system

Abstract

The system according to this embodiment aims to provide comprehensive life support to elderly people and users unfamiliar with technology. [Solution] The system according to the embodiment comprises a personalization assistant unit, a health monitoring unit, and an IoT device integration unit. The personalization assistant unit learns the user's lifestyle, health status, and interests, and provides personalized support. The health monitoring unit monitors the health status based on the information acquired by the personalization assistant unit and automatically makes an emergency contact if an abnormality is detected. The IoT device integration unit works with smart home devices based on the information monitored by the health monitoring unit to centrally manage lighting, temperature control, security, etc.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method 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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, comprehensive life support has not been sufficiently provided for the elderly and users unfamiliar with technology, and there is room for improvement.

[0005] The system according to the embodiment aims to provide comprehensive life support for the elderly and users unfamiliar with technology.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a personalization assistant unit, a health monitoring unit, and an IoT device integration unit. The personalization assistant unit learns the user's lifestyle, health status, and interests, and provides personalized support. The health monitoring unit monitors the user's health status based on the information acquired by the personalization assistant unit and automatically makes an emergency contact if an abnormality is detected. The IoT device integration unit works with smart home devices based on the information monitored by the health monitoring unit to centrally manage lighting, temperature control, security, and other functions. [Effects of the Invention]

[0007] The system according to this embodiment can provide comprehensive life support to elderly people and users who are unfamiliar with technology. [Brief explanation of the drawing]

[0008] [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. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single 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), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving 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 receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The AI-Powered Life Navigator, according to an embodiment of the present invention, is a personal AI assistant system for the elderly and those unfamiliar with technology. This system goes beyond mere technical support, becoming a comprehensive life support system that assists in all aspects of daily life. Utilizing the latest generative AI models and integrating speech recognition, natural language processing, and computer vision technologies, it seamlessly digitizes and enhances the user's life. For example, the AI-Powered Life Navigator learns the user's lifestyle, health status, and interests to provide personalized support. For instance, it learns the user's dietary preferences and exercise habits to provide appropriate dietary and exercise advice. Furthermore, the AI-Powered Life Navigator provides an intuitive operating environment combining voice, text, gestures, and visual guidance. For example, when a user gives a voice command, the system understands the command and performs the appropriate action. In addition, the AI-Powered Life Navigator integrates with smart home devices to centrally manage lighting, temperature control, security, and more. For example, when a user voice-commands "Raise the room temperature," the system operates the temperature control device to adjust the room temperature. AIPowered Life Navigator works in conjunction with wearable devices to monitor health status. If an abnormality is detected, it automatically initiates emergency contact. For example, if a user's heart rate becomes abnormally high, the system automatically notifies emergency contacts. AIPowered Life Navigator also uses AI to match users with similar interests, forming online communities. For instance, connecting users with shared hobbies can reduce feelings of loneliness. Furthermore, AIPowered Life Navigator uses AI to handle various procedures and applications, suggesting appropriate government services as needed. For example, it can handle pension application procedures, preparing the necessary documents for the user. AIPowered Life Navigator also provides household budget management and investment advice, protecting users from fraud and illegal transactions. For example, it analyzes the user's spending patterns and provides saving advice.Furthermore, AIPowered Life Navigator supports lifelong learning by suggesting online courses and learning content based on the user's interests. For example, if a user wants to learn a new language, it will suggest a suitable online course. In this way, AIPowered Life Navigator can significantly improve the quality of life for seniors and people unfamiliar with technology.

[0029] The AI-Powered Life Navigator according to this embodiment comprises a personalization assistant unit, a health monitoring unit, and an IoT device integration unit. The personalization assistant unit learns the user's lifestyle, health status, and interests, and provides personalized support. For example, the personalization assistant unit learns the user's dietary preferences and exercise habits and provides appropriate diet and exercise advice. The personalization assistant unit can also learn the user's sleep patterns and provide appropriate sleep advice. Furthermore, the personalization assistant unit can learn the user's hobbies and interests and suggest relevant information and activities. The health monitoring unit monitors the user's health status based on the information acquired by the personalization assistant unit and automatically makes emergency contact if an abnormality is detected. For example, the health monitoring unit can work in conjunction with a wearable device to monitor the user's heart rate, blood pressure, body temperature, etc. The health monitoring unit can also automatically notify emergency contacts if an abnormality is detected. Furthermore, the health monitoring unit can monitor the user's health status in real time and respond immediately if an abnormality occurs. The IoT device integration unit works with smart home devices based on information monitored by the health monitoring unit to centrally manage lighting, temperature control, security, and more. For example, if the user gives a voice command such as "Raise the room temperature," the IoT device integration unit will operate the temperature control device to adjust the room temperature. The IoT device integration unit can also adjust the room lighting by operating the lighting device if the user gives a voice command such as "Dim the lights." Furthermore, if the user gives a voice command such as "Turn on security," the IoT device integration unit can operate the security device to enhance home security. As a result, the AI-Powered Life Navigator according to this embodiment provides personalized support based on the user's lifestyle, health status, and interests, monitors their health status, and centrally manages smart home devices, thereby making the user's life more comfortable.

[0030] The Personalized Assistant unit learns the user's lifestyle, health status, and interests to provide personalized support. Specifically, the Personalized Assistant unit collects data about the user's daily life and analyzes this data using AI. For example, if a user uses an app to record their daily meals, the Personalized Assistant unit collects that data and analyzes the user's dietary preferences and nutritional balance. Based on this, it can provide the user with suggestions for healthy meals or recipes to supplement specific nutrients. Also, if a user uses a fitness tracker, it analyzes that data to evaluate the user's exercise habits and performance. This allows it to suggest the user the optimal exercise plan and training methods. Furthermore, the Personalized Assistant unit learns the user's sleep patterns and provides appropriate sleep advice. For example, if a user uses a smartwatch to record sleep data, it analyzes that data to evaluate the user's sleep quality and patterns. Based on this, it can suggest ways to improve the user's sleep environment or activities to help them relax. In addition, the Personalized Assistant unit learns the user's hobbies and interests and suggests relevant information and activities. For example, if a user likes a particular music genre or movie, it can recommend new artists or movies based on that information. Furthermore, if a user is interested in a particular sport or outdoor activity, the system can also recommend events and communities related to that activity. This allows the personalized assistant to provide individualized support across all aspects of the user's life, thereby improving their quality of life.

[0031] The Health Monitoring Department monitors the user's health status based on information acquired by the Personalized Assistant Department and automatically makes emergency contacts if an abnormality is detected. Specifically, the Health Monitoring Department works in conjunction with wearable devices and smartphone apps to monitor the user's vital signs such as heart rate, blood pressure, body temperature, and oxygen saturation in real time. This data is analyzed using AI, and alerts are issued if abnormal patterns or sudden fluctuations are detected. For example, if a user's heart rate suddenly increases, the Health Monitoring Department analyzes the information and, if it determines that there may be an abnormality, notifies the user and automatically contacts pre-registered emergency contacts. The Health Monitoring Department can also monitor the user's health status over the long term and analyze trends and patterns. This allows for early detection of changes in the user's health status and the implementation of preventative measures. Furthermore, the Health Monitoring Department can share the user's health data with medical institutions, allowing doctors and specialists to remotely monitor the user's health status and provide necessary advice and treatment. In this way, the Health Monitoring Department can comprehensively manage the user's health status and respond quickly if an abnormality occurs.

