system

Abstract

The system according to this embodiment aims to analyze health data, propose the most suitable insurance product for each individual, and centrally manage health insurance and medical records. [Solution] The system according to the embodiment comprises a health data analysis unit, an insurance product proposal unit, a health insurance management unit, and a medical record management unit. The health data analysis unit analyzes health data. The insurance product proposal unit proposes insurance products based on the data analyzed by the health data analysis unit. The health insurance management unit performs health insurance management based on the insurance products proposed by the insurance product proposal unit. The medical record management unit manages medical records based on the data managed by the health insurance management unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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 conventional technology, making use of health data to individually propose an optimal insurance product and centrally managing health insurance and medical records have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze health data, individually propose an optimal insurance product, and centrally manage health insurance and medical records.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a health data analysis unit, an insurance product proposal unit, a health insurance management unit, and a medical record management unit. The health data analysis unit analyzes health data. The insurance product proposal unit proposes insurance products based on the data analyzed by the health data analysis unit. The health insurance management unit manages health insurance based on the insurance products proposed by the insurance product proposal unit. The medical record management unit manages medical records based on the data managed by the health insurance management unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze health data, propose the most suitable insurance product for each individual, and centrally manage health insurance and medical records. [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 controls communication between a plurality of computers. Examples of communication standards applied 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 reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[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 life management support system according to an embodiment of the present invention is a system in which an AI agent supports the complex handling of various life management tasks in the era of 100-year lifespans. This life management support system provides functions such as health management monitoring, insurance product recommendations, health insurance management, medical record management, insurance claim support, subsidy application support, personal asset management, and digital inheritance management. For example, as health management monitoring, the life management support system uses AI to analyze health data acquired from wearable devices such as smartwatches and provides daily health management and improvement suggestions. Next, as insurance product recommendations, the life management support system uses AI to analyze an individual's situation and propose the most suitable insurance product. Furthermore, as health insurance management, the life management support system uses AI to automatically analyze documents related to health insurance and navigate the scope of application and procedures. A chatbot is also provided to simplify procedures. In addition, as medical record management, the life management support system aggregates medical records in the cloud, and AI performs data analysis and security management. It can also be shared with medical institutions. As insurance claim support, the life management support system uses AI to automatically generate insurance claim documents and provides real-time feedback on any missing information. The system allows for quick review and modification of application details. For subsidy applications, the AI ​​analyzes subsidy requirements and proposes applicable subsidies. It automatically generates application documents and manages progress. For personal asset management, the AI ​​analyzes an individual's asset situation and proposes an optimal asset management plan. Real-time risk management is also provided. For digital inheritance management, the AI ​​automatically generates necessary inheritance documents and provides a system for digital signatures and approvals. Real-time progress management is also provided. Furthermore, the system enhances the security of personal information through AI-driven automation and the introduction of blockchain technology. The AI ​​automatically encrypts and decrypts data, making it accessible only when needed. This eliminates the need for manual management of passwords, etc. This system allows you to entrust the complex tasks of information management, document creation, procedures, and applications to an AI agent, enriching your life.This allows the life management support system to handle everything from health data analysis and insurance product recommendations to health insurance management and medical record management in a comprehensive manner.

[0029] The life management support system according to this embodiment comprises a health data analysis unit, an insurance product proposal unit, a health insurance management unit, and a medical record management unit. The health data analysis unit analyzes health data. The health data analysis unit analyzes health data acquired from, for example, wearable devices such as smartwatches. Health data includes, but is not limited to, heart rate, blood pressure, and body temperature. The health data analysis unit analyzes health data using, for example, AI, and provides daily health management and improvement suggestions. The insurance product proposal unit proposes insurance products based on the data analyzed by the health data analysis unit. The insurance product proposal unit analyzes an individual's situation and proposes the most suitable insurance product. Insurance products include, for example, medical insurance and life insurance, but is not limited to, medical insurance and life insurance. The insurance product proposal unit analyzes an individual's situation using, for example, AI, and proposes the most suitable insurance product. The health insurance management unit manages health insurance based on the insurance products proposed by the insurance product proposal unit. The health insurance management unit automatically analyzes documents related to health insurance and provides guidance on the scope of application and procedures. The Health Insurance Management Department, for example, uses AI to automatically analyze health insurance documents and guide users through the scope of application and procedures. The Medical Records Management Department manages medical records based on the data managed by the Health Insurance Management Department. The Medical Records Management Department, for example, aggregates medical records in the cloud and performs data analysis and security management. Medical records include, but are not limited to, medical records and prescription information. The Medical Records Management Department, for example, uses AI to aggregate medical records in the cloud and performs data analysis and security management. As a result, the life management support system according to this embodiment can consistently perform everything from health data analysis to insurance product proposals, health insurance management, and medical record management.

[0030] The Health Data Analysis Department analyzes health data. For example, it analyzes health data acquired from wearable devices such as smartwatches. Health data includes, but is not limited to, heart rate, blood pressure, and body temperature. Specifically, smartwatches monitor the user's heart rate 24 hours a day and detect abnormal patterns. Blood pressure is measured regularly and daily fluctuations are recorded. Body temperature is particularly important for the early detection of fever signs. This data is transmitted to smartphones and cloud servers via Bluetooth® or Wi-Fi. The Health Data Analysis Department also uses AI to analyze health data and provide daily health management and improvement suggestions. The AI ​​uses machine learning algorithms to analyze large amounts of data and evaluate the user's health status. For example, if abnormal fluctuations in heart rate are detected, the AI ​​may suggest stress, lack of exercise, or potential heart disease. Blood pressure data is used to assess the risk of hypertension or hypotension and to suggest appropriate lifestyle improvements. Body temperature data helps in the early detection of infectious diseases and encourages medical consultations if necessary. This allows the health data analysis department to monitor users' health status in real time and provide appropriate advice. Furthermore, the health data analysis department can accumulate historical data and analyze long-term health trends. This enables users to understand changes in their health status and manage their health more effectively.

[0031] The Insurance Product Proposal Department proposes insurance products based on data analyzed by the Health Data Analysis Department. For example, the Insurance Product Proposal Department analyzes an individual's situation and proposes the most suitable insurance product. Insurance products include, but are not limited to, medical insurance and life insurance. Specifically, based on data provided by the Health Data Analysis Department, AI evaluates the user's health risks. For example, if data on heart rate and blood pressure indicates a high risk of heart disease or hypertension, the AI ​​proposes corresponding medical insurance. The department also considers the user's age, lifestyle, and medical history to select the most suitable insurance product. The Insurance Product Proposal Department uses AI to analyze an individual's situation and proposes the most suitable insurance product. The AI ​​compares the user's health data with a database of insurance products to select the most appropriate product. For example, it proposes life insurance to prepare for future risks for younger users, and medical insurance to reduce the burden of medical expenses for middle-aged and older users. Furthermore, the Insurance Product Proposal Department collects user feedback and continuously improves the accuracy of its proposals. This allows the insurance product proposal department to provide users with insurance products that are best suited to their health condition and lifestyle, and to support them in living with peace of mind.

[0032] The Health Insurance Management Department manages health insurance based on insurance products proposed by the Insurance Product Proposal Department. For example, the Health Insurance Management Department automatically analyzes health insurance documents and guides users through the scope of coverage and procedures. Specifically, AI automatically analyzes documents such as insurance application forms and medical expense statements submitted by users and extracts the necessary information. This simplifies complex procedures for users, allowing them to receive insurance benefits quickly. The Health Insurance Management Department uses AI to automatically analyze health insurance documents and guide users through the scope of coverage and procedures. The AI ​​uses natural language processing technology to understand the content of documents and guides users through the scope of coverage and necessary procedures. For example, it determines whether a specific treatment is covered by insurance and, if so, guides the user through the application process. It also lists the documents and information necessary for insurance claim procedures and provides them to the user. This allows the Health Insurance Management Department to support users in smoothly completing insurance procedures and expedite the receipt of insurance benefits. Furthermore, the Health Insurance Management Department also supports insurance contract renewal and modification procedures, ensuring users always have access to the most suitable insurance products. This will allow the Health Insurance Management Department to streamline users' insurance management and provide an environment where they can use medical services with peace of mind.

[0033] The Medical Records Management Department manages medical records based on data managed by the Health Insurance Management Department. For example, the Medical Records Management Department aggregates medical records in the cloud and performs data analysis and security management. Medical records include, but are not limited to, medical records and prescription information. Specifically, it aggregates users' medical records and prescription information on a cloud server and performs data analysis using AI. This allows for centralized management of users' medical history and the rapid provision of necessary information. The Medical Records Management Department uses AI to aggregate medical records in the cloud and performs data analysis and security management. The AI ​​analyzes users' medical records and evaluates changes in health status and treatment effectiveness. For example, it compares past medical records with current health data to understand the progress of treatment. It also analyzes prescription information to evaluate drug interactions and the risk of side effects. Furthermore, the Medical Records Management Department places a strong emphasis on security management and protects user privacy. The cloud server incorporates the latest security technologies and measures to prevent unauthorized access and data leaks. This allows the Medical Records Management Department to securely manage users' medical information and provide necessary information quickly and accurately. Furthermore, the Medical Records Management Department will strengthen its collaboration with medical institutions and insurance companies to smoothly support users' access to medical services. As a result, the Medical Records Management Department will be able to centrally manage users' medical information and provide medical services efficiently and safely.

[0034] The Health Data Analysis Department can analyze health data acquired from wearable devices such as smartwatches. For example, the Health Data Analysis Department can analyze data such as heart rate, blood pressure, and body temperature acquired from smartwatches. For example, the Health Data Analysis Department can use AI to analyze data acquired from smartwatches and provide suggestions for daily health management and improvement. For example, the Health Data Analysis Department can also analyze data acquired from fitness trackers and provide suggestions for improving exercise habits. This makes it possible to manage health more accurately by analyzing data acquired from wearable devices. Some or all of the above-described processes in the Health Data Analysis Department may be performed using AI, for example, or without AI. For example, the Health Data Analysis Department can input data acquired from smartwatches into a generating AI and have the generating AI perform the data analysis.

[0035] The insurance product proposal department can analyze an individual's situation and propose an appropriate insurance product. For example, the insurance product proposal department can analyze an individual's age, health condition, lifestyle, etc., and propose the optimal insurance product. For example, the insurance product proposal department can use AI to analyze an individual's situation and propose the optimal insurance product. For example, the insurance product proposal department can analyze an individual's income and expenditure data and propose the optimal insurance product. In this way, by proposing the optimal insurance product according to the individual's situation, it is possible to provide insurance products that meet the user's needs. Some or all of the above processing in the insurance product proposal department may be performed using AI, for example, or without AI. For example, the insurance product proposal department can input individual situation data into a generating AI and have the generating AI execute insurance product proposals.

[0036] The Health Insurance Management Department can automatically analyze health insurance documents and provide guidance on coverage and procedures. For example, the Health Insurance Management Department can automatically analyze health insurance documents such as health insurance cards and application forms. For example, the Health Insurance Management Department can use AI to automatically analyze health insurance documents and provide guidance on coverage and procedures. The Health Insurance Management Department can also use OCR technology to digitize and analyze health insurance documents. This simplifies health insurance procedures. Some or all of the above-described processes in the Health Insurance Management Department may be performed using AI, or not. For example, the Health Insurance Management Department can input health insurance documents into a generating AI and have the generating AI perform document analysis.

[0037] The Medical Records Management Department can consolidate medical records in the cloud and perform data analysis and security management. For example, the Medical Records Management Department can consolidate medical records such as consultation records and prescription information in the cloud. The Medical Records Management Department can use AI to consolidate medical records in the cloud and perform data analysis and security management. The Medical Records Management Department can also analyze medical records using big data analysis or machine learning algorithms. This makes data analysis and security management easier by consolidating medical records in the cloud. Some or all of the above processes in the Medical Records Management Department may be performed using AI, or not. For example, the Medical Records Management Department can input medical record data into a generating AI and have the generating AI perform data analysis.

[0038] The Insurance Claim Support Department automatically generates insurance claim documents and notifies users of any missing information in real time. For example, the Insurance Claim Support Department automatically generates insurance claim documents such as insurance claim forms and medical certificates. The Insurance Claim Support Department can also automatically generate insurance claim documents using AI and notify users of any missing information in real time. The Insurance Claim Support Department can also generate insurance claim documents using template-based generation or AI generation. This automates the creation of insurance claim documents and the feedback of missing information, thereby speeding up the application process. Some or all of the above-described processes in the Insurance Claim Support Department may be performed using AI, for example, or without AI. For example, the Insurance Claim Support Department can have a generation AI perform the generation of insurance claim documents.

[0039] The subsidy application department analyzes the conditions for subsidies and presents applicable subsidies. The subsidy application department analyzes the conditions for subsidies, such as income limits and eligible projects. The subsidy application department analyzes the conditions for subsidies, for example, using AI and presents applicable subsidies. The subsidy application department can also analyze the conditions for subsidies using data mining or rule-based analysis, for example. This makes it easier for users to find subsidies they can use by suggesting applicable subsidies. Some or all of the above processes in the subsidy application department may be performed using AI, for example, or not using AI. For example, the subsidy application department can input subsidy condition data into a generating AI and have the generating AI generate suggestions for applicable subsidies.

