] A means of receiving the user's biometric information and lifestyle data,

[0873] A means for generating personalized health guidance using an artificial intelligence module based on the aforementioned biometric information and lifestyle data,

[0874] A means for transmitting the aforementioned health guidance to the user terminal,

[0875] A means for generating and managing reminders based on the aforementioned health guidance,

[0876] A means of recording user activity history and evaluating progress,

[0877] Means for providing feedback based on the aforementioned progress,

[0878] A system that includes this.

[0879] (Claim 2)

[0880] The system according to claim 1, wherein the reminder operates based on the user's schedule information.

[0881] (Claim 3)

[0882] The system according to claim 1, wherein the feedback graphically visualizes the user's progress.

[0883] "Example 1"

[0884] (Claim 1)

[0885] Means for collecting users' health-related data,

[0886] A means for generating personalized health guidance using intelligent functions based on the aforementioned health-related data,

[0887] A means for transmitting the aforementioned health guidance to the user's communication device,

[0888] A means for generating and managing notifications based on the aforementioned health guidance,

[0889] A means of recording the history of user activity and evaluating progress,

[0890] A means of providing information to users based on the aforementioned progress,

[0891] A system that includes this.

[0892] (Claim 2)

[0893] The system according to claim 1, wherein the notification operates based on the user's schedule information.

[0894] (Claim 3)

[0895] The system according to claim 1, wherein the aforementioned information visually displays the user's progress.

[0896] "Application Example 1"

[0897] (Claim 1)

[0898] A device that receives the user's biometric information and lifestyle activity data,

[0899] A device that generates personalized health guidance using an intelligent module based on the aforementioned biological information and lifestyle activity data,

[0900] A device that transmits the aforementioned health guidance to the user's terminal,

[0901] A device for generating and managing notifications based on the aforementioned health guidance,

[0902] A device that records the user's activity history and evaluates their progress,

[0903] A device that provides feedback based on the aforementioned progress,

[0904] A device that processes data to provide health guidance and intellectual stimulation to users while they are traveling, and outputs it visually and audibly to a display device,

[0905] A system that includes this.

[0906] (Claim 2)

[0907] The system according to claim 1, wherein the notification operates based on the user's schedule information.

[0908] (Claim 3)

[0909] The system according to claim 1, wherein the feedback visually displays the user's progress.

[0910] "Example 2 of combining an emotion engine"

[0911] (Claim 1)

[0912] A means of receiving the user's biometric information and lifestyle data,

[0913] A means for generating personalized health guidance using the aforementioned biometric information and lifestyle data, as well as inference means for analyzing psychological state,

[0914] A means for transmitting the aforementioned health guidance to the user terminal,

[0915] A means for generating and managing time reminders based on the aforementioned health guidance and psychological state,

[0916] A means of recording a user's activity history and evaluating their progress in conjunction with their inferred psychological state,

[0917] A means for providing feedback based on the aforementioned progress and psychological state,

[0918] A system that includes this.

[0919] (Claim 2)

[0920] The system according to claim 1, wherein the time reminder operates based on the user's lifestyle schedule information and psychological state.

[0921] (Claim 3)

[0922] The system according to claim 1, wherein the feedback visualizes the user's progress in a graphical representation that includes emotional elements.

[0923] "Application example 2 when combining with an emotional engine"

[0924] (Claim 1)

[0925] A means of receiving the user's biometric information and lifestyle data,

[0926] A means for generating personalized health guidance using an artificial intelligence module based on the aforementioned biometric information and lifestyle data,

[0927] Means for transmitting the aforementioned health guidance to the user's visual device,

[0928] A means for generating and managing notifications based on the aforementioned health guidance,

[0929] A means of recording and evaluating the user's activity history,

[0930] A means of providing feedback based on the aforementioned process,

[0931] A means of detecting the user's emotional state,

[0932] A means of providing recommended products and entertainment based on the aforementioned emotional state,

[0933] A system that includes this.

[0934] (Claim 2)

[0935] The system according to claim 1, wherein the notification operates based on the user's time information.

[0936] (Claim 3)

[0937] The system according to claim 1, wherein the feedback graphically represents the user's progress. [Explanation of Symbols]

[0938] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving the user's biometric information and lifestyle data, A means for generating personalized health guidance using an artificial intelligence module based on the aforementioned biometric information and lifestyle data, A means for transmitting the aforementioned health guidance to the user terminal, A means for generating and managing reminders based on the aforementioned health guidance, A means of recording user activity history and evaluating progress, Means for providing feedback based on the aforementioned progress, A system that includes this.

2. The system according to claim 1, wherein the reminder operates based on the user's schedule information.

3. The system according to claim 1, wherein the feedback graphically visualizes the user's progress.

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