[0032] The IoT device integration unit works with smart home devices based on information monitored by the health monitoring unit to centrally manage lighting, temperature control, security, and more. Specifically, the IoT device integration unit automatically controls smart home devices in response to user voice commands and health status. For example, if a user says, "Raise the room temperature," the IoT device integration unit will operate the temperature control device to adjust the room temperature. Similarly, if a user says, "Dim the lights," it can operate the lighting device to adjust the room lighting. Furthermore, the IoT device integration unit can automatically control smart home devices based on the user's health status. For example, if the health monitoring unit determines that the user's stress level is high, the IoT device integration unit will automatically set relaxing lighting and music. Also, if an abnormality is detected while the user is sleeping, the IoT device integration unit can automatically turn on the lights and make an emergency call. In addition, the IoT device integration unit works with security devices to ensure user safety. For example, if a user says, "Turn on security," it can operate the security device to enhance home security. Furthermore, if the health monitoring unit detects an abnormality, the IoT device integration unit can automatically activate security devices and send emergency notifications. This allows the IoT device integration unit to comprehensively manage the user's living environment, providing a comfortable and safe lifestyle.

[0033] The virtual social network section uses AI to match users with similar interests, forming online communities. For example, by connecting users with shared hobbies, it reduces feelings of loneliness. It can match users who enjoy reading to form online reading clubs, or users who enjoy gardening to form online gardening clubs, or users who enjoy cooking to form online cooking clubs. In this way, by matching users with similar interests and forming online communities, it reduces feelings of loneliness among users.

[0034] The Administrative Service Coordination Department uses AI to handle various procedures and applications, and proposes appropriate administrative services as needed. For example, the Administrative Service Coordination Department can use AI to handle pension application procedures and prepare the necessary documents for the user. It can also use AI to handle tax return procedures and prepare the necessary documents for the user. Furthermore, it can use AI to handle passport application procedures and prepare the necessary documents for the user. In addition, it can use AI to handle procedures such as obtaining a resident registration certificate and prepare the necessary documents for the user. In this way, the Administrative Service Coordination Department simplifies procedures for users by having AI handle various procedures and applications and propose appropriate administrative services.

[0035] The Financial Advisory Department provides household budget management and investment advice, protecting users from fraud and illegal transactions. For example, it can analyze users' spending patterns and provide savings advice. It can also analyze users' income and expenses and set budgets. Furthermore, it can analyze users' investment portfolios and provide investment advice that balances risk and return. In addition, it can monitor users' financial transactions and detect fraud and illegal transactions. By providing household budget management and investment advice and protecting users from fraud and illegal transactions, it supports users' financial management.

[0036] The Continuing Education Support Department supports lifelong learning by suggesting online courses and learning content based on users' interests. For example, if a user wants to learn a new language, the Continuing Education Support Department can suggest an appropriate online course. For example, if a user wants to learn programming, the Continuing Education Support Department can also suggest an appropriate online course. Furthermore, if a user wants to learn cooking, the Continuing Education Support Department can also suggest an appropriate online course. In addition, if a user wants to learn history, the Continuing Education Support Department can also suggest an appropriate online course. In this way, by suggesting online courses and learning content based on users' interests and supporting lifelong learning, the department helps improve users' knowledge and skills.

[0037] The Personalized Assistant analyzes the user's past behavioral history and selects the most suitable support method. For example, the Personalized Assistant can suggest similar activities based on the user's past preferred activities. For example, the Personalized Assistant can analyze the user's past eating history and suggest a nutritionally balanced meal plan. Furthermore, for example, the Personalized Assistant can suggest an appropriate exercise program based on the user's past exercise history. In this way, by analyzing the user's past behavioral history, the Personalized Assistant provides the most suitable support method.

[0038] The personalized assistant dynamically updates its support content in response to changes in the user's lifestyle. For example, if the user starts a new hobby, the personalized assistant will suggest information and activities related to that hobby. For example, if the user's health condition changes, the personalized assistant can also suggest appropriate health management methods. Furthermore, if the user's daily routine changes, the personalized assistant can provide support tailored to the new routine. In this way, by updating support content in response to changes in the user's lifestyle, it always provides optimal support.

[0039] The personalized assistant unit takes into account the user's geographical location to provide region-specific support. For example, if the user is traveling, the personalized assistant unit can suggest local sightseeing information and restaurants. If the user is at home, for example, the personalized assistant unit can also suggest nearby events and services. Furthermore, if the user is in a specific region, the personalized assistant unit can provide weather and traffic information for that region. By providing region-specific support based on the user's geographical location, it can offer more appropriate support.

[0040] The Personalized Assistant analyzes the user's social media activity and provides relevant support. For example, the Personalized Assistant provides information related to topics the user has shown interest in on social media. For example, the Personalized Assistant can also suggest activities related to events and groups the user follows on social media. Furthermore, the Personalized Assistant can analyze the content of the user's social media posts and provide appropriate support. This allows for more relevant support based on the user's social media activity.

[0041] The health monitoring unit analyzes the user's past health data to improve the accuracy of anomaly detection. For example, the health monitoring unit analyzes the user's past heart rate data to detect abnormal patterns. For example, the health monitoring unit can also analyze the user's past sleep data to detect abnormal sleep patterns. Furthermore, for example, the health monitoring unit can analyze the user's past exercise data to detect abnormal exercise patterns. In this way, the accuracy of anomaly detection is improved by analyzing the user's past health data.

[0042] The health monitoring unit customizes health monitoring parameters based on the user's lifestyle. For example, if the user exercises regularly, the health monitoring unit will focus on monitoring heart rate during exercise. If the user is on a specific diet, the health monitoring unit can also monitor its effects. Furthermore, if the user has an irregular lifestyle, the health monitoring unit can perform monitoring that matches their lifestyle rhythm. By customizing health monitoring parameters based on the user's lifestyle, the unit provides more appropriate health management.

[0043] The health monitoring unit selects the most appropriate emergency contact, taking into account the user's geographical location. For example, if the user is at home, the health monitoring unit prioritizes nearby emergency contacts. If the user is traveling, for example, the health monitoring unit can also prioritize local emergency contacts. Furthermore, if the user is in a specific region, for example, the health monitoring unit can prioritize emergency contacts in that region. This allows for a faster emergency response by selecting the most appropriate emergency contact based on the user's geographical location.

[0044] The health monitoring unit analyzes users' social media activity to improve the accuracy of health monitoring. For example, if a user is very active on social media, the health monitoring unit can estimate their health status based on that activity. The health monitoring unit can also estimate a user's health status by analyzing the content of their social media posts. Furthermore, the health monitoring unit can also estimate a user's health status by analyzing their social media following relationships. By improving the accuracy of health monitoring based on users' social media activity, the unit can provide more appropriate health management.

[0045] The IoT device integration unit analyzes the user's past device operation history and selects the optimal operation method. For example, the IoT device integration unit suggests the optimal settings based on the lighting settings the user has previously preferred. For example, the IoT device integration unit can analyze the user's past temperature adjustment history and suggest the optimal temperature setting. Furthermore, for example, the IoT device integration unit can suggest the optimal music based on the user's past music playback history. In this way, by analyzing the user's past device operation history, it provides the optimal operation method.

[0046] The IoT device integration unit dynamically updates the settings of IoT devices based on the user's lifestyle. For example, if the user exercises regularly, the IoT device integration unit automatically adjusts the lighting and music settings for exercise. If the user has a habit of relaxing at a specific time of day, the IoT device integration unit can also provide settings tailored to that time. Furthermore, if the user's daily rhythm changes, the IoT device integration unit can provide settings that match the new rhythm. In this way, by dynamically updating the settings of IoT devices based on the user's lifestyle, the system always provides the optimal environment.

[0047] The IoT device integration unit provides region-specific device settings, taking into account the user's geographical location. For example, if the user is traveling, the IoT device integration unit can provide temperature settings tailored to the local climate. If the user is at home, for example, the IoT device integration unit can also provide lighting settings tailored to nearby events. Furthermore, if the user is in a specific region, the IoT device integration unit can provide device settings based on the weather information of that region. By providing region-specific device settings based on the user's geographical location, a more appropriate environment is provided.

[0048] The IoT device integration unit analyzes the user's social media activity and provides relevant device operations. For example, it can provide device operations related to topics the user has shown interest in on social media. It can also provide device operations tailored to events the user is following on social media. Furthermore, it can analyze the content of the user's social media posts and provide appropriate device operations. By providing relevant device operations based on the user's social media activity, it provides a more appropriate environment.