[0040] The Personal Asset Management Department analyzes an individual's asset situation and proposes an optimal asset management plan. The Personal Asset Management Department analyzes asset situations such as cash, stocks, and real estate. The Personal Asset Management Department also uses AI to analyze an individual's asset situation and propose an optimal asset management plan. Furthermore, the Personal Asset Management Department can analyze asset situations using financial analysis and risk analysis. This streamlines asset management by proposing an optimal asset management plan tailored to the individual's asset situation. Some or all of the above processes in the Personal Asset Management Department may be performed using AI, or not. For example, the Personal Asset Management Department can input asset situation data into a generating AI and have the generating AI generate asset management plan proposals.

[0041] The Digital Inheritance Management Department provides a system for automatically generating necessary inheritance documents and obtaining digital signatures and approvals. For example, the Digital Inheritance Management Department automatically generates necessary inheritance documents such as wills and inheritance tax returns. The Digital Inheritance Management Department provides a system for automatically generating necessary inheritance documents using AI and obtaining digital signatures and approvals. The Digital Inheritance Management Department can also generate necessary inheritance documents using template-based generation or AI generation. This allows for faster and more efficient procedures by digitizing inheritance processes. Some or all of the above-mentioned processes in the Digital Inheritance Management Department may be performed using AI, or not. For example, the Digital Inheritance Management Department can have a generation AI perform the generation of necessary inheritance documents.

[0042] The Security Management Department encrypts and decrypts data, making it accessible only when necessary. The Security Management Department uses methods such as AES encryption and RSA encryption to encrypt and decrypt data. The Security Management Department also uses AI to encrypt and decrypt data, making it accessible only when necessary. The Security Management Department can also perform access control, such as authentication, authorization, and log management. This enhances data security and improves the protection of personal information by making it accessible only when necessary. Some or all of the above processes performed by the Security Management Department may be carried out using AI, for example, or without AI. For example, the Security Management Department can have a generating AI perform data encryption and decryption.

[0043] The health data analysis unit can improve the accuracy of its analysis by referring to the user's past health history during health data analysis. For example, the health data analysis unit can refer to the user's past health checkup results and compare them with the user's current health status for analysis. The health data analysis unit can also refer to the user's past exercise history and reflect changes in exercise habits in the analysis. The health data analysis unit can also refer to the user's past meal records and analyze areas for improvement in dietary habits. In this way, the accuracy of health data analysis is improved by referring to past health history. Some or all of the above processes in the health data analysis unit may be performed using AI, for example, or without AI. For example, the health data analysis unit can input past health history data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0044] The health data analysis unit can integrate and analyze users' lifestyle and dietary data during health data analysis. For example, the health data analysis unit can integrate users' sleep data and dietary data to analyze the relationship between sleep quality and diet. For example, the health data analysis unit can integrate users' exercise data and dietary data to analyze the relationship between exercise effects and diet. For example, the health data analysis unit can integrate users' stress levels and dietary data to analyze the relationship between stress management and diet. This enables more comprehensive health management by integrating and analyzing lifestyle and dietary data. Some or all of the above processing in the health data analysis unit may be performed using AI, for example, or without AI. For example, the health data analysis unit can input lifestyle and dietary data into a generating AI and have the generating AI perform integrated data analysis.

[0045] The health data analysis unit can perform health data analysis while considering the user's geographical location information. For example, if the user lives at high altitude, the health data analysis unit can perform analysis while considering health risks specific to high altitude. For example, if the user lives in an urban area, the health data analysis unit can also perform analysis while considering health risks specific to urban areas. For example, if the user lives by the sea, the health data analysis unit can also perform analysis while considering health risks specific to coastal areas. This makes it possible to perform analysis that reflects region-specific health risks by considering geographical location information. Some or all of the above processing in the health data analysis unit may be performed using AI, for example, or without using AI. For example, the health data analysis unit can input geographical location information data into a generating AI and have the generating AI perform analysis that takes location information into account.

[0046] The health data analysis unit can analyze a user's social media activity and acquire relevant health data during health data analysis. For example, the health data analysis unit can estimate a user's stress level from their social media posts and incorporate this into the health data analysis. The health data analysis unit can also estimate a user's exercise habits from their social media activity and incorporate this into the health data analysis. For example, the health data analysis unit can estimate a user's diet from their social media activity and incorporate this into the health data analysis. This allows additional information about the user's health status to be obtained by analyzing their social media activity. Some or all of the above-described processes in the health data analysis unit may be performed using AI, for example, or without AI. For example, the health data analysis unit can input social media activity data into a generating AI and have the generating AI acquire relevant health data.

[0047] The insurance product proposal department can make optimal proposals by referring to the user's past insurance contract history when proposing insurance products. For example, the insurance product proposal department can refer to the user's past insurance contract history and propose similar insurance products. For example, the insurance product proposal department can analyze changes in contract details from the user's past insurance contract history and propose the most suitable insurance products. For example, the insurance product proposal department can predict the contract renewal period based on the user's past insurance contract history and make proposals at the appropriate time. In this way, the insurance product proposal department can propose the most suitable insurance products to the user by referring to past insurance contract history. Some or all of the above processes in the insurance product proposal department may be performed using AI, for example, or not using AI. For example, the insurance product proposal department can input past insurance contract history data into a generating AI and have the generating AI execute the optimal proposal.

[0048] The insurance product proposal department can customize proposals based on the user's family structure and life stage when proposing insurance products. For example, if the user is raising children, the department can propose an insurance product that covers the cost of the children's education. If the user is retired, the department can also propose an insurance product that covers the cost of living in retirement. If the user is newly married, the department can also propose an insurance product that takes into account future family plans. In this way, by proposing insurance products that are tailored to the user's family structure and life stage, the department can provide the user with the most suitable insurance product. Some or all of the above processing in the insurance product proposal department may be performed using AI, for example, or not. For example, the insurance product proposal department can input family structure and life stage data into a generating AI and have the generating AI perform the customization of proposals.

[0049] The insurance product proposal department can propose the most suitable insurance product when proposing an insurance product, taking into account the user's geographical location information. For example, if the user lives in an urban area, the insurance product proposal department can propose an insurance product that covers risks specific to urban areas. For example, if the user lives in a rural area, the insurance product proposal department can also propose an insurance product that covers risks specific to rural areas. For example, if the user lives overseas, the insurance product proposal department can also propose an insurance product that covers risks specific to overseas locations. In this way, by proposing insurance products while considering geographical location information, it is possible to provide insurance products that cover risks specific to a particular region. Some or all of the above processing in the insurance product proposal department may be performed using AI, for example, or without using AI. For example, the insurance product proposal department can input geographical location data into a generating AI and have the generating AI execute a proposal that takes location information into account.

[0050] The insurance product proposal department can analyze a user's social media activity and propose relevant insurance products when proposing insurance products. For example, the insurance product proposal department can estimate life events from a user's social media posts and propose relevant insurance products. For example, the insurance product proposal department can estimate a user's hobbies and interests from their social media activity and propose relevant insurance products. For example, the insurance product proposal department can estimate a user's health status from their social media activity and propose relevant insurance products. In this way, by analyzing social media activity, it is possible to propose insurance products based on the user's life events and interests. Some or all of the above processing in the insurance product proposal department may be performed using AI, for example, or without AI. For example, the insurance product proposal department can input social media activity data into a generating AI and have the generating AI execute the proposal of relevant insurance products.

[0051] The Health Insurance Management Department can improve the accuracy of health insurance management by referring to the user's past insurance usage history. For example, the Health Insurance Management Department can refer to the user's past insurance usage history and suggest similar insurance usage methods. For example, the Health Insurance Management Department can also prioritize the management of frequently used insurance items based on the user's past insurance usage history. For example, the Health Insurance Management Department can predict the timing of usage based on the user's past insurance usage history and manage at the appropriate time. In this way, the accuracy of health insurance management is improved by referring to past insurance usage history. Some or all of the above processes in the Health Insurance Management Department may be performed using AI, for example, or without AI. For example, the Health Insurance Management Department can input past insurance usage history data into a generating AI and have the generating AI perform the improvement of management accuracy.

[0052] The Health Insurance Management Department can integrate and manage users' health status and medical records when managing health insurance. For example, the Health Insurance Management Department can integrate users' health checkup results and insurance usage history to manage health insurance. For example, the Health Insurance Management Department can integrate users' medical records and insurance usage history to manage health insurance. For example, the Health Insurance Management Department can integrate users' health status and insurance usage history to manage health insurance. This enables more comprehensive health insurance management by integrating and managing health status and medical records. Some or all of the above processes in the Health Insurance Management Department may be performed using AI, for example, or without AI. For example, the Health Insurance Management Department can input health status and medical record data into a generating AI and have the generating AI perform integrated data management.

[0053] The Health Insurance Management Department can manage health insurance while taking into account the user's geographical location information. For example, if the user lives in an urban area, the Health Insurance Management Department can provide urban-specific health insurance management methods. For example, if the user lives in a rural area, the Health Insurance Management Department can also provide rural-specific health insurance management methods. For example, if the user lives overseas, the Health Insurance Management Department can also provide overseas-specific health insurance management methods. This makes region-specific health insurance management possible by taking geographical location information into account. Some or all of the above processing in the Health Insurance Management Department may be performed using AI, for example, or without AI. For example, the Health Insurance Management Department can input geographical location data into a generating AI and have the generating AI perform management that takes location information into account.

[0054] The Health Insurance Management Department can analyze users' social media activity and obtain relevant health insurance information when managing health insurance. For example, the Health Insurance Management Department can estimate users' interest in health insurance from their social media posts and provide relevant information. For example, the Health Insurance Management Department can collect questions and concerns about health insurance from users' social media activity and provide appropriate answers. For example, the Health Insurance Management Department can analyze trends related to health insurance from users' social media activity and provide the latest information. In this way, by analyzing social media activity, it is possible to provide information based on users' interests and questions about health insurance. Some or all of the above processes in the Health Insurance Management Department may be performed using AI, for example, or not using AI. For example, the Health Insurance Management Department can input social media activity data into a generating AI and have the generating AI perform the acquisition of relevant health insurance information.

[0055] The medical record management department can improve the accuracy of medical record management by referring to the user's past medical history. For example, the medical record management department can refer to the user's past medical history and propose a similar medical record management method. For example, the medical record management department can also prioritize the management of frequently used medical items based on the user's past medical history. For example, the medical record management department can predict the timing of use based on the user's past medical history and manage at the appropriate time. This improves the accuracy of medical record management by referring to past medical history. Some or all of the above processes in the medical record management department may be performed using AI, for example, or without AI. For example, the medical record management department can input past medical history data into a generating AI and have the generating AI perform the improvement of management accuracy.

[0056] The Medical Records Management Department can integrate and manage users' health data and insurance information when managing medical records. For example, the Medical Records Management Department can integrate users' health checkup results and medical records and manage them. For example, the Medical Records Management Department can also integrate users' insurance information and medical records and manage them. For example, the Medical Records Management Department can integrate users' health status and medical records and manage them. This enables more comprehensive medical record management by integrating and managing health data and insurance information. Some or all of the above processes in the Medical Records Management Department may be performed using AI, for example, or not using AI. For example, the Medical Records Management Department can input health data and insurance information data into a generating AI and have the generating AI perform integrated data management.

[0057] The medical records management department can manage medical records while taking into account the user's geographical location information. For example, if the user lives in an urban area, the medical records management department can provide a medical record management method specific to urban areas. For example, if the user lives in a rural area, the medical records management department can also provide a medical record management method specific to rural areas. For example, if the user lives overseas, the medical records management department can also provide a medical record management method specific to overseas locations. This makes it possible to manage medical records in a region that takes geographical location information into account. Some or all of the above processing in the medical records management department may be performed using AI, for example, or without AI. For example, the medical records management department can input geographical location data into a generating AI and have the generating AI perform management that takes location information into account.

[0058] The medical records management department can analyze users' social media activity and retrieve relevant medical records when managing medical records. For example, the medical records management department can estimate users' medical interests from their social media posts and provide relevant information. For example, the medical records management department can collect medical questions and inquiries from users' social media activity and provide appropriate answers. For example, the medical records management department can analyze medical trends from users' social media activity and provide the latest information. In this way, by analyzing social media activity, it is possible to provide information based on users' medical interests and inquiries. Some or all of the above processes in the medical records management department may be performed using AI, for example, or not using AI. For example, the medical records management department can input social media activity data into a generating AI and have the generating AI retrieve relevant medical records.

[0059] The insurance claim support unit can improve the accuracy of its support by referring to the user's past claim history when providing insurance claim support. For example, the insurance claim support unit can refer to the user's past insurance claim history and propose similar insurance claim support methods. For example, the insurance claim support unit can also prioritize support for frequently used insurance items based on the user's past insurance claim history. For example, the insurance claim support unit can predict the timing of an application based on the user's past insurance claim history and provide support at the appropriate time. This improves the accuracy of insurance claim support by referring to past claim history. Some or all of the above processes in the insurance claim support unit may be performed using AI, for example, or without AI. For example, the insurance claim support unit can input past claim history data into a generating AI and have the generating AI perform the task of improving the accuracy of support.