[0049] The virtual social networking unit analyzes the user's past communication history and selects the optimal matching method. For example, the virtual social networking unit suggests similar partners based on the characteristics of people the user has preferred to interact with in the past. The virtual social networking unit can also suggest the optimal matching method by analyzing the user's past communication patterns. Furthermore, the virtual social networking unit can suggest partners with common interests based on the user's past communication history. In this way, by analyzing the user's past communication history, it provides the optimal matching method.

[0050] The virtual social network section provides region-specific communities, taking into account the user's geographical location. For example, if a user is in a specific region, the virtual social network section will suggest events and groups in that region. For example, if a user is traveling, the virtual social network section can also suggest local communities. Furthermore, if a user is at home, the virtual social network section can also suggest nearby communities. By providing region-specific communities based on the user's geographical location, it offers more appropriate communities.

[0051] The Administrative Service Collaboration Department analyzes the user's past administrative procedure history and selects the most suitable suggestion method. For example, the Administrative Service Collaboration Department can suggest similar procedures based on the procedures the user has performed in the past. The Administrative Service Collaboration Department can also analyze the user's past procedure history and select the most suitable suggestion method. Furthermore, the Administrative Service Collaboration Department can suggest necessary documents and procedures based on the user's past procedure history. In this way, by analyzing the user's past administrative procedure history, it provides the most suitable suggestion method.

[0052] The Administrative Service Coordination Department provides region-specific administrative services, taking into account the user's geographical location. For example, if a user is in a specific region, the Administrative Service Coordination Department will suggest administrative services for that region. For example, if a user is traveling, the Administrative Service Coordination Department can also suggest local administrative services. Furthermore, if a user is at home, the Administrative Service Coordination Department can also suggest nearby administrative services. By providing region-specific administrative services based on the user's geographical location, the department aims to provide more appropriate administrative services.

[0053] The Financial Advisory Department analyzes the user's past financial history and selects the most appropriate advice method. For example, the Financial Advisory Department can analyze the user's past investment history and propose the most suitable investment method. For example, the Financial Advisory Department can also analyze the user's past spending history and provide savings advice. Furthermore, for example, the Financial Advisory Department can provide risk management advice based on the user's past financial history. In this way, by analyzing the user's past financial history, the department can provide the most appropriate advice method.

[0054] The Financial Advisory Department provides region-specific financial advice, taking into account the user's geographical location. For example, if the user is in a specific region, the Financial Advisory Department will suggest financial services in that region. For example, if the user is traveling, the Financial Advisory Department can also suggest local financial services. Furthermore, for example, if the user is at home, the Financial Advisory Department can suggest nearby financial services. By providing region-specific financial advice based on the user's geographical location, the department can offer more appropriate financial advice.

[0055] The Continuous Learning Support Unit analyzes the user's past learning history and selects the optimal learning method. For example, the Continuous Learning Support Unit analyzes the user's past learning history and proposes the optimal learning method. The Continuous Learning Support Unit can also suggest effective learning methods based on the user's past learning history. Furthermore, the Continuous Learning Support Unit can manage learning progress based on the user's past learning history. In this way, by analyzing the user's past learning history, it provides the optimal learning method.

[0056] The Continuous Learning Support Unit provides region-specific learning content, taking into account the user's geographical location. For example, if the user is in a specific region, the Continuous Learning Support Unit will suggest learning content relevant to that region. It can also suggest local learning content if the user is traveling. Furthermore, if the user is at home, the Continuous Learning Support Unit can suggest nearby learning content. By providing region-specific learning content based on the user's geographical location, the unit aims to deliver more appropriate learning content.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] AIPowered Life Navigator can also include a health management section that takes into account the user's geographical location to provide region-specific health management. For example, if the user is in a specific region, the health management section can suggest health management methods tailored to the climate and environment of that region. For example, if the user is traveling, the health management section can suggest health management methods tailored to the local climate and environment. Furthermore, if the user is at home, the health management section can suggest nearby medical institutions and health facilities. This allows for more appropriate health management by providing region-specific health management based on the user's geographical location.

[0059] AIPowered Life Navigator can also be equipped with a health management unit that analyzes the user's past health data and selects the optimal health management method. For example, the health management unit can analyze the user's past heart rate data and detect abnormal patterns. For example, the health management unit can analyze the user's past sleep data and detect abnormal sleep patterns. Furthermore, for example, the health management unit can analyze the user's past exercise data and detect abnormal exercise patterns. This improves the accuracy of anomaly detection by analyzing the user's past health data.

[0060] AIPowered Life Navigator can also include a health management unit that customizes health management parameters based on the user's lifestyle. For example, if the user exercises regularly, the health management unit will focus on monitoring heart rate during exercise. For example, if the user is on a specific diet, the health management unit can monitor its effects. Furthermore, if the user has an irregular lifestyle, the health management unit can monitor according to their lifestyle rhythm. This allows for more appropriate health management by customizing health management parameters based on the user's lifestyle.

[0061] AIPowered Life Navigator can also include a financial advisor section that takes into account the user's geographical location to provide region-specific financial advice. For example, if the user is in a specific region, the financial advisor section will suggest financial services in that region. For example, if the user is traveling, the financial advisor section can suggest local financial services. Furthermore, if the user is at home, the financial advisor section can suggest nearby financial services. This allows for more appropriate financial advice by providing region-specific advice based on the user's geographical location.

[0062] AIPowered Life Navigator can also include a financial advisor section that analyzes the user's past financial history and selects the optimal advice method. For example, the financial advisor section can analyze the user's past investment history and propose the best investment method. It can also analyze the user's past spending history and provide savings advice. Furthermore, the financial advisor section can provide risk management advice based on the user's past financial history. In this way, by analyzing the user's past financial history, it can provide the most appropriate advice method.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The Personalized Assistant learns the user's lifestyle, health status, and interests to provide personalized support. For example, it learns the user's dietary preferences and exercise habits to provide appropriate diet and exercise advice. It can also learn the user's sleep patterns to provide appropriate sleep advice. Furthermore, it can learn the user's hobbies and interests and suggest relevant information and activities. Step 2: The health monitoring unit monitors the user's health status based on information acquired by the personalization assistant unit and automatically sends an emergency notification if an abnormality is detected. For example, it can work in conjunction with wearable devices to monitor the user's heart rate, blood pressure, body temperature, etc. It can also automatically notify emergency contacts if an abnormality is detected. Furthermore, it can monitor the user's health status in real time and respond immediately if an abnormality occurs. Step 3: The IoT device integration unit works with smart home devices based on information monitored by the health monitoring unit to centrally manage lighting, temperature control, security, and more. For example, if the user says "Raise the room temperature," it will operate the temperature control device to adjust the room temperature. Similarly, if the user says "Dim the lights," it can operate the lighting device to adjust the room lighting. Furthermore, if the user says "Turn on security," it can operate the security device to enhance home security.

[0065] (Example of form 2) The AI-Powered Life Navigator, according to an embodiment of the present invention, is a personal AI assistant system for the elderly and those unfamiliar with technology. This system goes beyond mere technical support, becoming a comprehensive life support system that assists in all aspects of daily life. Utilizing the latest generative AI models and integrating speech recognition, natural language processing, and computer vision technologies, it seamlessly digitizes and enhances the user's life. For example, the AI-Powered Life Navigator learns the user's lifestyle, health status, and interests to provide personalized support. For instance, it learns the user's dietary preferences and exercise habits to provide appropriate dietary and exercise advice. Furthermore, the AI-Powered Life Navigator provides an intuitive operating environment combining voice, text, gestures, and visual guidance. For example, when a user gives a voice command, the system understands the command and performs the appropriate action. In addition, the AI-Powered Life Navigator integrates with smart home devices to centrally manage lighting, temperature control, security, and more. For example, when a user voice-commands "Raise the room temperature," the system operates the temperature control device to adjust the room temperature. AIPowered Life Navigator works in conjunction with wearable devices to monitor health status. If an abnormality is detected, it automatically initiates emergency contact. For example, if a user's heart rate becomes abnormally high, the system automatically notifies emergency contacts. AIPowered Life Navigator also uses AI to match users with similar interests, forming online communities. For instance, connecting users with shared hobbies can reduce feelings of loneliness. Furthermore, AIPowered Life Navigator uses AI to handle various procedures and applications, suggesting appropriate government services as needed. For example, it can handle pension application procedures, preparing the necessary documents for the user. AIPowered Life Navigator also provides household budget management and investment advice, protecting users from fraud and illegal transactions. For example, it analyzes the user's spending patterns and provides saving advice.Furthermore, AIPowered Life Navigator supports lifelong learning by suggesting online courses and learning content based on the user's interests. For example, if a user wants to learn a new language, it will suggest a suitable online course. In this way, AIPowered Life Navigator can significantly improve the quality of life for seniors and people unfamiliar with technology.

[0066] The AI-Powered Life Navigator according to this embodiment comprises a personalization assistant unit, a health monitoring unit, and an IoT device integration unit. The personalization assistant unit learns the user's lifestyle, health status, and interests, and provides personalized support. For example, the personalization assistant unit learns the user's dietary preferences and exercise habits and provides appropriate diet and exercise advice. The personalization assistant unit can also learn the user's sleep patterns and provide appropriate sleep advice. Furthermore, the personalization assistant unit can learn the user's hobbies and interests and suggest relevant information and activities. The health monitoring unit monitors the user's health status based on the information acquired by the personalization assistant unit and automatically makes emergency contact if an abnormality is detected. For example, the health monitoring unit can work in conjunction with a wearable device to monitor the user's heart rate, blood pressure, body temperature, etc. The health monitoring unit can also automatically notify emergency contacts if an abnormality is detected. Furthermore, the health monitoring unit can monitor the user's health status in real time and respond immediately if an abnormality occurs. The IoT device integration unit works with smart home devices based on information monitored by the health monitoring unit to centrally manage lighting, temperature control, security, and more. For example, if the user gives a voice command such as "Raise the room temperature," the IoT device integration unit will operate the temperature control device to adjust the room temperature. The IoT device integration unit can also adjust the room lighting by operating the lighting device if the user gives a voice command such as "Dim the lights." Furthermore, if the user gives a voice command such as "Turn on security," the IoT device integration unit can operate the security device to enhance home security. As a result, the AI-Powered Life Navigator according to this embodiment provides personalized support based on the user's lifestyle, health status, and interests, monitors their health status, and centrally manages smart home devices, thereby making the user's life more comfortable.

[0067] The Personalized Assistant unit learns the user's lifestyle, health status, and interests to provide personalized support. Specifically, the Personalized Assistant unit collects data about the user's daily life and analyzes this data using AI. For example, if a user uses an app to record their daily meals, the Personalized Assistant unit collects that data and analyzes the user's dietary preferences and nutritional balance. Based on this, it can provide the user with suggestions for healthy meals or recipes to supplement specific nutrients. Also, if a user uses a fitness tracker, it analyzes that data to evaluate the user's exercise habits and performance. This allows it to suggest the user the optimal exercise plan and training methods. Furthermore, the Personalized Assistant unit learns the user's sleep patterns and provides appropriate sleep advice. For example, if a user uses a smartwatch to record sleep data, it analyzes that data to evaluate the user's sleep quality and patterns. Based on this, it can suggest ways to improve the user's sleep environment or activities to help them relax. In addition, the Personalized Assistant unit learns the user's hobbies and interests and suggests relevant information and activities. For example, if a user likes a particular music genre or movie, it can recommend new artists or movies based on that information. Furthermore, if a user is interested in a particular sport or outdoor activity, the system can also recommend events and communities related to that activity. This allows the personalized assistant to provide individualized support across all aspects of the user's life, thereby improving their quality of life.

[0068] The Health Monitoring Department monitors the user's health status based on information acquired by the Personalized Assistant Department and automatically makes emergency contacts if an abnormality is detected. Specifically, the Health Monitoring Department works in conjunction with wearable devices and smartphone apps to monitor the user's vital signs such as heart rate, blood pressure, body temperature, and oxygen saturation in real time. This data is analyzed using AI, and alerts are issued if abnormal patterns or sudden fluctuations are detected. For example, if a user's heart rate suddenly increases, the Health Monitoring Department analyzes the information and, if it determines that there may be an abnormality, notifies the user and automatically contacts pre-registered emergency contacts. The Health Monitoring Department can also monitor the user's health status over the long term and analyze trends and patterns. This allows for early detection of changes in the user's health status and the implementation of preventative measures. Furthermore, the Health Monitoring Department can share the user's health data with medical institutions, allowing doctors and specialists to remotely monitor the user's health status and provide necessary advice and treatment. In this way, the Health Monitoring Department can comprehensively manage the user's health status and respond quickly if an abnormality occurs.

[0069] The IoT device integration unit works with smart home devices based on information monitored by the health monitoring unit to centrally manage lighting, temperature control, security, and more. Specifically, the IoT device integration unit automatically controls smart home devices in response to user voice commands and health status. For example, if a user says, "Raise the room temperature," the IoT device integration unit will operate the temperature control device to adjust the room temperature. Similarly, if a user says, "Dim the lights," it can operate the lighting device to adjust the room lighting. Furthermore, the IoT device integration unit can automatically control smart home devices based on the user's health status. For example, if the health monitoring unit determines that the user's stress level is high, the IoT device integration unit will automatically set relaxing lighting and music. Also, if an abnormality is detected while the user is sleeping, the IoT device integration unit can automatically turn on the lights and make an emergency call. In addition, the IoT device integration unit works with security devices to ensure user safety. For example, if a user says, "Turn on security," it can operate the security device to enhance home security. Furthermore, if the health monitoring unit detects an abnormality, the IoT device integration unit can automatically activate security devices and send emergency notifications. This allows the IoT device integration unit to comprehensively manage the user's living environment, providing a comfortable and safe lifestyle.

[0070] The virtual social network section uses AI to match users with similar interests, forming online communities. For example, by connecting users with shared hobbies, it reduces feelings of loneliness. It can match users who enjoy reading to form online reading clubs, or users who enjoy gardening to form online gardening clubs, or users who enjoy cooking to form online cooking clubs. In this way, by matching users with similar interests and forming online communities, it reduces feelings of loneliness among users.

[0071] The Administrative Service Coordination Department uses AI to handle various procedures and applications, and proposes appropriate administrative services as needed. For example, the Administrative Service Coordination Department can use AI to handle pension application procedures and prepare the necessary documents for the user. It can also use AI to handle tax return procedures and prepare the necessary documents for the user. Furthermore, it can use AI to handle passport application procedures and prepare the necessary documents for the user. In addition, it can use AI to handle procedures such as obtaining a resident registration certificate and prepare the necessary documents for the user. In this way, the Administrative Service Coordination Department simplifies procedures for users by having AI handle various procedures and applications and propose appropriate administrative services.

[0072] The Financial Advisory Department provides household budget management and investment advice, protecting users from fraud and illegal transactions. For example, it can analyze users' spending patterns and provide savings advice. It can also analyze users' income and expenses and set budgets. Furthermore, it can analyze users' investment portfolios and provide investment advice that balances risk and return. In addition, it can monitor users' financial transactions and detect fraud and illegal transactions. By providing household budget management and investment advice and protecting users from fraud and illegal transactions, it supports users' financial management.