[0060] The insurance claim support unit can provide support while considering the user's geographical location information. For example, if the user lives in an urban area, the insurance claim support unit can provide insurance claim support methods specific to urban areas. For example, if the user lives in a rural area, the insurance claim support unit can also provide insurance claim support methods specific to rural areas. For example, if the user lives overseas, the insurance claim support unit can also provide insurance claim support methods specific to overseas locations. This makes it possible to provide region-specific insurance claim support by considering geographical location information. Some or all of the above processing in the insurance claim support unit may be performed using AI, for example, or without AI. For example, the insurance claim support unit can input geographical location information data into a generating AI and have the generating AI perform support that takes location information into consideration.

[0061] The subsidy application department can improve the accuracy of its support by referring to the user's past application history when assisting with subsidy applications. For example, the subsidy application department can refer to the user's past subsidy application history and propose similar subsidy application support methods. For example, the subsidy application department can also prioritize support for frequently used subsidy items based on the user's past subsidy application history. For example, the subsidy application department can predict the application timing based on the user's past subsidy application history and provide support at the appropriate time. This improves the accuracy of subsidy application support by referring to past application history. Some or all of the above processes in the subsidy application department may be performed using AI, for example, or without AI. For example, the subsidy application department can input past application history data into a generating AI and have the generating AI perform the task of improving the accuracy of support.

[0062] The subsidy application unit can provide support for subsidy applications while taking into account the user's geographical location. For example, if the user lives in an urban area, the subsidy application unit can provide subsidy application support methods specific to urban areas. For example, if the user lives in a rural area, the subsidy application unit can also provide subsidy application support methods specific to rural areas. For example, if the user lives overseas, the subsidy application unit can also provide subsidy application support methods specific to overseas areas. This makes it possible to provide region-specific subsidy application support by taking geographical location information into account. Some or all of the above processing in the subsidy application unit may be performed using AI, for example, or without using AI. For example, the subsidy application unit can input geographical location data into a generating AI and have the generating AI perform support that takes location information into account.

[0063] The personal asset management unit can improve the accuracy of personal asset management by referring to the user's past asset management history. For example, the personal asset management unit can refer to the user's past asset management history and suggest similar asset management methods. For example, the personal asset management unit can also prioritize the management of frequently used asset items based on the user's past asset management history. For example, the personal asset management unit can predict the timing of management based on the user's past asset management history and perform management at the appropriate time. This improves the accuracy of personal asset management by referring to past asset management history. Some or all of the above processes in the personal asset management unit may be performed using AI, for example, or without AI. For example, the personal asset management unit can input past asset management history data into a generating AI and have the generating AI perform the improvement of management accuracy.

[0064] The personal asset management unit can integrate and manage the user's income and expenditure data when managing personal assets. For example, the personal asset management unit can integrate the user's income and expenditure data to manage the balance of income and expenses. For example, the personal asset management unit can also integrate the user's income and expenditure data to suggest saving points. For example, the personal asset management unit can integrate the user's income and expenditure data to suggest future asset management plans. This enables more comprehensive personal asset management by integrating and managing income and expenditure data. Some or all of the above processes in the personal asset management unit may be performed using AI, for example, or not using AI. For example, the personal asset management unit can input income and expenditure data into a generating AI and have the generating AI perform integrated data management.

[0065] The personal property management unit can manage personal property while taking into account the user's geographical location information. For example, if the user lives in an urban area, the personal property management unit can provide property management methods specific to urban areas. For example, if the user lives in a rural area, the personal property management unit can also provide property management methods specific to rural areas. For example, if the user lives overseas, the personal property management unit can also provide property management methods specific to overseas locations. This makes it possible to manage personal property in a region that takes geographical location information into account. Some or all of the above processing in the personal property management unit may be performed using AI, for example, or without AI. For example, the personal property management unit can input geographical location data into a generating AI and have the generating AI perform management that takes location information into account.

[0066] The Personal Asset Management Department can analyze a user's social media activity and obtain relevant asset management information when managing personal assets. For example, the Personal Asset Management Department can estimate a user's interest in asset management from their social media posts and provide relevant information. The Personal Asset Management Department can also collect questions and concerns about asset management from a user's social media activity and provide appropriate answers. The Personal Asset Management Department can also analyze trends in asset management from a user's social media activity and provide the latest information. In this way, by analyzing social media activity, it can provide information based on the user's interest in and questions about asset management. Some or all of the above processes in the Personal Asset Management Department may be performed using AI, for example, or not using AI. For example, the Personal Asset Management Department can input social media activity data into a generating AI and have the generating AI perform the acquisition of relevant asset management information.

[0067] The Digital Inheritance Management Department can improve the accuracy of digital inheritance management by referring to the user's past inheritance history. For example, the Digital Inheritance Management Department can refer to the user's past inheritance history and suggest similar inheritance management methods. For example, the Digital Inheritance Management Department can also prioritize the management of frequently used inheritance items based on the user's past inheritance history. For example, the Digital Inheritance Management Department can predict the timing of management based on the user's past inheritance history and perform management at the appropriate time. This improves the accuracy of digital inheritance management by referring to past inheritance history. Some or all of the above processes in the Digital Inheritance Management Department may be performed using AI, for example, or without AI. For example, the Digital Inheritance Management Department can input past inheritance history data into a generating AI and have the generating AI perform the improvement of management accuracy.

[0068] The Digital Inheritance Management Department can integrate and manage user asset data and family structure information during digital inheritance management. For example, the Digital Inheritance Management Department can integrate user asset data and family structure information to perform inheritance management. For example, the Digital Inheritance Management Department can integrate user asset data and family structure information to propose an inheritance plan. For example, the Digital Inheritance Management Department can integrate user asset data and family structure information to simplify inheritance procedures. This enables more comprehensive digital inheritance management by integrating and managing asset data and family structure information. Some or all of the above processes in the Digital Inheritance Management Department may be performed using AI, for example, or without AI. For example, the Digital Inheritance Management Department can input asset data and family structure information into a generating AI and have the generating AI perform integrated data management.

[0069] The Digital Inheritance Management Department can manage digital inheritances while taking into account the user's geographical location information. For example, if the user lives in an urban area, the Digital Inheritance Management Department can provide inheritance management methods specific to urban areas. For example, if the user lives in a rural area, the Digital Inheritance Management Department can also provide inheritance management methods specific to rural areas. For example, if the user lives overseas, the Digital Inheritance Management Department can also provide inheritance management methods specific to overseas locations. This enables region-specific digital inheritance management by taking geographical location information into account. Some or all of the above-described processes in the Digital Inheritance Management Department may be performed using AI, for example, or without AI. For example, the Digital Inheritance Management Department can input geographical location data into a generating AI and have the generating AI perform management that takes location information into account.

[0070] The Digital Inheritance Management Department can analyze users' social media activity and obtain relevant inheritance information during digital inheritance management. For example, the Digital Inheritance Management Department can estimate users' interest in inheritance from their social media posts and provide relevant information. For example, the Digital Inheritance Management Department can collect questions and concerns about inheritance from users' social media activity and provide appropriate answers. For example, the Digital Inheritance Management Department can analyze inheritance trends from users' social media activity and provide the latest information. In this way, by analyzing social media activity, it can provide information based on users' interests and questions about inheritance. Some or all of the above processes in the Digital Inheritance Management Department may be performed using AI, for example, or not using AI. For example, the Digital Inheritance Management Department can input social media activity data into a generating AI and have the generating AI perform the acquisition of relevant inheritance information.

[0071] The security management department can improve the accuracy of security management by referring to the user's past security history during security management. For example, the security management department can refer to the user's past security history and propose similar security management methods. For example, the security management department can also prioritize the management of frequently used security items based on the user's past security history. For example, the security management department can predict the timing of management based on the user's past security history and perform management at the appropriate time. In this way, the accuracy of security management is improved by referring to past security history. Some or all of the above processes in the security management department may be performed using AI, for example, or without AI. For example, the security management department can input past security history data into a generating AI and have the generating AI perform the improvement of management accuracy.

[0072] The security management department can perform security management while taking into account the user's geographical location information. For example, if the user lives in an urban area, the security management department can provide security management methods specific to urban areas. For example, if the user lives in a rural area, the security management department can also provide security management methods specific to rural areas. For example, if the user lives overseas, the security management department can also provide security management methods specific to overseas locations. This makes it possible to perform region-specific security management by taking geographical location information into consideration. Some or all of the above processing in the security management department may be performed using AI, for example, or without using AI. For example, the security management department can input geographical location data into a generating AI and have the generating AI perform management that takes location information into consideration.

[0073] The Security Management Department can analyze users' social media activity and obtain relevant security information during security management. For example, the Security Management Department can estimate users' security interests from their social media posts and provide relevant information. For example, the Security Management Department can collect security questions and concerns from users' social media activity and provide appropriate answers. For example, the Security Management Department can analyze security trends from users' social media activity and provide the latest information. In this way, by analyzing social media activity, it is possible to provide information based on users' security interests and questions. Some or all of the above processes in the Security Management Department may be performed using AI, for example, or not using AI. For example, the Security Management Department can input social media activity data into a generating AI and have the generating AI perform the acquisition of relevant security information.

[0074] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0075] The life management support system can improve the accuracy of health management monitoring by referencing the user's past health data. For example, it can refer to past health checkup results and analyze them in comparison to the current health status. It can also refer to past exercise history and reflect changes in exercise habits in the analysis. Furthermore, it can refer to past meal records and analyze areas for improvement in dietary habits. In this way, more accurate health management becomes possible by utilizing past health data.

[0076] The life management support system can integrate users' lifestyle and dietary data to monitor their health. For example, it can integrate sleep data and dietary data to analyze the relationship between sleep quality and diet. It can also integrate exercise data and dietary data to analyze the relationship between exercise effects and diet. Furthermore, it can integrate stress levels and dietary data to analyze the relationship between stress management and diet. By integrating and analyzing lifestyle and dietary data, it enables more comprehensive health management.

[0077] The life management support system can perform health management monitoring while considering the user's geographical location. For example, if the user lives in a high-altitude area, the system can analyze the health risks specific to high altitudes. Similarly, if the user lives in an urban area, the system can analyze the health risks specific to urban areas. Furthermore, if the user lives by the sea, the system can analyze the health risks specific to coastal areas. In this way, by considering geographical location in the analysis, it becomes possible to perform analyses that reflect region-specific health risks.

[0078] The life management support system can analyze a user's social media activity and obtain relevant health data. For example, it can estimate stress levels from a user's social media posts and incorporate this into the health data analysis. It can also estimate exercise habits from a user's social media activity and incorporate this into the health data analysis. Furthermore, it can estimate dietary habits from a user's social media activity and incorporate this into the health data analysis. In this way, additional information about the user's health status can be obtained by analyzing social media activity.

[0079] The life management support system can improve the accuracy of insurance product recommendations by referring to the user's past insurance contract history. For example, it can refer to past insurance contract history and recommend similar insurance products. It can also analyze changes in contract details from past insurance contract history and recommend the most suitable insurance product. Furthermore, it can predict contract renewal dates based on past insurance contract history and make recommendations at the appropriate time. In this way, by utilizing past insurance contract history, more accurate insurance product recommendations become possible.

[0080] The life management support system can customize insurance product recommendations based on the user's family structure and life stage. For example, if the user is raising children, it can suggest insurance products that cover the cost of their children's education. If the user is retired, it can suggest insurance products that cover living expenses in retirement. Furthermore, if the user is newly married, it can suggest insurance products that take into account their future family plans. This allows for insurance product recommendations tailored to family structure and life stage, resulting in more appropriate recommendations.

[0081] The life management support system can suggest insurance products while considering the user's geographical location. For example, if the user lives in an urban area, it can suggest insurance products that cover risks specific to urban areas. Similarly, if the user lives in a rural area, it can suggest insurance products that cover risks specific to rural areas. Furthermore, if the user lives overseas, it can suggest insurance products that cover risks specific to overseas locations. In this way, by suggesting insurance products while considering geographical location, it can provide insurance products that cover risks specific to a particular region.

[0082] The following briefly describes the processing flow for example form 1.

[0083] Step 1: The Health Data Analysis Department analyzes health data. For example, it analyzes health data such as heart rate, blood pressure, and body temperature obtained from wearable devices like smartwatches. The Health Data Analysis Department uses AI to provide daily health management and improvement suggestions. Step 2: The Insurance Product Proposal Department proposes insurance products based on the data analyzed by the Health Data Analysis Department. For example, it analyzes an individual's situation and proposes the most suitable insurance product, such as medical insurance or life insurance. The Insurance Product Proposal Department uses AI to analyze an individual's situation and propose the most suitable insurance product. Step 3: The Health Insurance Management Department manages health insurance based on the insurance products proposed by the Insurance Product Proposal Department. For example, it automatically analyzes health insurance documents and guides users through the scope of coverage and procedures. The Health Insurance Management Department uses AI to automatically analyze health insurance documents and guide users through the scope of coverage and procedures. Step 4: The Medical Records Management Department manages medical records based on data managed by the Health Insurance Management Department. For example, medical records are aggregated in the cloud, and data analysis and security management are performed. Medical records include medical records, prescription information, etc. The Medical Records Management Department uses AI to aggregate medical records in the cloud, and performs data analysis and security management.