[0073] The Continuing Education Support Department supports lifelong learning by suggesting online courses and learning content based on users' interests. For example, if a user wants to learn a new language, the Continuing Education Support Department can suggest an appropriate online course. For example, if a user wants to learn programming, the Continuing Education Support Department can also suggest an appropriate online course. Furthermore, if a user wants to learn cooking, the Continuing Education Support Department can also suggest an appropriate online course. In addition, if a user wants to learn history, the Continuing Education Support Department can also suggest an appropriate online course. In this way, by suggesting online courses and learning content based on users' interests and supporting lifelong learning, the department helps improve users' knowledge and skills.

[0074] The personalized assistant unit estimates the user's emotions and adjusts the support content based on the estimated emotions. For example, if the user is feeling stressed, the personalized assistant unit may suggest relaxing music or meditation. For example, if the user is happy, the personalized assistant unit may suggest positive feedback or new challenges. Also, if the user is tired, the personalized assistant unit may provide notifications to encourage rest or suggest light stretching. In this way, by adjusting the support content based on the user's emotions, more appropriate support is provided. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0075] The Personalized Assistant analyzes the user's past behavioral history and selects the most suitable support method. For example, the Personalized Assistant can suggest similar activities based on the user's past preferred activities. For example, the Personalized Assistant can analyze the user's past eating history and suggest a nutritionally balanced meal plan. Furthermore, for example, the Personalized Assistant can suggest an appropriate exercise program based on the user's past exercise history. In this way, by analyzing the user's past behavioral history, the Personalized Assistant provides the most suitable support method.

[0076] The personalized assistant dynamically updates its support content in response to changes in the user's lifestyle. For example, if the user starts a new hobby, the personalized assistant will suggest information and activities related to that hobby. For example, if the user's health condition changes, the personalized assistant can also suggest appropriate health management methods. Furthermore, if the user's daily routine changes, the personalized assistant can provide support tailored to the new routine. In this way, by updating support content in response to changes in the user's lifestyle, it always provides optimal support.

[0077] The personalized assistant unit estimates the user's emotions and prioritizes support based on those emotions. For example, if the user is feeling anxious, the personalized assistant unit will prioritize support that provides reassurance. If the user is excited, the personalized assistant unit may also prioritize activities that allow the user to release energy. Furthermore, if the user is depressed, the personalized assistant unit may also prioritize support that lifts their spirits. By prioritizing support based on the user's emotions, the system provides more effective support. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The personalized assistant unit takes into account the user's geographical location to provide region-specific support. For example, if the user is traveling, the personalized assistant unit can suggest local sightseeing information and restaurants. If the user is at home, for example, the personalized assistant unit can also suggest nearby events and services. Furthermore, if the user is in a specific region, the personalized assistant unit can provide weather and traffic information for that region. By providing region-specific support based on the user's geographical location, it can offer more appropriate support.

[0079] The Personalized Assistant analyzes the user's social media activity and provides relevant support. For example, the Personalized Assistant provides information related to topics the user has shown interest in on social media. For example, the Personalized Assistant can also suggest activities related to events and groups the user follows on social media. Furthermore, the Personalized Assistant can analyze the content of the user's social media posts and provide appropriate support. This allows for more relevant support based on the user's social media activity.

[0080] The health monitoring unit estimates the user's emotions and adjusts the frequency of health monitoring based on the estimated emotions. For example, if the user is stressed, the health monitoring unit increases the frequency of health monitoring. For example, if the user is relaxed, the health monitoring unit can also decrease the frequency of health monitoring. Furthermore, if the user is anxious, the health monitoring unit can appropriately adjust the frequency of health monitoring. This provides more appropriate health management by adjusting the frequency of health monitoring based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The health monitoring unit analyzes the user's past health data to improve the accuracy of anomaly detection. For example, the health monitoring unit analyzes the user's past heart rate data to detect abnormal patterns. For example, the health monitoring unit can also analyze the user's past sleep data to detect abnormal sleep patterns. Furthermore, for example, the health monitoring unit can analyze the user's past exercise data to detect abnormal exercise patterns. In this way, the accuracy of anomaly detection is improved by analyzing the user's past health data.

[0082] The health monitoring unit customizes health monitoring parameters based on the user's lifestyle. For example, if the user exercises regularly, the health monitoring unit will focus on monitoring heart rate during exercise. If the user is on a specific diet, the health monitoring unit can also monitor its effects. Furthermore, if the user has an irregular lifestyle, the health monitoring unit can perform monitoring that matches their lifestyle rhythm. By customizing health monitoring parameters based on the user's lifestyle, the unit provides more appropriate health management.

[0083] The health monitoring unit estimates the user's emotions and determines the priority of emergency contacts based on the estimated emotions. For example, if the user is experiencing strong anxiety, the health monitoring unit will prioritize emergency contacts. For example, if the user is relaxed, the health monitoring unit may lower the priority of emergency contacts. Furthermore, if the user is agitated, the health monitoring unit may appropriately adjust the priority of emergency contacts. This allows for more appropriate emergency responses by determining the priority of emergency contacts based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The health monitoring unit selects the most appropriate emergency contact, taking into account the user's geographical location. For example, if the user is at home, the health monitoring unit prioritizes nearby emergency contacts. If the user is traveling, for example, the health monitoring unit can also prioritize local emergency contacts. Furthermore, if the user is in a specific region, for example, the health monitoring unit can prioritize emergency contacts in that region. This allows for a faster emergency response by selecting the most appropriate emergency contact based on the user's geographical location.

[0085] The health monitoring unit analyzes users' social media activity to improve the accuracy of health monitoring. For example, if a user is very active on social media, the health monitoring unit can estimate their health status based on that activity. The health monitoring unit can also estimate a user's health status by analyzing the content of their social media posts. Furthermore, the health monitoring unit can also estimate a user's health status by analyzing their social media following relationships. By improving the accuracy of health monitoring based on users' social media activity, the unit can provide more appropriate health management.

[0086] The IoT device integration unit estimates the user's emotions and adjusts the operation of the IoT device based on the estimated emotions. For example, if the user is relaxed, the IoT device integration unit adjusts the lighting to a warmer color. For example, if the user is stressed, the IoT device integration unit can play music to help them relax. Furthermore, if the user is excited, the IoT device integration unit can lower the temperature to help them calm down. In this way, by adjusting the operation of the IoT device based on the user's emotions, a more comfortable environment is provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The IoT device integration unit analyzes the user's past device operation history and selects the optimal operation method. For example, the IoT device integration unit suggests the optimal settings based on the lighting settings the user has previously preferred. For example, the IoT device integration unit can analyze the user's past temperature adjustment history and suggest the optimal temperature setting. Furthermore, for example, the IoT device integration unit can suggest the optimal music based on the user's past music playback history. In this way, by analyzing the user's past device operation history, it provides the optimal operation method.

[0088] The IoT device integration unit dynamically updates the settings of IoT devices based on the user's lifestyle. For example, if the user exercises regularly, the IoT device integration unit automatically adjusts the lighting and music settings for exercise. If the user has a habit of relaxing at a specific time of day, the IoT device integration unit can also provide settings tailored to that time. Furthermore, if the user's daily rhythm changes, the IoT device integration unit can provide settings that match the new rhythm. In this way, by dynamically updating the settings of IoT devices based on the user's lifestyle, the system always provides the optimal environment.

[0089] The IoT device integration unit estimates the user's emotions and determines the priority of device operations based on the estimated emotions. For example, if the user is feeling anxious, the IoT device integration unit prioritizes device operations that provide a sense of security. For example, if the user is excited, the IoT device integration unit may also prioritize device operations that allow the user to release energy. Furthermore, if the user is feeling depressed, the IoT device integration unit may also prioritize device operations that lift their spirits. This provides more effective device operation by determining the priority of device operations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The IoT device integration unit provides region-specific device settings, taking into account the user's geographical location. For example, if the user is traveling, the IoT device integration unit can provide temperature settings tailored to the local climate. If the user is at home, for example, the IoT device integration unit can also provide lighting settings tailored to nearby events. Furthermore, if the user is in a specific region, the IoT device integration unit can provide device settings based on the weather information of that region. By providing region-specific device settings based on the user's geographical location, a more appropriate environment is provided.