[0084] (Example of form 2) The life management support system according to an embodiment of the present invention is a system in which an AI agent supports the complex handling of various life management tasks in the era of 100-year lifespans. This life management support system provides functions such as health management monitoring, insurance product recommendations, health insurance management, medical record management, insurance claim support, subsidy application support, personal asset management, and digital inheritance management. For example, as health management monitoring, the life management support system uses AI to analyze health data acquired from wearable devices such as smartwatches and provides daily health management and improvement suggestions. Next, as insurance product recommendations, the life management support system uses AI to analyze an individual's situation and propose the most suitable insurance product. Furthermore, as health insurance management, the life management support system uses AI to automatically analyze documents related to health insurance and navigate the scope of application and procedures. A chatbot is also provided to simplify procedures. In addition, as medical record management, the life management support system aggregates medical records in the cloud, and AI performs data analysis and security management. It can also be shared with medical institutions. As insurance claim support, the life management support system uses AI to automatically generate insurance claim documents and provides real-time feedback on any missing information. The system allows for quick review and modification of application details. For subsidy applications, the AI ​​analyzes subsidy requirements and proposes applicable subsidies. It automatically generates application documents and manages progress. For personal asset management, the AI ​​analyzes an individual's asset situation and proposes an optimal asset management plan. Real-time risk management is also provided. For digital inheritance management, the AI ​​automatically generates necessary inheritance documents and provides a system for digital signatures and approvals. Real-time progress management is also provided. Furthermore, the system enhances the security of personal information through AI-driven automation and the introduction of blockchain technology. The AI ​​automatically encrypts and decrypts data, making it accessible only when needed. This eliminates the need for manual management of passwords, etc. This system allows you to entrust the complex tasks of information management, document creation, procedures, and applications to an AI agent, enriching your life.This allows the life management support system to handle everything from health data analysis and insurance product recommendations to health insurance management and medical record management in a comprehensive manner.

[0085] The life management support system according to this embodiment comprises a health data analysis unit, an insurance product proposal unit, a health insurance management unit, and a medical record management unit. The health data analysis unit analyzes health data. The health data analysis unit analyzes health data acquired from, for example, wearable devices such as smartwatches. Health data includes, but is not limited to, heart rate, blood pressure, and body temperature. The health data analysis unit analyzes health data using, for example, AI, and provides daily health management and improvement suggestions. The insurance product proposal unit proposes insurance products based on the data analyzed by the health data analysis unit. The insurance product proposal unit analyzes an individual's situation and proposes the most suitable insurance product. Insurance products include, for example, medical insurance and life insurance, but is not limited to, medical insurance and life insurance. The insurance product proposal unit analyzes an individual's situation using, for example, AI, and proposes the most suitable insurance product. The health insurance management unit manages health insurance based on the insurance products proposed by the insurance product proposal unit. The health insurance management unit automatically analyzes documents related to health insurance and provides guidance on the scope of application and procedures. The Health Insurance Management Department, for example, uses AI to automatically analyze health insurance documents and guide users through the scope of application and procedures. The Medical Records Management Department manages medical records based on the data managed by the Health Insurance Management Department. The Medical Records Management Department, for example, aggregates medical records in the cloud and performs data analysis and security management. Medical records include, but are not limited to, medical records and prescription information. The Medical Records Management Department, for example, uses AI to aggregate medical records in the cloud and performs data analysis and security management. As a result, the life management support system according to this embodiment can consistently perform everything from health data analysis to insurance product proposals, health insurance management, and medical record management.

[0086] The Health Data Analysis Department analyzes health data. For example, it analyzes health data acquired from wearable devices such as smartwatches. Health data includes, but is not limited to, heart rate, blood pressure, and body temperature. Specifically, smartwatches monitor the user's heart rate 24 hours a day and detect abnormal patterns. Blood pressure is measured regularly and daily fluctuations are recorded. Body temperature is particularly important for the early detection of signs of fever. This data is transmitted to smartphones and cloud servers via Bluetooth or Wi-Fi. The Health Data Analysis Department uses AI, for example, to analyze health data and provide daily health management and improvement suggestions. The AI ​​uses machine learning algorithms to analyze large amounts of data and evaluate the user's health status. For example, if abnormal fluctuations in heart rate are detected, the AI ​​may suggest stress, lack of exercise, or the possibility of underlying heart disease. Blood pressure data is used to assess the risk of hypertension or hypotension and to suggest appropriate lifestyle improvements. Body temperature data helps in the early detection of infectious diseases and encourages medical consultations if necessary. This allows the health data analysis department to monitor users' health status in real time and provide appropriate advice. Furthermore, the health data analysis department can accumulate historical data and analyze long-term health trends. This enables users to understand changes in their health status and manage their health more effectively.

[0087] The Insurance Product Proposal Department proposes insurance products based on data analyzed by the Health Data Analysis Department. For example, the Insurance Product Proposal Department analyzes an individual's situation and proposes the most suitable insurance product. Insurance products include, but are not limited to, medical insurance and life insurance. Specifically, based on data provided by the Health Data Analysis Department, AI evaluates the user's health risks. For example, if data on heart rate and blood pressure indicates a high risk of heart disease or hypertension, the AI ​​proposes corresponding medical insurance. The department also considers the user's age, lifestyle, and medical history to select the most suitable insurance product. The Insurance Product Proposal Department uses AI to analyze an individual's situation and proposes the most suitable insurance product. The AI ​​compares the user's health data with a database of insurance products to select the most appropriate product. For example, it proposes life insurance to prepare for future risks for younger users, and medical insurance to reduce the burden of medical expenses for middle-aged and older users. Furthermore, the Insurance Product Proposal Department collects user feedback and continuously improves the accuracy of its proposals. This allows the insurance product proposal department to provide users with insurance products that are best suited to their health condition and lifestyle, and to support them in living with peace of mind.

[0088] The Health Insurance Management Department manages health insurance based on insurance products proposed by the Insurance Product Proposal Department. For example, the Health Insurance Management Department automatically analyzes health insurance documents and guides users through the scope of coverage and procedures. Specifically, AI automatically analyzes documents such as insurance application forms and medical expense statements submitted by users and extracts the necessary information. This simplifies complex procedures for users, allowing them to receive insurance benefits quickly. The Health Insurance Management Department uses AI to automatically analyze health insurance documents and guide users through the scope of coverage and procedures. The AI ​​uses natural language processing technology to understand the content of documents and guides users through the scope of coverage and necessary procedures. For example, it determines whether a specific treatment is covered by insurance and, if so, guides the user through the application process. It also lists the documents and information necessary for insurance claim procedures and provides them to the user. This allows the Health Insurance Management Department to support users in smoothly completing insurance procedures and expedite the receipt of insurance benefits. Furthermore, the Health Insurance Management Department also supports insurance contract renewal and modification procedures, ensuring users always have access to the most suitable insurance products. This will allow the Health Insurance Management Department to streamline users' insurance management and provide an environment where they can use medical services with peace of mind.

[0089] The Medical Records Management Department manages medical records based on data managed by the Health Insurance Management Department. For example, the Medical Records Management Department aggregates medical records in the cloud and performs data analysis and security management. Medical records include, but are not limited to, medical records and prescription information. Specifically, it aggregates users' medical records and prescription information on a cloud server and performs data analysis using AI. This allows for centralized management of users' medical history and the rapid provision of necessary information. The Medical Records Management Department uses AI to aggregate medical records in the cloud and performs data analysis and security management. The AI ​​analyzes users' medical records and evaluates changes in health status and treatment effectiveness. For example, it compares past medical records with current health data to understand the progress of treatment. It also analyzes prescription information to evaluate drug interactions and the risk of side effects. Furthermore, the Medical Records Management Department places a strong emphasis on security management and protects user privacy. The cloud server incorporates the latest security technologies and measures to prevent unauthorized access and data leaks. This allows the Medical Records Management Department to securely manage users' medical information and provide necessary information quickly and accurately. Furthermore, the Medical Records Management Department will strengthen its collaboration with medical institutions and insurance companies to smoothly support users' access to medical services. As a result, the Medical Records Management Department will be able to centrally manage users' medical information and provide medical services efficiently and safely.

[0090] The Health Data Analysis Department can analyze health data acquired from wearable devices such as smartwatches. For example, the Health Data Analysis Department can analyze data such as heart rate, blood pressure, and body temperature acquired from smartwatches. For example, the Health Data Analysis Department can use AI to analyze data acquired from smartwatches and provide suggestions for daily health management and improvement. For example, the Health Data Analysis Department can also analyze data acquired from fitness trackers and provide suggestions for improving exercise habits. This makes it possible to manage health more accurately by analyzing data acquired from wearable devices. Some or all of the above-described processes in the Health Data Analysis Department may be performed using AI, for example, or without AI. For example, the Health Data Analysis Department can input data acquired from smartwatches into a generating AI and have the generating AI perform the data analysis.

[0091] The insurance product proposal department can analyze an individual's situation and propose an appropriate insurance product. For example, the insurance product proposal department can analyze an individual's age, health condition, lifestyle, etc., and propose the optimal insurance product. For example, the insurance product proposal department can use AI to analyze an individual's situation and propose the optimal insurance product. For example, the insurance product proposal department can analyze an individual's income and expenditure data and propose the optimal insurance product. In this way, by proposing the optimal insurance product according to the individual's situation, it is possible to provide insurance products that meet the user's needs. Some or all of the above processing in the insurance product proposal department may be performed using AI, for example, or without AI. For example, the insurance product proposal department can input individual situation data into a generating AI and have the generating AI execute insurance product proposals.

[0092] The Health Insurance Management Department can automatically analyze health insurance documents and provide guidance on coverage and procedures. For example, the Health Insurance Management Department can automatically analyze health insurance documents such as health insurance cards and application forms. For example, the Health Insurance Management Department can use AI to automatically analyze health insurance documents and provide guidance on coverage and procedures. The Health Insurance Management Department can also use OCR technology to digitize and analyze health insurance documents. This simplifies health insurance procedures. Some or all of the above-described processes in the Health Insurance Management Department may be performed using AI, or not. For example, the Health Insurance Management Department can input health insurance documents into a generating AI and have the generating AI perform document analysis.

[0093] The Medical Records Management Department can consolidate medical records in the cloud and perform data analysis and security management. For example, the Medical Records Management Department can consolidate medical records such as consultation records and prescription information in the cloud. The Medical Records Management Department can use AI to consolidate medical records in the cloud and perform data analysis and security management. The Medical Records Management Department can also analyze medical records using big data analysis or machine learning algorithms. This makes data analysis and security management easier by consolidating medical records in the cloud. Some or all of the above processes in the Medical Records Management Department may be performed using AI, or not. For example, the Medical Records Management Department can input medical record data into a generating AI and have the generating AI perform data analysis.

[0094] The Insurance Claim Support Department automatically generates insurance claim documents and notifies users of any missing information in real time. For example, the Insurance Claim Support Department automatically generates insurance claim documents such as insurance claim forms and medical certificates. The Insurance Claim Support Department can also automatically generate insurance claim documents using AI and notify users of any missing information in real time. The Insurance Claim Support Department can also generate insurance claim documents using template-based generation or AI generation. This automates the creation of insurance claim documents and the feedback of missing information, thereby speeding up the application process. Some or all of the above-described processes in the Insurance Claim Support Department may be performed using AI, for example, or without AI. For example, the Insurance Claim Support Department can have a generation AI perform the generation of insurance claim documents.

[0095] The subsidy application department analyzes the conditions for subsidies and presents applicable subsidies. The subsidy application department analyzes the conditions for subsidies, such as income limits and eligible projects. The subsidy application department analyzes the conditions for subsidies, for example, using AI and presents applicable subsidies. The subsidy application department can also analyze the conditions for subsidies using data mining or rule-based analysis, for example. This makes it easier for users to find subsidies they can use by suggesting applicable subsidies. Some or all of the above processes in the subsidy application department may be performed using AI, for example, or not using AI. For example, the subsidy application department can input subsidy condition data into a generating AI and have the generating AI generate suggestions for applicable subsidies.

[0096] The Personal Asset Management Department analyzes an individual's asset situation and proposes an optimal asset management plan. The Personal Asset Management Department analyzes asset situations such as cash, stocks, and real estate. The Personal Asset Management Department also uses AI to analyze an individual's asset situation and propose an optimal asset management plan. Furthermore, the Personal Asset Management Department can analyze asset situations using financial analysis and risk analysis. This streamlines asset management by proposing an optimal asset management plan tailored to the individual's asset situation. Some or all of the above processes in the Personal Asset Management Department may be performed using AI, or not. For example, the Personal Asset Management Department can input asset situation data into a generating AI and have the generating AI generate asset management plan proposals.

[0097] The Digital Inheritance Management Department provides a system for automatically generating necessary inheritance documents and obtaining digital signatures and approvals. For example, the Digital Inheritance Management Department automatically generates necessary inheritance documents such as wills and inheritance tax returns. The Digital Inheritance Management Department provides a system for automatically generating necessary inheritance documents using AI and obtaining digital signatures and approvals. The Digital Inheritance Management Department can also generate necessary inheritance documents using template-based generation or AI generation. This allows for faster and more efficient procedures by digitizing inheritance processes. Some or all of the above-mentioned processes in the Digital Inheritance Management Department may be performed using AI, or not. For example, the Digital Inheritance Management Department can have a generation AI perform the generation of necessary inheritance documents.

[0098] The Security Management Department encrypts and decrypts data, making it accessible only when necessary. The Security Management Department uses methods such as AES encryption and RSA encryption to encrypt and decrypt data. The Security Management Department also uses AI to encrypt and decrypt data, making it accessible only when necessary. The Security Management Department can also perform access control, such as authentication, authorization, and log management. This enhances data security and improves the protection of personal information by making it accessible only when necessary. Some or all of the above processes performed by the Security Management Department may be carried out using AI, for example, or without AI. For example, the Security Management Department can have a generating AI perform data encryption and decryption.

[0099] The health data analysis unit can estimate the user's emotions and adjust the health data analysis method based on the estimated user emotions. For example, if the user is stressed, the health data analysis unit can select a data analysis method that promotes relaxation. For example, if the user is relaxed, the health data analysis unit can perform a detailed health data analysis and provide improvement suggestions. For example, if the user is in a hurry, the health data analysis unit can perform a concise and rapid health data analysis. This allows for more appropriate health management by adjusting the health data analysis method according to the user's emotions. 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. Some or all of the above processing in the health data analysis unit may be performed using AI, for example, or without AI. For example, the health data analysis unit can input the user's emotion data into a generative AI and have the generative AI perform adjustments to the analysis method based on emotions.

[0100] The health data analysis unit can improve the accuracy of its analysis by referring to the user's past health history during health data analysis. For example, the health data analysis unit can refer to the user's past health checkup results and compare them with the user's current health status for analysis. The health data analysis unit can also refer to the user's past exercise history and reflect changes in exercise habits in the analysis. The health data analysis unit can also refer to the user's past meal records and analyze areas for improvement in dietary habits. In this way, the accuracy of health data analysis is improved by referring to past health history. Some or all of the above processes in the health data analysis unit may be performed using AI, for example, or without AI. For example, the health data analysis unit can input past health history data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0101] The health data analysis unit can integrate and analyze users' lifestyle and dietary data during health data analysis. For example, the health data analysis unit can integrate users' sleep data and dietary data to analyze the relationship between sleep quality and diet. For example, the health data analysis unit can integrate users' exercise data and dietary data to analyze the relationship between exercise effects and diet. For example, the health data analysis unit can integrate users' stress levels and dietary data to analyze the relationship between stress management and diet. This enables more comprehensive health management by integrating and analyzing lifestyle and dietary data. Some or all of the above processing in the health data analysis unit may be performed using AI, for example, or without AI. For example, the health data analysis unit can input lifestyle and dietary data into a generating AI and have the generating AI perform integrated data analysis.

[0102] The health data analysis unit can estimate the user's emotions and prioritize health data based on the estimated emotions. For example, if the user is stressed, the health data analysis unit will prioritize analyzing stress-related health data. For example, if the user is relaxed, the health data analysis unit can also analyze overall health data in a balanced manner. For example, if the user is in a hurry, the health data analysis unit can prioritize analyzing only important health data. This allows for the prioritization of important data by determining the priority of health data according to the user's emotions. 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. Some or all of the above processing in the health data analysis unit may be performed using AI or not using AI. For example, the health data analysis unit can input the user's emotion data into a generative AI and have the generative AI perform emotion-based priority determination.

[0103] The health data analysis unit can perform health data analysis while considering the user's geographical location information. For example, if the user lives at high altitude, the health data analysis unit can perform analysis while considering health risks specific to high altitude. For example, if the user lives in an urban area, the health data analysis unit can also perform analysis while considering health risks specific to urban areas. For example, if the user lives by the sea, the health data analysis unit can also perform analysis while considering health risks specific to coastal areas. This makes it possible to perform analysis that reflects region-specific health risks by considering geographical location information. Some or all of the above processing in the health data analysis unit may be performed using AI, for example, or without using AI. For example, the health data analysis unit can input geographical location information data into a generating AI and have the generating AI perform analysis that takes location information into account.

[0104] The health data analysis unit can analyze a user's social media activity and acquire relevant health data during health data analysis. For example, the health data analysis unit can estimate a user's stress level from their social media posts and incorporate this into the health data analysis. The health data analysis unit can also estimate a user's exercise habits from their social media activity and incorporate this into the health data analysis. For example, the health data analysis unit can estimate a user's diet from their social media activity and incorporate this into the health data analysis. This allows additional information about the user's health status to be obtained by analyzing their social media activity. Some or all of the above-described processes in the health data analysis unit may be performed using AI, for example, or without AI. For example, the health data analysis unit can input social media activity data into a generating AI and have the generating AI acquire relevant health data.

[0105] The insurance product recommendation department can estimate the user's emotions and adjust its approach to recommending insurance products based on those emotions. For example, if the user is stressed, the department might recommend simple and easy-to-understand insurance products. If the user is relaxed, the department might provide more detailed information and increase the user's options. If the user is in a hurry, the department might prioritize recommending the most suitable insurance products to ensure a quick response. By adjusting the approach to recommending insurance products according to the user's emotions, more appropriate recommendations become possible. 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. Some or all of the above-described processes in the insurance product recommendation department may be performed using AI or not. For example, the insurance product recommendation department can input user emotion data into a generative AI and have the generative AI adjust its recommendation approach based on those emotions.

[0106] The insurance product proposal department can make optimal proposals by referring to the user's past insurance contract history when proposing insurance products. For example, the insurance product proposal department can refer to the user's past insurance contract history and propose similar insurance products. For example, the insurance product proposal department can analyze changes in contract details from the user's past insurance contract history and propose the most suitable insurance products. For example, the insurance product proposal department can predict the contract renewal period based on the user's past insurance contract history and make proposals at the appropriate time. In this way, the insurance product proposal department can propose the most suitable insurance products to the user by referring to past insurance contract history. Some or all of the above processes in the insurance product proposal department may be performed using AI, for example, or not using AI. For example, the insurance product proposal department can input past insurance contract history data into a generating AI and have the generating AI execute the optimal proposal.

[0107] The insurance product proposal department can customize proposals based on the user's family structure and life stage when proposing insurance products. For example, if the user is raising children, the department can propose an insurance product that covers the cost of the children's education. If the user is retired, the department can also propose an insurance product that covers the cost of living in retirement. If the user is newly married, the department can also propose an insurance product that takes into account future family plans. In this way, by proposing insurance products that are tailored to the user's family structure and life stage, the department can provide the user with the most suitable insurance product. Some or all of the above processing in the insurance product proposal department may be performed using AI, for example, or not. For example, the insurance product proposal department can input family structure and life stage data into a generating AI and have the generating AI perform the customization of proposals.

[0108] The insurance product recommendation department can estimate the user's emotions and determine the priority of insurance products to recommend based on the estimated emotions. For example, if the user is stressed, the insurance product recommendation department will prioritize recommending simple and easy-to-understand insurance products. If the user is relaxed, for example, the insurance product recommendation department can also provide detailed insurance product information and increase the options. If the user is in a hurry, for example, the insurance product recommendation department can prioritize recommending the most suitable insurance product in order to provide a quick recommendation. In this way, by determining the priority of insurance products according to the user's emotions, important insurance products can be recommended preferentially. 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. Some or all of the above processing in the insurance product recommendation department may be performed using AI or not using AI. For example, the insurance product recommendation department can input user emotion data into a generative AI and have the generative AI perform emotion-based priority determination.

[0109] The insurance product proposal department can propose the most suitable insurance product when proposing an insurance product, taking into account the user's geographical location information. For example, if the user lives in an urban area, the insurance product proposal department can propose an insurance product that covers risks specific to urban areas. For example, if the user lives in a rural area, the insurance product proposal department can also propose an insurance product that covers risks specific to rural areas. For example, if the user lives overseas, the insurance product proposal department can also propose an insurance product that covers risks specific to overseas locations. In this way, by proposing insurance products while considering geographical location information, it is possible to provide insurance products that cover risks specific to a particular region. Some or all of the above processing in the insurance product proposal department may be performed using AI, for example, or without using AI. For example, the insurance product proposal department can input geographical location data into a generating AI and have the generating AI execute a proposal that takes location information into account.

[0110] The insurance product proposal department can analyze a user's social media activity and propose relevant insurance products when proposing insurance products. For example, the insurance product proposal department can estimate life events from a user's social media posts and propose relevant insurance products. For example, the insurance product proposal department can estimate a user's hobbies and interests from their social media activity and propose relevant insurance products. For example, the insurance product proposal department can estimate a user's health status from their social media activity and propose relevant insurance products. In this way, by analyzing social media activity, it is possible to propose insurance products based on the user's life events and interests. Some or all of the above processing in the insurance product proposal department may be performed using AI, for example, or without AI. For example, the insurance product proposal department can input social media activity data into a generating AI and have the generating AI execute the proposal of relevant insurance products.

[0111] The Health Insurance Management Department can estimate a user's emotions and adjust the method of health insurance management based on the estimated emotions. For example, if a user is stressed, the Health Insurance Management Department can provide a simple and easy-to-understand method of health insurance management. For example, if a user is relaxed, the Health Insurance Management Department can provide detailed health insurance information and increase the options available. For example, if a user is in a hurry, the Health Insurance Management Department can prioritize providing the most important information to enable quick health insurance management. This allows for more appropriate health insurance management by adjusting the method of health insurance management according to the user's emotions. 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. Some or all of the above processing in the Health Insurance Management Department may be performed using AI or not. For example, the Health Insurance Management Department can input user emotion data into a generative AI and have the generative AI perform emotion-based adjustments to the management method.

[0112] The Health Insurance Management Department can improve the accuracy of health insurance management by referring to the user's past insurance usage history. For example, the Health Insurance Management Department can refer to the user's past insurance usage history and suggest similar insurance usage methods. For example, the Health Insurance Management Department can also prioritize the management of frequently used insurance items based on the user's past insurance usage history. For example, the Health Insurance Management Department can predict the timing of usage based on the user's past insurance usage history and manage at the appropriate time. In this way, the accuracy of health insurance management is improved by referring to past insurance usage history. Some or all of the above processes in the Health Insurance Management Department may be performed using AI, for example, or without AI. For example, the Health Insurance Management Department can input past insurance usage history data into a generating AI and have the generating AI perform the improvement of management accuracy.

[0113] The Health Insurance Management Department can integrate and manage users' health status and medical records when managing health insurance. For example, the Health Insurance Management Department can integrate users' health checkup results and insurance usage history to manage health insurance. For example, the Health Insurance Management Department can integrate users' medical records and insurance usage history to manage health insurance. For example, the Health Insurance Management Department can integrate users' health status and insurance usage history to manage health insurance. This enables more comprehensive health insurance management by integrating and managing health status and medical records. Some or all of the above processes in the Health Insurance Management Department may be performed using AI, for example, or without AI. For example, the Health Insurance Management Department can input health status and medical record data into a generating AI and have the generating AI perform integrated data management.

[0114] The Health Insurance Management Department can estimate a user's emotions and determine the priority of health insurance management based on those emotions. For example, if a user is stressed, the Health Insurance Management Department can prioritize providing a simple and easy-to-understand health insurance management method. If a user is relaxed, the Health Insurance Management Department can also provide detailed health insurance information and increase the options available. If a user is in a hurry, the Health Insurance Management Department can prioritize providing the most important information to enable quick health insurance management. This allows for the priority management of important information by determining the priority of health insurance management according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the Health Insurance Management Department may be performed using AI or not. For example, the Health Insurance Management Department can input user emotion data into a generative AI and have the generative AI perform emotion-based priority determination.

[0115] The Health Insurance Management Department can manage health insurance while taking into account the user's geographical location information. For example, if the user lives in an urban area, the Health Insurance Management Department can provide urban-specific health insurance management methods. For example, if the user lives in a rural area, the Health Insurance Management Department can also provide rural-specific health insurance management methods. For example, if the user lives overseas, the Health Insurance Management Department can also provide overseas-specific health insurance management methods. This makes region-specific health insurance management possible by taking geographical location information into account. Some or all of the above processing in the Health Insurance Management Department may be performed using AI, for example, or without AI. For example, the Health Insurance Management Department can input geographical location data into a generating AI and have the generating AI perform management that takes location information into account.

[0116] The Health Insurance Management Department can analyze users' social media activity and obtain relevant health insurance information when managing health insurance. For example, the Health Insurance Management Department can estimate users' interest in health insurance from their social media posts and provide relevant information. For example, the Health Insurance Management Department can collect questions and concerns about health insurance from users' social media activity and provide appropriate answers. For example, the Health Insurance Management Department can analyze trends related to health insurance from users' social media activity and provide the latest information. In this way, by analyzing social media activity, it is possible to provide information based on users' interests and questions about health insurance. Some or all of the above processes in the Health Insurance Management Department may be performed using AI, for example, or not using AI. For example, the Health Insurance Management Department can input social media activity data into a generating AI and have the generating AI perform the acquisition of relevant health insurance information.

[0117] The medical record management department can estimate the user's emotions and adjust the medical record management method based on the estimated emotions. For example, if the user is stressed, the medical record management department can provide a simple and easy-to-understand medical record management method. For example, if the user is relaxed, the medical record management department can provide detailed medical record information and increase the options available. For example, if the user is in a hurry, the medical record management department can prioritize providing the most important information to enable quick medical record management. This allows for more appropriate medical record management by adjusting the medical record management method according to the user's emotions. 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. Some or all of the above processing in the medical record management department may be performed using AI or not. For example, the medical record management department can input user emotion data into a generative AI and have the generative AI perform emotion-based adjustments to the management method.