[0091] The IoT device integration unit analyzes the user's social media activity and provides relevant device operations. For example, it can provide device operations related to topics the user has shown interest in on social media. It can also provide device operations tailored to events the user is following on social media. Furthermore, it can analyze the content of the user's social media posts and provide appropriate device operations. By providing relevant device operations based on the user's social media activity, it provides a more appropriate environment.

[0092] The virtual social network section estimates the user's emotions and adjusts the matching criteria based on the estimated emotions. For example, if a user is feeling lonely, the virtual social network section prioritizes matching them with someone who can empathize. For example, if a user is excited, the virtual social network section can also prioritize matching them with someone who can share the same excitement. Furthermore, if a user is relaxed, the virtual social network section can also prioritize matching them with someone who can have a calm conversation. In this way, by adjusting the matching criteria based on the user's emotions, it provides more appropriate matches. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The virtual social networking unit analyzes the user's past communication history and selects the optimal matching method. For example, the virtual social networking unit suggests similar partners based on the characteristics of people the user has preferred to interact with in the past. The virtual social networking unit can also suggest the optimal matching method by analyzing the user's past communication patterns. Furthermore, the virtual social networking unit can suggest partners with common interests based on the user's past communication history. In this way, by analyzing the user's past communication history, it provides the optimal matching method.

[0094] The virtual social network unit estimates the user's emotions and prioritizes communities based on those emotions. For example, if a user is feeling lonely, the virtual social network unit will prioritize suggesting active communities. If a user is relaxed, the virtual social network unit can also prioritize suggesting calm communities. Furthermore, if a user is excited, the virtual social network unit can prioritize suggesting energetic communities. By prioritizing communities based on the user's emotions, it provides more appropriate communities. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The virtual social network section provides region-specific communities, taking into account the user's geographical location. For example, if a user is in a specific region, the virtual social network section will suggest events and groups in that region. For example, if a user is traveling, the virtual social network section can also suggest local communities. Furthermore, if a user is at home, the virtual social network section can also suggest nearby communities. By providing region-specific communities based on the user's geographical location, it offers more appropriate communities.

[0096] The Administrative Service Coordination Department estimates the user's emotions and adjusts the suggested administrative services based on those emotions. For example, if the user is feeling anxious, the Administrative Service Coordination Department will prioritize suggesting administrative services that provide a sense of security. If the user is relaxed, the Administrative Service Coordination Department may also suggest services that allow for smooth processing of necessary procedures. Furthermore, if the user is agitated, the Administrative Service Coordination Department may suggest administrative services that can be responded to quickly. In this way, by adjusting the suggested administrative services based on the user's emotions, the department provides more appropriate administrative services. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The Administrative Service Collaboration Department analyzes the user's past administrative procedure history and selects the most suitable suggestion method. For example, the Administrative Service Collaboration Department can suggest similar procedures based on the procedures the user has performed in the past. The Administrative Service Collaboration Department can also analyze the user's past procedure history and select the most suitable suggestion method. Furthermore, the Administrative Service Collaboration Department can suggest necessary documents and procedures based on the user's past procedure history. In this way, by analyzing the user's past administrative procedure history, it provides the most suitable suggestion method.

[0098] The Administrative Service Coordination Department estimates the user's emotions and prioritizes administrative procedures based on those estimated emotions. For example, if the user is feeling anxious, the Administrative Service Coordination Department will prioritize procedures that provide reassurance. If the user is relaxed, the Administrative Service Coordination Department may also prioritize procedures that allow for smooth execution of necessary procedures. Furthermore, if the user is agitated, the Administrative Service Coordination Department may also prioritize procedures that require a quick response. By prioritizing administrative procedures based on the user's emotions, the department provides more appropriate administrative services. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The Administrative Service Coordination Department provides region-specific administrative services, taking into account the user's geographical location. For example, if a user is in a specific region, the Administrative Service Coordination Department will suggest administrative services for that region. For example, if a user is traveling, the Administrative Service Coordination Department can also suggest local administrative services. Furthermore, if a user is at home, the Administrative Service Coordination Department can also suggest nearby administrative services. By providing region-specific administrative services based on the user's geographical location, the department aims to provide more appropriate administrative services.

[0100] The financial advisory department estimates the user's emotions and adjusts the content of financial advice based on the estimated emotions. For example, if the user is feeling anxious, the financial advisory department may suggest low-risk investments. If the user is relaxed, for example, the financial advisory department may suggest investments that balance risk and return. Also, if the user is excited, for example, the financial advisory department may suggest high-risk investments. In this way, by adjusting the content of financial advice based on the user's emotions, more appropriate financial advice is provided. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The Financial Advisory Department analyzes the user's past financial history and selects the most appropriate advice method. For example, the Financial Advisory Department can analyze the user's past investment history and propose the most suitable investment method. For example, the Financial Advisory Department can also analyze the user's past spending history and provide savings advice. Furthermore, for example, the Financial Advisory Department can provide risk management advice based on the user's past financial history. In this way, by analyzing the user's past financial history, the department can provide the most appropriate advice method.

[0102] The financial advisory department estimates the user's emotions and prioritizes financial advice based on those emotions. For example, if the user is feeling anxious, the financial advisory department will prioritize advice that provides reassurance. If the user is relaxed, the financial advisory department may also prioritize advice that balances risk and return. Furthermore, if the user is excited, the financial advisory department may prioritize high-risk advice. By prioritizing financial advice based on the user's emotions, the system provides more appropriate financial advice. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The Financial Advisory Department provides region-specific financial advice, taking into account the user's geographical location. For example, if the user is in a specific region, the Financial Advisory Department will suggest financial services in that region. For example, if the user is traveling, the Financial Advisory Department can also suggest local financial services. Furthermore, for example, if the user is at home, the Financial Advisory Department can suggest nearby financial services. By providing region-specific financial advice based on the user's geographical location, the department can offer more appropriate financial advice.

[0104] The Continuous Learning Support Unit estimates the user's emotions and adjusts the suggested learning content based on those emotions. For example, if the user is excited, the Continuous Learning Support Unit will suggest challenging learning content. If the user is relaxed, the Continuous Learning Support Unit can also suggest relaxing learning content. Furthermore, if the user is feeling anxious, the Continuous Learning Support Unit can suggest reassuring learning content. By adjusting the suggested learning content based on the user's emotions, the unit provides more appropriate learning content. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The Continuous Learning Support Unit analyzes the user's past learning history and selects the optimal learning method. For example, the Continuous Learning Support Unit analyzes the user's past learning history and proposes the optimal learning method. The Continuous Learning Support Unit can also suggest effective learning methods based on the user's past learning history. Furthermore, the Continuous Learning Support Unit can manage learning progress based on the user's past learning history. In this way, by analyzing the user's past learning history, it provides the optimal learning method.

[0106] The continuous learning support unit estimates the user's emotions and prioritizes learning content based on those emotions. For example, if the user is excited, the unit will prioritize challenging learning content. If the user is relaxed, the unit may also prioritize relaxing learning content. Furthermore, if the user is feeling anxious, the unit may prioritize reassuring learning content. By prioritizing learning content based on the user's emotions, the unit provides more appropriate learning content. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0107] The Continuous Learning Support Unit provides region-specific learning content, taking into account the user's geographical location. For example, if the user is in a specific region, the Continuous Learning Support Unit will suggest learning content relevant to that region. It can also suggest local learning content if the user is traveling. Furthermore, if the user is at home, the Continuous Learning Support Unit can suggest nearby learning content. By providing region-specific learning content based on the user's geographical location, the unit aims to deliver more appropriate learning content.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] AIPowered Life Navigator can also include a stress management unit that estimates the user's emotions and manages the user's stress level based on those emotions. For example, if the user is experiencing high stress levels, the stress management unit may suggest relaxing music or meditation. It may also suggest deep breathing or light exercise if the user is feeling stressed. Furthermore, if the user is relaxed, the stress management unit may provide advice on how to maintain that state. This allows for more appropriate support by managing stress levels based on the user's emotions.

[0110] AIPowered Life Navigator can also include a motivation enhancement unit that estimates the user's emotions and improves their motivation based on those emotions. For example, if the user is feeling motivated, the motivation enhancement unit can suggest new challenges or goals. If the user is feeling discouraged, the motivation enhancement unit can also provide encouraging messages or share success stories. Furthermore, if the user is feeling a sense of accomplishment, the motivation enhancement unit can provide advice on how to maintain that feeling. This allows for more effective support by improving motivation based on the user's emotions.

[0111] AIPowered Life Navigator can also be equipped with a sleep management unit that estimates the user's emotions and improves the user's sleep quality based on those emotions. For example, if the user is feeling anxious, the sleep management unit can suggest relaxing music or meditation. If the user is feeling tired, the sleep management unit can also provide advice on creating a suitable sleep environment. Furthermore, if the user is relaxed, the sleep management unit can provide advice on maintaining that state. This allows for more appropriate support by improving sleep quality based on the user's emotions.

[0112] AIPowered Life Navigator can also be equipped with a meal management unit that estimates the user's emotions and improves the quality of the user's meals based on those emotions. For example, if the user is feeling stressed, the meal management unit can suggest relaxing meals and drinks. If the user is feeling tired, the meal management unit can also suggest nutritionally balanced meals. Furthermore, if the user is relaxed, the meal management unit can suggest meals to maintain that state. This provides more appropriate support by improving the quality of meals based on the user's emotions.

[0113] AIPowered Life Navigator can also be equipped with an exercise management unit that estimates the user's emotions and improves the quality of their exercise based on those emotions. For example, if the user is feeling stressed, the exercise management unit can suggest relaxing exercises or stretches. If the user is feeling tired, the exercise management unit can also suggest light exercises or exercises to refresh themselves. Furthermore, if the user is relaxed, the exercise management unit can suggest exercises to maintain that state. This provides more appropriate support by improving the quality of exercise based on the user's emotions.

[0114] AIPowered Life Navigator can also include a health management section that takes into account the user's geographical location to provide region-specific health management. For example, if the user is in a specific region, the health management section can suggest health management methods tailored to the climate and environment of that region. For example, if the user is traveling, the health management section can suggest health management methods tailored to the local climate and environment. Furthermore, if the user is at home, the health management section can suggest nearby medical institutions and health facilities. This allows for more appropriate health management by providing region-specific health management based on the user's geographical location.

[0115] AIPowered Life Navigator can also be equipped with a health management unit that analyzes the user's past health data and selects the optimal health management method. For example, the health management unit can analyze the user's past heart rate data and detect abnormal patterns. For example, the health management unit can analyze the user's past sleep data and detect abnormal sleep patterns. Furthermore, for example, the health management unit can analyze the user's past exercise data and detect abnormal exercise patterns. This improves the accuracy of anomaly detection by analyzing the user's past health data.

[0116] AIPowered Life Navigator can also include a health management unit that customizes health management parameters based on the user's lifestyle. For example, if the user exercises regularly, the health management unit will focus on monitoring heart rate during exercise. For example, if the user is on a specific diet, the health management unit can monitor its effects. Furthermore, if the user has an irregular lifestyle, the health management unit can monitor according to their lifestyle rhythm. This allows for more appropriate health management by customizing health management parameters based on the user's lifestyle.

[0117] AIPowered Life Navigator can also include a financial advisor section that takes into account the user's geographical location to provide region-specific financial advice. For example, if the user is in a specific region, the financial advisor section will suggest financial services in that region. For example, if the user is traveling, the financial advisor section can suggest local financial services. Furthermore, if the user is at home, the financial advisor section can suggest nearby financial services. This allows for more appropriate financial advice by providing region-specific advice based on the user's geographical location.

[0118] AIPowered Life Navigator can also include a financial advisor section that analyzes the user's past financial history and selects the optimal advice method. For example, the financial advisor section can analyze the user's past investment history and propose the best investment method. It can also analyze the user's past spending history and provide savings advice. Furthermore, the financial advisor section can provide risk management advice based on the user's past financial history. In this way, by analyzing the user's past financial history, it can provide the most appropriate advice method.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The Personalized Assistant learns the user's lifestyle, health status, and interests to provide personalized support. For example, it learns the user's dietary preferences and exercise habits to provide appropriate diet and exercise advice. It can also learn the user's sleep patterns to provide appropriate sleep advice. Furthermore, it can learn the user's hobbies and interests and suggest relevant information and activities. Step 2: The health monitoring unit monitors the user's health status based on information acquired by the personalization assistant unit and automatically sends an emergency notification if an abnormality is detected. For example, it can work in conjunction with wearable devices to monitor the user's heart rate, blood pressure, body temperature, etc. It can also automatically notify emergency contacts if an abnormality is detected. Furthermore, it can monitor the user's health status in real time and respond immediately if an abnormality occurs. Step 3: The IoT device integration unit works with smart home devices based on information monitored by the health monitoring unit to centrally manage lighting, temperature control, security, and more. For example, if the user says "Raise the room temperature," it will operate the temperature control device to adjust the room temperature. Similarly, if the user says "Dim the lights," it can operate the lighting device to adjust the room lighting. Furthermore, if the user says "Turn on security," it can operate the security device to enhance home security.

[0121] 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.

[0122] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0123] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] Each of the multiple elements mentioned above, including the Personalized Assistant Unit, Health Monitoring Unit, IoT Device Integration Unit, Virtual Social Network Unit, Administrative Service Collaboration Unit, Financial Advisor Unit, and Continuous Learning Support Unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the Personalized Assistant Unit is implemented by the control unit 46A of the smart device 14, which learns the user's lifestyle and health status and provides personalized support. The Health Monitoring Unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which monitors the user's health status in cooperation with a wearable device. The IoT Device Integration Unit is implemented by, for example, the control unit 46A of the smart device 14, which centrally manages smart home devices. The Virtual Social Network Unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which matches users with similar interests. The Administrative Service Collaboration Unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which acts as an agent for various procedures and applications. The financial advisory unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and provides household budget management and investment advice. The continuous learning support unit is implemented, for example, by the control unit 46A of the smart device 14, and proposes online courses based on the user's interests. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] 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.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0128] 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.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0130] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] 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.

[0132] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] 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.

[0138] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements mentioned above, including the Personalized Assistant Unit, Health Monitoring Unit, IoT Device Integration Unit, Virtual Social Network Unit, Administrative Service Collaboration Unit, Financial Advisor Unit, and Continuous Learning Support Unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the Personalized Assistant Unit is implemented by the control unit 46A of the smart glasses 214, which learns the user's lifestyle and health status and provides personalized support. The Health Monitoring Unit is implemented by the specific processing unit 290 of the data processing unit 12, which monitors the user's health status in cooperation with wearable devices. The IoT Device Integration Unit is implemented by the control unit 46A of the smart glasses 214, which centrally manages smart home devices. The Virtual Social Network Unit is implemented by the specific processing unit 290 of the data processing unit 12, which matches users with similar interests. The Administrative Service Collaboration Unit is implemented by the specific processing unit 290 of the data processing unit 12, which handles various procedures and applications on behalf of users. The financial advisory unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and provides household budget management and investment advice. The continuous learning support unit is implemented, for example, by the control unit 46A of the smart glasses 214, and suggests online courses based on the user's interests. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] 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.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0144] 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.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0146] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] 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.

[0148] 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.

[0149] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0153] 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.