[0118] The medical record management department can improve the accuracy of medical record management by referring to the user's past medical history. For example, the medical record management department can refer to the user's past medical history and propose a similar medical record management method. For example, the medical record management department can also prioritize the management of frequently used medical items based on the user's past medical history. For example, the medical record management department can predict the timing of use based on the user's past medical history and manage at the appropriate time. This improves the accuracy of medical record management by referring to past medical history. Some or all of the above processes in the medical record management department may be performed using AI, for example, or without AI. For example, the medical record management department can input past medical history data into a generating AI and have the generating AI perform the improvement of management accuracy.

[0119] The Medical Records Management Department can integrate and manage users' health data and insurance information when managing medical records. For example, the Medical Records Management Department can integrate users' health checkup results and medical records and manage them. For example, the Medical Records Management Department can also integrate users' insurance information and medical records and manage them. For example, the Medical Records Management Department can integrate users' health status and medical records and manage them. This enables more comprehensive medical record management by integrating and managing health data and insurance information. Some or all of the above processes in the Medical Records Management Department may be performed using AI, for example, or not using AI. For example, the Medical Records Management Department can input health data and insurance information data into a generating AI and have the generating AI perform integrated data management.

[0120] The medical records management department can estimate the user's emotions and prioritize medical records based on those emotions. For example, if the user is stressed, the medical records management department can prioritize providing a simple and easy-to-understand medical record management method. If the user is relaxed, the medical records management department can also provide detailed medical record information and increase the options available. If the user is in a hurry, the medical records management department can prioritize providing the most important information to enable quick medical record management. This allows for the priority management of important information by prioritizing medical records according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the medical records management department may be performed using AI or not. For example, the medical records management department can input user emotion data into a generative AI and have the generative AI perform emotion-based priority determination.

[0121] The medical records management department can manage medical records while taking into account the user's geographical location information. For example, if the user lives in an urban area, the medical records management department can provide a medical record management method specific to urban areas. For example, if the user lives in a rural area, the medical records management department can also provide a medical record management method specific to rural areas. For example, if the user lives overseas, the medical records management department can also provide a medical record management method specific to overseas locations. This makes it possible to manage medical records in a region that takes geographical location information into account. Some or all of the above processing in the medical records management department may be performed using AI, for example, or without AI. For example, the medical records management department can input geographical location data into a generating AI and have the generating AI perform management that takes location information into account.

[0122] The medical records management department can analyze users' social media activity and retrieve relevant medical records when managing medical records. For example, the medical records management department can estimate users' medical interests from their social media posts and provide relevant information. For example, the medical records management department can collect medical questions and inquiries from users' social media activity and provide appropriate answers. For example, the medical records management department can analyze medical trends from users' social media activity and provide the latest information. In this way, by analyzing social media activity, it is possible to provide information based on users' medical interests and inquiries. Some or all of the above processes in the medical records management department may be performed using AI, for example, or not using AI. For example, the medical records management department can input social media activity data into a generating AI and have the generating AI retrieve relevant medical records.

[0123] The insurance claim support unit can estimate the user's emotions and adjust the insurance claim support method based on the estimated emotions. For example, if the user is stressed, the insurance claim support unit can provide a simple and easy-to-understand insurance claim support method. For example, if the user is relaxed, the insurance claim support unit can also provide detailed insurance claim information and increase the options. For example, if the user is in a hurry, the insurance claim support unit can prioritize providing the most important information in order to provide insurance claim support quickly. This allows for more appropriate insurance claim support by adjusting the insurance claim support method according to the user's emotions. 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. Some or all of the above processing in the insurance claim support unit may be performed using AI, for example, or not using AI. For example, the insurance claim support unit can input user emotion data into a generative AI and have the generative AI perform adjustments to the support method based on emotions.

[0124] The insurance claim support unit can improve the accuracy of its support by referring to the user's past claim history when providing insurance claim support. For example, the insurance claim support unit can refer to the user's past insurance claim history and propose similar insurance claim support methods. For example, the insurance claim support unit can also prioritize support for frequently used insurance items based on the user's past insurance claim history. For example, the insurance claim support unit can predict the timing of an application based on the user's past insurance claim history and provide support at the appropriate time. This improves the accuracy of insurance claim support by referring to past claim history. Some or all of the above processes in the insurance claim support unit may be performed using AI, for example, or without AI. For example, the insurance claim support unit can input past claim history data into a generating AI and have the generating AI perform the task of improving the accuracy of support.

[0125] The insurance claim support unit can estimate the user's emotions and determine the priority of insurance claims based on the estimated emotions. For example, if the user is stressed, the insurance claim support unit can prioritize providing a simple and easy-to-understand insurance claim support method. For example, if the user is relaxed, the insurance claim support unit can also provide detailed insurance claim information and increase the options available. For example, if the user is in a hurry, the insurance claim support unit can prioritize providing the most important information in order to quickly assist with the insurance claim. This allows for priority support of important information by determining the priority of insurance claims according to the user's emotions. 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. Some or all of the above processing in the insurance claim support unit may be performed using AI or not using AI. For example, the insurance claim support unit can input user emotion data into a generative AI and have the generative AI perform emotion-based priority determination.

[0126] The insurance claim support unit can provide support while considering the user's geographical location information. For example, if the user lives in an urban area, the insurance claim support unit can provide insurance claim support methods specific to urban areas. For example, if the user lives in a rural area, the insurance claim support unit can also provide insurance claim support methods specific to rural areas. For example, if the user lives overseas, the insurance claim support unit can also provide insurance claim support methods specific to overseas locations. This makes it possible to provide region-specific insurance claim support by considering geographical location information. Some or all of the above processing in the insurance claim support unit may be performed using AI, for example, or without AI. For example, the insurance claim support unit can input geographical location information data into a generating AI and have the generating AI perform support that takes location information into consideration.

[0127] The subsidy application unit can estimate the user's emotions and adjust the subsidy application support method based on the estimated user emotions. For example, if the user is stressed, the subsidy application unit can provide a simple and easy-to-understand subsidy application support method. For example, if the user is relaxed, the subsidy application unit can also provide detailed subsidy application information and increase the options. For example, if the user is in a hurry, the subsidy application unit can prioritize providing the most important information in order to provide subsidy application support quickly. This makes it possible to provide more appropriate subsidy application support by adjusting the subsidy application support method according to the user's emotions. 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. Some or all of the above processing in the subsidy application unit may be performed using AI or not using AI. For example, the subsidy application unit can input user emotion data into a generative AI and have the generative AI perform adjustments to the support method based on emotions.

[0128] The subsidy application department can improve the accuracy of its support by referring to the user's past application history when assisting with subsidy applications. For example, the subsidy application department can refer to the user's past subsidy application history and propose similar subsidy application support methods. For example, the subsidy application department can also prioritize support for frequently used subsidy items based on the user's past subsidy application history. For example, the subsidy application department can predict the application timing based on the user's past subsidy application history and provide support at the appropriate time. This improves the accuracy of subsidy application support by referring to past application history. Some or all of the above processes in the subsidy application department may be performed using AI, for example, or without AI. For example, the subsidy application department can input past application history data into a generating AI and have the generating AI perform the task of improving the accuracy of support.

[0129] The subsidy application unit can estimate the user's emotions and determine the priority of subsidy applications based on the estimated emotions. For example, if the user is stressed, the subsidy application unit can prioritize providing simple and easy-to-understand subsidy application support methods. For example, if the user is relaxed, the subsidy application unit can also provide detailed subsidy application information and increase the options available. For example, if the user is in a hurry, the subsidy application unit can prioritize providing the most important information in order to quickly assist with the subsidy application. This allows for priority support of important information by determining the priority of subsidy applications according to the user's emotions. 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. Some or all of the above processing in the subsidy application unit may be performed using AI or not. For example, the subsidy application unit can input user emotion data into a generative AI and have the generative AI perform emotion-based priority determination.

[0130] The subsidy application unit can provide support for subsidy applications while taking into account the user's geographical location. For example, if the user lives in an urban area, the subsidy application unit can provide subsidy application support methods specific to urban areas. For example, if the user lives in a rural area, the subsidy application unit can also provide subsidy application support methods specific to rural areas. For example, if the user lives overseas, the subsidy application unit can also provide subsidy application support methods specific to overseas areas. This makes it possible to provide region-specific subsidy application support by taking geographical location information into account. Some or all of the above processing in the subsidy application unit may be performed using AI, for example, or without using AI. For example, the subsidy application unit can input geographical location data into a generating AI and have the generating AI perform support that takes location information into account.

[0131] The personal asset management unit can estimate the user's emotions and adjust the personal asset management method based on the estimated emotions. For example, if the user is stressed, the personal asset management unit can provide a simple and easy-to-understand personal asset management method. For example, if the user is relaxed, the personal asset management unit can provide detailed personal asset information and increase the options. For example, if the user is in a hurry, the personal asset management unit can prioritize providing the most important information to enable quick personal asset management. This allows for more appropriate personal asset management by adjusting the personal asset management method according to the user's emotions. 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. Some or all of the above processing in the personal asset management unit may be performed using AI or not. For example, the personal asset management unit can input user emotion data into a generative AI and have the generative AI perform emotion-based adjustments to the management method.

[0132] The personal asset management unit can improve the accuracy of personal asset management by referring to the user's past asset management history. For example, the personal asset management unit can refer to the user's past asset management history and suggest similar asset management methods. For example, the personal asset management unit can also prioritize the management of frequently used asset items based on the user's past asset management history. For example, the personal asset management unit can predict the timing of management based on the user's past asset management history and perform management at the appropriate time. This improves the accuracy of personal asset management by referring to past asset management history. Some or all of the above processes in the personal asset management unit may be performed using AI, for example, or without AI. For example, the personal asset management unit can input past asset management history data into a generating AI and have the generating AI perform the improvement of management accuracy.

[0133] The personal asset management unit can integrate and manage the user's income and expenditure data when managing personal assets. For example, the personal asset management unit can integrate the user's income and expenditure data to manage the balance of income and expenses. For example, the personal asset management unit can also integrate the user's income and expenditure data to suggest saving points. For example, the personal asset management unit can integrate the user's income and expenditure data to suggest future asset management plans. This enables more comprehensive personal asset management by integrating and managing income and expenditure data. Some or all of the above processes in the personal asset management unit may be performed using AI, for example, or not using AI. For example, the personal asset management unit can input income and expenditure data into a generating AI and have the generating AI perform integrated data management.

[0134] The personal asset management unit can estimate the user's emotions and determine priorities for personal asset management based on those emotions. For example, if the user is stressed, the personal asset management unit can prioritize providing a simple and easy-to-understand personal asset management method. If the user is relaxed, the personal asset management unit can also provide detailed personal asset information and increase the options available. If the user is in a hurry, the personal asset management unit can prioritize providing the most important information to enable quick personal asset management. This allows for the priority management of important information by determining priorities for personal asset management according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the personal asset management unit may be performed using AI or not. For example, the personal asset management unit can input user emotion data into a generative AI and have the generative AI perform emotion-based priority determination.

[0135] The personal property management unit can manage personal property while taking into account the user's geographical location information. For example, if the user lives in an urban area, the personal property management unit can provide property management methods specific to urban areas. For example, if the user lives in a rural area, the personal property management unit can also provide property management methods specific to rural areas. For example, if the user lives overseas, the personal property management unit can also provide property management methods specific to overseas locations. This makes it possible to manage personal property in a region that takes geographical location information into account. Some or all of the above processing in the personal property management unit may be performed using AI, for example, or without AI. For example, the personal property management unit can input geographical location data into a generating AI and have the generating AI perform management that takes location information into account.

[0136] The Personal Asset Management Department can analyze a user's social media activity and obtain relevant asset management information when managing personal assets. For example, the Personal Asset Management Department can estimate a user's interest in asset management from their social media posts and provide relevant information. The Personal Asset Management Department can also collect questions and concerns about asset management from a user's social media activity and provide appropriate answers. The Personal Asset Management Department can also analyze trends in asset management from a user's social media activity and provide the latest information. In this way, by analyzing social media activity, it can provide information based on the user's interest in and questions about asset management. Some or all of the above processes in the Personal Asset Management Department may be performed using AI, for example, or not using AI. For example, the Personal Asset Management Department can input social media activity data into a generating AI and have the generating AI perform the acquisition of relevant asset management information.

[0137] The Digital Inheritance Management Department can estimate the user's emotions and adjust the digital inheritance management method based on the estimated emotions. For example, if the user is stressed, the Digital Inheritance Management Department can provide a simple and easy-to-understand digital inheritance management method. For example, if the user is relaxed, the Digital Inheritance Management Department can also provide detailed digital inheritance information and increase the options. For example, if the user is in a hurry, the Digital Inheritance Management Department can prioritize providing the most important information to enable quick digital inheritance management. This allows for more appropriate digital inheritance management by adjusting the digital inheritance management method according to the user's emotions. 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. Some or all of the above processing in the Digital Inheritance Management Department may be performed using AI or not. For example, the Digital Inheritance Management Department can input user emotion data into a generative AI and have the generative AI perform emotion-based adjustments to the management method.

[0138] The Digital Inheritance Management Department can improve the accuracy of digital inheritance management by referring to the user's past inheritance history. For example, the Digital Inheritance Management Department can refer to the user's past inheritance history and suggest similar inheritance management methods. For example, the Digital Inheritance Management Department can also prioritize the management of frequently used inheritance items based on the user's past inheritance history. For example, the Digital Inheritance Management Department can predict the timing of management based on the user's past inheritance history and perform management at the appropriate time. This improves the accuracy of digital inheritance management by referring to past inheritance history. Some or all of the above processes in the Digital Inheritance Management Department may be performed using AI, for example, or without AI. For example, the Digital Inheritance Management Department can input past inheritance history data into a generating AI and have the generating AI perform the improvement of management accuracy.

[0139] The Digital Inheritance Management Department can integrate and manage user asset data and family structure information during digital inheritance management. For example, the Digital Inheritance Management Department can integrate user asset data and family structure information to perform inheritance management. For example, the Digital Inheritance Management Department can integrate user asset data and family structure information to propose an inheritance plan. For example, the Digital Inheritance Management Department can integrate user asset data and family structure information to simplify inheritance procedures. This enables more comprehensive digital inheritance management by integrating and managing asset data and family structure information. Some or all of the above processes in the Digital Inheritance Management Department may be performed using AI, for example, or without AI. For example, the Digital Inheritance Management Department can input asset data and family structure information into a generating AI and have the generating AI perform integrated data management.

[0140] The Digital Inheritance Management Department can estimate the user's emotions and determine the priorities of digital inheritance management based on those emotions. For example, if the user is stressed, the Digital Inheritance Management Department can prioritize providing a simple and easy-to-understand digital inheritance management method. For example, if the user is relaxed, the Digital Inheritance Management Department can also provide detailed digital inheritance information and increase the options available. For example, if the user is in a hurry, the Digital Inheritance Management Department can prioritize providing the most important information to enable quick digital inheritance management. This allows for the priority management of important information by determining the priorities of digital inheritance management according to the user's emotions. 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. Some or all of the above processing in the Digital Inheritance Management Department may be performed using AI or not. For example, the Digital Inheritance Management Department can input user emotion data into a generative AI and have the generative AI perform emotion-based priority determination.

[0141] The Digital Inheritance Management Department can manage digital inheritances while taking into account the user's geographical location information. For example, if the user lives in an urban area, the Digital Inheritance Management Department can provide inheritance management methods specific to urban areas. For example, if the user lives in a rural area, the Digital Inheritance Management Department can also provide inheritance management methods specific to rural areas. For example, if the user lives overseas, the Digital Inheritance Management Department can also provide inheritance management methods specific to overseas locations. This enables region-specific digital inheritance management by taking geographical location information into account. Some or all of the above-described processes in the Digital Inheritance Management Department may be performed using AI, for example, or without AI. For example, the Digital Inheritance Management Department can input geographical location data into a generating AI and have the generating AI perform management that takes location information into account.

[0142] The Digital Inheritance Management Department can analyze users' social media activity and obtain relevant inheritance information during digital inheritance management. For example, the Digital Inheritance Management Department can estimate users' interest in inheritance from their social media posts and provide relevant information. For example, the Digital Inheritance Management Department can collect questions and concerns about inheritance from users' social media activity and provide appropriate answers. For example, the Digital Inheritance Management Department can analyze inheritance trends from users' social media activity and provide the latest information. In this way, by analyzing social media activity, it can provide information based on users' interests and questions about inheritance. Some or all of the above processes in the Digital Inheritance Management Department may be performed using AI, for example, or not using AI. For example, the Digital Inheritance Management Department can input social media activity data into a generating AI and have the generating AI perform the acquisition of relevant inheritance information.

[0143] The security management department can estimate the user's emotions and adjust security management methods based on those estimated emotions. For example, if the user is stressed, the security management department can provide simple and easy-to-understand security management methods. For example, if the user is relaxed, the security management department can provide detailed security information and increase the options available. For example, if the user is in a hurry, the security management department can prioritize providing the most important information to enable rapid security management. This allows for more appropriate security management by adjusting security management methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the security management department may be performed using AI or not. For example, the security management department can input user emotion data into a generative AI and have the generative AI perform emotion-based adjustments to management methods.

[0144] The security management department can improve the accuracy of security management by referring to the user's past security history during security management. For example, the security management department can refer to the user's past security history and propose similar security management methods. For example, the security management department can also prioritize the management of frequently used security items based on the user's past security history. For example, the security management department can predict the timing of management based on the user's past security history and perform management at the appropriate time. In this way, the accuracy of security management is improved by referring to past security history. Some or all of the above processes in the security management department may be performed using AI, for example, or without AI. For example, the security management department can input past security history data into a generating AI and have the generating AI perform the improvement of management accuracy.

[0145] The security management department can estimate a user's emotions and determine security management priorities based on those emotions. For example, if a user is stressed, the security management department can prioritize providing simple and easy-to-understand security management methods. If a user is relaxed, the security management department can also provide detailed security information and increase the options available. If a user is in a hurry, the security management department can prioritize providing the most important information to enable rapid security management. This allows for the priority management of critical information by determining security management priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the security management department may be performed using AI or not. For example, the security management department can input user emotion data into a generative AI and have the generative AI perform emotion-based priority determination.

[0146] The security management department can perform security management while taking into account the user's geographical location information. For example, if the user lives in an urban area, the security management department can provide security management methods specific to urban areas. For example, if the user lives in a rural area, the security management department can also provide security management methods specific to rural areas. For example, if the user lives overseas, the security management department can also provide security management methods specific to overseas locations. This makes it possible to perform region-specific security management by taking geographical location information into consideration. Some or all of the above processing in the security management department may be performed using AI, for example, or without using AI. For example, the security management department can input geographical location data into a generating AI and have the generating AI perform management that takes location information into consideration.

[0147] The Security Management Department can analyze users' social media activity and obtain relevant security information during security management. For example, the Security Management Department can estimate users' security interests from their social media posts and provide relevant information. For example, the Security Management Department can collect security questions and concerns from users' social media activity and provide appropriate answers. For example, the Security Management Department can analyze security trends from users' social media activity and provide the latest information. In this way, by analyzing social media activity, it is possible to provide information based on users' security interests and questions. Some or all of the above processes in the Security Management Department may be performed using AI, for example, or not using AI. For example, the Security Management Department can input social media activity data into a generating AI and have the generating AI perform the acquisition of relevant security information.

[0148] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0149] The life management support system can estimate the user's emotions and adjust the health management monitoring method based on those emotions. For example, if the user is stressed, it can suggest relaxing exercises or diets. If the user is relaxed, it can perform a more detailed health data analysis and provide specific improvement suggestions. Furthermore, if the user is in a hurry, it can perform a concise and rapid health data analysis and provide actionable advice immediately. This enables health management monitoring tailored to the user's emotions, resulting in more effective health management.

[0150] The life management support system can improve the accuracy of health management monitoring by referencing the user's past health data. For example, it can refer to past health checkup results and analyze them in comparison to the current health status. It can also refer to past exercise history and reflect changes in exercise habits in the analysis. Furthermore, it can refer to past meal records and analyze areas for improvement in dietary habits. In this way, more accurate health management becomes possible by utilizing past health data.

[0151] The life management support system can integrate users' lifestyle and dietary data to monitor their health. For example, it can integrate sleep data and dietary data to analyze the relationship between sleep quality and diet. It can also integrate exercise data and dietary data to analyze the relationship between exercise effects and diet. Furthermore, it can integrate stress levels and dietary data to analyze the relationship between stress management and diet. By integrating and analyzing lifestyle and dietary data, it enables more comprehensive health management.

[0152] The life management support system can perform health management monitoring while considering the user's geographical location. For example, if the user lives in a high-altitude area, the system can analyze the health risks specific to high altitudes. Similarly, if the user lives in an urban area, the system can analyze the health risks specific to urban areas. Furthermore, if the user lives by the sea, the system can analyze the health risks specific to coastal areas. In this way, by considering geographical location in the analysis, it becomes possible to perform analyses that reflect region-specific health risks.

[0153] The life management support system can analyze a user's social media activity and obtain relevant health data. For example, it can estimate stress levels from a user's social media posts and incorporate this into the health data analysis. It can also estimate exercise habits from a user's social media activity and incorporate this into the health data analysis. Furthermore, it can estimate dietary habits from a user's social media activity and incorporate this into the health data analysis. In this way, additional information about the user's health status can be obtained by analyzing social media activity.

[0154] The life management support system can estimate the user's emotions and adjust the method of suggesting insurance products based on those emotions. For example, if the user is stressed, it can suggest simple and easy-to-understand insurance products. If the user is relaxed, it can provide detailed insurance product information and increase the options. Furthermore, if the user is in a hurry, it can prioritize suggesting the most suitable insurance products in order to provide a quick response. This enables insurance product suggestions that are tailored to the user's emotions, resulting in more appropriate recommendations.

[0155] The life management support system can improve the accuracy of insurance product recommendations by referring to the user's past insurance contract history. For example, it can refer to past insurance contract history and recommend similar insurance products. It can also analyze changes in contract details from past insurance contract history and recommend the most suitable insurance product. Furthermore, it can predict contract renewal dates based on past insurance contract history and make recommendations at the appropriate time. In this way, by utilizing past insurance contract history, more accurate insurance product recommendations become possible.

[0156] The life management support system can customize insurance product recommendations based on the user's family structure and life stage. For example, if the user is raising children, it can suggest insurance products that cover the cost of their children's education. If the user is retired, it can suggest insurance products that cover living expenses in retirement. Furthermore, if the user is newly married, it can suggest insurance products that take into account their future family plans. This allows for insurance product recommendations tailored to family structure and life stage, resulting in more appropriate recommendations.

[0157] The life management support system can estimate the user's emotions and prioritize insurance products based on those emotions. For example, if the user is stressed, it can prioritize suggesting simple and easy-to-understand insurance products. If the user is relaxed, it can provide detailed insurance product information and increase the options. Furthermore, if the user is in a hurry, it can prioritize suggesting the most suitable insurance product in order to provide a quick response. In this way, by prioritizing insurance products according to the user's emotions, it can prioritize suggesting important insurance products.

[0158] The life management support system can suggest insurance products while considering the user's geographical location. For example, if the user lives in an urban area, it can suggest insurance products that cover risks specific to urban areas. Similarly, if the user lives in a rural area, it can suggest insurance products that cover risks specific to rural areas. Furthermore, if the user lives overseas, it can suggest insurance products that cover risks specific to overseas locations. In this way, by suggesting insurance products while considering geographical location, it can provide insurance products that cover risks specific to a particular region.

[0159] The following briefly describes the processing flow for example form 2.

[0160] Step 1: The Health Data Analysis Department analyzes health data. For example, it analyzes health data such as heart rate, blood pressure, and body temperature obtained from wearable devices like smartwatches. The Health Data Analysis Department uses AI to provide daily health management and improvement suggestions. Step 2: The Insurance Product Proposal Department proposes insurance products based on the data analyzed by the Health Data Analysis Department. For example, it analyzes an individual's situation and proposes the most suitable insurance product, such as medical insurance or life insurance. The Insurance Product Proposal Department uses AI to analyze an individual's situation and propose the most suitable insurance product. Step 3: The Health Insurance Management Department manages health insurance based on the insurance products proposed by the Insurance Product Proposal Department. For example, it automatically analyzes health insurance documents and guides users through the scope of coverage and procedures. The Health Insurance Management Department uses AI to automatically analyze health insurance documents and guide users through the scope of coverage and procedures. Step 4: The Medical Records Management Department manages medical records based on data managed by the Health Insurance Management Department. For example, medical records are aggregated in the cloud, and data analysis and security management are performed. Medical records include medical records, prescription information, etc. The Medical Records Management Department uses AI to aggregate medical records in the cloud, and performs data analysis and security management.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements mentioned above, including the health data analysis department, insurance product proposal department, health insurance management department, medical record management department, insurance claim support department, subsidy application department, personal asset management department, digital inheritance management department, and security management department, is implemented by, for example, at least one of the smart device 14 and the data processing device 12. For example, the health data analysis department is implemented by the processor 46 of the smart device 14 and analyzes health data acquired from a smartwatch. The insurance product proposal department is implemented by, for example, the specific processing unit 290 of the data processing device 12 and analyzes an individual's situation to propose the most suitable insurance product. The health insurance management department is implemented by, for example, the specific processing unit 290 of the data processing device 12 and automatically analyzes documents related to health insurance and navigates the scope of application and procedures. The medical record management department is implemented by, for example, the specific processing unit 290 of the data processing device 12 and aggregates medical records in the cloud and performs data analysis and security management. The insurance claim support unit, for example, is implemented by the specific processing unit 290 of the data processing device 12, and automatically generates insurance claim documents and notifies the user of any missing information in real time. The subsidy application unit, for example, is implemented by the specific processing unit 290 of the data processing device 12, and analyzes the conditions for subsidies and presents applicable subsidies. The personal asset management unit, for example, is implemented by the specific processing unit 290 of the data processing device 12, and analyzes the individual's asset situation and proposes an optimal asset management plan. The digital inheritance management unit, for example, is implemented by the specific processing unit 290 of the data processing device 12, and automatically generates the necessary documents related to inheritance and provides a mechanism for obtaining digital signatures and approvals. The security management unit, for example, is implemented by the specific processing unit 290 of the data processing device 12, and encrypts and decrypts data, making it accessible only when necessary. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0165] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] Each of the multiple elements mentioned above, including the health data analysis department, insurance product proposal department, health insurance management department, medical record management department, insurance claim support department, subsidy application department, personal asset management department, digital inheritance management department, and security management department, is implemented by, for example, at least one of the smart glasses 214 and the data processing device 12. For example, the health data analysis department is implemented by the processor 46 of the smart glasses 214 and analyzes health data acquired from a smartwatch. The insurance product proposal department is implemented by, for example, the specific processing unit 290 of the data processing device 12 and analyzes an individual's situation to propose the most suitable insurance product. The health insurance management department is implemented by, for example, the specific processing unit 290 of the data processing device 12 and automatically analyzes documents related to health insurance and navigates the scope of application and procedures. The medical record management department is implemented by, for example, the specific processing unit 290 of the data processing device 12 and aggregates medical records in the cloud and performs data analysis and security management. The insurance claim support unit, for example, is implemented by the specific processing unit 290 of the data processing device 12, and automatically generates insurance claim documents and notifies the user of any missing information in real time. The subsidy application unit, for example, is implemented by the specific processing unit 290 of the data processing device 12, and analyzes the conditions for subsidies and presents applicable subsidies. The personal asset management unit, for example, is implemented by the specific processing unit 290 of the data processing device 12, and analyzes the individual's asset situation and proposes an optimal asset management plan. The digital inheritance management unit, for example, is implemented by the specific processing unit 290 of the data processing device 12, and automatically generates the necessary documents related to inheritance and provides a mechanism for obtaining digital signatures and approvals. The security management unit, for example, is implemented by the specific processing unit 290 of the data processing device 12, and encrypts and decrypts data, making it accessible only when necessary. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0181] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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).