[0154] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements described above, including the Personalized Assistant Unit, Health Monitoring Unit, IoT Device Integration Unit, Virtual Social Network Unit, Administrative Service Collaboration Unit, Financial Advisor Unit, and Continuous Learning Support Unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the Personalized Assistant Unit is implemented by the control unit 46A of the headset terminal 314, which learns the user's lifestyle and health status and provides personalized support. The Health Monitoring Unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which monitors the user's health status in cooperation with wearable devices. The IoT Device Integration Unit is implemented by, for example, the control unit 46A of the headset terminal 314, which centrally manages smart home devices. The Virtual Social Network Unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which matches users with similar interests. The Administrative Service Collaboration Unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which acts as an agent for various procedures and applications. The financial advisory unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and provides household budget management and investment advice. The continuous learning support unit is implemented, for example, by the control unit 46A of the headset terminal 314, and proposes online courses based on the user's interests. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0160] 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.

[0161] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0162] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0163] 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.

[0164] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0165] 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.

[0166] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0167] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0168] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0169] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0170] 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.

[0171] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0172] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0173] Each of the multiple elements mentioned above, including the Personalized Assistant Unit, Health Monitoring Unit, IoT Device Integration Unit, Virtual Social Network Unit, Administrative Service Collaboration Unit, Financial Advisor Unit, and Continuous Learning Support Unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the Personalized Assistant Unit is implemented by the control unit 46A of the robot 414, which learns the user's lifestyle and health status and provides personalized support. The Health Monitoring Unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which monitors the user's health status in cooperation with wearable devices. The IoT Device Integration Unit is implemented by, for example, the control unit 46A of the robot 414, which centrally manages smart home devices. The Virtual Social Network Unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which matches users with similar interests. The Administrative Service Collaboration Unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which acts as an agent for various procedures and applications. The financial advisory unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and provides household budget management and investment advice. The continuous learning support unit is implemented, for example, by the control unit 46A of the robot 414, and proposes online courses based on the user's interests. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0174] 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.

[0175] Figure 9 shows the 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.

[0176] 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.

[0177] 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.

[0178] 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, and motorcycles, 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 based, for example, 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.

[0179] 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."

[0180] 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.

[0181] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0190] 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 other things 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.

[0191] 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.

[0192] (Note 1) The Personalized Assistant unit learns the user's lifestyle, health status, and interests, and provides personalized support. A health monitoring unit monitors the health status based on information acquired by the aforementioned personalization assistant unit and automatically makes an emergency contact if an abnormality is detected. The system includes an IoT device integration unit that, based on information monitored by the aforementioned health monitoring unit, works in conjunction with smart home devices to centrally manage lighting, temperature control, security, and other functions. A system characterized by the following features. (Note 2) It features a virtual social network section where AI matches users with similar interests to form online communities. The system described in Appendix 1, characterized by the features described herein. (Note 3) The department includes an administrative service coordination unit where AI handles various procedures and applications on behalf of the user and proposes appropriate administrative services as needed. The system described in Appendix 1, characterized by the features described herein. (Note 4) It has a financial advisory department that provides household budget management and investment advice, and protects users from fraud and illegal transactions. The system described in Appendix 1, characterized by the features described herein. (Note 5) It has a Continuous Learning Support Department that proposes online courses and learning content based on users' interests and supports lifelong learning. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned personalization assistant unit is The system estimates the user's emotions and adjusts the support provided based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned personalization assistant unit is Analyze the user's past behavior history to select the optimal support method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned personalization assistant unit is The support content is dynamically updated in response to changes in the user's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned personalization assistant unit is The system estimates the user's emotions and determines support priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned personalization assistant unit is Providing region-specific support while taking the user's geographical location into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned personalization assistant unit is Analyze users' social media activity and provide relevant support. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned health monitoring unit, The system estimates the user's emotions and adjusts the frequency of health monitoring based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned health monitoring unit, Analyzing users' past health data improves the accuracy of anomaly detection. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned health monitoring unit, Customize health monitoring parameters based on the user's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned health monitoring unit, It estimates the user's emotions and prioritizes emergency contacts based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned health monitoring unit, The system selects the most suitable emergency contact, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned health monitoring unit, Analyze users' social media activity to improve the accuracy of health monitoring. The system described in Appendix 1, characterized by the features described herein. (Note 18) The IoT device integration unit is It estimates the user's emotions and adjusts the operation of IoT devices based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The IoT device integration unit is Analyze the user's past device operation history and select the optimal operation method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The IoT device integration unit is Dynamically update IoT device settings based on the user's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 21) The IoT device integration unit is It estimates the user's emotions and determines the priority of device operations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The IoT device integration unit is Provide region-specific device settings, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The IoT device integration unit is Analyzes users' social media activity and provides relevant device actions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned virtual social network section is It estimates the user's emotions and adjusts the matching criteria based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 25) The aforementioned virtual social network section is Analyze the user's past communication history and select the optimal matching method. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned virtual social network section is It estimates user sentiment and determines community priorities based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned virtual social network section is Providing region-specific communities while taking into account the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned Administrative Service Coordination Department, It estimates user sentiment and adjusts administrative service proposals based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 29) The aforementioned Administrative Service Coordination Department, We analyze the user's past administrative procedure history and select the most suitable proposal method. The system described in Appendix 3, characterized by the features described herein. (Note 30) The aforementioned Administrative Service Coordination Department, The system estimates user sentiment and determines the priority of administrative procedures based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned Administrative Service Coordination Department, Providing region-specific administrative services while taking into account the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned financial advisory department, The system estimates the user's emotions and adjusts the content of financial advice based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 33) The aforementioned financial advisory department, We analyze the user's past financial history and select the most appropriate advice method. The system described in Appendix 4, characterized by the features described herein. (Note 34) The aforementioned financial advisory department, It estimates the user's emotions and prioritizes financial advice based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 35) The aforementioned financial advisory department, Providing region-specific financial advice while taking into account the user's geographical location. The system described in Appendix 4, characterized by the features described herein. (Note 36) The aforementioned Continuous Learning Support Department, It estimates the user's emotions and adjusts the suggested learning content based on those estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 37) The aforementioned Continuous Learning Support Department, Analyze the user's past learning history and select the optimal learning method. The system described in Appendix 5, characterized by the features described herein. (Note 38) The aforementioned Continuous Learning Support Department, It estimates the user's emotions and prioritizes learning content based on those estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 39) The aforementioned Continuous Learning Support Department, Providing region-specific learning content while taking the user's geographical location into consideration. The system described in Appendix 5, characterized by the features described herein. [Explanation of Symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The Personalized Assistant unit learns the user's lifestyle, health status, and interests to provide personalized support, A health monitoring unit monitors the health status based on information acquired by the aforementioned personalization assistant unit and automatically makes an emergency contact if an abnormality is detected. The system includes an IoT device integration unit that, based on information monitored by the aforementioned health monitoring unit, works in conjunction with smart home devices to centrally manage lighting, temperature control, security, and other functions. A system characterized by the following features.

2. It features a virtual social network section where AI matches users with similar interests to form online communities. The system according to feature 1.

3. The system includes an administrative service coordination department where AI handles various procedures and applications and proposes appropriate administrative services as needed. The system according to feature 1.

4. It has a financial advisory department that provides household budget management and investment advice, and protects users from fraud and illegal transactions. The system according to feature 1.

5. It has a Continuous Learning Support Department that proposes online courses and learning content based on users' interests and supports lifelong learning. The system according to feature 1.

6. The aforementioned personalization assistant unit is The system estimates the user's emotions and adjusts the support provided based on those emotions. The system according to feature 1.

7. The aforementioned personalization assistant unit is Analyze the user's past behavior history to select the optimal support method. The system according to feature 1.

8. The aforementioned personalization assistant unit is The support content is dynamically updated in response to changes in the user's lifestyle. The system according to feature 1.

9. The aforementioned personalization assistant unit is The system estimates the user's emotions and determines support priorities based on those estimated emotions. The system according to feature 1.

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