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.).

[0193] 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.

[0194] 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.

[0195] 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.

[0196] Each of the multiple elements mentioned above, including the health data analysis department, insurance product proposal department, health insurance management department, medical record management department, insurance claim support department, subsidy application department, personal asset management department, digital inheritance management department, and security management department, is implemented by, for example, at least one of the headset terminal 314 and the data processing device 12. For example, the health data analysis department is implemented by the processor 46 of the headset terminal 314 and analyzes health data acquired from a smartwatch. The insurance product proposal department is implemented by, for example, the specific processing unit 290 of the data processing device 12 and analyzes the individual's situation to propose the most suitable insurance product. The health insurance management department is implemented by, for example, the specific processing unit 290 of the data processing device 12 and automatically analyzes documents related to health insurance and navigates the scope of application and procedures. The medical record management department is implemented by, for example, the specific processing unit 290 of the data processing device 12 and aggregates medical records in the cloud and performs data analysis and security management. The insurance claim support unit, for example, is implemented by the specific processing unit 290 of the data processing device 12, and automatically generates insurance claim documents and notifies the user of any missing information in real time. The subsidy application unit, for example, is implemented by the specific processing unit 290 of the data processing device 12, and analyzes the conditions for subsidies and presents applicable subsidies. The personal asset management unit, for example, is implemented by the specific processing unit 290 of the data processing device 12, and analyzes the individual's asset situation and proposes an optimal asset management plan. The digital inheritance management unit, for example, is implemented by the specific processing unit 290 of the data processing device 12, and automatically generates the necessary documents related to inheritance and provides a mechanism for obtaining digital signatures and approvals. The security management unit, for example, is implemented by the specific processing unit 290 of the data processing device 12, and encrypts and decrypts data, making it accessible only when necessary. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0197] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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).

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.).

[0210] 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.

[0211] 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.

[0212] 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.

[0213] Each of the multiple elements mentioned above, including the health data analysis department, insurance product proposal department, health insurance management department, medical record management department, insurance claim support department, subsidy application department, personal asset management department, digital inheritance management department, and security management department, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the health data analysis department is implemented by the processor 46 of the robot 414 and analyzes health data acquired from a smartwatch. The insurance product proposal department is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes an individual's situation to propose the most suitable insurance product. The health insurance management department is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically analyzes documents related to health insurance and navigates the scope of application and procedures. The medical record management department is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and aggregates medical records in the cloud and performs data analysis and security management. The insurance claim support unit, for example, is implemented by the specific processing unit 290 of the data processing device 12, and automatically generates insurance claim documents and notifies the user of any missing information in real time. The subsidy application unit, for example, is implemented by the specific processing unit 290 of the data processing device 12, and analyzes the conditions for subsidies and presents applicable subsidies. The personal asset management unit, for example, is implemented by the specific processing unit 290 of the data processing device 12, and analyzes the individual's asset situation and proposes an optimal asset management plan. The digital inheritance management unit, for example, is implemented by the specific processing unit 290 of the data processing device 12, and automatically generates the necessary documents related to inheritance and provides a mechanism for obtaining digital signatures and approvals. The security management unit, for example, is implemented by the specific processing unit 290 of the data processing device 12, and encrypts and decrypts data, making it accessible only when necessary. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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."

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] (Note 1) The Health Data Analysis Department analyzes health data, An insurance product proposal unit proposes insurance products based on the data analyzed by the aforementioned health data analysis unit, The Health Insurance Management Department manages health insurance based on the insurance products proposed by the aforementioned Insurance Product Proposal Department, The system includes a medical record management department that manages medical records based on data managed by the aforementioned health insurance management department. A system characterized by the following features. (Note 2) The aforementioned health data analysis unit, Analyzing health data acquired from wearable devices such as smartwatches. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned insurance product proposal unit is the system described in Appendix 1, characterized in that it analyzes an individual's situation and proposes an appropriate insurance product. (Note 4) The aforementioned health insurance management department is the system described in Appendix 1, characterized by its ability to automatically analyze documents related to health insurance and provide guidance on the scope of application and procedures. (Note 5) The aforementioned medical record management department is the system described in Appendix 1, characterized in that it centralizes medical records in the cloud and performs data analysis and security management. (Note 6) The system described in Appendix 1, characterized by having an insurance claim support unit that automatically generates insurance claim documents and notifies of missing information in real time. (Note 7) The system described in Appendix 1, characterized by having a subsidy application unit that analyzes the conditions for subsidies and presents applicable subsidies. (Note 8) We have a personal asset management department that analyzes individuals' financial situations and proposes optimal asset management plans. The system described in Appendix 1, characterized by the features described herein. (Note 9) We have a Digital Inheritance Management Department that automatically generates necessary inheritance-related documents and provides a system for obtaining digital signatures and approvals. The system described in Appendix 1, characterized by the features described herein. (Note 10) It includes a security management unit that encrypts and decrypts data, making it accessible only when necessary. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned health data analysis unit, We estimate the user's emotions and adjust the analysis method of health data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned health data analysis unit, When analyzing health data, we improve the accuracy of the analysis by referring to the user's past health history. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned health data analysis unit, When analyzing health data, the analysis integrates the user's lifestyle and dietary data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned health data analysis unit, It estimates the user's emotions and prioritizes health data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned health data analysis unit, When analyzing health data, the analysis takes into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned health data analysis unit, During health data analysis, we analyze users' social media activity and obtain relevant health data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned insurance product proposal department, The system estimates the user's emotions and adjusts how insurance products are proposed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned insurance product proposal department, When proposing insurance products, we refer to the user's past insurance contract history to make the most suitable recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned insurance product proposal department, When proposing insurance products, we customize the proposals based on the user's family structure and life stage. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned insurance product proposal department, It estimates the user's emotions and determines the priority of insurance products to suggest based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned insurance product proposal department, When proposing insurance products, we take the user's geographical location into consideration to suggest the most suitable insurance product. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned insurance product proposal department, When proposing insurance products, we analyze the user's social media activity and suggest relevant insurance products. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned Health Insurance Management Department, It estimates user sentiment and adjusts health insurance management methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned Health Insurance Management Department, When managing health insurance, we improve the accuracy of management by referring to the user's past insurance usage history. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned Health Insurance Management Department, When managing health insurance, the system integrates and manages the user's health status and medical records. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned Health Insurance Management Department, It estimates user sentiment and determines health insurance management priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned Health Insurance Management Department, When managing health insurance information, the system takes into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned Health Insurance Management Department, During health insurance management, the system analyzes users' social media activity and retrieves relevant health insurance information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned medical records management department, The system estimates the user's emotions and adjusts the medical record management method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned medical records management department, When managing medical records, referencing the user's past medical history improves the accuracy of management. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned medical records management department, When managing medical records, the system integrates and manages users' health data and insurance information. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned medical records management department, The system estimates the user's emotions and prioritizes medical records based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned medical records management department, When managing medical records, the system should take into account the user's geographical location. The system according to appended note 1, characterized in that... (Appended note 34) The medical record management department Analyzes the user's social media activities during medical record management and obtains relevant medical records The system according to appended note 1, characterized in that... (Appended note 35) The insurance application support department Estimates the user's emotions and adjusts the support method for insurance applications based on the estimated user emotions The system according to appended note 1, characterized in that... (Appended note 36) The insurance application support department Refers to the user's past application history to improve the accuracy of support during insurance application support The system according to appended note 1, characterized in that... (Appended note 37) The insurance application support department Estimates the user's emotions and determines the priority of insurance applications based on the estimated user emotions The system according to appended note 1, characterized in that... (Appended note 38) The insurance application support department Considers the user's geographical location information to provide support during insurance application support The system according to appended note 1, characterized in that... (Appended note 39) The subsidy application department Estimates the user's emotions and adjusts the support method for subsidy applications based on the estimated user emotions The system according to appended note 1, characterized in that... (Appended note 40) The subsidy application department Refers to the user's past application history to improve the accuracy of support during subsidy application support The system according to appended note 1, characterized in that... (Appended note 41) The subsidy application department Estimate the user's emotions and determine the priority of the subsidy application based on the estimated user emotions The system according to Appendix 1, characterized in that (Appendix 42) The subsidy application department When providing subsidy application support, consider the user's geographical location information for support The system according to Appendix 1, characterized in that (Appendix 43) The personal property management department Estimate the user's emotions and adjust the personal property management method based on the estimated user emotions The system according to Appendix 1, characterized in that (Appendix 44) The personal property management department When managing personal property, refer to the user's past property management history to improve the management accuracy The system according to Appendix 1, characterized in that (Appendix 45) The personal property management department When managing personal property, integrate the user's income and expenditure data for management The system according to Appendix 1, characterized in that (Appendix 46) The personal property management department<00​​​​​​​​​​​​​​​​​​​​​​​ It estimates user sentiment and adjusts the digital inheritance management method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 50) The aforementioned Digital Inheritance Management Department When managing digital inheritance, referencing the user's past inheritance history improves the accuracy of the management process. The system described in Appendix 1, characterized by the features described herein. (Note 51) The aforementioned Digital Inheritance Management Department During digital inheritance management, the system integrates and manages the user's asset data and family structure information. The system described in Appendix 1, characterized by the features described herein. (Note 52) The aforementioned Digital Inheritance Management Department It estimates user sentiment and prioritizes digital inheritance management based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 53) The aforementioned Digital Inheritance Management Department When managing digital inheritances, the system takes into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 54) The aforementioned Digital Inheritance Management Department During digital inheritance management, the system analyzes the user's social media activity and retrieves relevant inheritance information. The system described in Appendix 1, characterized by the features described herein. (Note 55) The aforementioned security management department, It estimates user sentiment and adjusts security management methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 56) The aforementioned security management department, During security management, referencing the user's past security history improves the accuracy of management. The system according to Appendix 1, characterized in that (Appendix 57) The security management department estimates the user's emotions and determines the priority of security management based on the estimated user emotions The system according to Appendix 1, characterized in that (Appendix 58) The security management department performs management considering the user's geographical location information during security management The system according to Appendix 1, characterized in that (Appendix 59) The security management department analyzes the user's social media activities and obtains relevant security information during security management The system according to Appendix 1, characterized in that

Explanation of Signs

[0233] 10, 210, 310, 410 Data processing system 12 Data processing device 14 Smart device 214 Smart glasses 314 Headset-type terminal 414 Robot

Claims

1. The Health Data Analysis Department analyzes health data, An insurance product proposal unit proposes insurance products based on the data analyzed by the aforementioned health data analysis unit, The Health Insurance Management Department manages health insurance based on the insurance products proposed by the aforementioned Insurance Product Proposal Department, The system includes a medical record management department that manages medical records based on data managed by the aforementioned health insurance management department. A system characterized by the following features.

2. The aforementioned health data analysis unit, Analyzing health data acquired from wearable devices such as smartwatches. The system according to feature 1.

3. The system according to claim 1, characterized in that the aforementioned insurance product proposal unit analyzes an individual's situation and proposes an appropriate insurance product.

4. The system according to claim 1, characterized in that the aforementioned health insurance management department automatically analyzes documents related to health insurance and provides guidance on the scope of application and procedures.

5. The system according to claim 1, characterized in that the aforementioned medical record management department aggregates medical records in the cloud and performs data analysis and security management.

6. The system according to claim 1, characterized in that it includes an insurance claim support unit that automatically generates insurance claim documents and notifies of missing information in real time.

7. The system according to claim 1, characterized in that it includes a subsidy application unit that analyzes the conditions for subsidies and presents applicable subsidies.

8. We have a personal asset management department that analyzes individuals' financial situations and proposes optimal asset management plans. The system according to feature 1.

9. We have a Digital Inheritance Management Department that automatically generates necessary inheritance-related documents and provides a system for obtaining digital signatures and approvals. The system according to feature 1.

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